Project 2 - TMDB Movie Analysis.ipynb

{
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# TMDB Movie Analysis: Exploring the Most Popular Movies, Highest Grossing Movies, and More!\n",
    "\n",
    "## Introduction\n",
    "\n",
    "How do we know if a certain movie is destined to be a Box Office success, or a Box Office flop?\n",
    "\n",
    "Is it the **million dollar casts** (\"Tom Hanks\", \"Chris Pratt\")? Could it be the **visionary director** (\"J.J. Abrams\", \"James Wan\")? What about the **genres** (Action, Adventure, Thriller)?\n",
    "\n",
    "In this analysis, we set out to analyze TMDB movie dataset to get insights and answer all our burning curiousity. \n",
    "\n",
    "The dataset is from [Kaggle: 5000 Movie Dataset](https://www.kaggle.com/tmdb/tmdb-movie-metadata) and contains information about 10,867 movies collected from The Movie Database (TMDb), including popularity, revenue, budget, cast and genres. \n",
    "\n",
    "_(This analysis is a case study in the Udacity Data Analyst Nanodegree Program.)_\n",
    "\n",
    "## Table of Contents\n",
    "\n",
    "<ul>\n",
    "<li><a href=\"#ask questions\">Asking Questions</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>\n",
    "\n",
    "\n",
    "***\n",
    "<a id='ask questions'></a>\n",
    "## Asking Questions\n",
    "\n",
    "Based on dataset, we are interested to know:\n",
    "\n",
    "- Q1: Does having a higher budget results in a higher revenue and profit?\n",
    "- Q2: Which genres are associated with high popularity?\n",
    "- Q3: Which director produces the highest revenue movies?\n",
    "- Q4: Which actor is associated with higher popularity and revenue profit?\n",
    "- Q5: Which year produced movies with highest grossing revenue?\n",
    "\n",
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='wrangling'></a>\n",
    "## Data Wrangling\n",
    "\n",
    "We will prepare the environment, load and assess dataset. Subsequently, we also clean and process the data for analysis.\n",
    "\n",
    "### Preparing the Environment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import libraries\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "\n",
    "# Import CSV\n",
    "df = pd.read_csv('/Users/katiehuang/Documents/Data Analytics/Udacity Data Analyst Nanodegree/Project 2 - TMDB Movie/tmdb-movies.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Assess Data\n",
    "\n",
    "In this step, we view the dataset and get basic information."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>imdb_id</th>\n",
       "      <th>popularity</th>\n",
       "      <th>budget</th>\n",
       "      <th>revenue</th>\n",
       "      <th>original_title</th>\n",
       "      <th>cast</th>\n",
       "      <th>homepage</th>\n",
       "      <th>director</th>\n",
       "      <th>tagline</th>\n",
       "      <th>...</th>\n",
       "      <th>overview</th>\n",
       "      <th>runtime</th>\n",
       "      <th>genres</th>\n",
       "      <th>production_companies</th>\n",
       "      <th>release_date</th>\n",
       "      <th>vote_count</th>\n",
       "      <th>vote_average</th>\n",
       "      <th>release_year</th>\n",
       "      <th>budget_adj</th>\n",
       "      <th>revenue_adj</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>135397</td>\n",
       "      <td>tt0369610</td>\n",
       "      <td>32.985763</td>\n",
       "      <td>150000000</td>\n",
       "      <td>1513528810</td>\n",
       "      <td>Jurassic World</td>\n",
       "      <td>Chris Pratt|Bryce Dallas Howard|Irrfan Khan|Vi...</td>\n",
       "      <td>http://www.jurassicworld.com/</td>\n",
       "      <td>Colin Trevorrow</td>\n",
       "      <td>The park is open.</td>\n",
       "      <td>...</td>\n",
       "      <td>Twenty-two years after the events of Jurassic ...</td>\n",
       "      <td>124</td>\n",
       "      <td>Action|Adventure|Science Fiction|Thriller</td>\n",
       "      <td>Universal Studios|Amblin Entertainment|Legenda...</td>\n",
       "      <td>06/09/2015</td>\n",
       "      <td>5562</td>\n",
       "      <td>6.5</td>\n",
       "      <td>2015</td>\n",
       "      <td>137999939.3</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>137999939.3</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>101199955.5</td>\n",
       "      <td>2.716190e+08</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 21 columns</p>\n",
       "</div>"
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      "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...   06/09/2015       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  137999939.3  1.392446e+09  \n",
       "1           7.1          2015  137999939.3  3.481613e+08  \n",
       "2           6.3          2015  101199955.5  2.716190e+08  \n",
       "\n",
       "[3 rows x 21 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# View first 3 rows\n",
    "df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 10866 entries, 0 to 10865\n",
      "Data columns (total 21 columns):\n",
      " #   Column                Non-Null Count  Dtype  \n",
      "---  ------                --------------  -----  \n",
      " 0   id                    10866 non-null  int64  \n",
      " 1   imdb_id               10856 non-null  object \n",
      " 2   popularity            10866 non-null  float64\n",
      " 3   budget                10866 non-null  int64  \n",
      " 4   revenue               10866 non-null  int64  \n",
      " 5   original_title        10866 non-null  object \n",
      " 6   cast                  10790 non-null  object \n",
      " 7   homepage              2936 non-null   object \n",
      " 8   director              10822 non-null  object \n",
      " 9   tagline               8042 non-null   object \n",
      " 10  keywords              9373 non-null   object \n",
      " 11  overview              10862 non-null  object \n",
      " 12  runtime               10866 non-null  int64  \n",
      " 13  genres                10843 non-null  object \n",
      " 14  production_companies  9836 non-null   object \n",
      " 15  release_date          10866 non-null  object \n",
      " 16  vote_count            10866 non-null  int64  \n",
      " 17  vote_average          10866 non-null  float64\n",
      " 18  release_year          10866 non-null  int64  \n",
      " 19  budget_adj            10866 non-null  float64\n",
      " 20  revenue_adj           10866 non-null  float64\n",
      "dtypes: float64(4), int64(6), object(11)\n",
      "memory usage: 1.7+ MB\n"
     ]
    }
   ],
   "source": [
    "# Basic information of dataset\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Handle Duplicates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check for duplicates\n",
    "df.duplicated().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As there is only 1 duplicated row, we drop the row."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Drop row and set inplace to True to reflect changes in dataset\n",
    "df.drop_duplicates(inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Handle Missing Values\n",
    "\n",
    "Using percentage to find missing values would give a clearer representation of the missing values in the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                       0.000000\n",
       "imdb_id                  0.092039\n",
       "popularity               0.000000\n",
       "budget                   0.000000\n",
       "revenue                  0.000000\n",
       "original_title           0.000000\n",
       "cast                     0.699494\n",
       "homepage                72.977451\n",
       "director                 0.404970\n",
       "tagline                 25.991717\n",
       "keywords                13.741371\n",
       "overview                 0.036815\n",
       "runtime                  0.000000\n",
       "genres                   0.211689\n",
       "production_companies     9.479982\n",
       "release_date             0.000000\n",
       "vote_count               0.000000\n",
       "vote_average             0.000000\n",
       "release_year             0.000000\n",
       "budget_adj               0.000000\n",
       "revenue_adj              0.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find percentage of null values\n",
    "df.isnull().sum()/len(df)*100"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will drop homepage, tagline, keywords and other unnecessary fields that we are not analysing.\n",
    "\n",
    "### Drop Columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Drop columns\n",
    "df.drop(['imdb_id','homepage','tagline','keywords','overview','production_companies','release_date',\n",
    "         'vote_count','budget_adj','revenue_adj'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>popularity</th>\n",
       "      <th>budget</th>\n",
       "      <th>revenue</th>\n",
       "      <th>original_title</th>\n",
       "      <th>cast</th>\n",
       "      <th>director</th>\n",
       "      <th>runtime</th>\n",
       "      <th>genres</th>\n",
       "      <th>vote_average</th>\n",
       "      <th>release_year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>135397</td>\n",
       "      <td>32.985763</td>\n",
       "      <td>150000000</td>\n",
       "      <td>1513528810</td>\n",
       "      <td>Jurassic World</td>\n",
       "      <td>Chris Pratt|Bryce Dallas Howard|Irrfan Khan|Vi...</td>\n",
       "      <td>Colin Trevorrow</td>\n",
       "      <td>124</td>\n",
       "      <td>Action|Adventure|Science Fiction|Thriller</td>\n",
       "      <td>6.5</td>\n",
       "      <td>2015</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>76341</td>\n",
       "      <td>28.419936</td>\n",
       "      <td>150000000</td>\n",
       "      <td>378436354</td>\n",
       "      <td>Mad Max: Fury Road</td>\n",
       "      <td>Tom Hardy|Charlize Theron|Hugh Keays-Byrne|Nic...</td>\n",
       "      <td>George Miller</td>\n",
       "      <td>120</td>\n",
       "      <td>Action|Adventure|Science Fiction|Thriller</td>\n",
       "      <td>7.1</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>262500</td>\n",
       "      <td>13.112507</td>\n",
       "      <td>110000000</td>\n",
       "      <td>295238201</td>\n",
       "      <td>Insurgent</td>\n",
       "      <td>Shailene Woodley|Theo James|Kate Winslet|Ansel...</td>\n",
       "      <td>Robert Schwentke</td>\n",
       "      <td>119</td>\n",
       "      <td>Adventure|Science Fiction|Thriller</td>\n",
       "      <td>6.3</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>140607</td>\n",
       "      <td>11.173104</td>\n",
       "      <td>200000000</td>\n",
       "      <td>2068178225</td>\n",
       "      <td>Star Wars: The Force Awakens</td>\n",
       "      <td>Harrison Ford|Mark Hamill|Carrie Fisher|Adam D...</td>\n",
       "      <td>J.J. Abrams</td>\n",
       "      <td>136</td>\n",
       "      <td>Action|Adventure|Science Fiction|Fantasy</td>\n",
       "      <td>7.5</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>168259</td>\n",
       "      <td>9.335014</td>\n",
       "      <td>190000000</td>\n",
       "      <td>1506249360</td>\n",
       "      <td>Furious 7</td>\n",
       "      <td>Vin Diesel|Paul Walker|Jason Statham|Michelle ...</td>\n",
       "      <td>James Wan</td>\n",
       "      <td>137</td>\n",
       "      <td>Action|Crime|Thriller</td>\n",
       "      <td>7.3</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       id  popularity     budget     revenue                original_title  \\\n",
       "0  135397   32.985763  150000000  1513528810                Jurassic World   \n",
       "1   76341   28.419936  150000000   378436354            Mad Max: Fury Road   \n",
       "2  262500   13.112507  110000000   295238201                     Insurgent   \n",
       "3  140607   11.173104  200000000  2068178225  Star Wars: The Force Awakens   \n",
       "4  168259    9.335014  190000000  1506249360                     Furious 7   \n",
       "\n",
       "                                                cast          director  \\\n",
       "0  Chris Pratt|Bryce Dallas Howard|Irrfan Khan|Vi...   Colin Trevorrow   \n",
       "1  Tom Hardy|Charlize Theron|Hugh Keays-Byrne|Nic...     George Miller   \n",
       "2  Shailene Woodley|Theo James|Kate Winslet|Ansel...  Robert Schwentke   \n",
       "3  Harrison Ford|Mark Hamill|Carrie Fisher|Adam D...       J.J. Abrams   \n",
       "4  Vin Diesel|Paul Walker|Jason Statham|Michelle ...         James Wan   \n",
       "\n",
       "   runtime                                     genres  vote_average  \\\n",
       "0      124  Action|Adventure|Science Fiction|Thriller           6.5   \n",
       "1      120  Action|Adventure|Science Fiction|Thriller           7.1   \n",
       "2      119         Adventure|Science Fiction|Thriller           6.3   \n",
       "3      136   Action|Adventure|Science Fiction|Fantasy           7.5   \n",
       "4      137                      Action|Crime|Thriller           7.3   \n",
       "\n",
       "   release_year  \n",
       "0          2015  \n",
       "1          2015  \n",
       "2          2015  \n",
       "3          2015  \n",
       "4          2015  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Confirm that colummns are dropped\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create Calculated Field"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a column for profit and profit margin\n",
    "df['profit'] = df['revenue'] - df['budget']\n",
    "df['profit_margin'] = df['profit']/df['revenue']*100"
   ]
  },
  {
   "cell_type": "code",
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   "outputs": [
    {
     "data": {
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       "      <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_average</th>\n",
       "      <th>release_year</th>\n",
       "      <th>profit</th>\n",
       "      <th>profit_margin</th>\n",
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       "      <th>count</th>\n",
       "      <td>10865.000000</td>\n",
       "      <td>10865.000000</td>\n",
       "      <td>1.086500e+04</td>\n",
       "      <td>1.086500e+04</td>\n",
       "      <td>10865.000000</td>\n",
       "      <td>10865.000000</td>\n",
       "      <td>10865.000000</td>\n",
       "      <td>1.086500e+04</td>\n",
       "      <td>6164.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>66066.374413</td>\n",
       "      <td>0.646446</td>\n",
       "      <td>1.462429e+07</td>\n",
       "      <td>3.982690e+07</td>\n",
       "      <td>102.071790</td>\n",
       "      <td>5.975012</td>\n",
       "      <td>2001.321859</td>\n",
       "      <td>2.520261e+07</td>\n",
       "      <td>-inf</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>92134.091971</td>\n",
       "      <td>1.000231</td>\n",
       "      <td>3.091428e+07</td>\n",
       "      <td>1.170083e+08</td>\n",
       "      <td>31.382701</td>\n",
       "      <td>0.935138</td>\n",
       "      <td>12.813260</td>\n",
       "      <td>9.659103e+07</td>\n",
       "      <td>NaN</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>1.500000</td>\n",
       "      <td>1960.000000</td>\n",
       "      <td>-4.139124e+08</td>\n",
       "      <td>-inf</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>10596.000000</td>\n",
       "      <td>0.207575</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>90.000000</td>\n",
       "      <td>5.400000</td>\n",
       "      <td>1995.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>-618.520453</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>20662.000000</td>\n",
       "      <td>0.383831</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>2006.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>47.321674</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>75612.000000</td>\n",
       "      <td>0.713857</td>\n",
       "      <td>1.500000e+07</td>\n",
       "      <td>2.400000e+07</td>\n",
       "      <td>111.000000</td>\n",
       "      <td>6.600000</td>\n",
       "      <td>2011.000000</td>\n",
       "      <td>9.092453e+06</td>\n",
       "      <td>85.279760</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>9.200000</td>\n",
       "      <td>2015.000000</td>\n",
       "      <td>2.544506e+09</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
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      "text/plain": [
       "                  id    popularity        budget       revenue       runtime  \\\n",
       "count   10865.000000  10865.000000  1.086500e+04  1.086500e+04  10865.000000   \n",
       "mean    66066.374413      0.646446  1.462429e+07  3.982690e+07    102.071790   \n",
       "std     92134.091971      1.000231  3.091428e+07  1.170083e+08     31.382701   \n",
       "min         5.000000      0.000065  0.000000e+00  0.000000e+00      0.000000   \n",
       "25%     10596.000000      0.207575  0.000000e+00  0.000000e+00     90.000000   \n",
       "50%     20662.000000      0.383831  0.000000e+00  0.000000e+00     99.000000   \n",
       "75%     75612.000000      0.713857  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_average  release_year        profit  profit_margin  \n",
       "count  10865.000000  10865.000000  1.086500e+04    6164.000000  \n",
       "mean       5.975012   2001.321859  2.520261e+07           -inf  \n",
       "std        0.935138     12.813260  9.659103e+07            NaN  \n",
       "min        1.500000   1960.000000 -4.139124e+08           -inf  \n",
       "25%        5.400000   1995.000000  0.000000e+00    -618.520453  \n",
       "50%        6.000000   2006.000000  0.000000e+00      47.321674  \n",
       "75%        6.600000   2011.000000  9.092453e+06      85.279760  \n",
       "max        9.200000   2015.000000  2.544506e+09     100.000000  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***\n",
    "\n",
    "<a id='eda'></a>\n",
    "## Exploratory Data Analysis\n",
    "\n",
    "## Q1: Does a higher budget results in higher revenue and profit?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
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       "      <th>count</th>\n",
       "      <td>10865.000000</td>\n",
       "      <td>10865.000000</td>\n",
       "      <td>1.086500e+04</td>\n",
       "      <td>1.086500e+04</td>\n",
       "      <td>10865.000000</td>\n",
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       "      <td>1.086500e+04</td>\n",
       "      <td>6164.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>66066.374413</td>\n",
       "      <td>0.646446</td>\n",
       "      <td>1.462429e+07</td>\n",
       "      <td>3.982690e+07</td>\n",
       "      <td>102.071790</td>\n",
       "      <td>5.975012</td>\n",
       "      <td>2001.321859</td>\n",
       "      <td>2.520261e+07</td>\n",
       "      <td>-inf</td>\n",
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       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>92134.091971</td>\n",
       "      <td>1.000231</td>\n",
       "      <td>3.091428e+07</td>\n",
       "      <td>1.170083e+08</td>\n",
       "      <td>31.382701</td>\n",
       "      <td>0.935138</td>\n",
       "      <td>12.813260</td>\n",
       "      <td>9.659103e+07</td>\n",
       "      <td>NaN</td>\n",
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       "    <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>1.500000</td>\n",
       "      <td>1960.000000</td>\n",
       "      <td>-4.139124e+08</td>\n",
       "      <td>-inf</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>10596.000000</td>\n",
       "      <td>0.207575</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>90.000000</td>\n",
       "      <td>5.400000</td>\n",
       "      <td>1995.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>-618.520453</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>20662.000000</td>\n",
       "      <td>0.383831</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>2006.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>47.321674</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>75612.000000</td>\n",
       "      <td>0.713857</td>\n",
       "      <td>1.500000e+07</td>\n",
       "      <td>2.400000e+07</td>\n",
       "      <td>111.000000</td>\n",
       "      <td>6.600000</td>\n",
       "      <td>2011.000000</td>\n",
       "      <td>9.092453e+06</td>\n",
       "      <td>85.279760</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>9.200000</td>\n",
       "      <td>2015.000000</td>\n",
       "      <td>2.544506e+09</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  id    popularity        budget       revenue       runtime  \\\n",
       "count   10865.000000  10865.000000  1.086500e+04  1.086500e+04  10865.000000   \n",
       "mean    66066.374413      0.646446  1.462429e+07  3.982690e+07    102.071790   \n",
       "std     92134.091971      1.000231  3.091428e+07  1.170083e+08     31.382701   \n",
       "min         5.000000      0.000065  0.000000e+00  0.000000e+00      0.000000   \n",
       "25%     10596.000000      0.207575  0.000000e+00  0.000000e+00     90.000000   \n",
       "50%     20662.000000      0.383831  0.000000e+00  0.000000e+00     99.000000   \n",
       "75%     75612.000000      0.713857  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_average  release_year        profit  profit_margin  \n",
       "count  10865.000000  10865.000000  1.086500e+04    6164.000000  \n",
       "mean       5.975012   2001.321859  2.520261e+07           -inf  \n",
       "std        0.935138     12.813260  9.659103e+07            NaN  \n",
       "min        1.500000   1960.000000 -4.139124e+08           -inf  \n",
       "25%        5.400000   1995.000000  0.000000e+00    -618.520453  \n",
       "50%        6.000000   2006.000000  0.000000e+00      47.321674  \n",
       "75%        6.600000   2011.000000  9.092453e+06      85.279760  \n",
       "max        9.200000   2015.000000  2.544506e+09     100.000000  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We observed something strange here. At least 50% of revenue are 0. And, if you look at profit column, there is a negative profit in the min row and same situation as revenue, half of it are recording $0 profit.\n",
    "\n",
    "Looking at the new column profit_margin, only 6,164 rows (57%) have profit margins which means the remaining rows have either a $0 in budget or revenue. \n",
    "\n",
    "We will create a subset where budget is more than 0."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.8/site-packages/numpy/lib/function_base.py:3961: RuntimeWarning: invalid value encountered in subtract\n",
      "  diff_b_a = subtract(b, a)\n"
     ]
    },
    {
     "data": {
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       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>popularity</th>\n",
       "      <th>budget</th>\n",
       "      <th>revenue</th>\n",
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>5169.000000</td>\n",
       "      <td>5169.000000</td>\n",
       "      <td>5.169000e+03</td>\n",
       "      <td>5.169000e+03</td>\n",
       "      <td>5169.000000</td>\n",
       "      <td>5169.000000</td>\n",
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       "      <td>5169.000000</td>\n",
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       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>45513.725479</td>\n",
       "      <td>0.992400</td>\n",
       "      <td>3.073958e+07</td>\n",
       "      <td>8.029101e+07</td>\n",
       "      <td>107.096150</td>\n",
       "      <td>6.032308</td>\n",
       "      <td>2001.251112</td>\n",
       "      <td>4.955143e+07</td>\n",
       "      <td>-inf</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>72730.345078</td>\n",
       "      <td>1.329262</td>\n",
       "      <td>3.890441e+07</td>\n",
       "      <td>1.594865e+08</td>\n",
       "      <td>22.810038</td>\n",
       "      <td>0.884125</td>\n",
       "      <td>11.776443</td>\n",
       "      <td>1.351328e+08</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.001117</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>1960.000000</td>\n",
       "      <td>-4.139124e+08</td>\n",
       "      <td>-inf</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>8840.000000</td>\n",
       "      <td>0.347958</td>\n",
       "      <td>6.000000e+06</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>93.000000</td>\n",
       "      <td>5.500000</td>\n",
       "      <td>1996.000000</td>\n",
       "      <td>-6.597680e+06</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>13008.000000</td>\n",
       "      <td>0.624024</td>\n",
       "      <td>1.700000e+07</td>\n",
       "      <td>2.100156e+07</td>\n",
       "      <td>103.000000</td>\n",
       "      <td>6.100000</td>\n",
       "      <td>2005.000000</td>\n",
       "      <td>2.507368e+06</td>\n",
       "      <td>16.956397</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>44943.000000</td>\n",
       "      <td>1.152417</td>\n",
       "      <td>4.000000e+07</td>\n",
       "      <td>8.945676e+07</td>\n",
       "      <td>117.000000</td>\n",
       "      <td>6.600000</td>\n",
       "      <td>2010.000000</td>\n",
       "      <td>5.279686e+07</td>\n",
       "      <td>69.288745</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>540.000000</td>\n",
       "      <td>8.400000</td>\n",
       "      <td>2015.000000</td>\n",
       "      <td>2.544506e+09</td>\n",
       "      <td>99.999902</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  id   popularity        budget       revenue      runtime  \\\n",
       "count    5169.000000  5169.000000  5.169000e+03  5.169000e+03  5169.000000   \n",
       "mean    45513.725479     0.992400  3.073958e+07  8.029101e+07   107.096150   \n",
       "std     72730.345078     1.329262  3.890441e+07  1.594865e+08    22.810038   \n",
       "min         5.000000     0.001117  1.000000e+00  0.000000e+00     0.000000   \n",
       "25%      8840.000000     0.347958  6.000000e+06  0.000000e+00    93.000000   \n",
       "50%     13008.000000     0.624024  1.700000e+07  2.100156e+07   103.000000   \n",
       "75%     44943.000000     1.152417  4.000000e+07  8.945676e+07   117.000000   \n",
       "max    417859.000000    32.985763  4.250000e+08  2.781506e+09   540.000000   \n",
       "\n",
       "       vote_average  release_year        profit  profit_margin  \n",
       "count   5169.000000   5169.000000  5.169000e+03    5169.000000  \n",
       "mean       6.032308   2001.251112  4.955143e+07           -inf  \n",
       "std        0.884125     11.776443  1.351328e+08            NaN  \n",
       "min        1.500000   1960.000000 -4.139124e+08           -inf  \n",
       "25%        5.500000   1996.000000 -6.597680e+06            NaN  \n",
       "50%        6.100000   2005.000000  2.507368e+06      16.956397  \n",
       "75%        6.600000   2010.000000  5.279686e+07      69.288745  \n",
       "max        8.400000   2015.000000  2.544506e+09      99.999902  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "budget = df.query('budget > 0')\n",
    "budget.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is a better representation to analyse revenue and profit."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Budget\n",
    "x_ticks = np.arange(1.000000e+00,5.000000e+08,5.000000e+07)\n",
    "\n",
    "budget['budget'].hist(bins=x_ticks)\n",
    "plt.title(\"Bar Chart of TMDB Movie Budget\")\n",
    "plt.xlabel(\"Budget (hundred million)\")\n",
    "plt.xticks(x_ticks)\n",
    "plt.ylabel(\"Number of TMDB Movie\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Revenue\n",
    "x_ticks = np.arange(0.000000e+00,3.000000e+09,5.000000e+08)\n",
    "\n",
    "budget['revenue'].hist(bins=x_ticks)\n",
    "plt.title(\"Bar Chart of TMDB Movie Revenue\")\n",
    "plt.xlabel(\"Revenue (billion)\")\n",
    "plt.xticks(x_ticks)\n",
    "plt.ylabel(\"Number of TMDB Movie\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Budget vs Revenue\n",
    "budget.plot(y='revenue',x='budget',kind='scatter')\n",
    "\n",
    "plt.title(\"Scatter Plot of TMDB Movie Budget vs. Revenue\")\n",
    "plt.xlabel(\"Budget (100 million)\")\n",
    "plt.ylabel(\"Revenue (100 million)\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### In general, movies with a higher budget results in a higher revenue."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Budget vs Profit\n",
    "budget.plot(y='profit',x='budget',kind='scatter')\n",
    "\n",
    "plt.title(\"Scatter Plot of TMDB Movie Budget vs. Profit\")\n",
    "plt.xlabel(\"Budget (100 million)\")\n",
    "plt.ylabel(\"Profit (100 million)\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### In general, movies with a higher budget results in a higher profit."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Based on an Investopedia article, [Why Movies Cost So Much To Make](https://www.investopedia.com/financial-edge/0611/why-movies-cost-so-much-to-make.aspx), \n",
    "\n",
    "> The average cost to produce a major studio movie has been around USD 65 million. But the production costs don't cover distribution and marketing, which adds another USD 35 million or so, on average, bringing the total cost to produce and market a major movie to right about USD 100 million. \n",
    "\n",
    "We will filter for budgets with USD 100 million and more. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>popularity</th>\n",
       "      <th>budget</th>\n",
       "      <th>revenue</th>\n",
       "      <th>runtime</th>\n",
       "      <th>vote_average</th>\n",
       "      <th>release_year</th>\n",
       "      <th>profit</th>\n",
       "      <th>profit_margin</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>282.000000</td>\n",
       "      <td>282.000000</td>\n",
       "      <td>2.820000e+02</td>\n",
       "      <td>2.820000e+02</td>\n",
       "      <td>282.000000</td>\n",
       "      <td>282.000000</td>\n",
       "      <td>282.000000</td>\n",
       "      <td>2.820000e+02</td>\n",
       "      <td>282.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>46172.187943</td>\n",
       "      <td>3.300546</td>\n",
       "      <td>1.544628e+08</td>\n",
       "      <td>4.778548e+08</td>\n",
       "      <td>123.031915</td>\n",
       "      <td>6.345035</td>\n",
       "      <td>2008.485816</td>\n",
       "      <td>3.233920e+08</td>\n",
       "      <td>-inf</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>62780.785336</td>\n",
       "      <td>3.535419</td>\n",
       "      <td>4.436161e+07</td>\n",
       "      <td>3.509745e+08</td>\n",
       "      <td>33.718360</td>\n",
       "      <td>0.736825</td>\n",
       "      <td>4.961935</td>\n",
       "      <td>3.362143e+08</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>22.000000</td>\n",
       "      <td>0.157586</td>\n",
       "      <td>1.020000e+08</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>76.000000</td>\n",
       "      <td>4.400000</td>\n",
       "      <td>1994.000000</td>\n",
       "      <td>-4.139124e+08</td>\n",
       "      <td>-inf</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2030.000000</td>\n",
       "      <td>1.507585</td>\n",
       "      <td>1.250000e+08</td>\n",
       "      <td>2.453954e+08</td>\n",
       "      <td>102.000000</td>\n",
       "      <td>5.800000</td>\n",
       "      <td>2005.000000</td>\n",
       "      <td>1.089377e+08</td>\n",
       "      <td>44.387952</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>14515.000000</td>\n",
       "      <td>2.437016</td>\n",
       "      <td>1.500000e+08</td>\n",
       "      <td>3.786594e+08</td>\n",
       "      <td>121.000000</td>\n",
       "      <td>6.350000</td>\n",
       "      <td>2010.000000</td>\n",
       "      <td>2.417142e+08</td>\n",
       "      <td>63.528171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>68723.250000</td>\n",
       "      <td>3.964661</td>\n",
       "      <td>1.750000e+08</td>\n",
       "      <td>6.303428e+08</td>\n",
       "      <td>137.000000</td>\n",
       "      <td>6.800000</td>\n",
       "      <td>2013.000000</td>\n",
       "      <td>4.886075e+08</td>\n",
       "      <td>75.959035</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>540.000000</td>\n",
       "      <td>8.100000</td>\n",
       "      <td>2015.000000</td>\n",
       "      <td>2.544506e+09</td>\n",
       "      <td>91.479435</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  id  popularity        budget       revenue     runtime  \\\n",
       "count     282.000000  282.000000  2.820000e+02  2.820000e+02  282.000000   \n",
       "mean    46172.187943    3.300546  1.544628e+08  4.778548e+08  123.031915   \n",
       "std     62780.785336    3.535419  4.436161e+07  3.509745e+08   33.718360   \n",
       "min        22.000000    0.157586  1.020000e+08  0.000000e+00   76.000000   \n",
       "25%      2030.000000    1.507585  1.250000e+08  2.453954e+08  102.000000   \n",
       "50%     14515.000000    2.437016  1.500000e+08  3.786594e+08  121.000000   \n",
       "75%     68723.250000    3.964661  1.750000e+08  6.303428e+08  137.000000   \n",
       "max    417859.000000   32.985763  4.250000e+08  2.781506e+09  540.000000   \n",
       "\n",
       "       vote_average  release_year        profit  profit_margin  \n",
       "count    282.000000    282.000000  2.820000e+02     282.000000  \n",
       "mean       6.345035   2008.485816  3.233920e+08           -inf  \n",
       "std        0.736825      4.961935  3.362143e+08            NaN  \n",
       "min        4.400000   1994.000000 -4.139124e+08           -inf  \n",
       "25%        5.800000   2005.000000  1.089377e+08      44.387952  \n",
       "50%        6.350000   2010.000000  2.417142e+08      63.528171  \n",
       "75%        6.800000   2013.000000  4.886075e+08      75.959035  \n",
       "max        8.100000   2015.000000  2.544506e+09      91.479435  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "blockbuster = budget.query('budget > 1.000000e+08')\n",
    "blockbuster.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(282, 13)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "blockbuster.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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OSaqmKCTNkHSvpKclrZL0pYQ6kvQDSaslrZR0YDVkdRzHGcnUVfHeXcA5Zva4pHHAMkl3mtlTkTofBfYO/+YBPwr/dxzHcQaJqs0ozOwVM3s8fL0FeBqYHqt2HHCNBTwMjJc0dZBFdRzHGdHUhI9C0izg3cAjsUPTgXWR9y0UKhPHcRxnAKm6opA0FlgCfNnMNscPJ5xiRa5zlqSlkpZu2LChv8V0HMcZsVRVUUiqJ1AS15rZzQlVWoAZkfdNwPqka5nZIjNrNrPmKVOm9L+wjuM4I5RqRj0J+BnwtJldWqTarcBpYfTTocCbZvbKoAnpOI7jVDXqaT7wKeAJScvDsq8DMwHM7ErgDuBoYDXwFnDG4IvpOI4zsqmaojCzB0j2QUTrGPD5wZHIcRzHSaLqzmxn5NLa1s6KdW/Q2tZebVEcxylBNU1PzgjmluUvc/6SldSnUnRms1x0/AEsmOuRz45Ti/iMwhl0WtvaOX/JSrZ3ZtnS3sX2ziznLVnpMwvHqVFcUTiDTsumbdSn8n969akULZu2VUkix3FK4YrCGXSaJoymM5vNK+vMZmmaMLpKEjmOUwpXFM6gM2lsIxcdfwCj6lOMa6xjVH2Ki44/gEljG6stmuM4Cbgz26kKC+ZOZ/7sybRs2kbThNGuJBynhnFF4VSNSWMbXUE4zhDATU+O4zhOSVxROI7jOCVxReE4juOUxBWF4ziOUxJXFI7jOE5JXFE4juM4JXFF4TiO45TEFYVTNTzNuOMMDXzBnVMVPM244wwdfEbhDDqeZtxxhhauKJxBx9OMO87QwhWFM+h4mnHHGVq4onAGHU8z7jhDC3dmO1XB04w7ztDBFYVTNTzNuOMMDdz05DiO45TEFYXjOI5TkooUhaSUpHdL+pikoyTt2h83l3SVpNckPVnk+JGS3pS0PPz7Zn/c13Ecx6mckj4KSXsB5wMfBJ4HNgCjgH0kvQX8GPi5mWWLX6UkVwOXA9eUqHO/mR3Ty+s7juM4faScM/tfgB8Bf29mFj0g6W3AKcCngJ/35uZmdp+kWb0513EcxxkcSioKMzu5xLHXgP/sb4ESOEzSCmA9cK6ZrUqqJOks4CyAmTNnDoJYjuM4I4OKw2MlvQeYFT3HzEqZjPqDx4HdzaxN0tHAb4C9kyqa2SJgEUBzc7Ml1XEcx3F6TkWKQtIvgL2A5UAmLDZK+xb6jJltjry+Q9IPJU02s40DeV/HcRxnB5XOKJqBfeN+ioFG0m7Aq2Zmkg4hiNJqHUwZHMdxRjqVKoongd2AV/rz5pKuB44EJktqAS4A6gHM7EpgIfAPkrqAbcAnBltZOY7jjHQqVRSTgackPQp0bxpgZgv6cvNSzvLw+OUE4bOO4zhOlahUUXxrIIVwek9rW7sn1nMcZ0CpSFGY2Z/C1dgHh0WPhuGxThXx7UQdxxkMKk3hcSLwKHACcCLwiKSFAymYUxrfTtRxnMGiUtPTPwEH52YRkqYAdwE3DZRgTmly24luZ0f2lNx2orVggnKTmOMMHypVFKmYqakVzzxbVWp5O9FqmMRcMTnOwFGpovi9pP8Brg/fnwTcMTAiOZWQ2070vFiHXO1OMmoSy812zluykvmzJw+YbO6rcZyBpVJn9lclHQ/MBwQsMrNfD6hkTllqcTvRwTaJVUMxOc5Io+JcT2a2BFgygLI4vaDWthMdbJNYrftqHGc4UNLPIOmB8P8tkjZH/rZI2lzqXGdkkjOJjapPMa6xjlH1qQE1idWyr8Zxhgvl0owfHv4/bnDEcYYDg2kSq1VfjeMMJ8rtcDex1HEze71/xXGGC4NpEqtFX43jDCfK+SiWEaQTV8IxA/bsd4mGGR62OTjUmq/GcYYT5UxPewyWIMMRD9t0HGc4UM70dGCp42b2eP+KM3zwsE3HcYYL5UxPl5Q4ZsBR/SjLsMLDNh3HGS6UMz29f7AEGW542KbjOMOFcqano8zsHkn/J+m4md08MGINfQYrbNOd5SMH/66dalHO9PQ+4B7g2IRjBriiKMFAh226s3zk4N+1U000HLegbm5utqVLl1ZbjAGlta2d+d+7h+2dO8xbo+pTPHj+UT7aHGb4d+0MBpKWmVlz0rFKNy6aJOkHkh6XtEzSZZIm9a+YTk/IOcuj5JzlzvDCv2un2lS6p8QNwAbgeGBh+PpXAyWUUx53lo8c/Lt2qk2limKimX3HzNaEf/8CjB9AuZwyDHbyPad6+HftVJtK04zfK+kTwOLw/ULgtwMjklMpnuNo5ODftVNNSjqzJW1hR66nMUAmPJQG2sxs5wGXsBeMBGf2cMDDPR2ndijlzC634G5A04tLugo4BnjNzPZPOC7gMuBo4C3gdE8bMjzwcE/HGTqU27hoVpnjktTUh/tfDXykxPGPAnuHf2cBP+rDvZwaIZoHa0t7F9s7s5y3ZCWtbe3VFs1xnATK+SgulpQCbiFIOb4BGAXMBt4PfAC4AGjpzc3N7L4yyug44BoL7GMPSxovaaqZvdKb+zm1gefBcpyhRTnT0wmS9gVOBf4OmEpgAnoauAP4rpltH0D5pgPrIu9bwrICRSHpLIJZBzNnzhxAkZy+4uGejjO0KBv1ZGZPAf80CLIkUWzDpMJCs0XAIgic2QMplNM3fPtSxxlaVBoeWy1agBmR903A+irJ4vSBeISTh3s6ztCh1hXFrcDZkm4A5gFvun9i6FEswsm3L3WcoUFVFYWk64EjgcmSWggc4/UAZnYlgR/kaGA1gW/kjOpI6vSWUjv9AT6jcJwhQEWKQtLdZvaBcmU9xcxOLnPcgM/35R5OdSkW4XTtIy/xwz+u9nUUjjMEKLeOYpSkiQQj/gmSJoZ/s4BpgyKhM6RJinDqyGS44t7Vvo7CcYYI5ZIC/j3B+ol3AI+Hr5cRrKu4YmBFGx60trWzYt0bI7YTTEpod/b794ZY6hjLmqfNdpwapdw6isuAyyR9wcz+a5BkGjZ4moqAeITTpq0dXHLnc3l12jPGmIZ0lSR0HKcUFe2ZDbyctG+275ldnFJO3JHouI1GOK1a/2ZinfVvbmP2rgOaXqzf8ISGzkiinDP7CHzP7KKU6ixGUpqKnneaSesoS5XXFj5TdEYa5RTFpvD/n5nZAwMtzFCiXGcxUtJU9KbTnLbLqB6V1xI+U3RGIuWc2bl1Cz8YaEGGEpVkPx2sXcmq6SzvbRbYrR0ZGtP5s4fGtNjakSlyRu3g+1c7I5FyM4qnJb0ITJG0MlIugmUOBwyYZDVMpWalgU5TccvylznvppWkUyKTNS5eOLgmkN6a15omjEYpQWZH5JNSGhKzrZEyU3ScKCVnFOGCuEMJVkYfG/k7hmS/xYigJ53FpLGNzJkxfkBmEufeuIL2rixvdWRo78pyzo0rBnVm0dtOcyjvAT1pbCMnHpS/BcuJzU1DQnbH6S3lTE+Y2V/NbA5Bau9x4d96M1s70MLVMp8/cjaNdapaR7dq/Zt0ZvLXInRmrGhE0UDQlw5/wdzpPHj+UfzyM/N48PyjhowzuLWtncXL8rdfWby0ZcSuk3FGBpWm8HgfcA3wIoHZaYakT5vZfQMoW00Sdd6COOuIPTll3sweKYn+CK3cvK2zR+UDRV/Ma0MxKeBIimZznByVJgW8FPiwmT0LIGkf4HrgoIESrBZJini54o+rOWXezLw6pTrN/gqt3Hl0Q4/KB5Kh2OH3FvdROCORShVFfU5JAJjZc5LqB0immqXcaLKcEujP0Mr9pu1MXQq6In1WXSoodwaOSWMb2X3iaJ59dWt32e4TfdGdM7wp66MIWSbpZ5KODP9+QpDzaURRajRZSahoT0Iry4W9ThrbmDeTAXpsAquEkZ6rKs7SNa15SgLg2Ve3snRNa5UkcpyBp1JF8TlgFfBF4EvAU2HZiKKU87YSJVCp2eKW5S8z/3v38MmfPsL8793DrctfLpClta2dxUsrc6r2trOvRI6Rxn3Pb+xReS3iyt/pKWVNT5JSwDIz25/AVzGiKea8rUQJ5EIrr3n4pe6yeGhlpeapSp2qvfWJ+ArkZOY07dKj8lrD04/0HM/rVVl4bBZYIWlmubojhaS1EcVmG5u2dnDT0nWsfnVLRaGVlZqnKlFMvV053RM5Rgq5UXh9XaogI5WAyeNGJdavpVF7X34PIxWfVQdU6syeCqyS9CjQbaA1swUDIlUN0JtRRHy2cdldz/HFG5Z3Hz/6XbuWnQVUap7KKabzYqPD+Kyjsyt2ra5sRaGcHt2zg+govCOToS6tvDUsjfWpvHap1VG7h/b2DJ9V76BSRXHhgEpRYyQ96JWuFciFiq5+dUueiQngjidepbEuf5SeZJ4qpgDiyqvcGobOrgyxNXlkLChPIn79coqo2HnDiaTOoi4FjXUpGtKF7VLLnYsr/57hinUH5fajGEXgtJ4NPEGQRbZrMASrFkkP+jk3riAlaEinKx4hLl/3RmL58QdO5+Y/v1yy801SAL0Zpb7Y+lbR8j2mjK3o+uUUZF9Gz0NBwSR1FqPr67ji1APZZXR9gey13Ln0RPk7rlijlJtR/BzoBO4HPgrsSxD1NGxJetBzZob2rkBHVjJCnDtjfGL5383fg3M+/PayHWR0EVuxUeqW7V1857dPFe2ki8nw+tYO5n/vnu7zvnHMvnzn9qeKjoKjMq5+dQvL173B3BnjmTCmodej52onNKyUYp3FftN2TvyMtd65DHSiyuGEK9YdlFMU+5rZuwAk/Qx4dOBFqi5JD3qcSkaIs3cdx2mHzeSah3aYn047bGb3Dm49+bElKa+0xIW3P0VHV/FOOkmGE5ubuPSu5/I69wtve4r6VL6LNuq4jvpcoua0j+2/W69Gz7mEhlE7/zk3rqgJ80ycnnYWQ6FzGUkr6fuKK9aAcoqiO3GQmXVJQ2MHsr4Qf9A7Mlky2WzeCuhKR4jfPu5dnHborO4ReG+3+UwcpWayBdE3lrWCTjouw9aODL974q/5nXtahU7vbJYnX36TkxY9RH0qRXtXho6Yw+O3T/6Vhti+EpW0TamEhkfs87aS58Lgm6x62ll45zK8cMVaXlHMkbQ5fC1gdPg+tx/FsMwXEX/QH1y9seIRYrwTm73ruD7vAz1pbCMnNjflzQw+sv+u3Lrir3n12jPGmIZ0wflRGVrb2guUTiZrvHvmeB5Zs6m7bE7TLnznt/nmqCROOGgGS/7c0sPRc++3Qq1WRFFPOwvvXJzhRElFYWaFvc4IIfqgVzpC7O9OLKd0xjSkC1Zh//7JV6lLia7sjpF5fQW7xCWZRr7ywX341989k1fvkTWbEpVOnDPmz+IrH96nR6Pn3m6FWssRRY4znKk0PHZAkPQR4DIgDfzUzP49dvxI4BZgTVh0s5l9ezBlzFFuhFisE9t36s5s7cjkdaKVmE6iSqc9k0WWb6qpS4u3OuLmqOQZRbmw2nufeS1Rho5M/vVTgohe6rXPZWtHJjB3ZXqm5GopomgoRGw5Tn9RNUUhKQ1cAXwIaAEek3SrmT0Vq3q/mR0z6AKGVNohJHViAEf/4H4a63aE1RqUnXUkKZ04cRt/jvVvbs8zdVUyyykWHfXVD7+dS+96Lu/cfafu3Gefy5iGdKKPotwMplYiimp1QZ3jDBTVnFEcAqw2sxcAJN0AHEeQcLAmiK/IPfv9exfN0No0YTTbOvOXmGzvDDq1jkxQ/tWbVgJGe5f1OI/TqPoUmaxRnw7+/7vDZ/GjP76QIPWODrgnYbVJEVpnvW8vjj+oqUBR9tXnsrUjQ2NatEdXN/fSbDbYEUVu/nJGItVUFNOBdZH3LcC8hHqHSVoBrAfONbNVSReTdBZwFsDMmX1PS5XUIVxy53Ncfu/q7pj/6GwjlIFoRx0nnRKYgB0dYgqxav1m9pu2c/e1kkbOmawhLLy80TR+J+J3E7DftB3J6XoSVvvg+UclRmgNhFO2acJolBLRZeNKqaKZQbUjimrJ/OU4g0U1FUVSiEu8l30c2N3M2iQdDfwG2DvpYma2CFgE0NzcXLy3rpBipqT2rizn3LiiYFT++SNnM6ouTWem+ML1TNaIf8S3OjOc+fPHMDNG19d1j5IvOv4AvhpZkJbJZunMQkcmUDIX3vZkQWMZsGlrR+m8UZksSWHOLZu2MacP5qSe0NeZQaXKayD8CLVi/nKcwaTS/SgGghZgRuR9E8GsoRsz22xmbeHrO4B6SZMHQ7hSC+86M8a3bnsyLwvn5fc+X+D8rU+L+hQ0plM0pOHihQdw8cI5xE3xnRmjK0teRs8t27sgnEFkzUjHOncrogr/Z9WOkNmkjLbnfvjttMfWTGzvzFYU4dSfLJg7ndvPPpwLjt2X288+vN9t/AOV9bPUniSOM1yp5oziMWBvSXsALwOfAE6JVpC0G/CqmZmkQwgU26BsJZbrEM5ZvJzOBH1RpxSdkdlGQzrNWUfsyRV/XN09Sm7efQIPrG6FsN7Sta9z0O4TsQKjUT7plLjwtlXhArdku31Xsg7jrY78GU3cVNOyaVuv/AP9zUA6hAfaj1Bt85fjDDZVUxThSu+zgf8hCI+9ysxWSfpcePxKYCHwD5K6gG3AJ8yKjaX7h6i5YsHc6ew7dWf+5j/vy8vCmhZkYx19ZzbLKfNmcsq8mWF67wwLf/xwXp1rHnqJGx59ic4yfXJSRFNdClJSdwTVJ5pncPVDawvqvXNq4QY6cVNNb/0D/cVAd+SD4UfwBXXOSKKq6yhCc9IdsbIrI68vBy4fLHmKjXL/46S5fPWmFaSVImNZLl44B6CojX3S2EZuWrou8R7ZBDUXX5/wkf125dYVr+TV6crCTX8/j/q6dHen/otHXgr9HgHplDhsr0klP2MtRA61bNqGxRoiKf1Ib2maMJrtsVTq27sy7kdwnF5SVUVRS5Qa5S6YO51pu4zivuc3csTek2neI+iMS5kfiq1NSDIZpWOK4vdP/pXGulSeL2FUfYr6ujRzItf9jxPnhCG3ARcvrGy/iGqbTsY0pPNMX1A8/UhviU88B3gi6jjDGlcUIaXMFdGsqT+4ZzWnHTaTbx/3rkTzQ7RTjq9N+Nj+u3HX06/mdZKBwztFZzYTKUvRmTD1iI+I+7JfRG9NJ0mRRD2NLtrakaEula8061Lk+Un6ErHUsmkbo+vr2NK+w18zur7OQ1gdp5e4oghJWjC3rbOLzq5MwU511zz0EqcdOqsglDTeKZ/Y3ERDGkQKI8t79p7M3c++lucfSElkLLZmwowLjg32iChnHirV4ZeaJQE97oiTlE4lK83jjGlIF8ysurJ0zyj66uiupRBWT/XhDAdcUUSIL5iTxJPrNyfWXb7ujTxFkdQp75hNBO+/c/tTLJgzLS/B30kHN9G8+8QCn8GCudOZN2ti2XQZpTqiYrOkax95iR9GorNKdcTRxITxz/fVm1YAor3EnhhJlJpR9Iejuxb8MOCpPpzhgyuKkJZN2woWzI2qSzO5SOcS90EUW6AXJS3xm+V5S0VYvLSFL31gHx48/6iCrUnPu2lldyBt0g5w5TqipJF1RybDFfeuTuzcc58jaXvUpMSEaaUKlk1WEl1UakbRXxFL1fbDeKoPZzhRzQV3NUUxc8Vhe03itMPyU4JEs6aWOj9OZyZbsNFPtBOcM2M8k8Y20trWzlcWrwg6864s7V1Z/t/iFbS2tXefF+2Iogv1WtvaaW1rZ0W4Z3d8cdjZ79+7YLWeZY1rH3kpb4HatQ+vzbt+R1e2wAGdsWxe1FWuzcqZeLZ2ZBhVn//TG1Wf6s6y219mo2ibDjY5hRclumug4wwlfEYRUspc8e3j3sWCA6YVRD3Fz49vLvTe2ZN4bO2mHXtTf2xfvvPb/JyHSZ3gQ39pLeiAM1njob+0csycaQBFO5wks1J0trJpaweX3Plc3jntGSuYZVx42yoa6go782zW8rLhQnKYcCmTWNOE0XTFVrF3ZbLddWvBbNRXaslP4jh9xRVFhPmzJ/P9hXPY2Ladw2dP6Z415MxA6ZT48X0vFE0KGN9c6LG1m7j97MPz9qN49tXNBXtYxzvBta1bE+V7+pU3mTFxJ5omjGZMQ7o7O22O7Z1ZLr/nOToyFCT8y4XVtmzaxqj6VN65DWkFCQsj1KdTBVufAtzxxfcW7K8RN/FUYptP8gflqLbZqD8YLgrPccAVRTe3LH+ZcxYv77ad16We5qTmmXz83dM498YVeauliyUFTLKtr39zG7uMbgACc1FcmdzwWEtBBNXuk8Ykyvjj+17gmodeojOb5Yz3zEqsE7d+xReyJY1oJQpnMEUir5Kc6tHIq0ps88X8QVE5h8PK5+Gg8BwHXFEAQed23k0r8hysXVm49tGXuPbRlwrq55ICdkZG7pff+zxdsRF4W3sXZ/78MRrSabqyxjHvmlowC+joynL0D+7n3L95OxN3amDujPEcttek7qyxUXKJAwF+cn/SXhTQFZsExBeyFRvpAnmL93KzgI/st1teR1cu3LMSZ/RIMssMB4XnOK4oCDq3tFIUS8CXRDwpYF0qRWcm/3wDOjN0ly/5c3IG046M8a937Niz+rTDZnLqvBl5Jqp0fnqmgv2yd8iVryxyTuIoSSPdb/7mibyV4EvXvt7jKCuoTAkk+XOSTHCO49QGrigIOrf4ordydCWEnSblceoN1zz0Eo0xR3LcXVBsK9Sk0qSRenSku/rVLYmLCpvG79S9FWp7V/D5urKld+erxDafZILLhQn3p7LwxW6O0z+4oiDo3C5eOIevRHwU5TixeSZL/tzS3Rme8Z5Z/OhPyeag3hDfWigecXTQzAk8+JfCjOuH7jmJZS9tKjArrVj3RtEOc3kYShvnov95hq4sRdeGFEvkV842PxjZXX2xm+P0H64oQnKd26r1m7nmoTXc9fSGkvXPmD+LM+bv2DoU6FdFkTQzuO4z83ix9S3mzhjPvc++lqgojnz7FH5w8ru7O+kHVm9k/vfuKdlhFktg2JBOFcycopRK5FfKNj/QPgpf7OY4/YsrigiTxjZyxD5T2PRWB396dgMQOJSTusrbVqznyvte6HY6n/3+2dSnVdQkVIq44/q0w2YWpPU48aAmPnnVo93vjwvXU8QZ21jX3UlX2mHO3nVcQQLDE5ubuHXF+oLrR0nyf1TCQPsofF9rx+lfXFHEaG1rj4TJFu/0/+ve1Xk+if+467lEH0VdChrrgqinkw9O3mwoqiQa0imad5/YvWnS8nVvMGvSTnzyqkfzOvyblrUUXAegZdNbkdfFO8zc8Zxp6NvHvYvTDp2Vl1tqVH0qrzNPojezgIH2UYykqCrHGQxcUcRYtX5zRX6KuFIopiSuOv1gdhnd0L0qOklRROnI7Ngz+9u3P4UI1jTEc62oyG6qb0VG+MUy4j758puctOihAnPU7F3Hda+TSOrMc+TSb/R2AdlAj/iH2mI3d7o7tY4rigL6b4Obrix0dmV5/tUtjGlI88DqjRWdJ+AbtzxZMoqqmDKLO8HjK6ABvn37U2UzvibtQlefEv9x0tzu1eG97dSKKbD+HPEPlcVu7nR3hgKuKGLsN22XXvsakjjzmmXdr4+YXXqb0hzbOnsWqhsl3tnHV0A3pNMVZXxN2oWuM2u8Y7dxRVOe94RKZmTQt9F2rS92c6e7M1Tw7LExJo1t5JIT5tBYl2KnhjTp+BC9D9y3ujBKCQpnAZWwU33yV/c3++3W/TrJVl9pxtfcnhFR4rvQ9ZZV6zcnKopVsb0/bln+cl5G21uXJy9YHKp4hllnqOCKIoEFc6fzv/94FNd/9lC+8qF9itZrrBOnH7Y79engdSXEcu+RFoyu7/le0RmzgmulBBPGNHS/z9nqo2nGL144h4sX5pcl2e/L7ULXN4rN1naUl0qjPlxwp7szVHDTU4S4mWPS2EbGNKS5+A/PFdRtSMNJzTO4Yek6GuvStFdoLoqPpDMGXdnyo/S37zqGta9vy0tCeNnd+ZFWKVFgQipmq08qi37+3J4R0dxUvQ2HjZNk3qtPi/2m7dL9fiSEuA41p7szcnFFERKkEl9BWikyluWbx+7H/tN2YUxDmrg7WMB1nzm0O2S1UlJAKpajqT4tvnDU3lx+7/Nks0ZnNpidtMey+619fVteyvI1G9q45M786wfO88KOPMlWHy+LO1W/8bF9Ez9Df4x2c+a9r4ap2zNZ4+KF+R1k8u58w2+0PVSc7s7IxhUF8bUTQUf7T79+klF1KTLZLI2xkfXYxjpebH2r7NancbJANu4fyFi4QZCor0uRyhoLD2zilhUv09a+o9OvTwWj+dy+ErcVWQz35PrN7DFlbNGMr0DiTCLuVP3Ob5/iG8cUphmvtCMr54SOrhNJ2hM8N9o+J5LivSuT5cHVG4dUVFAlzvhad7o7TlUVhaSPAJcBaeCnZvbvseMKjx8NvAWcbmaP97ccxdZObA8L4yPbbZ1dzJ0xvuzWp5WQAi658zk6IgLc9Pg64i7uuO262F7eL27cmpey48TmJn71WAvplOjMZDEzRtfX5YViJoXCWtbYf9ou3H724TywegOTx47isL0qi9pKCvnszeZG82dPzlOsGYOvLF4+ZKKCPPTVGS5UTVFISgNXAB8CWoDHJN1qZtG9Qj8K7B3+zQN+FP7fz/QsFFYSE8Y0cNHxB/DV0FzVlc0m7gi3U32atzqL2/WzBOsTOiJlDek0Zx2xJ1fEtjSNdo677ZzcUV736Et0ZHZkeE1aWZ3b0yIXipkUCtueMR5+oZXv/+HZ7hF9XQouPXFuyc4uaXbylcXLSadSNKRDs1Y4UykXFrpq/eaCrLld2aD8iH2mFJWhFvDQV2c4Uc2op0OA1Wb2gpl1ADcAx8XqHAdcYwEPA+MlTe1vQXLO1UpJES5IA0DBP4n3xtZJnNg8nWwZJSSC9QlROrNZTpk3k9vPPpwLjt2X288+vKBzfrH1LZKIbilajpxzOOe4jtJYJy6JKAkIOumv3rSiZORRUshnVxbau3ZEL11421OkY3Imh4WWj46qVTz01RlOVFNRTAfWRd63hGU9rQOApLMkLZW0dMOG0plf40TXTjRWoDDaM0ZnV4bzblpJe1eWtzoytHdleXjN69SnoLEuRUNaHD57Sl54akPCtQ34v+/bsyBc9YHVGznm8ge48LanOObyBwrWEBTL+GpWeSeaM2clOYiLXSat0p1dkhM6ThDxVD4sNEmBx6OjahUPfXWGE9VUFEk9crx7qqROUGi2yMyazax5ypSemyVyayd+8umDy84u6lJw85/X5+0IB4FjujMcPXdkrNvU8OD5R/HLz8zj60e/M/F6TRN26p49/PLvDmH8Tg2cd1PpNQQTxjQUpusALliwX7fSaaxLJTbgmIZ03vqJpPUW53747YmmtK5spmRnF79WY50K2jOTNS44dr+yazniix8b61JccsKcIWG6SWpTD311hirVdGa3ADMi75uAeChPJXX6jVya8UtOmMO5N66kI5M8Mu7Kwo1LS2dVhR2mhjkzxnevyUji9a0dHHP5AwBs78xSn1KBOSq+hqBl0zbGNtZ1+xsgiMbaf9ouPHj+Ud2O4wdXb+z2o0TDfuNROPEwzZZN22hMq8B38YWjymd4jV/rwdUbC9YKLJg7nY/sv1vZiKChHD46lGV3nCjVVBSPAXtL2gN4GfgEcEqszq3A2ZJuIHBiv2lmrwy0YLnQzaN/cH/iqLohnaIuJTpjC+Xi6y22d+WPvmfvOo73zp7E/ZFUHofuMYFL73ouL/w2riSCsnyzRdOE0XTE9ujO1YmGW/aks4qHaSqVv1F3Y12KU+bNLHp+sWsVk6HSsNChHD46lGV3nBxVMz2ZWRdwNvA/wNPAYjNbJelzkj4XVrsDeAFYDfwE+L+DIVtrWztbOzJcsGC/xNQcEgV7bDekVZBSI+4vaG1r5+E1r+eVPbZ2E3XxEyPsFDMT5Xhg9ca8Vdl1qd6n/U4iOf1H768/aWxj98zKcZyhRVXXUZjZHQTKIFp2ZeS1AZ8fTJnise/fPGY/Xt/aweX3ru4O78ztQx01p3z+yNksuu+FPFPQ6Pq6PHPRqvVvFmSlzWTJW0MRpbFOXPnJA9lv2i55C+fGNKQ5f8nKvGulUynmz55c9vP0JJbfTSeO44CvzM6j2ArlB88/ilPmzSyZLwngsrvzc0IV7rGQPHM48/A9+O//fREIfBSNaaGUuOj4Azhin7cB+R1+eyaLYrOV9q4s1z3yEl/4wN4lP09PY/nddOI4jiuKCKUS0SWZTaKdaGtbe8EmQfE1DftN25m6VP6mQ3Up+Mx79+Qz792ze7aQy+dUKsVGEpff+zynzJuZ5/Ae7on1HMcZeDzNeIS+xL7nNgmKMqounbfmYNLYRi49cS6NdWKn+jSNdeLSE+d2K5w5Yc6juFJKWrzVkC786iTl3c9j+QeX1rZ2Vqx7Y1ilQncc8BlFHn1J+1xpp9wbu3/StZMWYG/vzOaF4Hoa68HD8zo5wxn1ZCXvUKG5udmWLl3a6/N7u/3mrctfTlwv0B/Er/35I2dz+T3P561zaEyLxZ97T3eG2b5+HqcyWtvamf+9ewr27njw/KO8vZ0hg6RlZtacdMxnFAn01oE7kFFC8WsDXPHH1XnrHJRSolnJHdIDi/uCnOGOK4p+ZiA75fi13axUG7gvyBnuuKIYwvg6h9rAfUHOcMcVxRDHzUq1gSttZzjjisJx+glX2s5wxddROI7jOCVxReE4juOUxBWF4ziOUxJXFI7jOE5JXFE4juM4JXFF4TiO45TEFYXjOI5TElcUjuM4TklcUTiO4zglcUXhOI4zDBjIjbM8hYfjOM4QZ6A3zvIZheM4zhCmta2d85esZHtnli3tXWzvzHLekpX9OrNwReE4jjOEyW2cFSW3cVZ/4YrCcRxnCDMYG2e5onAcxxnC5DbOGlWfYlxjHaPqU/2+cVZVnNmSJgK/AmYBLwInmtmmhHovAluADNBVbONvx3GckcxAb5xVrRnFPwJ3m9newN3h+2K838zmupJwHMcpzqSxjcyZMX5ANs+qlqI4Dvh5+PrnwN9WSQ7HcRynDNVSFLua2SsA4f9vK1LPgD9IWibprFIXlHSWpKWSlm7YsKGfxXUcxxm5DJiPQtJdwG4Jh/6pB5eZb2brJb0NuFPSM2Z2X1JFM1sELAJobm62HgvsOI7jJDJgisLMPljsmKRXJU01s1ckTQVeK3KN9eH/r0n6NXAIkKgoHMdxnIGhWqanW4FPh68/DdwSryBpjKRxudfAh4EnB01Cx3EcBwCZDb6VRtIkYDEwE3gJOMHMXpc0DfipmR0taU/g1+EpdcB1ZvbdCq+/AVjbQ7EmAxt7eE6t4LJXB5e9Ogxl2aF25d/dzKYkHaiKoqhFJC0dqiG4Lnt1cNmrw1CWHYam/L4y23EcxymJKwrHcRynJK4odrCo2gL0AZe9Orjs1WEoyw5DUH73UTiO4zgl8RmF4ziOUxJXFI7jOE5JRpSikHSVpNckJS7cU8APJK2WtFLSgYMtYzEqkP1ISW9KWh7+fXOwZSyGpBmS7pX0tKRVkr6UUKcm275C2Wuy7SWNkvSopBWh7Bcm1KnVdq9E9pps9xyS0pL+LOn2hGM12e5FMbMR8wccARwIPFnk+NHA7wABhwKPVFvmHsh+JHB7teUsIttU4MDw9TjgOWDfodD2Fcpek20ftuXY8HU98Ahw6BBp90pkr8l2j8j3FeC6JBlrtd2L/Y2oGYUFCQVfL1HlOOAaC3gYGB/moqo6Fches5jZK2b2ePh6C/A0MD1WrSbbvkLZa5KwLdvCt/XhXzx6pVbbvRLZaxZJTcDHgJ8WqVKT7V6MEaUoKmA6sC7yvoUh0imEHBZO1X8nab9qC5OEpFnAuwlGiFFqvu1LyA412vah+WM5QeLNO81syLR7BbJDjbY78J/AeUC2yPGabfckXFHko4SyoTKKeZwgV8sc4L+A31RXnEIkjQWWAF82s83xwwmn1Ezbl5G9ZtvezDJmNhdoAg6RtH+sSs22ewWy12S7SzoGeM3MlpWqllBWE+2ehCuKfFqAGZH3TcD6KsnSI8xsc26qbmZ3APWSJldZrG4k1RN0tNea2c0JVWq27cvJXuttD2BmbwB/BD4SO1Sz7Z6jmOw13O7zgQWSXgRuAI6S9MtYnZpv9yiuKPK5FTgtjEg4FHjTwp34ah1Ju0lS+PoQgu+2tbpSBYRy/Qx42swuLVKtJtu+Etlrte0lTZE0Pnw9Gvgg8EysWq22e1nZa7XdzexrZtZkZrOATwD3mNknY9Vqst2LMWAbF9Uikq4niJSYLKkFuIDASYaZXQncQRCNsBp4CzijOpIWUoHsC4F/kNQFbAM+YWF4RQ0wH/gU8ERocwb4OkGa+Vpv+0pkr9W2nwr8XFKaoBNdbGa3S/oc1Hy7VyJ7rbZ7IkOk3RPxFB6O4zhOSdz05DiO45TEFYXjOI5TElcUjuM4TklcUTiO4zglcUXhOI4zxFGZpKGxujMVJLr8c5iQ8Ohy57iicKqCpEyY8XOFpMclvaeH5x+ZlJWzB+d/vcQxSbpH0s6SZlXy8PVShtMlXd7Ha7SVr1X03AWS/jF8/S1J54avr5a0MHz9U0n79vL6Z0uq6bDPYcTVFC6mLMY/E4Qbv5tgnccPy53gisKpFtvMbG6YfuFrwL8N8v2LKgqC+PYVCak6BgVJg7K+ycxuNbN/L1PnM2b2VC9vcRXwxV6e6/SApKShkvaS9HtJyyTdL+kduerAzuHrXahgRbgrCqcW2BnYBIUzBUmXSzo9fP0RSc9IegD4P5E6UyTdGc5MfixpbS6Vg6RPKtjXYHl4LC3p34HRYdm1CfKcCtwSeZ+W9BMF+yL8IVwpjKQ/SmoOX09WkLIhN1O4OXxIn5d0UUTWMyQ9J+lPBIv5cuVXS7pU0r3A94o95JL2kPSQpMckfSepMcNZ0DPhbOBJSddK+qCkB0N5DonIWXJGE/uMJ0t6Irzm9yJ12iR9N5wdPixpVwAzewt4MXc/Z9BZBHzBzA4CzmXHzOFbwCcVLNy9A/hCuQu5onCqRa6jfoYgFXNip5dD0ijgJ8CxwHuB3SKHLyBIk3Ag8GvCVdOS3gmcBMwPk8tlgFPN7B/ZMaM5NeF284FoQre9gSvMbD/gDeD4Cj7f3PDe7wJOUrAB0lTgwvD6HwLiJp19gA+a2TkUf8gvA35kZgcDfy1x/9lh3QOAdwCnAIeH1yo1m0pE0jTge8BR4Wc7WNLfhofHAA+Hs8P7gM9GTl1K8H05g4iCJJbvAW5UkFHgxwSr3QFOBq42syaC2fMvJJXUBSMqhYdTU2wLO28kHQZco8LsoFHeAawxs+fDc34JnBUeOxz4OICZ/V7SprD8A8BBwGMKUgKNJkhZXY6J4d4TOdaY2fLw9TJgVgXXuNvM3gxlfQrYHZgM/NHMNoTlvyJQDjluNLNM7CHPHWsM/5/PDkX1C4LOO4k1ZvZEeJ9VoTwm6YkK5Y9zcEz2awk20/oN0AHkZoHLCJRgjtcIvjtncEkBb+SesRhnEvozzOyhcBA2mRLPhisKp+qEP9bJwBSgi/yZ7qho1SKXSErZnCv/uZl9rYcidUlKmVluL4H2yLEMgcIhJmtUzqRzcs9aqZw5W8P/Sz3k5a6RdP9s5H2W3j33xdoYoDOSYyn6WSFol229uJ/TB8xss6Q1kk4wsxsVjDgOMLMVwEsEg6irw1n3KGBDqeu56cmpOqH9PU2Q+XMtsK+kRkm7EPygIcgcuoekvcL3J0cu8QBwYnitDwMTwvK7gYWS3hYemyhp9/BYp4L04Uk8C+xZgegvEsxYIEhQV45HgCMlTQrvfUJSpdCJvkbSCaHckjQnPPwgQaQKBL6UweIR4H2hLyZN0P5/quC8fYABiRpzdqAgaehDwNsltUg6k+D3caakFcAqgl31AM4BPhuWXw+cXi6Zos8onGoxWjuysQr4tJllgHWSFgMrgeeBPwOY2XZJZwG/lbSRQDnkTFUXAtdLOomg83oF2GJmGyX9M/CH0AbbCXyeQBktAlZKejzBT/Fbgky9q8t8hu8DiyV9Crin3Ac2s1ckfYvggX6FYOOddJHqpwI/CuWvJ9jXYAXwJeA6SV8i2CNjUAhl/xpwL8H3dYeZ3VLmNAhMZRcOqHAOZnZykUMFIbNhFNv8hLpF8eyxzpBHUiOQMbOu0N/xoxJmm0quN5VgP+MPla3sFEXSu4GvmNmnqi2L0zd8RuEMB2YSjOxTBI7Vz5apX5Jw9PwTSTtXay3FMGEy8I1qC+H0HZ9ROI7jOCVxZ7bjOI5TElcUjuM4TklcUTiO4zglcUXhOI7jlMQVheM4jlOS/w8bXc8Ry6QVAwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "blockbuster.plot(y='profit',x='budget',kind='scatter')\n",
    "\n",
    "plt.title(\"Scatter Plot of TMDB Movie Budget vs. Profit\")\n",
    "plt.xlabel(\"Budget (hundred million)\")\n",
    "plt.ylabel(\"Profit (billion)\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### We see a lot more outliers when it comes to Blockbuster movies with higher budget, but not necessarily having high profit. \n",
    "\n",
    "Let us find out the top 10 movies with highest budget."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>budget</th>\n",
       "      <th>revenue</th>\n",
       "      <th>profit</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>original_title</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>The Warrior's Way</th>\n",
       "      <td>425000000</td>\n",
       "      <td>11087569</td>\n",
       "      <td>-413912431</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pirates of the Caribbean: On Stranger Tides</th>\n",
       "      <td>380000000</td>\n",
       "      <td>1021683000</td>\n",
       "      <td>641683000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pirates of the Caribbean: At World's End</th>\n",
       "      <td>300000000</td>\n",
       "      <td>961000000</td>\n",
       "      <td>661000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Godzilla</th>\n",
       "      <td>290000000</td>\n",
       "      <td>908090363</td>\n",
       "      <td>618090363</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Avengers: Age of Ultron</th>\n",
       "      <td>280000000</td>\n",
       "      <td>1405035767</td>\n",
       "      <td>1125035767</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Superman Returns</th>\n",
       "      <td>270000000</td>\n",
       "      <td>391081192</td>\n",
       "      <td>121081192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tangled</th>\n",
       "      <td>260000000</td>\n",
       "      <td>591794936</td>\n",
       "      <td>331794936</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>John Carter</th>\n",
       "      <td>260000000</td>\n",
       "      <td>284139100</td>\n",
       "      <td>24139100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Spider-Man 3</th>\n",
       "      <td>258000000</td>\n",
       "      <td>890871626</td>\n",
       "      <td>632871626</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>The Lone Ranger</th>\n",
       "      <td>255000000</td>\n",
       "      <td>89289910</td>\n",
       "      <td>-165710090</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                budget     revenue      profit\n",
       "original_title                                                                \n",
       "The Warrior's Way                            425000000    11087569  -413912431\n",
       "Pirates of the Caribbean: On Stranger Tides  380000000  1021683000   641683000\n",
       "Pirates of the Caribbean: At World's End     300000000   961000000   661000000\n",
       "Godzilla                                     290000000   908090363   618090363\n",
       "Avengers: Age of Ultron                      280000000  1405035767  1125035767\n",
       "Superman Returns                             270000000   391081192   121081192\n",
       "Tangled                                      260000000   591794936   331794936\n",
       "John Carter                                  260000000   284139100    24139100\n",
       "Spider-Man 3                                 258000000   890871626   632871626\n",
       "The Lone Ranger                              255000000    89289910  -165710090"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Groupby Movie Title and Budget, Revenue and Profit for Top 10 Movies\n",
    "blockbuster_grouped = blockbuster.groupby('original_title')[['budget','revenue','profit']].sum().sort_values('budget',ascending=False).iloc[:10,:]\n",
    "blockbuster_grouped"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Not even a billion dollar budget can stop a movie from making losses. \"The Warrior's Way\" and \"The Lone Ranger\" are loss-making with the former making a USD 400 million loss. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***\n",
    "\n",
    "## Q2: Which genres are associated with high popularity?\n",
    "\n",
    "Before we dive into the analysis, we must first understand what it means by popularity and how is it calculated?\n",
    "\n",
    "#### On TMDB website, the popularity metric is calculated based on **Movies**, **TV Shows** and **People** where it measures the number of votes and views, and number of users who marked it as their \"favourite\" or added into their \"watchlist\". \n",
    "\n",
    "Source: [https://developers.themoviedb.org/3/getting-started/popularity](https://developers.themoviedb.org/3/getting-started/popularity)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    10865.000000\n",
       "mean         0.646446\n",
       "std          1.000231\n",
       "min          0.000065\n",
       "25%          0.207575\n",
       "50%          0.383831\n",
       "75%          0.713857\n",
       "max         32.985763\n",
       "Name: popularity, dtype: float64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['popularity'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is interesting to note that 75% of the data's popularity is 0.71 score and we have the other extreme, a score of 32.98 and not a rating of 10 as we would have expected from a generic popularity scheme. \n",
    "\n",
    "Why is that so? \n",
    "\n",
    "I did some research and found an answer on the TMDB forum where one of the staff, Travis Bell [shed some light](https://www.themoviedb.org/talk/5141d424760ee34da71431b0) on the popularity calculation.\n",
    "\n",
    "> ### Upper (_referring to the maximum value of the popularity metric_) no, this is the same as before. Lower, 0. It can be anywhere from 0 to essentially infinity.\n",
    "\n",
    "What he means is that ````the popularity score starts from 0 and runs to infinity. ````\n",
    "\n",
    "Hmm, that can be tough to measure. Let's get the average popularity and use it as our benchmark."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6464455549010583"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['popularity'].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since the popularity genres for each movies are in a single cell and separated by \"|\", then we will conduct the splitting first."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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>original_title</th>\n",
       "      <th>cast</th>\n",
       "      <th>director</th>\n",
       "      <th>runtime</th>\n",
       "      <th>genres</th>\n",
       "      <th>vote_average</th>\n",
       "      <th>release_year</th>\n",
       "      <th>profit</th>\n",
       "      <th>profit_margin</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>135397</td>\n",
       "      <td>32.985763</td>\n",
       "      <td>150000000</td>\n",
       "      <td>1513528810</td>\n",
       "      <td>Jurassic World</td>\n",
       "      <td>Chris Pratt|Bryce Dallas Howard|Irrfan Khan|Vi...</td>\n",
       "      <td>Colin Trevorrow</td>\n",
       "      <td>124</td>\n",
       "      <td>[Action, Adventure, Science Fiction, Thriller]</td>\n",
       "      <td>6.5</td>\n",
       "      <td>2015</td>\n",
       "      <td>1363528810</td>\n",
       "      <td>90.089386</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       id  popularity     budget     revenue  original_title  \\\n",
       "0  135397   32.985763  150000000  1513528810  Jurassic World   \n",
       "\n",
       "                                                cast         director  \\\n",
       "0  Chris Pratt|Bryce Dallas Howard|Irrfan Khan|Vi...  Colin Trevorrow   \n",
       "\n",
       "   runtime                                          genres  vote_average  \\\n",
       "0      124  [Action, Adventure, Science Fiction, Thriller]           6.5   \n",
       "\n",
       "   release_year      profit  profit_margin  \n",
       "0          2015  1363528810      90.089386  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['genres'] = df['genres'].str.split(\"|\") \n",
    "df.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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>original_title</th>\n",
       "      <th>cast</th>\n",
       "      <th>director</th>\n",
       "      <th>runtime</th>\n",
       "      <th>genres</th>\n",
       "      <th>vote_average</th>\n",
       "      <th>release_year</th>\n",
       "      <th>profit</th>\n",
       "      <th>profit_margin</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>135397</td>\n",
       "      <td>32.985763</td>\n",
       "      <td>150000000</td>\n",
       "      <td>1513528810</td>\n",
       "      <td>Jurassic World</td>\n",
       "      <td>Chris Pratt|Bryce Dallas Howard|Irrfan Khan|Vi...</td>\n",
       "      <td>Colin Trevorrow</td>\n",
       "      <td>124</td>\n",
       "      <td>Action</td>\n",
       "      <td>6.5</td>\n",
       "      <td>2015</td>\n",
       "      <td>1363528810</td>\n",
       "      <td>90.089386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>135397</td>\n",
       "      <td>32.985763</td>\n",
       "      <td>150000000</td>\n",
       "      <td>1513528810</td>\n",
       "      <td>Jurassic World</td>\n",
       "      <td>Chris Pratt|Bryce Dallas Howard|Irrfan Khan|Vi...</td>\n",
       "      <td>Colin Trevorrow</td>\n",
       "      <td>124</td>\n",
       "      <td>Adventure</td>\n",
       "      <td>6.5</td>\n",
       "      <td>2015</td>\n",
       "      <td>1363528810</td>\n",
       "      <td>90.089386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>135397</td>\n",
       "      <td>32.985763</td>\n",
       "      <td>150000000</td>\n",
       "      <td>1513528810</td>\n",
       "      <td>Jurassic World</td>\n",
       "      <td>Chris Pratt|Bryce Dallas Howard|Irrfan Khan|Vi...</td>\n",
       "      <td>Colin Trevorrow</td>\n",
       "      <td>124</td>\n",
       "      <td>Science Fiction</td>\n",
       "      <td>6.5</td>\n",
       "      <td>2015</td>\n",
       "      <td>1363528810</td>\n",
       "      <td>90.089386</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       id  popularity     budget     revenue  original_title  \\\n",
       "0  135397   32.985763  150000000  1513528810  Jurassic World   \n",
       "0  135397   32.985763  150000000  1513528810  Jurassic World   \n",
       "0  135397   32.985763  150000000  1513528810  Jurassic World   \n",
       "\n",
       "                                                cast         director  \\\n",
       "0  Chris Pratt|Bryce Dallas Howard|Irrfan Khan|Vi...  Colin Trevorrow   \n",
       "0  Chris Pratt|Bryce Dallas Howard|Irrfan Khan|Vi...  Colin Trevorrow   \n",
       "0  Chris Pratt|Bryce Dallas Howard|Irrfan Khan|Vi...  Colin Trevorrow   \n",
       "\n",
       "   runtime           genres  vote_average  release_year      profit  \\\n",
       "0      124           Action           6.5          2015  1363528810   \n",
       "0      124        Adventure           6.5          2015  1363528810   \n",
       "0      124  Science Fiction           6.5          2015  1363528810   \n",
       "\n",
       "   profit_margin  \n",
       "0      90.089386  \n",
       "0      90.089386  \n",
       "0      90.089386  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "genre = df.explode('genres')\n",
    "genre.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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",
       "Fantasy             916\n",
       "Mystery             810\n",
       "Animation           699\n",
       "Documentary         520\n",
       "Music               408\n",
       "History             334\n",
       "War                 270\n",
       "Foreign             188\n",
       "TV Movie            167\n",
       "Western             165\n",
       "Name: genres, dtype: int64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "genre['genres'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=[8,6])\n",
    "genre['genres'].value_counts().plot.barh()\n",
    "plt.title(\"Bar Chart of Genre Count in TMBd Movies\")\n",
    "plt.ylabel(\"Genres\")\n",
    "plt.xlabel(\"Count\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Highest count of genres are Drama, Comedy and Thriller. Does it mean that they're most popular?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "scrolled": true
   },
   "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>popularity</th>\n",
       "      <th>revenue</th>\n",
       "      <th>profit</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>genres</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Adventure</th>\n",
       "      <td>1.154259</td>\n",
       "      <td>1.131379e+08</td>\n",
       "      <td>7.559417e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Science Fiction</th>\n",
       "      <td>1.001548</td>\n",
       "      <td>7.014056e+07</td>\n",
       "      <td>4.516788e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fantasy</th>\n",
       "      <td>0.992840</td>\n",
       "      <td>9.631366e+07</td>\n",
       "      <td>6.370107e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Action</th>\n",
       "      <td>0.926274</td>\n",
       "      <td>7.279473e+07</td>\n",
       "      <td>4.506691e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Animation</th>\n",
       "      <td>0.852182</td>\n",
       "      <td>7.525606e+07</td>\n",
       "      <td>5.209628e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Family</th>\n",
       "      <td>0.786668</td>\n",
       "      <td>7.243318e+07</td>\n",
       "      <td>4.907384e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Crime</th>\n",
       "      <td>0.744930</td>\n",
       "      <td>4.236866e+07</td>\n",
       "      <td>2.470486e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Thriller</th>\n",
       "      <td>0.741563</td>\n",
       "      <td>4.172842e+07</td>\n",
       "      <td>2.452072e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>War</th>\n",
       "      <td>0.727683</td>\n",
       "      <td>4.760518e+07</td>\n",
       "      <td>2.671330e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mystery</th>\n",
       "      <td>0.690012</td>\n",
       "      <td>4.021757e+07</td>\n",
       "      <td>2.409830e+07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 popularity       revenue        profit\n",
       "genres                                                 \n",
       "Adventure          1.154259  1.131379e+08  7.559417e+07\n",
       "Science Fiction    1.001548  7.014056e+07  4.516788e+07\n",
       "Fantasy            0.992840  9.631366e+07  6.370107e+07\n",
       "Action             0.926274  7.279473e+07  4.506691e+07\n",
       "Animation          0.852182  7.525606e+07  5.209628e+07\n",
       "Family             0.786668  7.243318e+07  4.907384e+07\n",
       "Crime              0.744930  4.236866e+07  2.470486e+07\n",
       "Thriller           0.741563  4.172842e+07  2.452072e+07\n",
       "War                0.727683  4.760518e+07  2.671330e+07\n",
       "Mystery            0.690012  4.021757e+07  2.409830e+07"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "genre_mean = genre.groupby('genres')[['popularity','revenue','profit']].mean().sort_values('popularity', ascending=False)\n",
    "genre_mean.iloc[:10,:].query(\"popularity > 0.6464455549010583\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Doesn't seem like it. Here, we see completely different set of popular genres.\n",
    "\n",
    "### Adventure, Science Fiction and Fantasy are the most popular genres. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***\n",
    "\n",
    "## Q3: Which director produces the highest revenue movies?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Woody Allen                 45\n",
       "Clint Eastwood              34\n",
       "Martin Scorsese             29\n",
       "Steven Spielberg            29\n",
       "Ridley Scott                23\n",
       "                            ..\n",
       "Jeffrey Porter               1\n",
       "Larry Shaw                   1\n",
       "Bill Pohlad                  1\n",
       "Andy Hamilton|Guy Jenkin     1\n",
       "Harold P. Warren             1\n",
       "Name: director, Length: 5067, dtype: int64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['director'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>director</th>\n",
       "      <th>runtime</th>\n",
       "      <th>genres</th>\n",
       "      <th>vote_average</th>\n",
       "      <th>release_year</th>\n",
       "      <th>profit</th>\n",
       "      <th>profit_margin</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>135397</td>\n",
       "      <td>32.985763</td>\n",
       "      <td>150000000</td>\n",
       "      <td>1513528810</td>\n",
       "      <td>Jurassic World</td>\n",
       "      <td>Chris Pratt|Bryce Dallas Howard|Irrfan Khan|Vi...</td>\n",
       "      <td>[Colin Trevorrow]</td>\n",
       "      <td>124</td>\n",
       "      <td>[Action, Adventure, Science Fiction, Thriller]</td>\n",
       "      <td>6.5</td>\n",
       "      <td>2015</td>\n",
       "      <td>1363528810</td>\n",
       "      <td>90.089386</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       id  popularity     budget     revenue  original_title  \\\n",
       "0  135397   32.985763  150000000  1513528810  Jurassic World   \n",
       "\n",
       "                                                cast           director  \\\n",
       "0  Chris Pratt|Bryce Dallas Howard|Irrfan Khan|Vi...  [Colin Trevorrow]   \n",
       "\n",
       "   runtime                                          genres  vote_average  \\\n",
       "0      124  [Action, Adventure, Science Fiction, Thriller]           6.5   \n",
       "\n",
       "   release_year      profit  profit_margin  \n",
       "0          2015  1363528810      90.089386  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['director'] = df['director'].str.split(\"|\") \n",
    "df.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>original_title</th>\n",
       "      <th>cast</th>\n",
       "      <th>director</th>\n",
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       "      <th>profit_margin</th>\n",
       "    </tr>\n",
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       "      <th>0</th>\n",
       "      <td>135397</td>\n",
       "      <td>32.985763</td>\n",
       "      <td>150000000</td>\n",
       "      <td>1513528810</td>\n",
       "      <td>Jurassic World</td>\n",
       "      <td>Chris Pratt|Bryce Dallas Howard|Irrfan Khan|Vi...</td>\n",
       "      <td>Colin Trevorrow</td>\n",
       "      <td>124</td>\n",
       "      <td>[Action, Adventure, Science Fiction, Thriller]</td>\n",
       "      <td>6.5</td>\n",
       "      <td>2015</td>\n",
       "      <td>1363528810</td>\n",
       "      <td>90.089386</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       id  popularity     budget     revenue  original_title  \\\n",
       "0  135397   32.985763  150000000  1513528810  Jurassic World   \n",
       "\n",
       "                                                cast         director  \\\n",
       "0  Chris Pratt|Bryce Dallas Howard|Irrfan Khan|Vi...  Colin Trevorrow   \n",
       "\n",
       "   runtime                                          genres  vote_average  \\\n",
       "0      124  [Action, Adventure, Science Fiction, Thriller]           6.5   \n",
       "\n",
       "   release_year      profit  profit_margin  \n",
       "0          2015  1363528810      90.089386  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "directors_df = df.explode('director')\n",
    "directors_df.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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",
       "                     ..\n",
       "Mike Maguire          1\n",
       "Tom Kuntz             1\n",
       "John Simpson          1\n",
       "Simon Hunter          1\n",
       "Harold P. Warren      1\n",
       "Name: director, Length: 5362, dtype: int64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "directors_df['director'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>revenue</th>\n",
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       "    <tr>\n",
       "      <th>director</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Pierre Coffin</th>\n",
       "      <td>8.903356e+08</td>\n",
       "      <td>8.173356e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>David Yates</th>\n",
       "      <td>8.308591e+08</td>\n",
       "      <td>6.758591e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Colin Trevorrow</th>\n",
       "      <td>7.587683e+08</td>\n",
       "      <td>6.833933e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Robert Stromberg</th>\n",
       "      <td>7.584104e+08</td>\n",
       "      <td>5.784104e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lee Unkrich</th>\n",
       "      <td>7.439073e+08</td>\n",
       "      <td>6.191573e+08</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       revenue        profit\n",
       "director                                    \n",
       "Pierre Coffin     8.903356e+08  8.173356e+08\n",
       "David Yates       8.308591e+08  6.758591e+08\n",
       "Colin Trevorrow   7.587683e+08  6.833933e+08\n",
       "Robert Stromberg  7.584104e+08  5.784104e+08\n",
       "Lee Unkrich       7.439073e+08  6.191573e+08"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "directors_df.groupby('director')[['revenue','profit']].mean().sort_values('revenue', ascending=False).head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>revenue</th>\n",
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       "      <th>director</th>\n",
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       "      <th></th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Pierre Coffin</th>\n",
       "      <td>8.903356e+08</td>\n",
       "      <td>8.173356e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Colin Trevorrow</th>\n",
       "      <td>7.587683e+08</td>\n",
       "      <td>6.833933e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>David Yates</th>\n",
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       "      <td>6.758591e+08</td>\n",
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       "      <th>Lee Unkrich</th>\n",
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       "      <td>6.191573e+08</td>\n",
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       "      <th>Robert Stromberg</th>\n",
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      ],
      "text/plain": [
       "                       revenue        profit\n",
       "director                                    \n",
       "Pierre Coffin     8.903356e+08  8.173356e+08\n",
       "Colin Trevorrow   7.587683e+08  6.833933e+08\n",
       "David Yates       8.308591e+08  6.758591e+08\n",
       "Lee Unkrich       7.439073e+08  6.191573e+08\n",
       "Robert Stromberg  7.584104e+08  5.784104e+08"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "directors_df.groupby('director')[['revenue','profit']].mean().sort_values('profit', ascending=False).head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### On average, the director producing the movie with the highest revenue and profit is Pierre Coffin. \n",
    "\n",
    "Let us find out what are the movies produced by Pierre Coffin."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>popularity</th>\n",
       "      <th>budget</th>\n",
       "      <th>revenue</th>\n",
       "      <th>original_title</th>\n",
       "      <th>cast</th>\n",
       "      <th>director</th>\n",
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       "      <th>release_year</th>\n",
       "      <th>profit</th>\n",
       "      <th>profit_margin</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>211672</td>\n",
       "      <td>7.404165</td>\n",
       "      <td>74000000</td>\n",
       "      <td>1156730962</td>\n",
       "      <td>Minions</td>\n",
       "      <td>Sandra Bullock|Jon Hamm|Michael Keaton|Allison...</td>\n",
       "      <td>Pierre Coffin</td>\n",
       "      <td>91</td>\n",
       "      <td>[Family, Animation, Adventure, Comedy]</td>\n",
       "      <td>6.5</td>\n",
       "      <td>2015</td>\n",
       "      <td>1082730962</td>\n",
       "      <td>93.602661</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5434</th>\n",
       "      <td>93456</td>\n",
       "      <td>3.928789</td>\n",
       "      <td>76000000</td>\n",
       "      <td>970761885</td>\n",
       "      <td>Despicable Me 2</td>\n",
       "      <td>Steve Carell|Kristen Wiig|Benjamin Bratt|Miran...</td>\n",
       "      <td>Pierre Coffin</td>\n",
       "      <td>98</td>\n",
       "      <td>[Animation, Comedy, Family]</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2013</td>\n",
       "      <td>894761885</td>\n",
       "      <td>92.171098</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1924</th>\n",
       "      <td>20352</td>\n",
       "      <td>3.791881</td>\n",
       "      <td>69000000</td>\n",
       "      <td>543513985</td>\n",
       "      <td>Despicable Me</td>\n",
       "      <td>Steve Carell|Jason Segel|Russell Brand|Julie A...</td>\n",
       "      <td>Pierre Coffin</td>\n",
       "      <td>95</td>\n",
       "      <td>[Action, Animation, Family]</td>\n",
       "      <td>7.1</td>\n",
       "      <td>2010</td>\n",
       "      <td>474513985</td>\n",
       "      <td>87.304834</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          id  popularity    budget     revenue   original_title  \\\n",
       "8     211672    7.404165  74000000  1156730962          Minions   \n",
       "5434   93456    3.928789  76000000   970761885  Despicable Me 2   \n",
       "1924   20352    3.791881  69000000   543513985    Despicable Me   \n",
       "\n",
       "                                                   cast       director  \\\n",
       "8     Sandra Bullock|Jon Hamm|Michael Keaton|Allison...  Pierre Coffin   \n",
       "5434  Steve Carell|Kristen Wiig|Benjamin Bratt|Miran...  Pierre Coffin   \n",
       "1924  Steve Carell|Jason Segel|Russell Brand|Julie A...  Pierre Coffin   \n",
       "\n",
       "      runtime                                  genres  vote_average  \\\n",
       "8          91  [Family, Animation, Adventure, Comedy]           6.5   \n",
       "5434       98             [Animation, Comedy, Family]           7.0   \n",
       "1924       95             [Action, Animation, Family]           7.1   \n",
       "\n",
       "      release_year      profit  profit_margin  \n",
       "8             2015  1082730962      93.602661  \n",
       "5434          2013   894761885      92.171098  \n",
       "1924          2010   474513985      87.304834  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "directors_df.query('director == \"Pierre Coffin\"').sort_values('profit', ascending=False).head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Not surprisingly, it's the Despicable Me movie series which is well-loved by both kids and adults."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***\n",
    "\n",
    "## Q4: Which actor is associated with higher popularity, revenue and profit?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Louis C.K.                                                                        6\n",
       "William Shatner|Leonard Nimoy|DeForest Kelley|James Doohan|George Takei           5\n",
       "Bill Burr                                                                         4\n",
       "George Carlin                                                                     3\n",
       "Pierre Coffin                                                                     3\n",
       "                                                                                 ..\n",
       "Ray Stevenson|Vincent D'Onofrio|Val Kilmer|Christopher Walken|Linda Cardellini    1\n",
       "Freida Pinto|Riz Ahmed|Roshan Seth|Kalki Koechlin|Anurag Kashyap                  1\n",
       "William Hurt|Paul Giamatti|James Woods|Billy Crudup|Topher Grace                  1\n",
       "Dennis Quaid|Tony Oller|Aimee Teegarden|Stephen Lunsford|Devon Werkheiser         1\n",
       "Harold P. Warren|Tom Neyman|John Reynolds|Diane Mahree|Stephanie Nielson          1\n",
       "Name: cast, Length: 10719, dtype: int64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['cast'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count          10789\n",
       "unique         10719\n",
       "top       Louis C.K.\n",
       "freq               6\n",
       "Name: cast, dtype: object"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['cast'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <td>Jurassic World</td>\n",
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      "text/plain": [
       "       id  popularity     budget     revenue  original_title  \\\n",
       "0  135397   32.985763  150000000  1513528810  Jurassic World   \n",
       "\n",
       "                                                cast           director  \\\n",
       "0  Chris Pratt|Bryce Dallas Howard|Irrfan Khan|Vi...  [Colin Trevorrow]   \n",
       "\n",
       "   runtime                                          genres  vote_average  \\\n",
       "0      124  [Action, Adventure, Science Fiction, Thriller]           6.5   \n",
       "\n",
       "   release_year      profit  profit_margin  \n",
       "0          2015  1363528810      90.089386  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['cast'] = df['cast'].astype(str)\n",
    "df.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>135397</td>\n",
       "      <td>32.985763</td>\n",
       "      <td>150000000</td>\n",
       "      <td>1513528810</td>\n",
       "      <td>Jurassic World</td>\n",
       "      <td>[Chris Pratt, Bryce Dallas Howard, Irrfan Khan...</td>\n",
       "      <td>[Colin Trevorrow]</td>\n",
       "      <td>124</td>\n",
       "      <td>[Action, Adventure, Science Fiction, Thriller]</td>\n",
       "      <td>6.5</td>\n",
       "      <td>2015</td>\n",
       "      <td>1363528810</td>\n",
       "      <td>90.089386</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       id  popularity     budget     revenue  original_title  \\\n",
       "0  135397   32.985763  150000000  1513528810  Jurassic World   \n",
       "\n",
       "                                                cast           director  \\\n",
       "0  [Chris Pratt, Bryce Dallas Howard, Irrfan Khan...  [Colin Trevorrow]   \n",
       "\n",
       "   runtime                                          genres  vote_average  \\\n",
       "0      124  [Action, Adventure, Science Fiction, Thriller]           6.5   \n",
       "\n",
       "   release_year      profit  profit_margin  \n",
       "0          2015  1363528810      90.089386  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['cast'] = df['cast'].str.split(\"|\")\n",
    "df.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>director</th>\n",
       "      <th>runtime</th>\n",
       "      <th>genres</th>\n",
       "      <th>vote_average</th>\n",
       "      <th>release_year</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>135397</td>\n",
       "      <td>32.985763</td>\n",
       "      <td>150000000</td>\n",
       "      <td>1513528810</td>\n",
       "      <td>Jurassic World</td>\n",
       "      <td>Chris Pratt</td>\n",
       "      <td>[Colin Trevorrow]</td>\n",
       "      <td>124</td>\n",
       "      <td>[Action, Adventure, Science Fiction, Thriller]</td>\n",
       "      <td>6.5</td>\n",
       "      <td>2015</td>\n",
       "      <td>1363528810</td>\n",
       "      <td>90.089386</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       id  popularity     budget     revenue  original_title         cast  \\\n",
       "0  135397   32.985763  150000000  1513528810  Jurassic World  Chris Pratt   \n",
       "\n",
       "            director  runtime                                          genres  \\\n",
       "0  [Colin Trevorrow]      124  [Action, Adventure, Science Fiction, Thriller]   \n",
       "\n",
       "   vote_average  release_year      profit  profit_margin  \n",
       "0           6.5          2015  1363528810      90.089386  "
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cast_df = df.explode('cast')\n",
    "cast_df.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "nan                      76\n",
       "Robert De Niro           72\n",
       "Samuel L. Jackson        71\n",
       "Bruce Willis             62\n",
       "Nicolas Cage             61\n",
       "                         ..\n",
       "Aran Bell                 1\n",
       "Rebecca Houseknecht       1\n",
       "Joan Sebastian Zamora     1\n",
       "Miko Fogarty              1\n",
       "Stephanie Nielson         1\n",
       "Name: cast, Length: 19027, dtype: int64"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cast_df['cast'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>popularity</th>\n",
       "      <th>revenue</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cast</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Harrison Ford</th>\n",
       "      <td>80.138138</td>\n",
       "      <td>8922840695</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tom Cruise</th>\n",
       "      <td>59.140741</td>\n",
       "      <td>8812436186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tom Hanks</th>\n",
       "      <td>66.540270</td>\n",
       "      <td>8418142612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Emma Watson</th>\n",
       "      <td>53.598217</td>\n",
       "      <td>8176916698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ian McKellen</th>\n",
       "      <td>61.067864</td>\n",
       "      <td>8115046043</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               popularity     revenue\n",
       "cast                                 \n",
       "Harrison Ford   80.138138  8922840695\n",
       "Tom Cruise      59.140741  8812436186\n",
       "Tom Hanks       66.540270  8418142612\n",
       "Emma Watson     53.598217  8176916698\n",
       "Ian McKellen    61.067864  8115046043"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cast_df.groupby('cast')[['popularity','revenue']].sum().sort_values('revenue',ascending=False).head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>popularity</th>\n",
       "      <th>budget</th>\n",
       "      <th>revenue</th>\n",
       "      <th>original_title</th>\n",
       "      <th>cast</th>\n",
       "      <th>director</th>\n",
       "      <th>runtime</th>\n",
       "      <th>genres</th>\n",
       "      <th>vote_average</th>\n",
       "      <th>release_year</th>\n",
       "      <th>profit</th>\n",
       "      <th>profit_margin</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>140607</td>\n",
       "      <td>11.173104</td>\n",
       "      <td>200000000</td>\n",
       "      <td>2068178225</td>\n",
       "      <td>Star Wars: The Force Awakens</td>\n",
       "      <td>Harrison Ford</td>\n",
       "      <td>[J.J. Abrams]</td>\n",
       "      <td>136</td>\n",
       "      <td>[Action, Adventure, Science Fiction, Fantasy]</td>\n",
       "      <td>7.5</td>\n",
       "      <td>2015</td>\n",
       "      <td>1868178225</td>\n",
       "      <td>90.329654</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       id  popularity     budget     revenue                original_title  \\\n",
       "3  140607   11.173104  200000000  2068178225  Star Wars: The Force Awakens   \n",
       "\n",
       "            cast       director  runtime  \\\n",
       "3  Harrison Ford  [J.J. Abrams]      136   \n",
       "\n",
       "                                          genres  vote_average  release_year  \\\n",
       "3  [Action, Adventure, Science Fiction, Fantasy]           7.5          2015   \n",
       "\n",
       "       profit  profit_margin  \n",
       "3  1868178225      90.329654  "
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cast_df.query('cast == \"Harrison Ford\"').sort_values('revenue',ascending=False).head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
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       "    }\n",
       "\n",
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       "        text-align: right;\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>popularity</th>\n",
       "      <th>budget</th>\n",
       "      <th>revenue</th>\n",
       "      <th>original_title</th>\n",
       "      <th>cast</th>\n",
       "      <th>director</th>\n",
       "      <th>runtime</th>\n",
       "      <th>genres</th>\n",
       "      <th>vote_average</th>\n",
       "      <th>release_year</th>\n",
       "      <th>profit</th>\n",
       "      <th>profit_margin</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3382</th>\n",
       "      <td>56292</td>\n",
       "      <td>2.95274</td>\n",
       "      <td>145000000</td>\n",
       "      <td>694713380</td>\n",
       "      <td>Mission: Impossible - Ghost Protocol</td>\n",
       "      <td>Tom Cruise</td>\n",
       "      <td>[Brad Bird]</td>\n",
       "      <td>133</td>\n",
       "      <td>[Action, Thriller, Adventure]</td>\n",
       "      <td>6.7</td>\n",
       "      <td>2011</td>\n",
       "      <td>549713380</td>\n",
       "      <td>79.128083</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         id  popularity     budget    revenue  \\\n",
       "3382  56292     2.95274  145000000  694713380   \n",
       "\n",
       "                            original_title        cast     director  runtime  \\\n",
       "3382  Mission: Impossible - Ghost Protocol  Tom Cruise  [Brad Bird]      133   \n",
       "\n",
       "                             genres  vote_average  release_year     profit  \\\n",
       "3382  [Action, Thriller, Adventure]           6.7          2011  549713380   \n",
       "\n",
       "      profit_margin  \n",
       "3382      79.128083  "
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cast_df.query('cast == \"Tom Cruise\"').sort_values('revenue',ascending=False).head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "    }\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>original_title</th>\n",
       "      <th>cast</th>\n",
       "      <th>director</th>\n",
       "      <th>runtime</th>\n",
       "      <th>genres</th>\n",
       "      <th>vote_average</th>\n",
       "      <th>release_year</th>\n",
       "      <th>profit</th>\n",
       "      <th>profit_margin</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1930</th>\n",
       "      <td>10193</td>\n",
       "      <td>2.711136</td>\n",
       "      <td>200000000</td>\n",
       "      <td>1063171911</td>\n",
       "      <td>Toy Story 3</td>\n",
       "      <td>Tom Hanks</td>\n",
       "      <td>[Lee Unkrich]</td>\n",
       "      <td>103</td>\n",
       "      <td>[Animation, Family, Comedy]</td>\n",
       "      <td>7.5</td>\n",
       "      <td>2010</td>\n",
       "      <td>863171911</td>\n",
       "      <td>81.188367</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         id  popularity     budget     revenue original_title       cast  \\\n",
       "1930  10193    2.711136  200000000  1063171911    Toy Story 3  Tom Hanks   \n",
       "\n",
       "           director  runtime                       genres  vote_average  \\\n",
       "1930  [Lee Unkrich]      103  [Animation, Family, Comedy]           7.5   \n",
       "\n",
       "      release_year     profit  profit_margin  \n",
       "1930          2010  863171911      81.188367  "
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cast_df.query('cast == \"Tom Hanks\"').sort_values('revenue',ascending=False).head(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Actors associated with higher revenue and profit are Harrison Ford, Tom Hanks and Tom Cruise."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***\n",
    "\n",
    "## Q5: Which year produced movies with highest grossing revenue?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>director</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1386</th>\n",
       "      <td>19995</td>\n",
       "      <td>9.432768</td>\n",
       "      <td>237000000</td>\n",
       "      <td>2781505847</td>\n",
       "      <td>Avatar</td>\n",
       "      <td>[Sam Worthington, Zoe Saldana, Sigourney Weave...</td>\n",
       "      <td>[James Cameron]</td>\n",
       "      <td>162</td>\n",
       "      <td>[Action, Adventure, Fantasy, Science Fiction]</td>\n",
       "      <td>7.1</td>\n",
       "      <td>2009</td>\n",
       "      <td>2544505847</td>\n",
       "      <td>91.479435</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         id  popularity     budget     revenue original_title  \\\n",
       "1386  19995    9.432768  237000000  2781505847         Avatar   \n",
       "\n",
       "                                                   cast         director  \\\n",
       "1386  [Sam Worthington, Zoe Saldana, Sigourney Weave...  [James Cameron]   \n",
       "\n",
       "      runtime                                         genres  vote_average  \\\n",
       "1386      162  [Action, Adventure, Fantasy, Science Fiction]           7.1   \n",
       "\n",
       "      release_year      profit  profit_margin  \n",
       "1386          2009  2544505847      91.479435  "
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sort_values('revenue',ascending=False).head(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Avatar produced in 2009 has the highest grossing revenue.\n",
    "\n",
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='conclusions'></a>\n",
    "## Conclusions\n",
    "\n",
    "Let us revisit the questions and summarize our conclusion.\n",
    "\n",
    "#### Q1: Does having a higher budget results in a higher revenue and profit?\n",
    "\n",
    "> ### In most cases, a higher budget does results in higher revenue and profit. However, the analysis shows that there are outlier where a high budget results in losses.\n",
    "\n",
    "#### Q2: Which genres are associated with high popularity?\n",
    "\n",
    "> ### Adventure, Science Fiction and Fantasy are the most popular genres.\n",
    "\n",
    "#### Q3: Which director produces the highest revenue movies?\n",
    "\n",
    "> ### Pierre Coffin is the director who produced the highest revenue movie with the Despicable Me movies.\n",
    "\n",
    "#### Q4: Which actor is associated with higher popularity, revenue and profit?\n",
    "\n",
    "> ### Actors associated with higher revenue and profit are Harrison Ford, Tom Hanks and Tom Cruise.\n",
    "\n",
    "#### Q5: Which year produced movies with highest grossing revenue?\n",
    "\n",
    "> ### Avatar produced in 2009 has the highest grossing revenue at USD 2.7 billion.\n",
    "\n",
    "In conclusion, the factors contributing to a Box Office success is not only having a billion dollar budget, the  right genre, director, and star-studded casts also contributed to the revenue.\n",
    "\n",
    "### Limitations of Dataset\n",
    "1. 43% of dataset have USD 0 budget, hence the profit analysis is representative of the remaining 57% of dataset only.\n",
    "2. The revenue and budget were not denominated in currency, so it’s unsure whether they are in USD, or another currency.\n",
    "3. There were multiple genres in each movie, hence we split the genres such that each genre is allocated to 1 row.\n",
    "\n",
    "### Reference\n",
    "1. https://pandas.pydata.org/docs/user_guide/index.html\n",
    "2. https://www.investopedia.com/financial-edge/0611/why-movies-cost-so-much-to-make.aspx\n",
    "3. https://developers.themoviedb.org/3/getting-started/popularity\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***"
   ]
  }
 ],
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