report.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Machine Learning Foundation Nanodegree\n",
    "# Project: Investigate a TMDb movie Database"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----\n",
    "## Table of Contents\n",
    "\n",
    "- [Introduction](#Introduction)\n",
    " - [Questions to be Answered](#Questions-to-be-Answered)\n",
    " - [Wrangling & Cleaning](#What-needs-to-be-Wrangled-and-Cleaned)\n",
    "- [Data Cleaning](#Data-Cleaning)\n",
    "- [Data Wrangling](#Data-Wrangling)\n",
    "- [Exploratory Data Analysis](#Exploratory-Data-Analysis)\n",
    " - [Question 1](#Q1)\n",
    "   - [A](#1A)\n",
    "   - [B](#1B)\n",
    "   - [C](#1C)\n",
    "   - [D](#1D)\n",
    "   - [E](#1E)\n",
    "   - [F](#1F)\n",
    " - [Question 2](#Q2)\n",
    "   - [A](#2A)\n",
    "   - [B](#2B)\n",
    "   - [C](#2C)\n",
    "   - [D](#2D)\n",
    "   - [E](#2E)\n",
    "   - [F](#2F)\n",
    "   - [G](#2G)\n",
    "   - [H](#2H)\n",
    "- [Conclusion](#Conclusion)\n",
    "\n",
    "-----\n",
    "## Introduction\n",
    "\n",
    "In this project of my Data Analysis, I am investigating a TMDb movies database file which has collection of important detials of about 10k+ movies, including their details of budget, revenue, release dates, etc.\n",
    "\n",
    "Let's take a glimpse at TMDb movie database csv file..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>imdb_id</th>\n",
       "      <th>popularity</th>\n",
       "      <th>budget</th>\n",
       "      <th>revenue</th>\n",
       "      <th>original_title</th>\n",
       "      <th>cast</th>\n",
       "      <th>homepage</th>\n",
       "      <th>director</th>\n",
       "      <th>tagline</th>\n",
       "      <th>...</th>\n",
       "      <th>overview</th>\n",
       "      <th>runtime</th>\n",
       "      <th>genres</th>\n",
       "      <th>production_companies</th>\n",
       "      <th>release_date</th>\n",
       "      <th>vote_count</th>\n",
       "      <th>vote_average</th>\n",
       "      <th>release_year</th>\n",
       "      <th>budget_adj</th>\n",
       "      <th>revenue_adj</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>135397</td>\n",
       "      <td>tt0369610</td>\n",
       "      <td>32.985763</td>\n",
       "      <td>150000000</td>\n",
       "      <td>1513528810</td>\n",
       "      <td>Jurassic World</td>\n",
       "      <td>Chris Pratt|Bryce Dallas Howard|Irrfan Khan|Vi...</td>\n",
       "      <td>http://www.jurassicworld.com/</td>\n",
       "      <td>Colin Trevorrow</td>\n",
       "      <td>The park is open.</td>\n",
       "      <td>...</td>\n",
       "      <td>Twenty-two years after the events of Jurassic ...</td>\n",
       "      <td>124</td>\n",
       "      <td>Action|Adventure|Science Fiction|Thriller</td>\n",
       "      <td>Universal Studios|Amblin Entertainment|Legenda...</td>\n",
       "      <td>6/9/2015</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>5/13/2015</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>3/18/2015</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",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>140607</td>\n",
       "      <td>tt2488496</td>\n",
       "      <td>11.173104</td>\n",
       "      <td>200000000</td>\n",
       "      <td>2068178225</td>\n",
       "      <td>Star Wars: The Force Awakens</td>\n",
       "      <td>Harrison Ford|Mark Hamill|Carrie Fisher|Adam D...</td>\n",
       "      <td>http://www.starwars.com/films/star-wars-episod...</td>\n",
       "      <td>J.J. Abrams</td>\n",
       "      <td>Every generation has a story.</td>\n",
       "      <td>...</td>\n",
       "      <td>Thirty years after defeating the Galactic Empi...</td>\n",
       "      <td>136</td>\n",
       "      <td>Action|Adventure|Science Fiction|Fantasy</td>\n",
       "      <td>Lucasfilm|Truenorth Productions|Bad Robot</td>\n",
       "      <td>12/15/2015</td>\n",
       "      <td>5292</td>\n",
       "      <td>7.5</td>\n",
       "      <td>2015</td>\n",
       "      <td>183999919.0</td>\n",
       "      <td>1.902723e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>168259</td>\n",
       "      <td>tt2820852</td>\n",
       "      <td>9.335014</td>\n",
       "      <td>190000000</td>\n",
       "      <td>1506249360</td>\n",
       "      <td>Furious 7</td>\n",
       "      <td>Vin Diesel|Paul Walker|Jason Statham|Michelle ...</td>\n",
       "      <td>http://www.furious7.com/</td>\n",
       "      <td>James Wan</td>\n",
       "      <td>Vengeance Hits Home</td>\n",
       "      <td>...</td>\n",
       "      <td>Deckard Shaw seeks revenge against Dominic Tor...</td>\n",
       "      <td>137</td>\n",
       "      <td>Action|Crime|Thriller</td>\n",
       "      <td>Universal Pictures|Original Film|Media Rights ...</td>\n",
       "      <td>4/1/2015</td>\n",
       "      <td>2947</td>\n",
       "      <td>7.3</td>\n",
       "      <td>2015</td>\n",
       "      <td>174799923.1</td>\n",
       "      <td>1.385749e+09</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       id    imdb_id  popularity     budget     revenue  \\\n",
       "0  135397  tt0369610   32.985763  150000000  1513528810   \n",
       "1   76341  tt1392190   28.419936  150000000   378436354   \n",
       "2  262500  tt2908446   13.112507  110000000   295238201   \n",
       "3  140607  tt2488496   11.173104  200000000  2068178225   \n",
       "4  168259  tt2820852    9.335014  190000000  1506249360   \n",
       "\n",
       "                 original_title  \\\n",
       "0                Jurassic World   \n",
       "1            Mad Max: Fury Road   \n",
       "2                     Insurgent   \n",
       "3  Star Wars: The Force Awakens   \n",
       "4                     Furious 7   \n",
       "\n",
       "                                                cast  \\\n",
       "0  Chris Pratt|Bryce Dallas Howard|Irrfan Khan|Vi...   \n",
       "1  Tom Hardy|Charlize Theron|Hugh Keays-Byrne|Nic...   \n",
       "2  Shailene Woodley|Theo James|Kate Winslet|Ansel...   \n",
       "3  Harrison Ford|Mark Hamill|Carrie Fisher|Adam D...   \n",
       "4  Vin Diesel|Paul Walker|Jason Statham|Michelle ...   \n",
       "\n",
       "                                            homepage          director  \\\n",
       "0                      http://www.jurassicworld.com/   Colin Trevorrow   \n",
       "1                        http://www.madmaxmovie.com/     George Miller   \n",
       "2     http://www.thedivergentseries.movie/#insurgent  Robert Schwentke   \n",
       "3  http://www.starwars.com/films/star-wars-episod...       J.J. Abrams   \n",
       "4                           http://www.furious7.com/         James Wan   \n",
       "\n",
       "                         tagline  ...  \\\n",
       "0              The park is open.  ...   \n",
       "1             What a Lovely Day.  ...   \n",
       "2     One Choice Can Destroy You  ...   \n",
       "3  Every generation has a story.  ...   \n",
       "4            Vengeance Hits Home  ...   \n",
       "\n",
       "                                            overview runtime  \\\n",
       "0  Twenty-two years after the events of Jurassic ...     124   \n",
       "1  An apocalyptic story set in the furthest reach...     120   \n",
       "2  Beatrice Prior must confront her inner demons ...     119   \n",
       "3  Thirty years after defeating the Galactic Empi...     136   \n",
       "4  Deckard Shaw seeks revenge against Dominic Tor...     137   \n",
       "\n",
       "                                      genres  \\\n",
       "0  Action|Adventure|Science Fiction|Thriller   \n",
       "1  Action|Adventure|Science Fiction|Thriller   \n",
       "2         Adventure|Science Fiction|Thriller   \n",
       "3   Action|Adventure|Science Fiction|Fantasy   \n",
       "4                      Action|Crime|Thriller   \n",
       "\n",
       "                                production_companies release_date vote_count  \\\n",
       "0  Universal Studios|Amblin Entertainment|Legenda...     6/9/2015       5562   \n",
       "1  Village Roadshow Pictures|Kennedy Miller Produ...    5/13/2015       6185   \n",
       "2  Summit Entertainment|Mandeville Films|Red Wago...    3/18/2015       2480   \n",
       "3          Lucasfilm|Truenorth Productions|Bad Robot   12/15/2015       5292   \n",
       "4  Universal Pictures|Original Film|Media Rights ...     4/1/2015       2947   \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",
       "3           7.5          2015  183999919.0  1.902723e+09  \n",
       "4           7.3          2015  174799923.1  1.385749e+09  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "#reading tmdb csv file and storing that to a variable\n",
    "glimpse_tmdb = pd.read_csv('data.csv')\n",
    "\n",
    "#calling out first 5 rows (excluding headers) of tmdb database\n",
    "glimpse_tmdb.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### What can we say about the dataset provided?\n",
    "<ul>\n",
    "    <li>The columns *'budget', 'revenue', 'budget_adj', 'revenue_adj'* has not given us the currency but for this dataset we will assume that it is in dollars.</li>\n",
    "    <li>The vote count for each movie is not similar, for example, the movie *'Mad Max : Fury Road'* has *6k+* votes while *Sinister 2* has only *331 votes* (as seen above). Since the votes of the movies vary so much the *vote_average* column also is effected by it. So we cannot calculate or assume that movie with highest votes or rating was more successful since the voters of each film vary.</li>\n",
    "</ul>  \n",
    "\n",
    "### What Questions can be brainstormed?\n",
    "Looking at this database...\n",
    "<ul>\n",
    "<li>The first question comes in my mind is which movie gained the most profit or we can also kind of say that which movie has been the people's favourite?</li>\n",
    "\n",
    "<li>Since this is just the glimpse of the database, the glimpse of the data just shows the movies in the year 2015, but there are also other movies released in different years so the Second question comes in my mind is in which year the movies made the most profit?</li>\n",
    "\n",
    "<li>Finally my curious mind wanted to know what are the similar characteristics of movies which have gained highest profits?</li>\n",
    "</ul>\n",
    "\n",
    "### What needs to be Wrangled and Cleaned\n",
    "Based on the questions brainstormed above, we want to know do we have all the valid values of the variables that we want to calculate and how can this data be trimmed so we can only have the columns we need. This will also make our dataset clean and easy for us to calculate what we want.\n",
    "<ul>\n",
    "<li>As you can see in this database of movies there are lots of movies where the budget or revenue have a value of '0' which means that the values of those variables of those movies has not been recorded. Calculating the profits of these movies would lead to inappropriate results. So we need to delete these rows.</li>\n",
    "\n",
    "<li>Also this dataset has some duplicate rows. We have to clean that too for appropriate results.</li>\n",
    "\n",
    "<li>We will also calculate the average runtime of the movies so in case if we have a runtime of a movie '0' then we need to replace it with `NaN`.</li>\n",
    "\n",
    "<li>The *'release_date'* column must be converted into date format.</li>\n",
    "\n",
    "<li>Checking if all columns are in the desired data type, if not then we have to change it.</li>\n",
    "\n",
    "<li>Mentioning the country currency in the desired columns.</li>\n",
    "\n",
    "<li>Finally, we will also remove unnecessory columns such as *'id', 'imdb_id', 'popularity', 'budget_adj', 'revenue_adj', 'homepage', 'keywords', 'overview', 'production_companies', 'vote_count' and 'vote_average'.* </li>\n",
    "</ul>\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "### Questions to be Answered\n",
    "<ol>\n",
    "    <li>General questions about the dataset.</li>\n",
    "        <ol type = 'a'>\n",
    "            <li>Which movie earns the most and least profit?</li>\n",
    "            <li>Which movie had the greatest and least runtime?</li>\n",
    "            <li>Which movie had the greatest and least budget?</li>\n",
    "            <li>Which movie had the greatest and least revenue?</li>\n",
    "            <li>What is the average runtime of all movies?</li>\n",
    "            <li>In which year we had the most movies making profits?</li>\n",
    "        </ol>\n",
    "    <li>What are the similar characteristics does the most profitable movie have?</li>\n",
    "        <ol type = 'a'>\n",
    "            <li>Average duration of movies.</li>\n",
    "            <li>Average Budget.</li>\n",
    "            <li>Average revenue.</li>\n",
    "            <li>Average profits.</li>\n",
    "            <li>Which director directed most films?</li>\n",
    "            <li>Whcih cast has appeared the most?</li>\n",
    "            <li>Which genre were more successful?</li>\n",
    "            <li>Which month released highest number of movies in all of the years? And which month made the most profit?</li>\n",
    "        </ol>\n",
    "</ol>\n",
    "\n",
    "\n",
    "-----\n",
    "\n",
    "\n",
    "## Data Cleaning\n",
    "\n",
    "**Before answering the above questions we need a clean dataset which has columns and rows we need for calculations.**\n",
    "\n",
    "First, lets clean up the columns.\n",
    "We will only keep the columns we need and remove the rest of them.\n",
    "\n",
    "Columns to delete -  `id, imdb_id, popularity, budget_adj, revenue_adj, homepage, keywords, overview, production_companies, vote_count and vote_average.`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#importing all the nescessory libraries we need for our analysis\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "#this variable will store the database of tmdb movies into a dataframe\n",
    "movie_data = pd.read_csv('data.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's see how many entries we have of movies in this dataset and features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "We have 10865 total entries of movies and 21 columns/features of it.\n"
     ]
    }
   ],
   "source": [
    "rows, col = movie_data.shape\n",
    "#since 'rows' includes count of a header, we need to remove its count.\n",
    "print('We have {} total entries of movies and {} columns/features of it.'.format(rows-1, col))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_14033/636607395.py:6: FutureWarning: In a future version of pandas all arguments of DataFrame.drop except for the argument 'labels' will be keyword-only.\n",
      "  movie_data = movie_data.drop(del_col, 1)\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "</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>original_title</th>\n",
       "      <th>cast</th>\n",
       "      <th>director</th>\n",
       "      <th>tagline</th>\n",
       "      <th>runtime</th>\n",
       "      <th>genres</th>\n",
       "      <th>release_date</th>\n",
       "      <th>release_year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\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>The park is open.</td>\n",
       "      <td>124</td>\n",
       "      <td>Action|Adventure|Science Fiction|Thriller</td>\n",
       "      <td>6/9/2015</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\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>What a Lovely Day.</td>\n",
       "      <td>120</td>\n",
       "      <td>Action|Adventure|Science Fiction|Thriller</td>\n",
       "      <td>5/13/2015</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\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>One Choice Can Destroy You</td>\n",
       "      <td>119</td>\n",
       "      <td>Adventure|Science Fiction|Thriller</td>\n",
       "      <td>3/18/2015</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      budget     revenue      original_title  \\\n",
       "0  150000000  1513528810      Jurassic World   \n",
       "1  150000000   378436354  Mad Max: Fury Road   \n",
       "2  110000000   295238201           Insurgent   \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",
       "\n",
       "                      tagline  runtime  \\\n",
       "0           The park is open.      124   \n",
       "1          What a Lovely Day.      120   \n",
       "2  One Choice Can Destroy You      119   \n",
       "\n",
       "                                      genres release_date  release_year  \n",
       "0  Action|Adventure|Science Fiction|Thriller     6/9/2015          2015  \n",
       "1  Action|Adventure|Science Fiction|Thriller    5/13/2015          2015  \n",
       "2         Adventure|Science Fiction|Thriller    3/18/2015          2015  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#lets give a list of movies that needs to be deleted\n",
    "del_col = [ 'id', 'imdb_id', 'popularity', 'budget_adj', 'revenue_adj', 'homepage', 'keywords', 'overview', 'production_companies', 'vote_count', 'vote_average']\n",
    "\n",
    " \n",
    "#deleting the columns from the database\n",
    "movie_data = movie_data.drop(del_col, 1)\n",
    "#now take a look at this new dataset\n",
    "movie_data.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The difference between the database before and now can be seen. This database is soothing to eyes as it only contains columns that are needed for analysis. \n",
    "\n",
    "Now lets clean for any duplicate rows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "We now have 10864 total entries of movies and 10 columns/features of it.\n"
     ]
    }
   ],
   "source": [
    "#will drop duplicate rows but will keep the first one\n",
    "movie_data.drop_duplicates(keep = 'first', inplace = True)\n",
    "\n",
    "rows, col = movie_data.shape\n",
    "print('We now have {} total entries of movies and {} columns/features of it.'.format(rows-1, col))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So we had one duplicate copy of a movie. Now we have 10864 movie entries.\n",
    "\n",
    "**Now, lets figure out which movies have a value of `'0'` in their `budget` or `revenue`, and then deleting those movies from database.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "After cleaning, we now have only 3853 entries of movies.\n"
     ]
    }
   ],
   "source": [
    "#giving list of column names that needs to be checked\n",
    "check_row = ['budget', 'revenue']\n",
    "\n",
    "#this will replace the value of '0' to NaN of columns given in the list\n",
    "movie_data[check_row] = movie_data[check_row].replace(0, np.NaN)\n",
    "\n",
    "#now we will drop any row which has NaN values in any of the column of the list (check_row) \n",
    "movie_data.dropna(subset = check_row, inplace = True)\n",
    "\n",
    "rows, col = movie_data.shape\n",
    "print('After cleaning, we now have only {} entries of movies.'.format(rows-1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you saw in the previous dataset from having 10k+ rows and 21 columns we have now come down to 3853 rows and 10 columns. These many columns are needed for analysis and we have all the rows that have valid values for our calculations.\n",
    "\n",
    "Now as we are done with cleaning the dataset, let's move on to data wrangling phase.\n",
    "<br>\n",
    "\n",
    "-----\n",
    "\n",
    "## Data Wrangling\n",
    "\n",
    "**Now first lets check if we have any movie with a runtime value of `0`. If we have any, we will replace with `NaN`.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "#replacing 0 with NaN of runtime column of the dataframe\n",
    "movie_data['runtime'] = movie_data['runtime'].replace(0, np.NaN)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Now we need to convert the `'release_date'` column to date format**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>original_title</th>\n",
       "      <th>cast</th>\n",
       "      <th>director</th>\n",
       "      <th>tagline</th>\n",
       "      <th>runtime</th>\n",
       "      <th>genres</th>\n",
       "      <th>release_date</th>\n",
       "      <th>release_year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>150000000.0</td>\n",
       "      <td>1.513529e+09</td>\n",
       "      <td>Jurassic World</td>\n",
       "      <td>Chris Pratt|Bryce Dallas Howard|Irrfan Khan|Vi...</td>\n",
       "      <td>Colin Trevorrow</td>\n",
       "      <td>The park is open.</td>\n",
       "      <td>124</td>\n",
       "      <td>Action|Adventure|Science Fiction|Thriller</td>\n",
       "      <td>2015-06-09</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>150000000.0</td>\n",
       "      <td>3.784364e+08</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>What a Lovely Day.</td>\n",
       "      <td>120</td>\n",
       "      <td>Action|Adventure|Science Fiction|Thriller</td>\n",
       "      <td>2015-05-13</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        budget       revenue      original_title  \\\n",
       "0  150000000.0  1.513529e+09      Jurassic World   \n",
       "1  150000000.0  3.784364e+08  Mad Max: Fury Road   \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",
       "\n",
       "              tagline  runtime                                     genres  \\\n",
       "0   The park is open.      124  Action|Adventure|Science Fiction|Thriller   \n",
       "1  What a Lovely Day.      120  Action|Adventure|Science Fiction|Thriller   \n",
       "\n",
       "  release_date  release_year  \n",
       "0   2015-06-09          2015  \n",
       "1   2015-05-13          2015  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#calling the column which need to be formatted in datetime and storing those values in them\n",
    "movie_data.release_date = pd.to_datetime(movie_data['release_date'])\n",
    "\n",
    "#showing the dataset\n",
    "movie_data.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you see, the `'release_date'` column has been changed to date format. (year-month-day)\n",
    "\n",
    "**Lets see if all the columns are in the format that we want for our calculations.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "budget                   float64\n",
       "revenue                  float64\n",
       "original_title            object\n",
       "cast                      object\n",
       "director                  object\n",
       "tagline                   object\n",
       "runtime                    int64\n",
       "genres                    object\n",
       "release_date      datetime64[ns]\n",
       "release_year               int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#shwoing the datatypes of all the columns\n",
    "movie_data.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see we have float values for `'budget'` and `'revenue'` columns, since we dont need float but in int datatype, lets convert them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "budget                     int64\n",
       "revenue                    int64\n",
       "original_title            object\n",
       "cast                      object\n",
       "director                  object\n",
       "tagline                   object\n",
       "runtime                    int64\n",
       "genres                    object\n",
       "release_date      datetime64[ns]\n",
       "release_year               int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#applymap function changes the columns data type to the type 'argument' we pass\n",
    "change_coltype = ['budget', 'revenue']\n",
    "\n",
    "movie_data[change_coltype] = movie_data[change_coltype].applymap(np.int64)\n",
    "#showing the datatypes of all columns\n",
    "movie_data.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now all columns are in the desired format.\n",
    "\n",
    "**Since the values in the column `'budget'` and `'revenue'` shows us in Currency of US (as assumed earlier), lets change the name of these columns for convenience.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "#rename function renames the columns, the key as being the old name and its value new name of it in form of dictionary.\n",
    "movie_data.rename(columns = {'budget' : 'budget_(in_US-Dollars)', 'revenue' : 'revenue_(in_US-Dollars)'}, inplace = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Since now we have the columns, rows and format of the dataset in right way, its time to investigate the data for the questions asked.**\n",
    "<br>\n",
    "\n",
    "----\n",
    "\n",
    "## Exploratory Data Analysis\n",
    "\n",
    "**Before answering the questions, lets figure out the profits of each movie.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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_(in_US-Dollars)</th>\n",
       "      <th>revenue_(in_US-Dollars)</th>\n",
       "      <th>profit_(in_US_Dollars)</th>\n",
       "      <th>original_title</th>\n",
       "      <th>cast</th>\n",
       "      <th>director</th>\n",
       "      <th>tagline</th>\n",
       "      <th>runtime</th>\n",
       "      <th>genres</th>\n",
       "      <th>release_date</th>\n",
       "      <th>release_year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>150000000</td>\n",
       "      <td>1513528810</td>\n",
       "      <td>1363528810</td>\n",
       "      <td>Jurassic World</td>\n",
       "      <td>Chris Pratt|Bryce Dallas Howard|Irrfan Khan|Vi...</td>\n",
       "      <td>Colin Trevorrow</td>\n",
       "      <td>The park is open.</td>\n",
       "      <td>124</td>\n",
       "      <td>Action|Adventure|Science Fiction|Thriller</td>\n",
       "      <td>2015-06-09</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>150000000</td>\n",
       "      <td>378436354</td>\n",
       "      <td>228436354</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>What a Lovely Day.</td>\n",
       "      <td>120</td>\n",
       "      <td>Action|Adventure|Science Fiction|Thriller</td>\n",
       "      <td>2015-05-13</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   budget_(in_US-Dollars)  revenue_(in_US-Dollars)  profit_(in_US_Dollars)  \\\n",
       "0               150000000               1513528810              1363528810   \n",
       "1               150000000                378436354               228436354   \n",
       "\n",
       "       original_title                                               cast  \\\n",
       "0      Jurassic World  Chris Pratt|Bryce Dallas Howard|Irrfan Khan|Vi...   \n",
       "1  Mad Max: Fury Road  Tom Hardy|Charlize Theron|Hugh Keays-Byrne|Nic...   \n",
       "\n",
       "          director             tagline  runtime  \\\n",
       "0  Colin Trevorrow   The park is open.      124   \n",
       "1    George Miller  What a Lovely Day.      120   \n",
       "\n",
       "                                      genres release_date  release_year  \n",
       "0  Action|Adventure|Science Fiction|Thriller   2015-06-09          2015  \n",
       "1  Action|Adventure|Science Fiction|Thriller   2015-05-13          2015  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#assigning a new column which will hold the profit values of each movie\n",
    "\n",
    "#the insert function's first argument is an index number given to locate the column, second argument takes the name of the new column...\n",
    "#...and last but not least it takes the calculation values to output for specific column\n",
    "\n",
    "#To calculate profit of each movie, we need to substract the budget from the revenue of each movie\n",
    "movie_data.insert(2, 'profit_(in_US_Dollars)', movie_data['revenue_(in_US-Dollars)'] - movie_data['budget_(in_US-Dollars)'])\n",
    "\n",
    "#for just in case situations or for convenience, we change the data type to int\n",
    "movie_data['profit_(in_US_Dollars)'] = movie_data['profit_(in_US_Dollars)'].apply(np.int64)\n",
    "\n",
    "#showing the dataset\n",
    "movie_data.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "movie_data.to_csv(\"clean_data.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All the profits of the movies are now listed and our now a part of our dataset.\n",
    "\n",
    "**Now let's dig deep and answer the questions!**\n",
    "\n",
    "> ### Q1\n",
    ">> ### 1A\n",
    "#### Which movie earns the most and least profit?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>1386</th>\n",
       "      <th>2244</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>budget_(in_US-Dollars)</th>\n",
       "      <td>237000000</td>\n",
       "      <td>425000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>revenue_(in_US-Dollars)</th>\n",
       "      <td>2781505847</td>\n",
       "      <td>11087569</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>profit_(in_US_Dollars)</th>\n",
       "      <td>2544505847</td>\n",
       "      <td>-413912431</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>original_title</th>\n",
       "      <td>Avatar</td>\n",
       "      <td>The Warrior's Way</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cast</th>\n",
       "      <td>Sam Worthington|Zoe Saldana|Sigourney Weaver|S...</td>\n",
       "      <td>Kate Bosworth|Jang Dong-gun|Geoffrey Rush|Dann...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>director</th>\n",
       "      <td>James Cameron</td>\n",
       "      <td>Sngmoo Lee</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tagline</th>\n",
       "      <td>Enter the World of Pandora.</td>\n",
       "      <td>Assassin. Hero. Legend.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>runtime</th>\n",
       "      <td>162</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>genres</th>\n",
       "      <td>Action|Adventure|Fantasy|Science Fiction</td>\n",
       "      <td>Adventure|Fantasy|Action|Western|Thriller</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>release_date</th>\n",
       "      <td>2009-12-10 00:00:00</td>\n",
       "      <td>2010-12-02 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>release_year</th>\n",
       "      <td>2009</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                      1386  \\\n",
       "budget_(in_US-Dollars)                                           237000000   \n",
       "revenue_(in_US-Dollars)                                         2781505847   \n",
       "profit_(in_US_Dollars)                                          2544505847   \n",
       "original_title                                                      Avatar   \n",
       "cast                     Sam Worthington|Zoe Saldana|Sigourney Weaver|S...   \n",
       "director                                                     James Cameron   \n",
       "tagline                                        Enter the World of Pandora.   \n",
       "runtime                                                                162   \n",
       "genres                            Action|Adventure|Fantasy|Science Fiction   \n",
       "release_date                                           2009-12-10 00:00:00   \n",
       "release_year                                                          2009   \n",
       "\n",
       "                                                                      2244  \n",
       "budget_(in_US-Dollars)                                           425000000  \n",
       "revenue_(in_US-Dollars)                                           11087569  \n",
       "profit_(in_US_Dollars)                                          -413912431  \n",
       "original_title                                           The Warrior's Way  \n",
       "cast                     Kate Bosworth|Jang Dong-gun|Geoffrey Rush|Dann...  \n",
       "director                                                        Sngmoo Lee  \n",
       "tagline                                            Assassin. Hero. Legend.  \n",
       "runtime                                                                100  \n",
       "genres                           Adventure|Fantasy|Action|Western|Thriller  \n",
       "release_date                                           2010-12-02 00:00:00  \n",
       "release_year                                                          2010  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Let's define a function which calculates lowest and highest values of columns\n",
    "#taking column name as arguments\n",
    "\n",
    "def highest_lowest(column_name):\n",
    "    \n",
    "    #highest\n",
    "    #taking the index value of the highest number in profit column\n",
    "    highest_id = movie_data[column_name].idxmax()\n",
    "    #calling by index number,storing that row info to a variable\n",
    "    highest_details = pd.DataFrame(movie_data.loc[highest_id])\n",
    "    \n",
    "    #lowest\n",
    "    #same processing as above\n",
    "    lowest_id = movie_data[column_name].idxmin()\n",
    "    lowest_details = pd.DataFrame(movie_data.loc[lowest_id])\n",
    "    \n",
    "    #concatenating two dataframes\n",
    "    two_in_one_data = pd.concat([highest_details, lowest_details], axis = 1)\n",
    "    \n",
    "    return two_in_one_data\n",
    "\n",
    "#calling the function and passing the argument\n",
    "highest_lowest('profit_(in_US_Dollars)')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The column names for the dataframes above are the index number. The first column shows the highest profit made by a movie and second column shows the highest in loss movie in this dataset. \n",
    "\n",
    "As we can see the Directed by `James Cameron`, `Avatar` film has the highest profit in all, making over `$2.5B` in profit in this dataset. May be the highest till now of entire human race but we can't say for sure as this dataset doesn't have all the films released till date.\n",
    "\n",
    "And the most in loss movie in this dataset is `The Warriors Way`. Going in loss by more than `$400M` was directed by `Singmoo Lee`.\n",
    "\n",
    "Let's continue with our investigation! It's fun!\n",
    "\n",
    ">> ### 1B\n",
    "#### Which movie had the greatest and least runtime?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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",
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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>2107</th>\n",
       "      <th>5162</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>budget_(in_US-Dollars)</th>\n",
       "      <td>18000000</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>revenue_(in_US-Dollars)</th>\n",
       "      <td>871279</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>profit_(in_US_Dollars)</th>\n",
       "      <td>-17128721</td>\n",
       "      <td>-5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>original_title</th>\n",
       "      <td>Carlos</td>\n",
       "      <td>Kid's Story</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cast</th>\n",
       "      <td>Edgar RamÃ­rez|Alexander Scheer|Fadi Abi Samra...</td>\n",
       "      <td>Clayton Watson|Keanu Reeves|Carrie-Anne Moss|K...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>director</th>\n",
       "      <td>Olivier Assayas</td>\n",
       "      <td>Shinichiro Watanabe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tagline</th>\n",
       "      <td>The man who hijacked the world</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>runtime</th>\n",
       "      <td>338</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>genres</th>\n",
       "      <td>Crime|Drama|Thriller|History</td>\n",
       "      <td>Science Fiction|Animation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>release_date</th>\n",
       "      <td>2010-05-19 00:00:00</td>\n",
       "      <td>2003-06-02 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>release_year</th>\n",
       "      <td>2010</td>\n",
       "      <td>2003</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                      2107  \\\n",
       "budget_(in_US-Dollars)                                            18000000   \n",
       "revenue_(in_US-Dollars)                                             871279   \n",
       "profit_(in_US_Dollars)                                           -17128721   \n",
       "original_title                                                      Carlos   \n",
       "cast                     Edgar RamÃ­rez|Alexander Scheer|Fadi Abi Samra...   \n",
       "director                                                   Olivier Assayas   \n",
       "tagline                                     The man who hijacked the world   \n",
       "runtime                                                                338   \n",
       "genres                                        Crime|Drama|Thriller|History   \n",
       "release_date                                           2010-05-19 00:00:00   \n",
       "release_year                                                          2010   \n",
       "\n",
       "                                                                      5162  \n",
       "budget_(in_US-Dollars)                                                  10  \n",
       "revenue_(in_US-Dollars)                                                  5  \n",
       "profit_(in_US_Dollars)                                                  -5  \n",
       "original_title                                                 Kid's Story  \n",
       "cast                     Clayton Watson|Keanu Reeves|Carrie-Anne Moss|K...  \n",
       "director                                               Shinichiro Watanabe  \n",
       "tagline                                                                NaN  \n",
       "runtime                                                                 15  \n",
       "genres                                           Science Fiction|Animation  \n",
       "release_date                                           2003-06-02 00:00:00  \n",
       "release_year                                                          2003  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#as our calculations seems to be same as previous one for different column, lets call the function by passing desired argument\n",
    "\n",
    "highest_lowest('runtime')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Hmm..! So again the first column shows the runtime of the highest and second the lowest with column names as the index number. \n",
    "\n",
    "I have never heard a runtime of a movie so long, yes we would in old times, but in the 21st century, no! Runtime of `338 min`, that's approx 3.5 hrs! So `Carlos` movie has the highest runtime.\n",
    "\n",
    "The name of the movie with shortest runtime is `Kid's Story`, runtime of just 15 min! Woah! I have never seen such a short movie in my lifetime. Have to check this out though! \n",
    "\n",
    "As you see both movies have one thing in common, negative profits! The `Kid's Story` having a budget of `10` dollars and revenue `5` just doesn't seem right to me. It might be but i have never heard or seen a movie with such a short budget. But this what our dataset shows. Okay now let's move forward!\n",
    "\n",
    "\n",
    ">> ### 1C\n",
    "#### Which movie had the greatest and least budget?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>2244</th>\n",
       "      <th>2618</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>budget_(in_US-Dollars)</th>\n",
       "      <td>425000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>revenue_(in_US-Dollars)</th>\n",
       "      <td>11087569</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>profit_(in_US_Dollars)</th>\n",
       "      <td>-413912431</td>\n",
       "      <td>99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>original_title</th>\n",
       "      <td>The Warrior's Way</td>\n",
       "      <td>Lost &amp; Found</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cast</th>\n",
       "      <td>Kate Bosworth|Jang Dong-gun|Geoffrey Rush|Dann...</td>\n",
       "      <td>David Spade|Sophie Marceau|Ever Carradine|Step...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>director</th>\n",
       "      <td>Sngmoo Lee</td>\n",
       "      <td>Jeff Pollack</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tagline</th>\n",
       "      <td>Assassin. Hero. Legend.</td>\n",
       "      <td>A comedy about a guy who would do anything to ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>runtime</th>\n",
       "      <td>100</td>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>genres</th>\n",
       "      <td>Adventure|Fantasy|Action|Western|Thriller</td>\n",
       "      <td>Comedy|Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>release_date</th>\n",
       "      <td>2010-12-02 00:00:00</td>\n",
       "      <td>1999-04-23 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>release_year</th>\n",
       "      <td>2010</td>\n",
       "      <td>1999</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                      2244  \\\n",
       "budget_(in_US-Dollars)                                           425000000   \n",
       "revenue_(in_US-Dollars)                                           11087569   \n",
       "profit_(in_US_Dollars)                                          -413912431   \n",
       "original_title                                           The Warrior's Way   \n",
       "cast                     Kate Bosworth|Jang Dong-gun|Geoffrey Rush|Dann...   \n",
       "director                                                        Sngmoo Lee   \n",
       "tagline                                            Assassin. Hero. Legend.   \n",
       "runtime                                                                100   \n",
       "genres                           Adventure|Fantasy|Action|Western|Thriller   \n",
       "release_date                                           2010-12-02 00:00:00   \n",
       "release_year                                                          2010   \n",
       "\n",
       "                                                                      2618  \n",
       "budget_(in_US-Dollars)                                                   1  \n",
       "revenue_(in_US-Dollars)                                                100  \n",
       "profit_(in_US_Dollars)                                                  99  \n",
       "original_title                                                Lost & Found  \n",
       "cast                     David Spade|Sophie Marceau|Ever Carradine|Step...  \n",
       "director                                                      Jeff Pollack  \n",
       "tagline                  A comedy about a guy who would do anything to ...  \n",
       "runtime                                                                 95  \n",
       "genres                                                      Comedy|Romance  \n",
       "release_date                                           1999-04-23 00:00:00  \n",
       "release_year                                                          1999  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#as our calculations seems to be same as previous one for different column, lets call the function by passing desired argument\n",
    "\n",
    "highest_lowest('budget_(in_US-Dollars)')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is Interesting. Same in format as above dataframes, we can see that `The Warriors Way` had the highest budget of all movies in the dataset of about `$425M`. This same movie also had the highest loss. So it makes sense that having the highest budget in all makes the film more harder to have higher revenues and earn more profits.\n",
    "\n",
    "And the least budget of all, the `Lost & Found` movie of `$1` has made me thinking how can a movie with `95 min` of runtime managed with such low budget! Also making revenue of `$100` and earning a profit `$99`, this may be a local movie release. Because it's kind of impossible to have such low budget and earning low revenues if it has released internationally.\n",
    "    \n",
    "Now let's look at the revenue side of the movies...!\n",
    "\n",
    "\n",
    ">> ### 1D\n",
    "#### Which movie had the greatest and least revenue?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>1386</th>\n",
       "      <th>5067</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>budget_(in_US-Dollars)</th>\n",
       "      <td>237000000</td>\n",
       "      <td>6000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>revenue_(in_US-Dollars)</th>\n",
       "      <td>2781505847</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>profit_(in_US_Dollars)</th>\n",
       "      <td>2544505847</td>\n",
       "      <td>-5999998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>original_title</th>\n",
       "      <td>Avatar</td>\n",
       "      <td>Shattered Glass</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cast</th>\n",
       "      <td>Sam Worthington|Zoe Saldana|Sigourney Weaver|S...</td>\n",
       "      <td>Hayden Christensen|Peter Sarsgaard|ChloÃ« Sevi...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>director</th>\n",
       "      <td>James Cameron</td>\n",
       "      <td>Billy Ray</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tagline</th>\n",
       "      <td>Enter the World of Pandora.</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>runtime</th>\n",
       "      <td>162</td>\n",
       "      <td>94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>genres</th>\n",
       "      <td>Action|Adventure|Fantasy|Science Fiction</td>\n",
       "      <td>Drama|History</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>release_date</th>\n",
       "      <td>2009-12-10 00:00:00</td>\n",
       "      <td>2003-11-14 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>release_year</th>\n",
       "      <td>2009</td>\n",
       "      <td>2003</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                      1386  \\\n",
       "budget_(in_US-Dollars)                                           237000000   \n",
       "revenue_(in_US-Dollars)                                         2781505847   \n",
       "profit_(in_US_Dollars)                                          2544505847   \n",
       "original_title                                                      Avatar   \n",
       "cast                     Sam Worthington|Zoe Saldana|Sigourney Weaver|S...   \n",
       "director                                                     James Cameron   \n",
       "tagline                                        Enter the World of Pandora.   \n",
       "runtime                                                                162   \n",
       "genres                            Action|Adventure|Fantasy|Science Fiction   \n",
       "release_date                                           2009-12-10 00:00:00   \n",
       "release_year                                                          2009   \n",
       "\n",
       "                                                                      5067  \n",
       "budget_(in_US-Dollars)                                             6000000  \n",
       "revenue_(in_US-Dollars)                                                  2  \n",
       "profit_(in_US_Dollars)                                            -5999998  \n",
       "original_title                                             Shattered Glass  \n",
       "cast                     Hayden Christensen|Peter Sarsgaard|ChloÃ« Sevi...  \n",
       "director                                                         Billy Ray  \n",
       "tagline                                                                NaN  \n",
       "runtime                                                                 94  \n",
       "genres                                                       Drama|History  \n",
       "release_date                                           2003-11-14 00:00:00  \n",
       "release_year                                                          2003  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#again, we will call our function! \n",
    "highest_lowest('revenue_(in_US-Dollars)')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Interesting results again! `Avatar` movie also earning the most profit movie has made most revenue too! Making a revenue of more than `$2.7B`, it makes a logical sense that the more revenue you earn the more profit you gain and as we saw in earlier result, `The Warriors Way` having most budget had less chance of making profits. It really looks that there's correlation between profit and budget/revenue but we can't say just by looking at few results.\n",
    "\n",
    "Having lowest revenue of `$2`, `Shattered Glass` movie seems like couldn't sell much tickets.\n",
    "\n",
    "Let's keep exploring!\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ">> ### 1E\n",
    "#### What is the average runtime of all movies?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "#giving a function which calculates average of a particular column\n",
    "def average_func(column_name):\n",
    "    \n",
    "    return movie_data[column_name].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "109.22029060716139"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#calling function to show the mean\n",
    "average_func('runtime')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**The average runtime of all movies in this dataset is `109 mins` approx.\n",
    "We want to get a deeper look and understanding of runtime of all movies so Let's plot it. **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11274790>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plotting a histogram of runtime of movies\n",
    "\n",
    "#gives styles to bg plot\n",
    "sns.set_style('darkgrid')\n",
    "\n",
    "#chaging the label size, this will change the size of all plots that we plot from now!\n",
    "plt.rc('xtick', labelsize = 10)\n",
    "plt.rc('ytick', labelsize = 10)\n",
    "\n",
    "#giving the figure size(width, height)\n",
    "plt.figure(figsize=(9,6), dpi = 100)\n",
    "#x-axis label name\n",
    "plt.xlabel('Runtime of Movies', fontsize = 15)\n",
    "#y-axis label name\n",
    "plt.ylabel('Number of Movies', fontsize=15)\n",
    "#title of the graph\n",
    "plt.title('Runtime distribution of all the movies', fontsize=18)\n",
    "\n",
    "#giving a histogram plot\n",
    "plt.hist(movie_data['runtime'], rwidth = 0.9, bins =31)\n",
    "#displays the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above graph shows us that how many movies lie between the time interval x1 to x2. For example, as you can see the tallest bar here is time interval between `85-100 min`(approx) and around 1000 movies out of 3855 movies have the runtime between these time intervals. So we can also say from this graph that mode time of movies is around `85-110 min`, has the highest concentration of data points around this time interval. The distribution of this graph is positively skewed or right skewed!\n",
    "\n",
    "**Let's dig deep and figure out the outliers of this distribution.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10c9da10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#giving two plots, thr first is the box plot, second is plots of runtime for movies\n",
    "#giving figure size\n",
    "plt.figure(figsize=(9,7), dpi = 105)\n",
    "\n",
    "#using seaborn to plot\n",
    "#plotting box plot\n",
    "sns.boxplot(movie_data['runtime'], linewidth = 3)\n",
    "#showing the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11140870>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plots the data points of runtime of movies\n",
    "\n",
    "#giving figure size\n",
    "plt.figure(figsize=(10,5), dpi = 105)\n",
    "\n",
    "sns.swarmplot(movie_data['runtime'], color = 'grey')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    3854.000000\n",
       "mean      109.220291\n",
       "std        19.922820\n",
       "min        15.000000\n",
       "25%        95.000000\n",
       "50%       106.000000\n",
       "75%       119.000000\n",
       "max       338.000000\n",
       "Name: runtime, dtype: float64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#getting specific runtime points at x positions\n",
    "movie_data['runtime'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "WooW! Both visualizations above shows us the overall distribution of runtime of movies by plotting the points where they lie in the ditribution and how many movies percent of movies lie below the runtime.\n",
    "\n",
    "What's amazing about box-plot is that it gives us an overall idea of how spread the ditribution in our case the runtime of movies are. First of all what we get from this visualization is how many outliers we have, the min and max points, the median and IQR.\n",
    "\n",
    "As we already saw in our previous calculations of least and highest runtime, this is the appropriate visualization in the comparison of other movies runtime. By looking at the box-plot we don't get the exact values, for example you can guess that the median is will around 100-110 min but by giving the describe function above we get the exact values.\n",
    "\n",
    "So by looking at both, visualiztions and calculations, we can say that..\n",
    "<ul>\n",
    "    <li>There are 25% of movies having a runtime of less than `95 min`</li>\n",
    "    <li>There are 50% of movies having a runtime of less than `109 min`. This is also the median of runtimes.</li>\n",
    "    <li>There are 75% of movies having a runtime of less than `119 min`</li>\n",
    "    <li>50% of movies have a runtime of between 95 min and `119 min`. This is also our IQR.</li>\n",
    "</ul>\n",
    "\n",
    "As we can see there are more movies after the 3rd quartile range than the 1st. This makes the mean of the runtime pull towards the right or increases it.\n",
    "\n",
    "Now lets answer our next question!\n",
    "\n",
    ">> ### 1F\n",
    "#### In which year we had the most movies making profits?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x110fca90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Line plot used for this\n",
    "#Since we want to know the profits of movies for every year we need to group all the movies for those years\n",
    "\n",
    "#the groupby function below collects all the movies for that year and then the profits of all those movies for that years is been added\n",
    "#and storing all this in variable\n",
    "profits_each_year = movie_data.groupby('release_year')['profit_(in_US_Dollars)'].sum()\n",
    "\n",
    "#giving the figure size(width, height)\n",
    "plt.figure(figsize=(12,6), dpi = 130)\n",
    "\n",
    "#labeling x-axis\n",
    "plt.xlabel('Release Year of Movies', fontsize = 12)\n",
    "#labeling y-axis\n",
    "plt.ylabel('Total Profits made by Movies', fontsize = 12)\n",
    "#title of a the plot\n",
    "plt.title('Calculating Total Profits made by all movies in year which it released.')\n",
    "\n",
    "#plotting what needs to be plotted\n",
    "plt.plot(profits_each_year)\n",
    "\n",
    "#showing the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Before i explain lets understand what the y axis shows us. Each values in the y-axis is been multiplied to `'1e10'` (as shown above the plot). Since the profits of movies are high, having 9+ digits, cannot fit the axis. So for example at the year 2010, the y-aixs value is around `1.35`, which means that the profit at that year made by al movies released in that year is `1.35x1e10 =  13500000000` which is `13.5 billion dollars`.\n",
    "\n",
    "The year `2015`, shows us the highest peak, having the highest profit than in any year, of more than `18 billion dollars`. This graph doesn't exactly prove us that every year pass by, the profits of movies will increase but when we see in terms of decades it does show significant uprise in profits. At the year `2000`, profits were around `8 biilion dollars`, but in just 15 years it increased by 10+ biilion dollars. Last 15 years had a significant rise in profits compared to any other decades as we can see in the graph.\n",
    "\n",
    "Not every year had same amount of movies released, the year `2015` had the most movie releases than in any other year. The more old the movies, the more less releases at that year (atleast this is what the dataset shows us).\n",
    "\n",
    "This dataset also doesn't show all the movies that has been released in each year. If it would the graph might would show some different trend.\n",
    "\n",
    "Also to note, In the dataset, there were also movies that had negative profits which drags down the the profits of other movies in those years. So we are not just calculating the movies which made profits, but also which went in loss! The highest profit making movie `Avatar` in 2009 alone drags the profit up by `2.5 billion dollars` out of `14 billion dollars`(calculations below).\n",
    "\n",
    "For convenience, i have shown which year had the most profit. Also we will take a look at the profits of each year with exact figures."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2015"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#this answers our question\n",
    "#shows which year made the highest profit\n",
    "profits_each_year.idxmax()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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>profit_(in_US_Dollars)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>release_year</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2011</th>\n",
       "      <td>14966694704</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012</th>\n",
       "      <td>16596845507</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>15782743325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>16676201357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>19032145273</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              profit_(in_US_Dollars)\n",
       "release_year                        \n",
       "2011                     14966694704\n",
       "2012                     16596845507\n",
       "2013                     15782743325\n",
       "2014                     16676201357\n",
       "2015                     19032145273"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#storing the values in the the form of DataFrame just to get a clean and better visual output\n",
    "profits_each_year = pd.DataFrame(profits_each_year)\n",
    "#printing out\n",
    "profits_each_year.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`2015` was the year where movies made the highest profit of about `19+ billion dollars` which released in that year.\n",
    "\n",
    "We are now done with exploring the dataset given. Now we want to find similar characteristics of most profitable movies.\n",
    "\n",
    "So we need to dig deeper, and that's what we as Data Analysts do! ;)\n",
    "<br>\n",
    "\n",
    "-----\n",
    "<br>\n",
    "> ### Q2\n",
    ">> ### 2A\n",
    "#### Average runtime of movies\n",
    "\n",
    "Before answering this question, we need to first clean the dataset so we only have the data of movies that made profit not loss. Also we need movies not only who just made profit by some dollars but we need movies who made significant profits and then analyzing similar characteristics of it.\n",
    "\n",
    "**Let's take only the movies who made profits of 50M dollars or more.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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_(in_US-Dollars)</th>\n",
       "      <th>revenue_(in_US-Dollars)</th>\n",
       "      <th>profit_(in_US_Dollars)</th>\n",
       "      <th>original_title</th>\n",
       "      <th>cast</th>\n",
       "      <th>director</th>\n",
       "      <th>tagline</th>\n",
       "      <th>runtime</th>\n",
       "      <th>genres</th>\n",
       "      <th>release_date</th>\n",
       "      <th>release_year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>150000000</td>\n",
       "      <td>1513528810</td>\n",
       "      <td>1363528810</td>\n",
       "      <td>Jurassic World</td>\n",
       "      <td>Chris Pratt|Bryce Dallas Howard|Irrfan Khan|Vi...</td>\n",
       "      <td>Colin Trevorrow</td>\n",
       "      <td>The park is open.</td>\n",
       "      <td>124</td>\n",
       "      <td>Action|Adventure|Science Fiction|Thriller</td>\n",
       "      <td>2015-06-09</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>150000000</td>\n",
       "      <td>378436354</td>\n",
       "      <td>228436354</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>What a Lovely Day.</td>\n",
       "      <td>120</td>\n",
       "      <td>Action|Adventure|Science Fiction|Thriller</td>\n",
       "      <td>2015-05-13</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   budget_(in_US-Dollars)  revenue_(in_US-Dollars)  profit_(in_US_Dollars)  \\\n",
       "1               150000000               1513528810              1363528810   \n",
       "2               150000000                378436354               228436354   \n",
       "\n",
       "       original_title                                               cast  \\\n",
       "1      Jurassic World  Chris Pratt|Bryce Dallas Howard|Irrfan Khan|Vi...   \n",
       "2  Mad Max: Fury Road  Tom Hardy|Charlize Theron|Hugh Keays-Byrne|Nic...   \n",
       "\n",
       "          director             tagline  runtime  \\\n",
       "1  Colin Trevorrow   The park is open.      124   \n",
       "2    George Miller  What a Lovely Day.      120   \n",
       "\n",
       "                                      genres release_date  release_year  \n",
       "1  Action|Adventure|Science Fiction|Thriller   2015-06-09          2015  \n",
       "2  Action|Adventure|Science Fiction|Thriller   2015-05-13          2015  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#assinging new dataframe which holds values only of movies having profit $50M or more\n",
    "profit_movie_data = movie_data[movie_data['profit_(in_US_Dollars)'] >= 50000000]\n",
    "\n",
    "#reindexing new dataframe\n",
    "profit_movie_data.index = range(len(profit_movie_data))\n",
    "#will initialize dataframe from 1 instead of 0\n",
    "profit_movie_data.index = profit_movie_data.index + 1\n",
    "\n",
    "#showing the dataset\n",
    "profit_movie_data.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1338"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#number of rows of a dataframe\n",
    "len(profit_movie_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we have the appropriate data to work on. From 3855 rows to 1338 rows, meaning more than 2500+ movies approx made profits less than \\$50M.\n",
    "\n",
    "Let's analyze the data now!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "#giving a new average function since we have a different dataset\n",
    "def prof_avg_fuc(column_name):\n",
    "    return profit_movie_data[column_name].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "113.66741405082212"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#mean of runtime\n",
    "prof_avg_fuc('runtime')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Interesting! The mean time for movies making significant profits is likely similar to the mean runtime of movies that we found before of `109.2` which included movies having less than $50M profits. Difference of 4 minutes.\n",
    "\n",
    ">> ### 2B\n",
    "#### Average Budget of Movies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "60444957.760837071"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#calling the function\n",
    "prof_avg_fuc('budget_(in_US-Dollars)')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The average budget of movies of $50M club in profit is around `$60M dollars`.\n",
    "\n",
    ">> ### 2C\n",
    "#### Average Revenue of Movies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "254957662.59491777"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#calling the function\n",
    "prof_avg_fuc('revenue_(in_US-Dollars)')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The average revenue of movies of $50M club in profit is around `$255M dollars`.\n",
    "\n",
    ">> ### 2D\n",
    "#### Average Profit of Movies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "194512704.83408073"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#calling the function\n",
    "prof_avg_fuc('profit_(in_US_Dollars)')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The average profits of movies of $50M club in profit is around `$194M dollars`.\n",
    "\n",
    ">> ### 2E\n",
    "#### Which directer directed most films?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "#since we have multiple questions answers being similar in logic and code, we will give function which will make our life easier\n",
    "\n",
    "#function which will take any column as argument from which data is need to be extracted and keep track of count\n",
    "def extract_data(column_name):\n",
    "    #will take a column, and separate the string by '|'\n",
    "    all_data = profit_movie_data[column_name].str.cat(sep = '|')\n",
    "    \n",
    "    #giving pandas series and storing the values separately\n",
    "    all_data = pd.Series(all_data.split('|'))\n",
    "    \n",
    "    #this will us value in descending order\n",
    "    count = all_data.value_counts(ascending = False)\n",
    "    \n",
    "    return count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Steven Spielberg    23\n",
       "Robert Zemeckis     13\n",
       "Clint Eastwood      12\n",
       "Tim Burton          11\n",
       "Tony Scott          10\n",
       "dtype: int64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#this will variable will store the return value from a function\n",
    "director_count = extract_data('director')\n",
    "#shwoing top 5 values\n",
    "director_count.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Voila!! `'Steven Spielberg'` takes the crown!** Directing `23 movies` over `$50M+` in profit is no joke! Also the other directors following along the list such as `'Robert Zemeckis', 'Clint Eastwood', 'Tim Burton' etc` prove to be really great directors. Movies directed by these directors is more likely for a movie to make huge profits, the higher the movies they direct that earn huge profits, the higher the probability for a movie to go for success! Since we don't really know how many movies the directors directed in total in their lifetime, we can't say for sure that movies directed by above directors will always earn this much but gives us the idea that how much likely it is when it is directed by them.\n",
    "\n",
    "Let's dig up for the next question!\n",
    "\n",
    ">> ### 2F\n",
    "#### Whcih cast has appeared the most?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Tom Cruise            27\n",
       "Brad Pitt             25\n",
       "Tom Hanks             22\n",
       "Sylvester Stallone    21\n",
       "Cameron Diaz          20\n",
       "dtype: int64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#this will variable will store the return value from a function\n",
    "cast_count = extract_data('cast')\n",
    "#shwoing top 5 values\n",
    "cast_count.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Mind Blowing results atleast to me! `'Tome Cruise'` takes the crown for appearing the most in movies profiting more than $50M. Other actors well deserved it as you can see above! Directors hiring these actors will have higher probability of making huge profits also this doesn't mean that actors other than these acting in a film will make less profit. Famous actors such as `'Tom Cruise', 'Brad Pitt', 'Tom Hanks'`, etc have huge fanbase, making the audience attract to the movie more than actors other than these hance this would affect the revenue of the movie but not all time necessory, ultimately it comes down to storyline and other influential factors. By looking at this dataset we can atleast say that these actors acting in a film has the higher probability of attraction to a movie, hence increasing the advantage of earing high profits!\n",
    "\n",
    "As we said for the directors, goes for the actors too as well! SInce we don't really know how many movies these actors have acted in total in their lifetime, we can't always be sure that movies acted by these actors will always earn this much but gives us the idea that how much likely it is when it is acted by them.\n",
    "\n",
    "Let's dig more and find similar characteristics of these movies.\n",
    "\n",
    ">> ### 2G\n",
    "#### Which genre were more successful?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Comedy       492\n",
       "Drama        481\n",
       "Action       464\n",
       "Thriller     405\n",
       "Adventure    379\n",
       "dtype: int64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#this will variable will store the return value from a function\n",
    "genre_count = extract_data('genres')\n",
    "#shwoing top 5 values\n",
    "genre_count.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Lets visualize this with a plot.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10e67890>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#we want plot to plot points in descending order top to bottom\n",
    "#since our count is in descending order and graph plot points from bottom to top, our graph will be in ascending order form top to bottom\n",
    "#hence lets give the series in ascending order\n",
    "genre_count.sort_values(ascending = True, inplace = True)\n",
    "\n",
    "#initializing plot\n",
    "ax = genre_count.plot.barh(color = '#007482', fontsize = 15)\n",
    "\n",
    "#giving a title\n",
    "ax.set(title = 'The Most filmed genres')\n",
    "\n",
    "#x-label\n",
    "ax.set_xlabel('Number of Movies', color = 'g', fontsize = '18')\n",
    "\n",
    "#giving the figure size(width, height)\n",
    "ax.figure.set_size_inches(12, 10)\n",
    "\n",
    "#shwoing the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Another amazing results. `Action, Drama and Comedy genres` are the most as visualized but Comedy takes the prize, about `492 movies` have genres comedy which make $50M+ in profit. In comparison, even `Adventure` and `Thriller` really play the role. These five genres have more number of movies than rest of the genres as shown by visualization. Probability of earning more than \\$50M for these genres are higher, but still other genres do count too again it depends on lots of other influential factors that come in play. `Western, war, history, music, documentary` and the most least `foreign` genres have less probability to make this much in profit as in comparison to other genre.\n",
    "\n",
    "This also doesn't prove that if you have a movie with an `Action, comedy and drama` genre in it will have a guarantee to make more than $50M but it would have a significant interest and attraction to the population.\n",
    "\n",
    "Let's find one more key characteristic of these movies.\n",
    "\n",
    ">> ### 2H\n",
    "#### Which month released highest number of movies in all of the years? And which month made the most profit?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10e2a3d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#for answering this question we need to group all of the months of years and then calculate the profits of those months\n",
    "#giving a new dataframe which gives 'release-date' as index\n",
    "index_release_date = profit_movie_data.set_index('release_date')\n",
    "\n",
    "#now we need to group all the data by month, since release date is in form of index, we extract month from it\n",
    "groupby_index = index_release_date.groupby([(index_release_date.index.month)])\n",
    "\n",
    "#this will give us how many movies are released in each month\n",
    "monthly_movie_count = groupby_index['profit_(in_US_Dollars)'].count()\n",
    "\n",
    "#converting table to a dataframe\n",
    "monthly_movie_count= pd.DataFrame(monthly_movie_count)\n",
    "\n",
    "#giving a list of months\n",
    "month_list = ['January', 'February', 'March', 'April', 'May', 'June', 'July', 'August', 'September', 'October', 'November', 'December']\n",
    "\n",
    "monthly_movie_count_bar = sns.barplot(x = monthly_movie_count.index, y = monthly_movie_count['profit_(in_US_Dollars)'], data = monthly_movie_count)\n",
    "\n",
    "#setting size of the graph\n",
    "monthly_movie_count_bar.figure.set_size_inches(15,8)\n",
    "\n",
    "#setting the title and customizing\n",
    "monthly_movie_count_bar.axes.set_title('Number of Movies released in each month', color=\"r\", fontsize = 25, alpha = 0.6)\n",
    "\n",
    "#setting x-label\n",
    "monthly_movie_count_bar.set_xlabel(\"Months\", color=\"g\", fontsize = 25)\n",
    "#setting y-label\n",
    "monthly_movie_count_bar.set_ylabel(\"Number of Movies\", color=\"y\", fontsize = 35)\n",
    "\n",
    "#customizing axes values\n",
    "monthly_movie_count_bar.tick_params(labelsize = 15, labelcolor=\"black\")\n",
    "\n",
    "#rotating the x-axis values to make it readable\n",
    "monthly_movie_count_bar.set_xticklabels(month_list, rotation = 30, size = 18)\n",
    "\n",
    "#shows the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10db58b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#finding the second part of this question\n",
    "\n",
    "#now since the data is grouped by month, we add 'profit_(in_US_Dollars)' values to respective months, saving all this to a new var\n",
    "monthly_profit = groupby_index['profit_(in_US_Dollars)'].sum()\n",
    "\n",
    "#converting table to a dataframe\n",
    "monthly_profit = pd.DataFrame(monthly_profit)\n",
    "\n",
    "#giving seaborn bar plot to visualize the data\n",
    "#giving values to our graph\n",
    "monthly_profit_bar = sns.barplot(x = monthly_profit.index, y = monthly_profit['profit_(in_US_Dollars)'], data = monthly_profit)\n",
    "\n",
    "#setting size of the graph\n",
    "monthly_profit_bar.figure.set_size_inches(15,8)\n",
    "\n",
    "#setting the title and customizing\n",
    "monthly_profit_bar.axes.set_title('Profits made by movies at their released months', color=\"r\", fontsize = 25, alpha = 0.6)\n",
    "\n",
    "#setting x-label\n",
    "monthly_profit_bar.set_xlabel(\"Months\", color=\"g\", fontsize = 25)\n",
    "#setting y-label\n",
    "monthly_profit_bar.set_ylabel(\"Profits\", color=\"y\", fontsize = 35)\n",
    "\n",
    "#customizing axes values\n",
    "monthly_profit_bar.tick_params(labelsize = 15, labelcolor=\"black\")\n",
    "\n",
    "#rotating the x-axis values to make it readable\n",
    "monthly_profit_bar.set_xticklabels(month_list, rotation = 30, size = 18)\n",
    "\n",
    "#shows the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Seeing the both visualizations of both graphs we see similar trend. Where there are more movie released there is more profit and vice versa but just not for one month i.e `December`. December is the month where most movie release but when compared to profits it ranks second. This means that december month has high release rate but less profit margin. The month of June where we have around `165 movie` releases, which is second highest, is the highest in terms of making profits.\n",
    "\n",
    "Also one more thing is we earlier finded which movie had made the most profit in our dataset, We came up with the answer of movie, `'Avatar'`, and the release month for this movie is in december, also the highest in loss movie had also released in december but that isn't being counted here. Knowing this that you have the highest release rate and highest profit making movie in same month of `December` but falls short in front of `June` month in terms of making profits makes me think that the month of June had movies with significant high profits where in december it didn't had that much high, making it short in terms of profit even though having the advantage of highest release rate.\n",
    "\n",
    "This visualization doesn't prove us that if we release a movie in those months we will earn more $50M. It just makes us think that the chances are higher, again it depends on other influential factors, such as directors, story, cast etc.\n",
    "\n",
    "-----"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusion\n",
    "\n",
    "As i have answered the questions that i thought would be interesting to dig into, i want to wrap up all my findings in this way ....\n",
    "\n",
    "\n",
    "**Q. If i wanted to show one of the best and most profitable movie, who would i hire as director and cast, which genre would i choose and also at what month would i release the movie in? **\n",
    "\n",
    "**Ans.** I would..\n",
    "\n",
    "**Choose any director from this** - Steven Spielberg, Robert Zemeckis, Ron Howard, Tony Scott, Ridley Scott.\n",
    "\n",
    "**Choose any cast from this** - Actors - Tom Cruise, Brad Pitt, Tom Hanks, Sylvester Stallone, Denzel Washington.\n",
    "\n",
    "Actress - Julia Roberts, Anne Hathaway, Angelina Jolie, Scarlett Johansson.\n",
    "                            \n",
    "**Choose these genre** - Action, Adventure, Thriller, Comedy, Drama.\n",
    "\n",
    "**Choose these release months** - May, June, July, November, December.\n",
    "\n",
    "By doing all this, my probability of making a profitable movie would be higher and obviously i will take care of other influential factors too. ;) And also the runtime of the movie will be around 110 min (mean runtime of movies with \\$50M+). \n",
    "\n",
    "**Limitations** -  I want to make it clear, it's not 100 percent guaranteed solution that this formula is gonna work, meaning we are going to earn more than \\$50M! But it shows us that we have high probability of making high profits if we had similar characteristics as such. All these directors, actors, genres and released dates have a common trend of attraction. If we release a movie with these characteristics, it gives people high expectations from this movie. Thus attracting more people towards the movie but it ultimately comes down to story mainly and also other important influential factors. People having higher expectations gives us less probability of meeting their expectations. Even if the movie was worth, people's high expectations would lead in biased results ultimately effecting the profits. We also see this in real life specially in sequels of movies. This was just one example of an influantial factor that would lead to different results, there are many that have to be taken care of!\n",
    "\n",
    "And that's my conclusion! \n",
    "\n",
    "------"
   ]
  }
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