student.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Final Project Submission\n",
    "\n",
    "Please fill out:\n",
    "* Student name: Calvin Omwega\n",
    "* Student pace: Full time\n",
    "* Scheduled project review date/time: N/A\n",
    "* Instructor name: Maryann Mwikali\n",
    "* Blog post URL:N/A\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.Introduction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1.1 Overview\n",
    "The project's primary purpose is to generate insights that can assist Microsoft in entering and succeeding in the movie industry. We will utilize exploratory data analysis to get actionable insights to assist Microsoft determine what kind of films to develop. This project will leverage existing movie data to analyze box office trends, genre performance, and financial metrics to recommend strategic directions for Microsoft's new movie studio."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1.2 General Objectives\n",
    "1. Identify Successful Film Genres: Determine which movie genres are performing best at the box office, both domestically and internationally.\n",
    "\n",
    "2. Analyze Budgets versus Returns: Examine the relationship between film budgets and their financial success to identify optimal investment levels.\n",
    "\n",
    "3. Understand Market Trends: Capture current trends in movie genres and preferences, including the most popular genre\n",
    "\n",
    "4. Provide Strategic Recommendations: Translate findings into actionable strategies for genre selection, and budget allocation for Microsoft's studio."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1.3 Key Questions \n",
    "1. Which movie genres yield the highest box office earnings and ROI?\n",
    "Aims to identify genres that consistently perform well, offering a safer investment for Microsoft.\n",
    "2. What is the optimal budget range for producing a profitable movie?\n",
    "Seeks to find a budget sweet spot that maximizes earnings without unnecessarily inflating costs.\n",
    "3. How do movie budgets correlate with box office success?\n",
    "Investigates whether higher budgets lead to higher gross earnings or if diminishing returns set in beyond a certain point."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. Data Sets\n",
    "The data sets are stored in the 'zippedData' folder which were sourced from:\n",
    "\n",
    "- Box Office MojoLinks\n",
    "    - bom.movie_gross.csv\n",
    "- TheMovieDBLinks\n",
    "    - tmdb.movies.csv\n",
    "- The Numbers\n",
    "    - tn.movie_budgets.csv\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2.2 Iporting the libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 824,
   "metadata": {},
   "outputs": [],
   "source": [
    "# importing relevant libraries\n",
    "import pandas as pd # Imports the pandas library, used for data manipulation and analysis, and aliases it as `pd`.\n",
    "import matplotlib.pyplot as plt # Imports the pyplot module from matplotlib, a plotting library, and aliases it as `plt`.\n",
    "import seaborn as sns # Imports seaborn, a statistical data visualization library based on matplotlib, and aliases it as `sns`.\n",
    "import ast # For converting string representations of lists into actual lists in the 'tmdb.movies.csv'\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2.3 Loading and exploring the data sets\n",
    "This will be done by displaying the first few collumns of the data sets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 825,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Loading the relevant datasets\n",
    "df_gross = pd.read_csv('bom.movie_gross.csv')\n",
    "df_tmdb = pd.read_csv('tmdb.movies.csv')\n",
    "df_budgets = pd.read_csv('tn.movie_budgets.csv')\n",
    "df_rt = pd.read_csv('rt.movie_info.tsv', delimiter='\\t')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 826,
   "metadata": {},
   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>synopsis</th>\n",
       "      <th>rating</th>\n",
       "      <th>genre</th>\n",
       "      <th>director</th>\n",
       "      <th>writer</th>\n",
       "      <th>theater_date</th>\n",
       "      <th>dvd_date</th>\n",
       "      <th>currency</th>\n",
       "      <th>box_office</th>\n",
       "      <th>runtime</th>\n",
       "      <th>studio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>This gritty, fast-paced, and innovative police...</td>\n",
       "      <td>R</td>\n",
       "      <td>Action and Adventure|Classics|Drama</td>\n",
       "      <td>William Friedkin</td>\n",
       "      <td>Ernest Tidyman</td>\n",
       "      <td>Oct 9, 1971</td>\n",
       "      <td>Sep 25, 2001</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>104 minutes</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>New York City, not-too-distant-future: Eric Pa...</td>\n",
       "      <td>R</td>\n",
       "      <td>Drama|Science Fiction and Fantasy</td>\n",
       "      <td>David Cronenberg</td>\n",
       "      <td>David Cronenberg|Don DeLillo</td>\n",
       "      <td>Aug 17, 2012</td>\n",
       "      <td>Jan 1, 2013</td>\n",
       "      <td>$</td>\n",
       "      <td>600,000</td>\n",
       "      <td>108 minutes</td>\n",
       "      <td>Entertainment One</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5</td>\n",
       "      <td>Illeana Douglas delivers a superb performance ...</td>\n",
       "      <td>R</td>\n",
       "      <td>Drama|Musical and Performing Arts</td>\n",
       "      <td>Allison Anders</td>\n",
       "      <td>Allison Anders</td>\n",
       "      <td>Sep 13, 1996</td>\n",
       "      <td>Apr 18, 2000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>116 minutes</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6</td>\n",
       "      <td>Michael Douglas runs afoul of a treacherous su...</td>\n",
       "      <td>R</td>\n",
       "      <td>Drama|Mystery and Suspense</td>\n",
       "      <td>Barry Levinson</td>\n",
       "      <td>Paul Attanasio|Michael Crichton</td>\n",
       "      <td>Dec 9, 1994</td>\n",
       "      <td>Aug 27, 1997</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>128 minutes</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NR</td>\n",
       "      <td>Drama|Romance</td>\n",
       "      <td>Rodney Bennett</td>\n",
       "      <td>Giles Cooper</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>200 minutes</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id                                           synopsis rating  \\\n",
       "0   1  This gritty, fast-paced, and innovative police...      R   \n",
       "1   3  New York City, not-too-distant-future: Eric Pa...      R   \n",
       "2   5  Illeana Douglas delivers a superb performance ...      R   \n",
       "3   6  Michael Douglas runs afoul of a treacherous su...      R   \n",
       "4   7                                                NaN     NR   \n",
       "\n",
       "                                 genre          director  \\\n",
       "0  Action and Adventure|Classics|Drama  William Friedkin   \n",
       "1    Drama|Science Fiction and Fantasy  David Cronenberg   \n",
       "2    Drama|Musical and Performing Arts    Allison Anders   \n",
       "3           Drama|Mystery and Suspense    Barry Levinson   \n",
       "4                        Drama|Romance    Rodney Bennett   \n",
       "\n",
       "                            writer  theater_date      dvd_date currency  \\\n",
       "0                   Ernest Tidyman   Oct 9, 1971  Sep 25, 2001      NaN   \n",
       "1     David Cronenberg|Don DeLillo  Aug 17, 2012   Jan 1, 2013        $   \n",
       "2                   Allison Anders  Sep 13, 1996  Apr 18, 2000      NaN   \n",
       "3  Paul Attanasio|Michael Crichton   Dec 9, 1994  Aug 27, 1997      NaN   \n",
       "4                     Giles Cooper           NaN           NaN      NaN   \n",
       "\n",
       "  box_office      runtime             studio  \n",
       "0        NaN  104 minutes                NaN  \n",
       "1    600,000  108 minutes  Entertainment One  \n",
       "2        NaN  116 minutes                NaN  \n",
       "3        NaN  128 minutes                NaN  \n",
       "4        NaN  200 minutes                NaN  "
      ]
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     "execution_count": 826,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_rt.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 827,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
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       "      <th>studio</th>\n",
       "      <th>domestic_gross</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Toy Story 3</td>\n",
       "      <td>BV</td>\n",
       "      <td>415000000.0</td>\n",
       "      <td>652000000</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Alice in Wonderland (2010)</td>\n",
       "      <td>BV</td>\n",
       "      <td>334200000.0</td>\n",
       "      <td>691300000</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Harry Potter and the Deathly Hallows Part 1</td>\n",
       "      <td>WB</td>\n",
       "      <td>296000000.0</td>\n",
       "      <td>664300000</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Inception</td>\n",
       "      <td>WB</td>\n",
       "      <td>292600000.0</td>\n",
       "      <td>535700000</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Shrek Forever After</td>\n",
       "      <td>P/DW</td>\n",
       "      <td>238700000.0</td>\n",
       "      <td>513900000</td>\n",
       "      <td>2010</td>\n",
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      "text/plain": [
       "                                         title studio  domestic_gross  \\\n",
       "0                                  Toy Story 3     BV     415000000.0   \n",
       "1                   Alice in Wonderland (2010)     BV     334200000.0   \n",
       "2  Harry Potter and the Deathly Hallows Part 1     WB     296000000.0   \n",
       "3                                    Inception     WB     292600000.0   \n",
       "4                          Shrek Forever After   P/DW     238700000.0   \n",
       "\n",
       "  foreign_gross  year  \n",
       "0     652000000  2010  \n",
       "1     691300000  2010  \n",
       "2     664300000  2010  \n",
       "3     535700000  2010  \n",
       "4     513900000  2010  "
      ]
     },
     "execution_count": 827,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Displaying the first few rows of the dataset to understand its structure and its data types.\n",
    "df_gross.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 828,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>genre_ids</th>\n",
       "      <th>id</th>\n",
       "      <th>original_language</th>\n",
       "      <th>original_title</th>\n",
       "      <th>popularity</th>\n",
       "      <th>release_date</th>\n",
       "      <th>title</th>\n",
       "      <th>vote_average</th>\n",
       "      <th>vote_count</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>[12, 14, 10751]</td>\n",
       "      <td>12444</td>\n",
       "      <td>en</td>\n",
       "      <td>Harry Potter and the Deathly Hallows: Part 1</td>\n",
       "      <td>33.533</td>\n",
       "      <td>19/11/2010</td>\n",
       "      <td>Harry Potter and the Deathly Hallows: Part 1</td>\n",
       "      <td>7.7</td>\n",
       "      <td>10788</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>[14, 12, 16, 10751]</td>\n",
       "      <td>10191</td>\n",
       "      <td>en</td>\n",
       "      <td>How to Train Your Dragon</td>\n",
       "      <td>28.734</td>\n",
       "      <td>26/03/2010</td>\n",
       "      <td>How to Train Your Dragon</td>\n",
       "      <td>7.7</td>\n",
       "      <td>7610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>[12, 28, 878]</td>\n",
       "      <td>10138</td>\n",
       "      <td>en</td>\n",
       "      <td>Iron Man 2</td>\n",
       "      <td>28.515</td>\n",
       "      <td>07/05/2010</td>\n",
       "      <td>Iron Man 2</td>\n",
       "      <td>6.8</td>\n",
       "      <td>12368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>[16, 35, 10751]</td>\n",
       "      <td>862</td>\n",
       "      <td>en</td>\n",
       "      <td>Toy Story</td>\n",
       "      <td>28.005</td>\n",
       "      <td>22/11/1995</td>\n",
       "      <td>Toy Story</td>\n",
       "      <td>7.9</td>\n",
       "      <td>10174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>[28, 878, 12]</td>\n",
       "      <td>27205</td>\n",
       "      <td>en</td>\n",
       "      <td>Inception</td>\n",
       "      <td>27.920</td>\n",
       "      <td>16/07/2010</td>\n",
       "      <td>Inception</td>\n",
       "      <td>8.3</td>\n",
       "      <td>22186</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "   Unnamed: 0            genre_ids     id original_language  \\\n",
       "0           0      [12, 14, 10751]  12444                en   \n",
       "1           1  [14, 12, 16, 10751]  10191                en   \n",
       "2           2        [12, 28, 878]  10138                en   \n",
       "3           3      [16, 35, 10751]    862                en   \n",
       "4           4        [28, 878, 12]  27205                en   \n",
       "\n",
       "                                 original_title  popularity release_date  \\\n",
       "0  Harry Potter and the Deathly Hallows: Part 1      33.533   19/11/2010   \n",
       "1                      How to Train Your Dragon      28.734   26/03/2010   \n",
       "2                                    Iron Man 2      28.515   07/05/2010   \n",
       "3                                     Toy Story      28.005   22/11/1995   \n",
       "4                                     Inception      27.920   16/07/2010   \n",
       "\n",
       "                                          title  vote_average  vote_count  \n",
       "0  Harry Potter and the Deathly Hallows: Part 1           7.7       10788  \n",
       "1                      How to Train Your Dragon           7.7        7610  \n",
       "2                                    Iron Man 2           6.8       12368  \n",
       "3                                     Toy Story           7.9       10174  \n",
       "4                                     Inception           8.3       22186  "
      ]
     },
     "execution_count": 828,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# This is still displaying the data sets and the first few rows of the datasets to understand its structure\n",
    "df_tmdb.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 829,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>release_date</th>\n",
       "      <th>movie</th>\n",
       "      <th>production_budget</th>\n",
       "      <th>domestic_gross</th>\n",
       "      <th>worldwide_gross</th>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Dec 18, 2009</td>\n",
       "      <td>Avatar</td>\n",
       "      <td>$425,000,000</td>\n",
       "      <td>$760,507,625</td>\n",
       "      <td>$2,776,345,279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>May 20, 2011</td>\n",
       "      <td>Pirates of the Caribbean: On Stranger Tides</td>\n",
       "      <td>$410,600,000</td>\n",
       "      <td>$241,063,875</td>\n",
       "      <td>$1,045,663,875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Jun 7, 2019</td>\n",
       "      <td>Dark Phoenix</td>\n",
       "      <td>$350,000,000</td>\n",
       "      <td>$42,762,350</td>\n",
       "      <td>$149,762,350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>May 1, 2015</td>\n",
       "      <td>Avengers: Age of Ultron</td>\n",
       "      <td>$330,600,000</td>\n",
       "      <td>$459,005,868</td>\n",
       "      <td>$1,403,013,963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Dec 15, 2017</td>\n",
       "      <td>Star Wars Ep. VIII: The Last Jedi</td>\n",
       "      <td>$317,000,000</td>\n",
       "      <td>$620,181,382</td>\n",
       "      <td>$1,316,721,747</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id  release_date                                        movie  \\\n",
       "0   1  Dec 18, 2009                                       Avatar   \n",
       "1   2  May 20, 2011  Pirates of the Caribbean: On Stranger Tides   \n",
       "2   3   Jun 7, 2019                                 Dark Phoenix   \n",
       "3   4   May 1, 2015                      Avengers: Age of Ultron   \n",
       "4   5  Dec 15, 2017            Star Wars Ep. VIII: The Last Jedi   \n",
       "\n",
       "  production_budget domestic_gross worldwide_gross  \n",
       "0      $425,000,000   $760,507,625  $2,776,345,279  \n",
       "1      $410,600,000   $241,063,875  $1,045,663,875  \n",
       "2      $350,000,000    $42,762,350    $149,762,350  \n",
       "3      $330,600,000   $459,005,868  $1,403,013,963  \n",
       "4      $317,000,000   $620,181,382  $1,316,721,747  "
      ]
     },
     "execution_count": 829,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# This is the last display of the last data set being used\n",
    "df_budgets.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3.0 Data Cleaning\n",
    "Given our objective, we focus on relevant fields like genres, budgets, and box office earnings. Cleaning involves:\n",
    "\n",
    "Converting data types: Ensuring numerical fields are correctly formatted for calculations.\n",
    "Handling missing values: Deciding whether to fill in missing data or drop rows based on our analysis needs.\n",
    "Extracting and standardizing genres: Particularly from tmdb.movies.csv, as genres are critical for our analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 830,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 2002 entries, 0 to 3353\n",
      "Data columns (total 5 columns):\n",
      " #   Column          Non-Null Count  Dtype  \n",
      "---  ------          --------------  -----  \n",
      " 0   title           2002 non-null   object \n",
      " 1   studio          2002 non-null   object \n",
      " 2   domestic_gross  2002 non-null   float64\n",
      " 3   foreign_gross   2002 non-null   float64\n",
      " 4   year            2002 non-null   int64  \n",
      "dtypes: float64(2), int64(1), object(2)\n",
      "memory usage: 93.8+ KB\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-830-e253b5b12437>:8: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_gross_clean['domestic_gross'] = df_gross_clean['domestic_gross'].astype(float)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>title</th>\n",
       "      <th>studio</th>\n",
       "      <th>domestic_gross</th>\n",
       "      <th>foreign_gross</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Toy Story 3</td>\n",
       "      <td>BV</td>\n",
       "      <td>415000000.0</td>\n",
       "      <td>652000000.0</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Alice in Wonderland (2010)</td>\n",
       "      <td>BV</td>\n",
       "      <td>334200000.0</td>\n",
       "      <td>691300000.0</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Harry Potter and the Deathly Hallows Part 1</td>\n",
       "      <td>WB</td>\n",
       "      <td>296000000.0</td>\n",
       "      <td>664300000.0</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Inception</td>\n",
       "      <td>WB</td>\n",
       "      <td>292600000.0</td>\n",
       "      <td>535700000.0</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Shrek Forever After</td>\n",
       "      <td>P/DW</td>\n",
       "      <td>238700000.0</td>\n",
       "      <td>513900000.0</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3275</th>\n",
       "      <td>I Still See You</td>\n",
       "      <td>LGF</td>\n",
       "      <td>1400.0</td>\n",
       "      <td>1500000.0</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3286</th>\n",
       "      <td>The Catcher Was a Spy</td>\n",
       "      <td>IFC</td>\n",
       "      <td>725000.0</td>\n",
       "      <td>229000.0</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3309</th>\n",
       "      <td>Time Freak</td>\n",
       "      <td>Grindstone</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>256000.0</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3342</th>\n",
       "      <td>Reign of Judges: Title of Liberty - Concept Short</td>\n",
       "      <td>Darin Southa</td>\n",
       "      <td>93200.0</td>\n",
       "      <td>5200.0</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3353</th>\n",
       "      <td>Antonio Lopez 1970: Sex Fashion &amp; Disco</td>\n",
       "      <td>FM</td>\n",
       "      <td>43200.0</td>\n",
       "      <td>30000.0</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2002 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                  title        studio  \\\n",
       "0                                           Toy Story 3            BV   \n",
       "1                            Alice in Wonderland (2010)            BV   \n",
       "2           Harry Potter and the Deathly Hallows Part 1            WB   \n",
       "3                                             Inception            WB   \n",
       "4                                   Shrek Forever After          P/DW   \n",
       "...                                                 ...           ...   \n",
       "3275                                    I Still See You           LGF   \n",
       "3286                              The Catcher Was a Spy           IFC   \n",
       "3309                                         Time Freak    Grindstone   \n",
       "3342  Reign of Judges: Title of Liberty - Concept Short  Darin Southa   \n",
       "3353            Antonio Lopez 1970: Sex Fashion & Disco            FM   \n",
       "\n",
       "      domestic_gross  foreign_gross  year  \n",
       "0        415000000.0    652000000.0  2010  \n",
       "1        334200000.0    691300000.0  2010  \n",
       "2        296000000.0    664300000.0  2010  \n",
       "3        292600000.0    535700000.0  2010  \n",
       "4        238700000.0    513900000.0  2010  \n",
       "...              ...            ...   ...  \n",
       "3275          1400.0      1500000.0  2018  \n",
       "3286        725000.0       229000.0  2018  \n",
       "3309         10000.0       256000.0  2018  \n",
       "3342         93200.0         5200.0  2018  \n",
       "3353         43200.0        30000.0  2018  \n",
       "\n",
       "[2002 rows x 5 columns]"
      ]
     },
     "execution_count": 830,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Cleaning the data set 'df_gross'\n",
    "# first we will start with converting 'foreign_gross' to numeric, errors='coerce' turns non-numeric values into NaN\n",
    "df_gross['foreign_gross'] = pd.to_numeric(df_gross['foreign_gross'], errors='coerce')\n",
    "\n",
    "# Then create a new DataFrame, df_gross_clean, by removing all rows from df_gross that contain NaN values in any column.\n",
    "df_gross_clean = df_gross.dropna()\n",
    "# Converts the 'domestic_gross' column in the df_gross_clean DataFrame to float type, ensuring numeric operations can be performed.\n",
    "df_gross_clean['domestic_gross'] = df_gross_clean['domestic_gross'].astype(float)\n",
    "\n",
    "# This then Displays information about df_gross_clean DataFrame, such as the number of entries, the total number of columns, \n",
    "# the data types of each column, and the amount of memory usage.\n",
    "df_gross_clean.info()\n",
    "# Outputs the df_gross_clean DataFrame, showing its contents. This is useful for verifying the results of the data cleaning process.\n",
    "df_gross_clean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 831,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>title</th>\n",
       "      <th>studio</th>\n",
       "      <th>domestic_gross</th>\n",
       "      <th>foreign_gross</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>toy story 3</td>\n",
       "      <td>BV</td>\n",
       "      <td>415000000.0</td>\n",
       "      <td>652000000.0</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>alice in wonderland (2010)</td>\n",
       "      <td>BV</td>\n",
       "      <td>334200000.0</td>\n",
       "      <td>691300000.0</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>harry potter and the deathly hallows part 1</td>\n",
       "      <td>WB</td>\n",
       "      <td>296000000.0</td>\n",
       "      <td>664300000.0</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>inception</td>\n",
       "      <td>WB</td>\n",
       "      <td>292600000.0</td>\n",
       "      <td>535700000.0</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>shrek forever after</td>\n",
       "      <td>P/DW</td>\n",
       "      <td>238700000.0</td>\n",
       "      <td>513900000.0</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3382</th>\n",
       "      <td>the quake</td>\n",
       "      <td>Magn.</td>\n",
       "      <td>6200.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3383</th>\n",
       "      <td>edward ii (2018 re-release)</td>\n",
       "      <td>FM</td>\n",
       "      <td>4800.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3384</th>\n",
       "      <td>el pacto</td>\n",
       "      <td>Sony</td>\n",
       "      <td>2500.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3385</th>\n",
       "      <td>the swan</td>\n",
       "      <td>Synergetic</td>\n",
       "      <td>2400.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3386</th>\n",
       "      <td>an actor prepares</td>\n",
       "      <td>Grav.</td>\n",
       "      <td>1700.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3387 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                            title      studio  domestic_gross  \\\n",
       "0                                     toy story 3          BV     415000000.0   \n",
       "1                      alice in wonderland (2010)          BV     334200000.0   \n",
       "2     harry potter and the deathly hallows part 1          WB     296000000.0   \n",
       "3                                       inception          WB     292600000.0   \n",
       "4                             shrek forever after        P/DW     238700000.0   \n",
       "...                                           ...         ...             ...   \n",
       "3382                                    the quake       Magn.          6200.0   \n",
       "3383                  edward ii (2018 re-release)          FM          4800.0   \n",
       "3384                                     el pacto        Sony          2500.0   \n",
       "3385                                     the swan  Synergetic          2400.0   \n",
       "3386                            an actor prepares       Grav.          1700.0   \n",
       "\n",
       "      foreign_gross  year  \n",
       "0       652000000.0  2010  \n",
       "1       691300000.0  2010  \n",
       "2       664300000.0  2010  \n",
       "3       535700000.0  2010  \n",
       "4       513900000.0  2010  \n",
       "...             ...   ...  \n",
       "3382            NaN  2018  \n",
       "3383            NaN  2018  \n",
       "3384            NaN  2018  \n",
       "3385            NaN  2018  \n",
       "3386            NaN  2018  \n",
       "\n",
       "[3387 rows x 5 columns]"
      ]
     },
     "execution_count": 831,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# This code converts the 'title' column in the df_gross DataFrame to string type, then applies two string methods:\n",
    "# .str.lower() converts all text to lowercase, ensuring consistency in case.\n",
    "# .str.strip() removes leading and trailing whitespace from the text, cleaning up the data.\n",
    "df_gross['title'] = df_gross['title'].astype(str).str.lower().str.strip()\n",
    "df_budgets['movie'] = df_budgets['movie'].astype(str).str.lower().str.strip()\n",
    "df_gross"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 832,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>release_date</th>\n",
       "      <th>movie</th>\n",
       "      <th>production_budget</th>\n",
       "      <th>domestic_gross</th>\n",
       "      <th>worldwide_gross</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Dec 18, 2009</td>\n",
       "      <td>avatar</td>\n",
       "      <td>425000000</td>\n",
       "      <td>760507625</td>\n",
       "      <td>2776345279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>May 20, 2011</td>\n",
       "      <td>pirates of the caribbean: on stranger tides</td>\n",
       "      <td>410600000</td>\n",
       "      <td>241063875</td>\n",
       "      <td>1045663875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Jun 7, 2019</td>\n",
       "      <td>dark phoenix</td>\n",
       "      <td>350000000</td>\n",
       "      <td>42762350</td>\n",
       "      <td>149762350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>May 1, 2015</td>\n",
       "      <td>avengers: age of ultron</td>\n",
       "      <td>330600000</td>\n",
       "      <td>459005868</td>\n",
       "      <td>1403013963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Dec 15, 2017</td>\n",
       "      <td>star wars ep. viii: the last jedi</td>\n",
       "      <td>317000000</td>\n",
       "      <td>620181382</td>\n",
       "      <td>1316721747</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id  release_date                                        movie  \\\n",
       "0   1  Dec 18, 2009                                       avatar   \n",
       "1   2  May 20, 2011  pirates of the caribbean: on stranger tides   \n",
       "2   3   Jun 7, 2019                                 dark phoenix   \n",
       "3   4   May 1, 2015                      avengers: age of ultron   \n",
       "4   5  Dec 15, 2017            star wars ep. viii: the last jedi   \n",
       "\n",
       "   production_budget  domestic_gross  worldwide_gross  \n",
       "0          425000000       760507625       2776345279  \n",
       "1          410600000       241063875       1045663875  \n",
       "2          350000000        42762350        149762350  \n",
       "3          330600000       459005868       1403013963  \n",
       "4          317000000       620181382       1316721747  "
      ]
     },
     "execution_count": 832,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Cleaning 'df_budgets': remove '$' and ',' from 'production_budget', 'domestic_gross', 'worldwide_gross', convert to int\n",
    "df_budgets['production_budget'] = df_budgets['production_budget'].replace('[\\$,]', '', regex=True).astype(int)\n",
    "df_budgets['domestic_gross'] = df_budgets['domestic_gross'].replace('[\\$,]', '', regex=True).astype(int)\n",
    "df_budgets['worldwide_gross'] = df_budgets['worldwide_gross'].replace('[\\$,]', '', regex=True).astype(int)\n",
    "\n",
    "df_budgets.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3.1 Data cleaning continued\n",
    "more conversions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 833,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0                                            avatar\n",
       "1       pirates of the caribbean: on stranger tides\n",
       "2                                      dark phoenix\n",
       "3                           avengers: age of ultron\n",
       "4                 star wars ep. viii: the last jedi\n",
       "                           ...                     \n",
       "5777                                         red 11\n",
       "5778                                      following\n",
       "5779                  return to the land of wonders\n",
       "5780                           a plague so pleasant\n",
       "5781                              my date with drew\n",
       "Name: movie, Length: 5782, dtype: object"
      ]
     },
     "execution_count": 833,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Ensures that all entries in the 'title' column of the df_gross DataFrame are of string type.\n",
    "df_gross['title'] = df_gross['title'].astype(str)\n",
    "# Ensures that all entries in the 'movie' column of the df_budgets DataFrame are of string type.\n",
    "df_budgets['movie'] = df_budgets['movie'].astype(str)\n",
    "# Converts all characters in the 'title' column of df_gross to lowercase and removes leading and trailing whitespace.\n",
    "df_gross['title'] = df_gross['title'].str.lower().str.strip()\n",
    "# Converts all characters in the 'movie' column of df_budgets to lowercase and removes leading and trailing whitespace.\n",
    "df_budgets['movie'] = df_budgets['movie'].str.lower().str.strip()\n",
    "# Outputs the modified 'title' column from the df_gross DataFrame, showing the cleaned titles.\n",
    "df_gross['title']\n",
    "df_budgets['movie'] # Outputs the modified 'movie' column from the df_budgets DataFrame, showing the cleaned movie names.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 834,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        harry potter and the deathly hallows: part 1\n",
       "1                            how to train your dragon\n",
       "2                                          iron man 2\n",
       "3                                           toy story\n",
       "4                                           inception\n",
       "                             ...                     \n",
       "26512                           laboratory conditions\n",
       "26513                                 _exhibit_84xxx_\n",
       "26514                                    the last one\n",
       "26515                                    trailer made\n",
       "26516                                      the church\n",
       "Name: title, Length: 26517, dtype: object"
      ]
     },
     "execution_count": 834,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# This standardizes the movie titles\n",
    "df_tmdb['title'] = df_tmdb['title'].str.lower()\n",
    "df_tmdb['title']\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4.0 Merging Data sets\n",
    "To analyze performance by genre, we need to merge the datasets. This could involve complex joins on movie titles or IDs, ensuring we align data correctly across different sources."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 835,
   "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>title</th>\n",
       "      <th>studio</th>\n",
       "      <th>domestic_gross_x</th>\n",
       "      <th>foreign_gross</th>\n",
       "      <th>year</th>\n",
       "      <th>id</th>\n",
       "      <th>release_date</th>\n",
       "      <th>movie</th>\n",
       "      <th>production_budget</th>\n",
       "      <th>domestic_gross_y</th>\n",
       "      <th>worldwide_gross</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>toy story 3</td>\n",
       "      <td>BV</td>\n",
       "      <td>415000000.0</td>\n",
       "      <td>652000000.0</td>\n",
       "      <td>2010</td>\n",
       "      <td>47</td>\n",
       "      <td>Jun 18, 2010</td>\n",
       "      <td>toy story 3</td>\n",
       "      <td>200000000</td>\n",
       "      <td>415004880</td>\n",
       "      <td>1068879522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>inception</td>\n",
       "      <td>WB</td>\n",
       "      <td>292600000.0</td>\n",
       "      <td>535700000.0</td>\n",
       "      <td>2010</td>\n",
       "      <td>38</td>\n",
       "      <td>Jul 16, 2010</td>\n",
       "      <td>inception</td>\n",
       "      <td>160000000</td>\n",
       "      <td>292576195</td>\n",
       "      <td>835524642</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>shrek forever after</td>\n",
       "      <td>P/DW</td>\n",
       "      <td>238700000.0</td>\n",
       "      <td>513900000.0</td>\n",
       "      <td>2010</td>\n",
       "      <td>27</td>\n",
       "      <td>May 21, 2010</td>\n",
       "      <td>shrek forever after</td>\n",
       "      <td>165000000</td>\n",
       "      <td>238736787</td>\n",
       "      <td>756244673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>the twilight saga: eclipse</td>\n",
       "      <td>Sum.</td>\n",
       "      <td>300500000.0</td>\n",
       "      <td>398000000.0</td>\n",
       "      <td>2010</td>\n",
       "      <td>53</td>\n",
       "      <td>Jun 30, 2010</td>\n",
       "      <td>the twilight saga: eclipse</td>\n",
       "      <td>68000000</td>\n",
       "      <td>300531751</td>\n",
       "      <td>706102828</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>iron man 2</td>\n",
       "      <td>Par.</td>\n",
       "      <td>312400000.0</td>\n",
       "      <td>311500000.0</td>\n",
       "      <td>2010</td>\n",
       "      <td>15</td>\n",
       "      <td>May 7, 2010</td>\n",
       "      <td>iron man 2</td>\n",
       "      <td>170000000</td>\n",
       "      <td>312433331</td>\n",
       "      <td>621156389</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        title studio  domestic_gross_x  foreign_gross  year  \\\n",
       "0                 toy story 3     BV       415000000.0    652000000.0  2010   \n",
       "1                   inception     WB       292600000.0    535700000.0  2010   \n",
       "2         shrek forever after   P/DW       238700000.0    513900000.0  2010   \n",
       "3  the twilight saga: eclipse   Sum.       300500000.0    398000000.0  2010   \n",
       "4                  iron man 2   Par.       312400000.0    311500000.0  2010   \n",
       "\n",
       "   id  release_date                       movie  production_budget  \\\n",
       "0  47  Jun 18, 2010                 toy story 3          200000000   \n",
       "1  38  Jul 16, 2010                   inception          160000000   \n",
       "2  27  May 21, 2010         shrek forever after          165000000   \n",
       "3  53  Jun 30, 2010  the twilight saga: eclipse           68000000   \n",
       "4  15   May 7, 2010                  iron man 2          170000000   \n",
       "\n",
       "   domestic_gross_y  worldwide_gross  \n",
       "0         415004880       1068879522  \n",
       "1         292576195        835524642  \n",
       "2         238736787        756244673  \n",
       "3         300531751        706102828  \n",
       "4         312433331        621156389  "
      ]
     },
     "execution_count": 835,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Merging datasets on a common key, 'title' for simplicity\n",
    "df_merged = pd.merge(df_gross, df_budgets, left_on='title', right_on='movie', how='inner')\n",
    "df_merged.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 836,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Merge was successful, with 1287 rows.\n"
     ]
    }
   ],
   "source": [
    "# Verifying if the merge was successful\n",
    "if not df_merged.empty:\n",
    "    print(\"Merge was successful, with {} rows.\".format(len(df_merged)))\n",
    "else:\n",
    "    print(\"Merge failed, resulting in an empty DataFrame.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 837,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Error: 'primary_genre' column not found in df_tmdb\n"
     ]
    }
   ],
   "source": [
    "# Further merge to include primary_genre. Ensure 'title' or another unique identifier is used for accurate merging.\n",
    "if 'primary_genre' in df_tmdb.columns:\n",
    "    df_final = pd.merge(df_merged, df_tmdb[['title', 'primary_genre']], on='title', how='left')\n",
    "    print(df_final.head())\n",
    "else:\n",
    "      print(\"Error: 'primary_genre' column not found in df_tmdb\")\n",
    "# Now df_final contains financials, and primary_genre which can be used for in-depth genre performance analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5.0 In-depth Analysis \n",
    "With a clean, merged dataset, we delve into analysis, focusing on:\n",
    "\n",
    "Performance by Genre: Calculating average and median box office earnings and Return on Investment (ROI) for different genres.\n",
    "Budget Analysis: Examining how movie budgets correlate with box office success, potentially identifying optimal budget ranges."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 838,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    4.344398\n",
       "1    4.222029\n",
       "2    3.583301\n",
       "3    9.383865\n",
       "4    2.653861\n",
       "Name: ROI, dtype: float64"
      ]
     },
     "execution_count": 838,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "# Calculating the ROI (Return on Investment)\n",
    "df_merged['ROI'] = (df_merged['worldwide_gross'] - df_merged['production_budget']) / df_merged['production_budget']\n",
    "df_merged['ROI'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 839,
   "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>primary_genre</th>\n",
       "      <th>worldwide_gross</th>\n",
       "      <th>ROI</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Action</td>\n",
       "      <td>2.963086e+08</td>\n",
       "      <td>2.276092</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Adventure</td>\n",
       "      <td>3.239360e+08</td>\n",
       "      <td>1.872695</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Animation</td>\n",
       "      <td>4.059225e+08</td>\n",
       "      <td>2.949700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Comedy</td>\n",
       "      <td>8.393497e+07</td>\n",
       "      <td>2.784083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Crime</td>\n",
       "      <td>8.712315e+07</td>\n",
       "      <td>1.677500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Documentary</td>\n",
       "      <td>8.643220e+07</td>\n",
       "      <td>2.059894</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Drama</td>\n",
       "      <td>7.401881e+07</td>\n",
       "      <td>2.828283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Family</td>\n",
       "      <td>2.376324e+08</td>\n",
       "      <td>2.714474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Fantasy</td>\n",
       "      <td>1.989176e+08</td>\n",
       "      <td>3.625845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>History</td>\n",
       "      <td>9.046027e+07</td>\n",
       "      <td>3.491323</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Horror</td>\n",
       "      <td>8.638707e+07</td>\n",
       "      <td>15.711045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Music</td>\n",
       "      <td>4.476660e+07</td>\n",
       "      <td>0.306509</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Mystery</td>\n",
       "      <td>1.228626e+08</td>\n",
       "      <td>8.147390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Romance</td>\n",
       "      <td>5.362835e+07</td>\n",
       "      <td>2.807441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Science Fiction</td>\n",
       "      <td>2.443196e+08</td>\n",
       "      <td>2.951853</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>TV Movie</td>\n",
       "      <td>2.223831e+08</td>\n",
       "      <td>3.447661</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Thriller</td>\n",
       "      <td>9.960484e+07</td>\n",
       "      <td>3.554031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Unknown</td>\n",
       "      <td>5.244728e+07</td>\n",
       "      <td>2.170166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>War</td>\n",
       "      <td>1.817333e+08</td>\n",
       "      <td>1.977346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Western</td>\n",
       "      <td>3.580314e+08</td>\n",
       "      <td>1.802993</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      primary_genre  worldwide_gross        ROI\n",
       "0            Action     2.963086e+08   2.276092\n",
       "1         Adventure     3.239360e+08   1.872695\n",
       "2         Animation     4.059225e+08   2.949700\n",
       "3            Comedy     8.393497e+07   2.784083\n",
       "4             Crime     8.712315e+07   1.677500\n",
       "5       Documentary     8.643220e+07   2.059894\n",
       "6             Drama     7.401881e+07   2.828283\n",
       "7            Family     2.376324e+08   2.714474\n",
       "8           Fantasy     1.989176e+08   3.625845\n",
       "9           History     9.046027e+07   3.491323\n",
       "10           Horror     8.638707e+07  15.711045\n",
       "11            Music     4.476660e+07   0.306509\n",
       "12          Mystery     1.228626e+08   8.147390\n",
       "13          Romance     5.362835e+07   2.807441\n",
       "14  Science Fiction     2.443196e+08   2.951853\n",
       "15         TV Movie     2.223831e+08   3.447661\n",
       "16         Thriller     9.960484e+07   3.554031\n",
       "17          Unknown     5.244728e+07   2.170166\n",
       "18              War     1.817333e+08   1.977346\n",
       "19          Western     3.580314e+08   1.802993"
      ]
     },
     "execution_count": 839,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Group by 'primary_genre' to find average worldwide gross and ROI\n",
    "avg_genre_performance = df_final.groupby('primary_genre').agg({'worldwide_gross':'mean', 'ROI':'mean'}).reset_index()\n",
    "avg_genre_performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 840,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average ROI: 3.3076326171896193\n"
     ]
    }
   ],
   "source": [
    "# Finding the average ROI using base Python, showcasing iteration over rows\n",
    "total_roi = 0 # Initializes a variable to store the total ROI to 0.\n",
    "for index, row in df_merged.iterrows(): # Loops through each row in the df_merged DataFrame to calculate the total ROI.\n",
    "    total_roi += row['ROI'] # Adds the ROI for each movie to the total ROI.\n",
    "average_roi = total_roi / len(df_merged) # Calculates the average ROI by dividing the total ROI by the number of entries in df_merged.\n",
    "print(f\"Average ROI: {average_roi}\")# Prints the calculated average ROI."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5.1 In-depth Analysis continued\n",
    "This section takes the data sets and makes sure the merged data is used to find the movies and then later the genres with the highest ROI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 841,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                           title        ROI\n",
      "0                    toy story 3   4.344398\n",
      "1                      inception   4.222029\n",
      "2            shrek forever after   3.583301\n",
      "3     the twilight saga: eclipse   9.383865\n",
      "4                     iron man 2   2.653861\n",
      "...                          ...        ...\n",
      "1266     paul, apostle of christ   4.105900\n",
      "1270                   hell fest   2.294406\n",
      "1271        unfriended: dark web  15.434588\n",
      "1272                     upgrade   4.517762\n",
      "1273                      unsane   8.496621\n",
      "\n",
      "[513 rows x 2 columns]\n"
     ]
    }
   ],
   "source": [
    "# Finding the average ROI using base Python, showcasing iteration over rows\n",
    "high_roi_movies = df_merged[df_merged['ROI'] > 2.0] # Filters movies in df_merged with an ROI greater than 2.0, indicating a high return on investment.\n",
    "print(high_roi_movies[['title', 'ROI']])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5.2 In-depth Analysis based on budget\n",
    "This takes the merged data and categorizes the movie tiers from lowest to highest based on their budgets. Meaning the movies with the lowest budgets are compared to the movies with the highest budget and it takes a look at their ROI."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 842,
   "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_tier</th>\n",
       "      <th>ROI</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Low</td>\n",
       "      <td>6.735857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Medium</td>\n",
       "      <td>2.356749</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>High</td>\n",
       "      <td>1.918749</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Very High</td>\n",
       "      <td>2.181144</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  budget_tier       ROI\n",
       "0         Low  6.735857\n",
       "1      Medium  2.356749\n",
       "2        High  1.918749\n",
       "3   Very High  2.181144"
      ]
     },
     "execution_count": 842,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Analyzing budget ranges. We categorize movies into budget tiers before calculating average ROI for each tier.\n",
    "df_final['budget_tier'] = pd.qcut(df_final['production_budget'], 4, labels=[\"Low\", \"Medium\", \"High\", \"Very High\"]) # Categorizes movies in df_final into budget tiers based on their production budget. \n",
    "# It uses quartiles to create 4 budget tiers: Low, Medium, High, and Very High.\n",
    "budget_tier_performance = df_final.groupby('budget_tier').agg({'ROI':'mean'}).reset_index() # Groups movies in df_final by their budget tier and calculates the average ROI for each tier.\n",
    "# This helps to understand how budget size relates to return on investment.\n",
    "budget_tier_performance"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5.3 Linking genre_id to genres \n",
    "since the df_tmdb file has genre ids and the ids all link to a name of a genres we had to take and link the data from the data sets. One limitation is that since no sql was used/imported, it was done mannually. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 843,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0                   [Adventure, Fantasy, Family]\n",
       "1        [Fantasy, Adventure, Animation, Family]\n",
       "2           [Adventure, Action, Science Fiction]\n",
       "3                    [Animation, Comedy, Family]\n",
       "4           [Action, Science Fiction, Adventure]\n",
       "                          ...                   \n",
       "26512                            [Horror, Drama]\n",
       "26513                          [Drama, Thriller]\n",
       "26514               [Fantasy, Action, Adventure]\n",
       "26515                [Family, Adventure, Action]\n",
       "26516                         [Thriller, Horror]\n",
       "Name: genre_names, Length: 26517, dtype: object"
      ]
     },
     "execution_count": 843,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Expandeding genre ID to name mapping\n",
    "# This was done because in the df_tmdb file the generes are under genre_id and the ids match up to genres from the data base which was recovered to come up with a conclusion. \n",
    "#genre_id_to_name just converts the id to a name to make it easier to understand. \n",
    "genre_id_to_name = {\n",
    "    28: \"Action\",\n",
    "    12: \"Adventure\",\n",
    "    16: \"Animation\",\n",
    "    35: \"Comedy\",\n",
    "    80: \"Crime\",\n",
    "    99: \"Documentary\",\n",
    "    18: \"Drama\",\n",
    "    10751: \"Family\",\n",
    "    14: \"Fantasy\",\n",
    "    36: \"History\",\n",
    "    27: \"Horror\",\n",
    "    10402: \"Music\",\n",
    "    9648: \"Mystery\",\n",
    "    10749: \"Romance\",\n",
    "    878: \"Science Fiction\",\n",
    "    10770: \"TV Movie\",\n",
    "    53: \"Thriller\",\n",
    "    10752: \"War\",\n",
    "    37: \"Western\"\n",
    "}\n",
    "# The df_tmdb['genre_ids'] might contain string representations of lists\n",
    "def safely_convert_string_to_list(string):\n",
    "    try:\n",
    "        # We then evaluate the string as a Python literal (list)\n",
    "        return ast.literal_eval(string)\n",
    "    except ValueError:\n",
    "        # This then returns an empty list if there's an error (e.g., if the string can't be converted to a list)\n",
    "        return []\n",
    "\n",
    "# Convert 'genre_ids' from string representations of lists to actual lists (if necessary)\n",
    "df_tmdb['genre_ids'] = df_tmdb['genre_ids'].apply(lambda x: safely_convert_string_to_list(x) if isinstance(x, str) else x)\n",
    "\n",
    "# Mapping genre IDs to names\n",
    "def map_genre_ids_to_names(ids):\n",
    "    return [genre_id_to_name.get(genre_id, \"Unknown\") for genre_id in ids]\n",
    "\n",
    "df_tmdb['genre_names'] = df_tmdb['genre_ids'].apply(map_genre_ids_to_names)\n",
    "df_tmdb['genre_names']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 844,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        Adventure\n",
       "1          Fantasy\n",
       "2        Adventure\n",
       "3        Animation\n",
       "4           Action\n",
       "           ...    \n",
       "26512       Horror\n",
       "26513        Drama\n",
       "26514      Fantasy\n",
       "26515       Family\n",
       "26516     Thriller\n",
       "Name: primary_genre, Length: 26517, dtype: object"
      ]
     },
     "execution_count": 844,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Function to extract primary genre name from genre_ids list\n",
    "def extract_primary_genre(genre_ids_list, genre_mapping):\n",
    "    if not genre_ids_list:  # Check if genre_ids list is empty\n",
    "        return \"Unknown\"  # Return 'Unknown' if no genre ID is available\n",
    "    primary_genre_id = genre_ids_list[0]  # Assuming the first genre is the primary\n",
    "    return genre_mapping.get(primary_genre_id, \"Unknown\")  # Map genre ID to name, return 'Unknown' if ID not found\n",
    "\n",
    "# This applies the function to each row in df_tmdb to create a new 'primary_genre' column\n",
    "df_tmdb['primary_genre'] = df_tmdb['genre_ids'].apply(lambda x: extract_primary_genre(x, genre_id_to_name))\n",
    "df_tmdb['primary_genre']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 6.0 Data Visualization\n",
    "Visualizations help communicate our findings effectively. We might create:\n",
    "\n",
    "Bar charts of average earnings by genre: To identify which genres perform best at the box office.\n",
    "\n",
    "Bar charts of average ROI by budget tier: This is to identify the ROI with the tier meaning movies with budgets from low to very high budgets and how the ROI relates to the budgets.\n",
    "\n",
    "Bar charts of Number of movies by genres in the TMDB Dataset: To identify the most popular genre that can later be used to make an informed decision. \n",
    "\n",
    "Scatter plots of budget vs. ROI: To explore the relationship between investment and return.\n",
    "\n",
    "Scatter plots of production budget vs. worldwide gross: To explore the correlation between the budget and the worldwide gross."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 845,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Scatter plot for Average worldwide gross by primary genre\n",
    "plt.rcParams.update({'font.size': 16})  # Adjusts the size of the font in the graphs\n",
    "# Calculates the average worldwide gross by primary genre\n",
    "avg_gross_by_genre = df_final.groupby('primary_genre')['worldwide_gross'].mean().sort_values(ascending=False)\n",
    "\n",
    "# Visualization\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.barplot(x=avg_gross_by_genre.values, y=avg_gross_by_genre.index)\n",
    "plt.title('Average Worldwide Gross by Primary Genre')\n",
    "plt.xlabel('Average Worldwide Gross ($)')\n",
    "plt.ylabel('Primary Genre')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 846,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting average ROI by budget tier\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.barplot(x='budget_tier', y='ROI', data=budget_tier_performance.sort_values('ROI', ascending=False))\n",
    "plt.title('Average ROI by Budget Tier')\n",
    "plt.xlabel('Budget Tier')\n",
    "plt.ylabel('ROI')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 847,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Scatter plot of production budget vs. worldwide gross\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.scatterplot(x='production_budget', y='worldwide_gross', data=df_merged)\n",
    "plt.title('Production Budget vs. Worldwide Gross')\n",
    "plt.xlabel('Production Budget ($)')\n",
    "plt.ylabel('Worldwide Gross ($)')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 848,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting budget vs. ROI\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.scatterplot(x='production_budget', y='ROI', data=df_merged)\n",
    "plt.title('Scatter plot for Budget vs. ROI')\n",
    "plt.xlabel('Production Budget ($)')\n",
    "plt.ylabel('Return on Investment (ROI)')\n",
    "plt.xscale('log')  # Using a logarithmic scale for better visualization\n",
    "plt.yscale('symlog')  # Symmetrical log scale to handle negative ROI values\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 851,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Pearson correlation coefficient between production budget and ROI is: -0.06959210502134314\n"
     ]
    }
   ],
   "source": [
    "# Getting data from the DataFrame df_merged that contains 'production_budget' and 'ROI' columns\n",
    "# Here's a calculation of the Pearson correlation coefficient:\n",
    "\n",
    "correlation = df_merged['production_budget'].corr(df_merged['ROI'])\n",
    "print(f\"The Pearson correlation coefficient between production budget and ROI is: {correlation}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 850,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Number of movies by genres in the TMDB Dataset\n",
    "# Explode 'genre_names' for visualization\n",
    "df_tmdb_exploded = df_tmdb.explode('genre_names')\n",
    "\n",
    "# Count movies by genre\n",
    "genre_counts = df_tmdb_exploded['genre_names'].value_counts()\n",
    "\n",
    "# Plot\n",
    "plt.figure(figsize=(10, 8))\n",
    "sns.barplot(y=genre_counts.index, x=genre_counts.values)\n",
    "plt.title('Number of Movies by Genre in TMDB Dataset')\n",
    "plt.xlabel('Number of Movies')\n",
    "plt.ylabel('Genre')\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "learn-env",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}