index.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![Microsoft-Studios-Banner](images/microsoft-studios-banner.png)\n",
    "\n",
    "# **Microsoft Movie Studio EDA**\n",
    "\n",
    "---\n",
    "\n",
    "### Author: [Femi Kamau](https://github.com/ctrl-Karugu)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Overview\n",
    "* Following the creation of movie studio, we have been tasked by Microsoft, who have no idea about making films, to identify what makes a film perform well at the box office. After identifying return on investment (RoI) as the primary metric of success, we narrowed down the datasets provided to the top 200 most grossing movies worldwide then calculated the RoI for each. After plotting several scatter and bar plots comparing runtime, production budget, gross revenue, release date, genre, directors, writers, and rating, the analysis identified the following:\n",
    "    * The best time to release a film is during Summer.\n",
    "    * Films directed by Kyle Balda, Pierre Coffin, Chris Rennaud, David F. Sandberg, and James Wan perform the best, whereas those Gary Dauberman were the most successful of all the other writers.\n",
    "    * Lastly we that length of a film, gross revenue and rating, and have no impact on the RoI of a film."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Business Problem\n",
    "* Microsoft has decided to enter the original video content scene by creating a new movie studio. However, they don't know anything about creating movies. In order to solve this problem, the analysis shall be centered around answering the following question:\n",
    "\n",
    "   > ##### What types of films are currently performing the best at the box office?\n",
    "\n",
    "* According to this [Holywood Reporter](https://www.hollywoodreporter.com/movies/movie-news/what-is-profitable-movie-ever-1269879/) article, we see that the metric used to determine performance of a movie at the box office may vary. Therefore, it is important to clarify that within this analysis, **the metric that shall be used to determine the success of a movie will be based on the return on investment (RoI) of the highest grossing films**. RoI is an important performance measure used by businesses to evaluate the profitability of an investment or compare the efficiency of a number of different investments.\n",
    "\n",
    "* In order to further understand the types of movies that are currently performing the best at the box office, this analysis will look into the impact following features have on the RoI:\n",
    " \n",
    "  * Runtime\n",
    "\n",
    "  * Production Budget\n",
    "\n",
    "  * Gross Revenue\n",
    "\n",
    "  * Release Date\n",
    "\n",
    "  * Genre\n",
    " \n",
    "  * Directors\n",
    "\n",
    "  * Writers\n",
    "\n",
    "  * Rating"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Understanding\n",
    ">The goal is to explore the general structure of the datasets that we are dealing with, as well as to verify the quality of the data itself.\n",
    "\n",
    "* Within this phase analysis, we will be examining datasets obtained from two renowned film database websites: [The Numbers](https://www.the-numbers.com/) and [IMDB](https://www.imdb.com/)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Load Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# NumPy for numerical analysis\n",
    "import numpy as np\n",
    "\n",
    "# Pandas for data analysis\n",
    "import pandas as pd\n",
    "\n",
    "# Matplotlib.pyplot, Seaborn, and Plotly.express for data visualization\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# Sqlite3 for database management\n",
    "import sqlite3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Load Data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function is created to load the data\n",
    "def load_data(path, data_format):\n",
    "    \n",
    "    # if statement used to differentiate data format. Checks if data is in 'csv' format\n",
    "    if data_format.lower() == 'csv':\n",
    "        data = pd.read_csv(path, compression='gzip') # 'compression' parameter used since the file is compressed\n",
    "    # elif statement used to check if data is in 'db' format\n",
    "    elif data_format.lower() == 'db':\n",
    "        conn = sqlite3.connect(path) # returns a Connection object that we will use to interact with the database\n",
    "        return conn\n",
    "    # else statement returns an error if the data format is not recognized\n",
    "    else:\n",
    "        raise ValueError('Data format not recognized')\n",
    "    \n",
    "    return data # returns the DataFrame under the variable name 'data'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Dataset 1: The Numbers - Movie Budgets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>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>$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": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load the movie budgets as a pandas DataFrame and assign it to the variable 'movie_budgets_df'\n",
    "movie_budgets_df = load_data('data/tn.movie_budgets.csv.gz', 'csv')\n",
    "\n",
    "# Preview the first few rows of the 'movie_budgets_df' DataFrame\n",
    "movie_budgets_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 5782 entries, 0 to 5781\n",
      "Data columns (total 6 columns):\n",
      " #   Column             Non-Null Count  Dtype \n",
      "---  ------             --------------  ----- \n",
      " 0   id                 5782 non-null   int64 \n",
      " 1   release_date       5782 non-null   object\n",
      " 2   movie              5782 non-null   object\n",
      " 3   production_budget  5782 non-null   object\n",
      " 4   domestic_gross     5782 non-null   object\n",
      " 5   worldwide_gross    5782 non-null   object\n",
      "dtypes: int64(1), object(5)\n",
      "memory usage: 271.2+ KB\n"
     ]
    }
   ],
   "source": [
    "# General description of the 'movie_budgets_df' data\n",
    "movie_budgets_df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Dataset 2: IMDB\n",
    "* The ERD of the IMDB dataset is shown below:\n",
    "  \n",
    "![IMDB Data ERD](images/imdb_data_erd.jpeg)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>movie_basics</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>directors</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>known_for</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>movie_akas</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>movie_ratings</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>persons</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>principals</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>writers</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            name\n",
       "0   movie_basics\n",
       "1      directors\n",
       "2      known_for\n",
       "3     movie_akas\n",
       "4  movie_ratings\n",
       "5        persons\n",
       "6     principals\n",
       "7        writers"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load the imdb data into a DataFrame which returns a Connection object\n",
    "conn = load_data('data/im.db/im.db', 'db')\n",
    "\n",
    "# View the tables within the idmb database as a DataFrame\n",
    "imdb_tables = pd.read_sql(\"\"\"SELECT name FROM sqlite_master WHERE type = 'table';\"\"\", conn)\n",
    "imdb_tables"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Examination of `movie_basics` table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>movie_id</th>\n",
       "      <th>primary_title</th>\n",
       "      <th>original_title</th>\n",
       "      <th>start_year</th>\n",
       "      <th>runtime_minutes</th>\n",
       "      <th>genres</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>tt0063540</td>\n",
       "      <td>Sunghursh</td>\n",
       "      <td>Sunghursh</td>\n",
       "      <td>2013</td>\n",
       "      <td>175.0</td>\n",
       "      <td>Action,Crime,Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>tt0066787</td>\n",
       "      <td>One Day Before the Rainy Season</td>\n",
       "      <td>Ashad Ka Ek Din</td>\n",
       "      <td>2019</td>\n",
       "      <td>114.0</td>\n",
       "      <td>Biography,Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>tt0069049</td>\n",
       "      <td>The Other Side of the Wind</td>\n",
       "      <td>The Other Side of the Wind</td>\n",
       "      <td>2018</td>\n",
       "      <td>122.0</td>\n",
       "      <td>Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>tt0069204</td>\n",
       "      <td>Sabse Bada Sukh</td>\n",
       "      <td>Sabse Bada Sukh</td>\n",
       "      <td>2018</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Comedy,Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>tt0100275</td>\n",
       "      <td>The Wandering Soap Opera</td>\n",
       "      <td>La Telenovela Errante</td>\n",
       "      <td>2017</td>\n",
       "      <td>80.0</td>\n",
       "      <td>Comedy,Drama,Fantasy</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    movie_id                    primary_title              original_title  \\\n",
       "0  tt0063540                        Sunghursh                   Sunghursh   \n",
       "1  tt0066787  One Day Before the Rainy Season             Ashad Ka Ek Din   \n",
       "2  tt0069049       The Other Side of the Wind  The Other Side of the Wind   \n",
       "3  tt0069204                  Sabse Bada Sukh             Sabse Bada Sukh   \n",
       "4  tt0100275         The Wandering Soap Opera       La Telenovela Errante   \n",
       "\n",
       "   start_year  runtime_minutes                genres  \n",
       "0        2013            175.0    Action,Crime,Drama  \n",
       "1        2019            114.0       Biography,Drama  \n",
       "2        2018            122.0                 Drama  \n",
       "3        2018              NaN          Comedy,Drama  \n",
       "4        2017             80.0  Comedy,Drama,Fantasy  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Preview the tables of interest in the IMDB data\n",
    "movie_basics_df = pd.read_sql(\"\"\"\n",
    "                      SELECT * \n",
    "                        FROM  movie_basics AS mb \"\"\", conn)\n",
    "\n",
    "movie_basics_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 146144 entries, 0 to 146143\n",
      "Data columns (total 6 columns):\n",
      " #   Column           Non-Null Count   Dtype  \n",
      "---  ------           --------------   -----  \n",
      " 0   movie_id         146144 non-null  object \n",
      " 1   primary_title    146144 non-null  object \n",
      " 2   original_title   146123 non-null  object \n",
      " 3   start_year       146144 non-null  int64  \n",
      " 4   runtime_minutes  114405 non-null  float64\n",
      " 5   genres           140736 non-null  object \n",
      "dtypes: float64(1), int64(1), object(4)\n",
      "memory usage: 6.7+ MB\n"
     ]
    }
   ],
   "source": [
    "movie_basics_df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Examination of `persons` table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>person_id</th>\n",
       "      <th>primary_name</th>\n",
       "      <th>birth_year</th>\n",
       "      <th>death_year</th>\n",
       "      <th>primary_profession</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>nm0061671</td>\n",
       "      <td>Mary Ellen Bauder</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>miscellaneous,production_manager,producer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>nm0061865</td>\n",
       "      <td>Joseph Bauer</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>composer,music_department,sound_department</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>nm0062070</td>\n",
       "      <td>Bruce Baum</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>miscellaneous,actor,writer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>nm0062195</td>\n",
       "      <td>Axel Baumann</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>camera_department,cinematographer,art_department</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>nm0062798</td>\n",
       "      <td>Pete Baxter</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>production_designer,art_department,set_decorator</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   person_id       primary_name  birth_year  death_year  \\\n",
       "0  nm0061671  Mary Ellen Bauder         NaN         NaN   \n",
       "1  nm0061865       Joseph Bauer         NaN         NaN   \n",
       "2  nm0062070         Bruce Baum         NaN         NaN   \n",
       "3  nm0062195       Axel Baumann         NaN         NaN   \n",
       "4  nm0062798        Pete Baxter         NaN         NaN   \n",
       "\n",
       "                                 primary_profession  \n",
       "0         miscellaneous,production_manager,producer  \n",
       "1        composer,music_department,sound_department  \n",
       "2                        miscellaneous,actor,writer  \n",
       "3  camera_department,cinematographer,art_department  \n",
       "4  production_designer,art_department,set_decorator  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Preview the tables of interest in the IMDB data\n",
    "persons_df = pd.read_sql(\"\"\"\n",
    "                      SELECT * \n",
    "                        FROM  persons\n",
    "                      ;\n",
    "                      \"\"\", conn)\n",
    "\n",
    "persons_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 606648 entries, 0 to 606647\n",
      "Data columns (total 5 columns):\n",
      " #   Column              Non-Null Count   Dtype  \n",
      "---  ------              --------------   -----  \n",
      " 0   person_id           606648 non-null  object \n",
      " 1   primary_name        606648 non-null  object \n",
      " 2   birth_year          82736 non-null   float64\n",
      " 3   death_year          6783 non-null    float64\n",
      " 4   primary_profession  555308 non-null  object \n",
      "dtypes: float64(2), object(3)\n",
      "memory usage: 23.1+ MB\n"
     ]
    }
   ],
   "source": [
    "persons_df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Examination of `movie_ratings` table"
   ]
  },
  {
   "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>movie_id</th>\n",
       "      <th>averagerating</th>\n",
       "      <th>numvotes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>tt10356526</td>\n",
       "      <td>8.3</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>tt10384606</td>\n",
       "      <td>8.9</td>\n",
       "      <td>559</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>tt1042974</td>\n",
       "      <td>6.4</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>tt1043726</td>\n",
       "      <td>4.2</td>\n",
       "      <td>50352</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>tt1060240</td>\n",
       "      <td>6.5</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     movie_id  averagerating  numvotes\n",
       "0  tt10356526            8.3        31\n",
       "1  tt10384606            8.9       559\n",
       "2   tt1042974            6.4        20\n",
       "3   tt1043726            4.2     50352\n",
       "4   tt1060240            6.5        21"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Preview the tables of interest in the IMDB data\n",
    "movie_ratings_df = pd.read_sql(\"\"\"\n",
    "                      SELECT * \n",
    "                        FROM  movie_ratings\n",
    "                      ;\n",
    "                      \"\"\", conn)\n",
    "\n",
    "movie_ratings_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 73856 entries, 0 to 73855\n",
      "Data columns (total 3 columns):\n",
      " #   Column         Non-Null Count  Dtype  \n",
      "---  ------         --------------  -----  \n",
      " 0   movie_id       73856 non-null  object \n",
      " 1   averagerating  73856 non-null  float64\n",
      " 2   numvotes       73856 non-null  int64  \n",
      "dtypes: float64(1), int64(1), object(1)\n",
      "memory usage: 1.7+ MB\n"
     ]
    }
   ],
   "source": [
    "movie_ratings_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>movie_id</th>\n",
       "      <th>ordering</th>\n",
       "      <th>title</th>\n",
       "      <th>region</th>\n",
       "      <th>language</th>\n",
       "      <th>types</th>\n",
       "      <th>attributes</th>\n",
       "      <th>is_original_title</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>tt0369610</td>\n",
       "      <td>10</td>\n",
       "      <td>Джурасик свят</td>\n",
       "      <td>BG</td>\n",
       "      <td>bg</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>tt0369610</td>\n",
       "      <td>11</td>\n",
       "      <td>Jurashikku warudo</td>\n",
       "      <td>JP</td>\n",
       "      <td>None</td>\n",
       "      <td>imdbDisplay</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>tt0369610</td>\n",
       "      <td>12</td>\n",
       "      <td>Jurassic World: O Mundo dos Dinossauros</td>\n",
       "      <td>BR</td>\n",
       "      <td>None</td>\n",
       "      <td>imdbDisplay</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>tt0369610</td>\n",
       "      <td>13</td>\n",
       "      <td>O Mundo dos Dinossauros</td>\n",
       "      <td>BR</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>short title</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>tt0369610</td>\n",
       "      <td>14</td>\n",
       "      <td>Jurassic World</td>\n",
       "      <td>FR</td>\n",
       "      <td>None</td>\n",
       "      <td>imdbDisplay</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    movie_id  ordering                                    title region  \\\n",
       "0  tt0369610        10                            Джурасик свят     BG   \n",
       "1  tt0369610        11                        Jurashikku warudo     JP   \n",
       "2  tt0369610        12  Jurassic World: O Mundo dos Dinossauros     BR   \n",
       "3  tt0369610        13                  O Mundo dos Dinossauros     BR   \n",
       "4  tt0369610        14                           Jurassic World     FR   \n",
       "\n",
       "  language        types   attributes  is_original_title  \n",
       "0       bg         None         None                0.0  \n",
       "1     None  imdbDisplay         None                0.0  \n",
       "2     None  imdbDisplay         None                0.0  \n",
       "3     None         None  short title                0.0  \n",
       "4     None  imdbDisplay         None                0.0  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movie_akas_df = pd.read_sql(\"\"\"\n",
    "                      SELECT * \n",
    "                        FROM  movie_akas\n",
    "                      ;\n",
    "                      \"\"\", conn)\n",
    "\n",
    "movie_akas_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 331703 entries, 0 to 331702\n",
      "Data columns (total 8 columns):\n",
      " #   Column             Non-Null Count   Dtype  \n",
      "---  ------             --------------   -----  \n",
      " 0   movie_id           331703 non-null  object \n",
      " 1   ordering           331703 non-null  int64  \n",
      " 2   title              331703 non-null  object \n",
      " 3   region             278410 non-null  object \n",
      " 4   language           41715 non-null   object \n",
      " 5   types              168447 non-null  object \n",
      " 6   attributes         14925 non-null   object \n",
      " 7   is_original_title  331678 non-null  float64\n",
      "dtypes: float64(1), int64(1), object(6)\n",
      "memory usage: 20.2+ MB\n"
     ]
    }
   ],
   "source": [
    "movie_akas_df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Read `directors` table into dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "directors_df = pd.read_sql(\"\"\"\n",
    "                      SELECT DISTINCT * \n",
    "                        FROM  directors\n",
    "                      ;\n",
    "                      \"\"\", conn)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Read `writers` table into dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "writers_df = pd.read_sql(\"\"\"\n",
    "                      SELECT * \n",
    "                        FROM  writers\n",
    "                      ;\n",
    "                      \"\"\", conn)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have loaded the necessary libraries and datasets into our environment. We can close the SQL Server connection."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Close the connection to the IMDB database\n",
    "conn.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Preparation\n",
    "> The data preparation phase uses the insights derived from the data understanding stage and will be used to prepare the data for analysis.\n",
    "\n",
    "* Based on the metric of success we are using used to determine the best performing films in the box office, we first need to identify and clean the relevant data that shall be used to calculate the RoI. Furthermore, the business problem is looking for the types of films that are **currently** performing the best, therefore, we will narrow our analysis to the films that have been released within the past 10 years.\n",
    "\n",
    "We will be obtaining the RoI information from the 'movie_budgets_df' DataFrame:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>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>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",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>Dec 18, 2015</td>\n",
       "      <td>Star Wars Ep. VII: The Force Awakens</td>\n",
       "      <td>$306,000,000</td>\n",
       "      <td>$936,662,225</td>\n",
       "      <td>$2,053,311,220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>Apr 27, 2018</td>\n",
       "      <td>Avengers: Infinity War</td>\n",
       "      <td>$300,000,000</td>\n",
       "      <td>$678,815,482</td>\n",
       "      <td>$2,048,134,200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5761</th>\n",
       "      <td>62</td>\n",
       "      <td>Dec 31, 2014</td>\n",
       "      <td>Stories of Our Lives</td>\n",
       "      <td>$15,000</td>\n",
       "      <td>$0</td>\n",
       "      <td>$0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5771</th>\n",
       "      <td>72</td>\n",
       "      <td>May 19, 2015</td>\n",
       "      <td>Family Motocross</td>\n",
       "      <td>$10,000</td>\n",
       "      <td>$0</td>\n",
       "      <td>$0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5772</th>\n",
       "      <td>73</td>\n",
       "      <td>Jan 13, 2012</td>\n",
       "      <td>Newlyweds</td>\n",
       "      <td>$9,000</td>\n",
       "      <td>$4,584</td>\n",
       "      <td>$4,584</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5777</th>\n",
       "      <td>78</td>\n",
       "      <td>Dec 31, 2018</td>\n",
       "      <td>Red 11</td>\n",
       "      <td>$7,000</td>\n",
       "      <td>$0</td>\n",
       "      <td>$0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5780</th>\n",
       "      <td>81</td>\n",
       "      <td>Sep 29, 2015</td>\n",
       "      <td>A Plague So Pleasant</td>\n",
       "      <td>$1,400</td>\n",
       "      <td>$0</td>\n",
       "      <td>$0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1666 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      id  release_date                                 movie  \\\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",
       "5      6  Dec 18, 2015  Star Wars Ep. VII: The Force Awakens   \n",
       "6      7  Apr 27, 2018                Avengers: Infinity War   \n",
       "...   ..           ...                                   ...   \n",
       "5761  62  Dec 31, 2014                  Stories of Our Lives   \n",
       "5771  72  May 19, 2015                      Family Motocross   \n",
       "5772  73  Jan 13, 2012                             Newlyweds   \n",
       "5777  78  Dec 31, 2018                                Red 11   \n",
       "5780  81  Sep 29, 2015                  A Plague So Pleasant   \n",
       "\n",
       "     production_budget domestic_gross worldwide_gross  \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  \n",
       "5         $306,000,000   $936,662,225  $2,053,311,220  \n",
       "6         $300,000,000   $678,815,482  $2,048,134,200  \n",
       "...                ...            ...             ...  \n",
       "5761           $15,000             $0              $0  \n",
       "5771           $10,000             $0              $0  \n",
       "5772            $9,000         $4,584          $4,584  \n",
       "5777            $7,000             $0              $0  \n",
       "5780            $1,400             $0              $0  \n",
       "\n",
       "[1666 rows x 6 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Limit the analysis to films that have been released within the past 10 years\n",
    "movie_budgets_df = movie_budgets_df[movie_budgets_df['release_date'].apply(lambda x: int(x.split(\" \")[-1]) >= 2012)]\n",
    "\n",
    "movie_budgets_df # Preview the updated 'movie_budgets_df' DataFrame"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we need to obtain the Top 200 highest grossing films"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe thead th {\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>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>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",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>Dec 18, 2015</td>\n",
       "      <td>Star Wars Ep. VII: The Force Awakens</td>\n",
       "      <td>306000000</td>\n",
       "      <td>936662225</td>\n",
       "      <td>2053311220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>Apr 27, 2018</td>\n",
       "      <td>Avengers: Infinity War</td>\n",
       "      <td>300000000</td>\n",
       "      <td>678815482</td>\n",
       "      <td>2048134200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5761</th>\n",
       "      <td>62</td>\n",
       "      <td>Dec 31, 2014</td>\n",
       "      <td>Stories of Our Lives</td>\n",
       "      <td>15000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5771</th>\n",
       "      <td>72</td>\n",
       "      <td>May 19, 2015</td>\n",
       "      <td>Family Motocross</td>\n",
       "      <td>10000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5772</th>\n",
       "      <td>73</td>\n",
       "      <td>Jan 13, 2012</td>\n",
       "      <td>Newlyweds</td>\n",
       "      <td>9000</td>\n",
       "      <td>4584</td>\n",
       "      <td>4584</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5777</th>\n",
       "      <td>78</td>\n",
       "      <td>Dec 31, 2018</td>\n",
       "      <td>Red 11</td>\n",
       "      <td>7000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5780</th>\n",
       "      <td>81</td>\n",
       "      <td>Sep 29, 2015</td>\n",
       "      <td>A Plague So Pleasant</td>\n",
       "      <td>1400</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1666 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      id  release_date                                 movie  \\\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",
       "5      6  Dec 18, 2015  Star Wars Ep. VII: The Force Awakens   \n",
       "6      7  Apr 27, 2018                Avengers: Infinity War   \n",
       "...   ..           ...                                   ...   \n",
       "5761  62  Dec 31, 2014                  Stories of Our Lives   \n",
       "5771  72  May 19, 2015                      Family Motocross   \n",
       "5772  73  Jan 13, 2012                             Newlyweds   \n",
       "5777  78  Dec 31, 2018                                Red 11   \n",
       "5780  81  Sep 29, 2015                  A Plague So Pleasant   \n",
       "\n",
       "      production_budget  domestic_gross  worldwide_gross  \n",
       "2             350000000        42762350        149762350  \n",
       "3             330600000       459005868       1403013963  \n",
       "4             317000000       620181382       1316721747  \n",
       "5             306000000       936662225       2053311220  \n",
       "6             300000000       678815482       2048134200  \n",
       "...                 ...             ...              ...  \n",
       "5761              15000               0                0  \n",
       "5771              10000               0                0  \n",
       "5772               9000            4584             4584  \n",
       "5777               7000               0                0  \n",
       "5780               1400               0                0  \n",
       "\n",
       "[1666 rows x 6 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Define a function to convert the production_budget, domestic_gross, and worldwide_gross columns to numeric values\n",
    "def convert_to_numeric(df, column):\n",
    "    df[column] = pd.to_numeric(df[column].str.replace(\",\", \"\").str.replace(\"$\", \"\"))\n",
    "    \n",
    "    return df\n",
    "\n",
    "convert_to_numeric(movie_budgets_df, 'production_budget') # convert the production_budget column to numeric values\n",
    "convert_to_numeric(movie_budgets_df, 'domestic_gross') # convert the domestic_gross column to numeric values\n",
    "convert_to_numeric(movie_budgets_df, 'worldwide_gross') # convert the worldwide_gross column to numeric values\n",
    "\n",
    "movie_budgets_df # Preview the updated 'movie_budgets_df' DataFrame\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <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>5</th>\n",
       "      <td>6</td>\n",
       "      <td>Dec 18, 2015</td>\n",
       "      <td>Star Wars Ep. VII: The Force Awakens</td>\n",
       "      <td>306000000</td>\n",
       "      <td>936662225</td>\n",
       "      <td>2053311220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>Apr 27, 2018</td>\n",
       "      <td>Avengers: Infinity War</td>\n",
       "      <td>300000000</td>\n",
       "      <td>678815482</td>\n",
       "      <td>2048134200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>34</td>\n",
       "      <td>Jun 12, 2015</td>\n",
       "      <td>Jurassic World</td>\n",
       "      <td>215000000</td>\n",
       "      <td>652270625</td>\n",
       "      <td>1648854864</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>67</td>\n",
       "      <td>Apr 3, 2015</td>\n",
       "      <td>Furious 7</td>\n",
       "      <td>190000000</td>\n",
       "      <td>353007020</td>\n",
       "      <td>1518722794</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>27</td>\n",
       "      <td>May 4, 2012</td>\n",
       "      <td>The Avengers</td>\n",
       "      <td>225000000</td>\n",
       "      <td>623279547</td>\n",
       "      <td>1517935897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>327</th>\n",
       "      <td>28</td>\n",
       "      <td>Dec 21, 2016</td>\n",
       "      <td>Passengers</td>\n",
       "      <td>110000000</td>\n",
       "      <td>100014699</td>\n",
       "      <td>302239672</td>\n",
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       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>25</td>\n",
       "      <td>May 31, 2019</td>\n",
       "      <td>Godzilla: King of the Monsters</td>\n",
       "      <td>170000000</td>\n",
       "      <td>85576941</td>\n",
       "      <td>299276941</td>\n",
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       "    <tr>\n",
       "      <th>698</th>\n",
       "      <td>99</td>\n",
       "      <td>Jun 8, 2018</td>\n",
       "      <td>Oceanâs 8</td>\n",
       "      <td>70000000</td>\n",
       "      <td>140218711</td>\n",
       "      <td>297115976</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>329</th>\n",
       "      <td>30</td>\n",
       "      <td>Sep 30, 2016</td>\n",
       "      <td>Miss Peregrineâs Home for Peculiar Children</td>\n",
       "      <td>110000000</td>\n",
       "      <td>87242834</td>\n",
       "      <td>295986876</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>321</th>\n",
       "      <td>22</td>\n",
       "      <td>Mar 20, 2015</td>\n",
       "      <td>The Divergent Series: Insurgent</td>\n",
       "      <td>110000000</td>\n",
       "      <td>130179072</td>\n",
       "      <td>295075882</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>200 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     id  release_date                                          movie  \\\n",
       "5     6  Dec 18, 2015           Star Wars Ep. VII: The Force Awakens   \n",
       "6     7  Apr 27, 2018                         Avengers: Infinity War   \n",
       "33   34  Jun 12, 2015                                 Jurassic World   \n",
       "66   67   Apr 3, 2015                                      Furious 7   \n",
       "26   27   May 4, 2012                                   The Avengers   \n",
       "..   ..           ...                                            ...   \n",
       "327  28  Dec 21, 2016                                     Passengers   \n",
       "124  25  May 31, 2019                 Godzilla: King of the Monsters   \n",
       "698  99   Jun 8, 2018                                    Oceanâs 8   \n",
       "329  30  Sep 30, 2016  Miss Peregrineâs Home for Peculiar Children   \n",
       "321  22  Mar 20, 2015                The Divergent Series: Insurgent   \n",
       "\n",
       "     production_budget  domestic_gross  worldwide_gross  \n",
       "5            306000000       936662225       2053311220  \n",
       "6            300000000       678815482       2048134200  \n",
       "33           215000000       652270625       1648854864  \n",
       "66           190000000       353007020       1518722794  \n",
       "26           225000000       623279547       1517935897  \n",
       "..                 ...             ...              ...  \n",
       "327          110000000       100014699        302239672  \n",
       "124          170000000        85576941        299276941  \n",
       "698           70000000       140218711        297115976  \n",
       "329          110000000        87242834        295986876  \n",
       "321          110000000       130179072        295075882  \n",
       "\n",
       "[200 rows x 6 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find the Top 200 films with the worldwide gross\n",
    "movie_budgets_df = movie_budgets_df.sort_values(by='worldwide_gross', ascending=False)[:200]\n",
    "movie_budgets_df # Preview the updated 'movie_budgets_df' DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>RoI</th>\n",
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       "      <td>682.711400</td>\n",
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       "      <th>33</th>\n",
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       "      <td>215000000</td>\n",
       "      <td>652270625</td>\n",
       "      <td>1648854864</td>\n",
       "      <td>766.909239</td>\n",
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       "      <th>66</th>\n",
       "      <td>67</td>\n",
       "      <td>Apr 3, 2015</td>\n",
       "      <td>Furious 7</td>\n",
       "      <td>190000000</td>\n",
       "      <td>353007020</td>\n",
       "      <td>1518722794</td>\n",
       "      <td>799.327786</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>27</td>\n",
       "      <td>May 4, 2012</td>\n",
       "      <td>The Avengers</td>\n",
       "      <td>225000000</td>\n",
       "      <td>623279547</td>\n",
       "      <td>1517935897</td>\n",
       "      <td>674.638176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>327</th>\n",
       "      <td>28</td>\n",
       "      <td>Dec 21, 2016</td>\n",
       "      <td>Passengers</td>\n",
       "      <td>110000000</td>\n",
       "      <td>100014699</td>\n",
       "      <td>302239672</td>\n",
       "      <td>274.763338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>25</td>\n",
       "      <td>May 31, 2019</td>\n",
       "      <td>Godzilla: King of the Monsters</td>\n",
       "      <td>170000000</td>\n",
       "      <td>85576941</td>\n",
       "      <td>299276941</td>\n",
       "      <td>176.045259</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>698</th>\n",
       "      <td>99</td>\n",
       "      <td>Jun 8, 2018</td>\n",
       "      <td>Oceanâs 8</td>\n",
       "      <td>70000000</td>\n",
       "      <td>140218711</td>\n",
       "      <td>297115976</td>\n",
       "      <td>424.451394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>329</th>\n",
       "      <td>30</td>\n",
       "      <td>Sep 30, 2016</td>\n",
       "      <td>Miss Peregrineâs Home for Peculiar Children</td>\n",
       "      <td>110000000</td>\n",
       "      <td>87242834</td>\n",
       "      <td>295986876</td>\n",
       "      <td>269.078978</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>321</th>\n",
       "      <td>22</td>\n",
       "      <td>Mar 20, 2015</td>\n",
       "      <td>The Divergent Series: Insurgent</td>\n",
       "      <td>110000000</td>\n",
       "      <td>130179072</td>\n",
       "      <td>295075882</td>\n",
       "      <td>268.250802</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>200 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     id  release_date                                          movie  \\\n",
       "5     6  Dec 18, 2015           Star Wars Ep. VII: The Force Awakens   \n",
       "6     7  Apr 27, 2018                         Avengers: Infinity War   \n",
       "33   34  Jun 12, 2015                                 Jurassic World   \n",
       "66   67   Apr 3, 2015                                      Furious 7   \n",
       "26   27   May 4, 2012                                   The Avengers   \n",
       "..   ..           ...                                            ...   \n",
       "327  28  Dec 21, 2016                                     Passengers   \n",
       "124  25  May 31, 2019                 Godzilla: King of the Monsters   \n",
       "698  99   Jun 8, 2018                                    Oceanâs 8   \n",
       "329  30  Sep 30, 2016  Miss Peregrineâs Home for Peculiar Children   \n",
       "321  22  Mar 20, 2015                The Divergent Series: Insurgent   \n",
       "\n",
       "     production_budget  domestic_gross  worldwide_gross         RoI  \n",
       "5            306000000       936662225       2053311220  671.016739  \n",
       "6            300000000       678815482       2048134200  682.711400  \n",
       "33           215000000       652270625       1648854864  766.909239  \n",
       "66           190000000       353007020       1518722794  799.327786  \n",
       "26           225000000       623279547       1517935897  674.638176  \n",
       "..                 ...             ...              ...         ...  \n",
       "327          110000000       100014699        302239672  274.763338  \n",
       "124          170000000        85576941        299276941  176.045259  \n",
       "698           70000000       140218711        297115976  424.451394  \n",
       "329          110000000        87242834        295986876  269.078978  \n",
       "321          110000000       130179072        295075882  268.250802  \n",
       "\n",
       "[200 rows x 7 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a new column called 'RoI' that calculates the return on investment (RoI) for each film\n",
    "# The formula for calculating RoI is to divide the film’s box office earnings by the production budget and multiplying the result by 100\n",
    "movie_budgets_df['RoI'] = movie_budgets_df['worldwide_gross'] / movie_budgets_df['production_budget'] * 100\n",
    "\n",
    "movie_budgets_df # Preview the updated 'movie_budgets_df' DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>3517</th>\n",
       "      <td>18</td>\n",
       "      <td>May 25, 2012</td>\n",
       "      <td>Les Intouchables</td>\n",
       "      <td>10800000</td>\n",
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       "      <td>484873045</td>\n",
       "      <td>4489.565231</td>\n",
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       "      <th>3342</th>\n",
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       "      <td>Jun 6, 2014</td>\n",
       "      <td>The Fault in Our Stars</td>\n",
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       "      <td>124872350</td>\n",
       "      <td>307166834</td>\n",
       "      <td>2559.723617</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2491</th>\n",
       "      <td>92</td>\n",
       "      <td>Dec 9, 2016</td>\n",
       "      <td>La La Land</td>\n",
       "      <td>20000000</td>\n",
       "      <td>151101803</td>\n",
       "      <td>426351163</td>\n",
       "      <td>2131.755815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3001</th>\n",
       "      <td>2</td>\n",
       "      <td>Aug 11, 2017</td>\n",
       "      <td>Annabelle: Creation</td>\n",
       "      <td>15000000</td>\n",
       "      <td>102092201</td>\n",
       "      <td>305384865</td>\n",
       "      <td>2035.899100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1623</th>\n",
       "      <td>24</td>\n",
       "      <td>Sep 8, 2017</td>\n",
       "      <td>It</td>\n",
       "      <td>35000000</td>\n",
       "      <td>327481748</td>\n",
       "      <td>697457969</td>\n",
       "      <td>1992.737054</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>79</td>\n",
       "      <td>Jul 22, 2016</td>\n",
       "      <td>Star Trek Beyond</td>\n",
       "      <td>185000000</td>\n",
       "      <td>158848340</td>\n",
       "      <td>335802233</td>\n",
       "      <td>181.514721</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>73</td>\n",
       "      <td>Nov 25, 2015</td>\n",
       "      <td>The Good Dinosaur</td>\n",
       "      <td>187500000</td>\n",
       "      <td>123087120</td>\n",
       "      <td>333771037</td>\n",
       "      <td>178.011220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>25</td>\n",
       "      <td>May 31, 2019</td>\n",
       "      <td>Godzilla: King of the Monsters</td>\n",
       "      <td>170000000</td>\n",
       "      <td>85576941</td>\n",
       "      <td>299276941</td>\n",
       "      <td>176.045259</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>May 25, 2018</td>\n",
       "      <td>Solo: A Star Wars Story</td>\n",
       "      <td>275000000</td>\n",
       "      <td>213767512</td>\n",
       "      <td>393151347</td>\n",
       "      <td>142.964126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>32</td>\n",
       "      <td>May 18, 2012</td>\n",
       "      <td>Battleship</td>\n",
       "      <td>220000000</td>\n",
       "      <td>65233400</td>\n",
       "      <td>313477717</td>\n",
       "      <td>142.489871</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>200 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      id  release_date                           movie  production_budget  \\\n",
       "3517  18  May 25, 2012                Les Intouchables           10800000   \n",
       "3342  43   Jun 6, 2014          The Fault in Our Stars           12000000   \n",
       "2491  92   Dec 9, 2016                      La La Land           20000000   \n",
       "3001   2  Aug 11, 2017             Annabelle: Creation           15000000   \n",
       "1623  24   Sep 8, 2017                              It           35000000   \n",
       "...   ..           ...                             ...                ...   \n",
       "78    79  Jul 22, 2016                Star Trek Beyond          185000000   \n",
       "72    73  Nov 25, 2015               The Good Dinosaur          187500000   \n",
       "124   25  May 31, 2019  Godzilla: King of the Monsters          170000000   \n",
       "11    12  May 25, 2018         Solo: A Star Wars Story          275000000   \n",
       "31    32  May 18, 2012                      Battleship          220000000   \n",
       "\n",
       "      domestic_gross  worldwide_gross          RoI  \n",
       "3517        13182281        484873045  4489.565231  \n",
       "3342       124872350        307166834  2559.723617  \n",
       "2491       151101803        426351163  2131.755815  \n",
       "3001       102092201        305384865  2035.899100  \n",
       "1623       327481748        697457969  1992.737054  \n",
       "...              ...              ...          ...  \n",
       "78         158848340        335802233   181.514721  \n",
       "72         123087120        333771037   178.011220  \n",
       "124         85576941        299276941   176.045259  \n",
       "11         213767512        393151347   142.964126  \n",
       "31          65233400        313477717   142.489871  \n",
       "\n",
       "[200 rows x 7 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Sort the DataFrame according to the 'RoI' column\n",
    "movie_budgets_df = movie_budgets_df.sort_values(by='RoI', ascending=False)\n",
    "\n",
    "movie_budgets_df # Preview the updated 'movie_budgets_df' DataFrame"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we have identified and cleaned the data that is being used to get films that are performing the best accodring to our metric, we need to understand what makes them successful. For this stage of data preparation, we shall be looking to integrate the key features of the top performing films into one dataset if possible. These features include Genre, Runtime (Length), Director(s) & Writer(s), Cast, Gross Revenue, Rating, and  Production Budget."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The release_date column has 178 unique values\n",
      "The movie column has 200 unique values\n"
     ]
    }
   ],
   "source": [
    "# Identify a unique column within the 'movie_budgets_df' DataFrame that can be used to integrate data from other sources.\n",
    "print(f\"The release_date column has {len(movie_budgets_df.release_date.unique())} unique values\") # identify number the unique release_date values\n",
    "print(f\"The movie column has {len(movie_budgets_df.movie.unique())} unique values\") # identify number of unique movie names"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since this dataset if from a different source than the rest, using the 'id' column to join the other datasets would not be a good decision. Therefore, we will be using the 'movie' column to join other values since it is a unique identifier within this dataset and will most probably also appear in the other datasets.\n",
    "The first DataFrame that we shall be looking join to the 'movie_data_df' DataFrame will be the 'movie_basics_df'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 222 entries, 3342 to 31\n",
      "Data columns (total 12 columns):\n",
      " #   Column             Non-Null Count  Dtype  \n",
      "---  ------             --------------  -----  \n",
      " 0   id                 222 non-null    int64  \n",
      " 1   release_date       222 non-null    object \n",
      " 2   movie              222 non-null    object \n",
      " 3   production_budget  222 non-null    int64  \n",
      " 4   domestic_gross     222 non-null    int64  \n",
      " 5   worldwide_gross    222 non-null    int64  \n",
      " 6   RoI                222 non-null    float64\n",
      " 7   movie_id           222 non-null    object \n",
      " 8   primary_title      222 non-null    object \n",
      " 9   start_year         222 non-null    int64  \n",
      " 10  runtime_minutes    211 non-null    float64\n",
      " 11  genres             219 non-null    object \n",
      "dtypes: float64(2), int64(5), object(5)\n",
      "memory usage: 22.5+ KB\n",
      "None\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 235 entries, 3342 to 31\n",
      "Data columns (total 12 columns):\n",
      " #   Column             Non-Null Count  Dtype  \n",
      "---  ------             --------------  -----  \n",
      " 0   id                 235 non-null    int64  \n",
      " 1   release_date       235 non-null    object \n",
      " 2   movie              235 non-null    object \n",
      " 3   production_budget  235 non-null    int64  \n",
      " 4   domestic_gross     235 non-null    int64  \n",
      " 5   worldwide_gross    235 non-null    int64  \n",
      " 6   RoI                235 non-null    float64\n",
      " 7   movie_id           235 non-null    object \n",
      " 8   original_title     235 non-null    object \n",
      " 9   start_year         235 non-null    int64  \n",
      " 10  runtime_minutes    224 non-null    float64\n",
      " 11  genres             232 non-null    object \n",
      "dtypes: float64(2), int64(5), object(5)\n",
      "memory usage: 23.9+ KB\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "# We shall be using either the original_title/primary_title column to join the two DataFrames\n",
    "\n",
    "# Try joining the 'movie_budgets_df' and 'movie_titles_df' DataFrames on the 'original_title' column first\n",
    "option_1 = movie_budgets_df.join(movie_basics_df.set_index('original_title'), on='movie', how='inner')\n",
    "print(option_1.info())\n",
    "\n",
    "# Try joining the 'movie_budgets_df' and 'movie_titles_df' DataFrames on the 'primary_title' column\n",
    "option_2 = movie_budgets_df.join(movie_basics_df.set_index('primary_title'), on='movie', how='inner')\n",
    "print(option_2.info())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There seems to a one-to-many relationship in both options hence why we have more than 200 films returned in the joined DataFrame. This is understandable as it is possible for movies to share names. In order to fix this problem, we will first begin by dropping the movies in the 'movie_basics_df' DataFrame that are older than 10 years. This will reduce the chances of having a movie that shares the same name with another movie."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>movie_id</th>\n",
       "      <th>primary_title</th>\n",
       "      <th>original_title</th>\n",
       "      <th>start_year</th>\n",
       "      <th>runtime_minutes</th>\n",
       "      <th>genres</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>tt0063540</td>\n",
       "      <td>Sunghursh</td>\n",
       "      <td>Sunghursh</td>\n",
       "      <td>2013</td>\n",
       "      <td>175.0</td>\n",
       "      <td>Action,Crime,Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>tt0066787</td>\n",
       "      <td>One Day Before the Rainy Season</td>\n",
       "      <td>Ashad Ka Ek Din</td>\n",
       "      <td>2019</td>\n",
       "      <td>114.0</td>\n",
       "      <td>Biography,Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>tt0069049</td>\n",
       "      <td>The Other Side of the Wind</td>\n",
       "      <td>The Other Side of the Wind</td>\n",
       "      <td>2018</td>\n",
       "      <td>122.0</td>\n",
       "      <td>Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>tt0069204</td>\n",
       "      <td>Sabse Bada Sukh</td>\n",
       "      <td>Sabse Bada Sukh</td>\n",
       "      <td>2018</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Comedy,Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>tt0100275</td>\n",
       "      <td>The Wandering Soap Opera</td>\n",
       "      <td>La Telenovela Errante</td>\n",
       "      <td>2017</td>\n",
       "      <td>80.0</td>\n",
       "      <td>Comedy,Drama,Fantasy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146139</th>\n",
       "      <td>tt9916538</td>\n",
       "      <td>Kuambil Lagi Hatiku</td>\n",
       "      <td>Kuambil Lagi Hatiku</td>\n",
       "      <td>2019</td>\n",
       "      <td>123.0</td>\n",
       "      <td>Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146140</th>\n",
       "      <td>tt9916622</td>\n",
       "      <td>Rodolpho Teóphilo - O Legado de um Pioneiro</td>\n",
       "      <td>Rodolpho Teóphilo - O Legado de um Pioneiro</td>\n",
       "      <td>2015</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Documentary</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146141</th>\n",
       "      <td>tt9916706</td>\n",
       "      <td>Dankyavar Danka</td>\n",
       "      <td>Dankyavar Danka</td>\n",
       "      <td>2013</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Comedy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146142</th>\n",
       "      <td>tt9916730</td>\n",
       "      <td>6 Gunn</td>\n",
       "      <td>6 Gunn</td>\n",
       "      <td>2017</td>\n",
       "      <td>116.0</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146143</th>\n",
       "      <td>tt9916754</td>\n",
       "      <td>Chico Albuquerque - Revelações</td>\n",
       "      <td>Chico Albuquerque - Revelações</td>\n",
       "      <td>2013</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Documentary</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>121395 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         movie_id                                primary_title  \\\n",
       "0       tt0063540                                    Sunghursh   \n",
       "1       tt0066787              One Day Before the Rainy Season   \n",
       "2       tt0069049                   The Other Side of the Wind   \n",
       "3       tt0069204                              Sabse Bada Sukh   \n",
       "4       tt0100275                     The Wandering Soap Opera   \n",
       "...           ...                                          ...   \n",
       "146139  tt9916538                          Kuambil Lagi Hatiku   \n",
       "146140  tt9916622  Rodolpho Teóphilo - O Legado de um Pioneiro   \n",
       "146141  tt9916706                              Dankyavar Danka   \n",
       "146142  tt9916730                                       6 Gunn   \n",
       "146143  tt9916754               Chico Albuquerque - Revelações   \n",
       "\n",
       "                                     original_title  start_year  \\\n",
       "0                                         Sunghursh        2013   \n",
       "1                                   Ashad Ka Ek Din        2019   \n",
       "2                        The Other Side of the Wind        2018   \n",
       "3                                   Sabse Bada Sukh        2018   \n",
       "4                             La Telenovela Errante        2017   \n",
       "...                                             ...         ...   \n",
       "146139                          Kuambil Lagi Hatiku        2019   \n",
       "146140  Rodolpho Teóphilo - O Legado de um Pioneiro        2015   \n",
       "146141                              Dankyavar Danka        2013   \n",
       "146142                                       6 Gunn        2017   \n",
       "146143               Chico Albuquerque - Revelações        2013   \n",
       "\n",
       "        runtime_minutes                genres  \n",
       "0                 175.0    Action,Crime,Drama  \n",
       "1                 114.0       Biography,Drama  \n",
       "2                 122.0                 Drama  \n",
       "3                   NaN          Comedy,Drama  \n",
       "4                  80.0  Comedy,Drama,Fantasy  \n",
       "...                 ...                   ...  \n",
       "146139            123.0                 Drama  \n",
       "146140              NaN           Documentary  \n",
       "146141              NaN                Comedy  \n",
       "146142            116.0                  None  \n",
       "146143              NaN           Documentary  \n",
       "\n",
       "[121395 rows x 6 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Drop the films in the 'movie_basics_df' that are older than 10 years\n",
    "movie_basics_df = movie_basics_df[movie_basics_df['start_year'] >= 2012]\n",
    "\n",
    "movie_basics_df # Preview the updated 'movie_basics_df' DataFrame"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we check to see if that has reduced out one-to-many relationships."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 213 entries, 3342 to 31\n",
      "Data columns (total 12 columns):\n",
      " #   Column             Non-Null Count  Dtype  \n",
      "---  ------             --------------  -----  \n",
      " 0   id                 213 non-null    int64  \n",
      " 1   release_date       213 non-null    object \n",
      " 2   movie              213 non-null    object \n",
      " 3   production_budget  213 non-null    int64  \n",
      " 4   domestic_gross     213 non-null    int64  \n",
      " 5   worldwide_gross    213 non-null    int64  \n",
      " 6   RoI                213 non-null    float64\n",
      " 7   movie_id           213 non-null    object \n",
      " 8   primary_title      213 non-null    object \n",
      " 9   start_year         213 non-null    int64  \n",
      " 10  runtime_minutes    204 non-null    float64\n",
      " 11  genres             211 non-null    object \n",
      "dtypes: float64(2), int64(5), object(5)\n",
      "memory usage: 21.6+ KB\n",
      "None\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 221 entries, 3342 to 31\n",
      "Data columns (total 12 columns):\n",
      " #   Column             Non-Null Count  Dtype  \n",
      "---  ------             --------------  -----  \n",
      " 0   id                 221 non-null    int64  \n",
      " 1   release_date       221 non-null    object \n",
      " 2   movie              221 non-null    object \n",
      " 3   production_budget  221 non-null    int64  \n",
      " 4   domestic_gross     221 non-null    int64  \n",
      " 5   worldwide_gross    221 non-null    int64  \n",
      " 6   RoI                221 non-null    float64\n",
      " 7   movie_id           221 non-null    object \n",
      " 8   original_title     221 non-null    object \n",
      " 9   start_year         221 non-null    int64  \n",
      " 10  runtime_minutes    212 non-null    float64\n",
      " 11  genres             219 non-null    object \n",
      "dtypes: float64(2), int64(5), object(5)\n",
      "memory usage: 22.4+ KB\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "option_1 = movie_budgets_df.join(movie_basics_df.set_index('original_title'), on='movie', how='inner')\n",
    "print(option_1.info())\n",
    "\n",
    "option_2 = movie_budgets_df.join(movie_basics_df.set_index('primary_title'), on='movie', how='inner')\n",
    "print(option_2.info())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see that this has slighly reduced the one-to-many relationships. However, there is still some more cleaning to be done. Based on the two join options, we will be looking to join using the 'original_title' column. The missing values in the 'runtime_minutes' column will make task of narrowing the data down to the Top 200 films faster, and there are fewer duplicate records to deal with."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <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",
       "      <th>RoI</th>\n",
       "      <th>movie_id</th>\n",
       "      <th>primary_title</th>\n",
       "      <th>start_year</th>\n",
       "      <th>runtime_minutes</th>\n",
       "      <th>genres</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3342</th>\n",
       "      <td>43</td>\n",
       "      <td>Jun 6, 2014</td>\n",
       "      <td>The Fault in Our Stars</td>\n",
       "      <td>12000000</td>\n",
       "      <td>124872350</td>\n",
       "      <td>307166834</td>\n",
       "      <td>2559.723617</td>\n",
       "      <td>tt2582846</td>\n",
       "      <td>The Fault in Our Stars</td>\n",
       "      <td>2014</td>\n",
       "      <td>126.0</td>\n",
       "      <td>Drama,Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2491</th>\n",
       "      <td>92</td>\n",
       "      <td>Dec 9, 2016</td>\n",
       "      <td>La La Land</td>\n",
       "      <td>20000000</td>\n",
       "      <td>151101803</td>\n",
       "      <td>426351163</td>\n",
       "      <td>2131.755815</td>\n",
       "      <td>tt3783958</td>\n",
       "      <td>La La Land</td>\n",
       "      <td>2016</td>\n",
       "      <td>128.0</td>\n",
       "      <td>Comedy,Drama,Music</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3001</th>\n",
       "      <td>2</td>\n",
       "      <td>Aug 11, 2017</td>\n",
       "      <td>Annabelle: Creation</td>\n",
       "      <td>15000000</td>\n",
       "      <td>102092201</td>\n",
       "      <td>305384865</td>\n",
       "      <td>2035.899100</td>\n",
       "      <td>tt5140878</td>\n",
       "      <td>Annabelle: Creation</td>\n",
       "      <td>2017</td>\n",
       "      <td>109.0</td>\n",
       "      <td>Horror,Mystery,Thriller</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1623</th>\n",
       "      <td>24</td>\n",
       "      <td>Sep 8, 2017</td>\n",
       "      <td>It</td>\n",
       "      <td>35000000</td>\n",
       "      <td>327481748</td>\n",
       "      <td>697457969</td>\n",
       "      <td>1992.737054</td>\n",
       "      <td>tt1396484</td>\n",
       "      <td>It</td>\n",
       "      <td>2017</td>\n",
       "      <td>135.0</td>\n",
       "      <td>Horror,Thriller</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2865</th>\n",
       "      <td>66</td>\n",
       "      <td>Apr 6, 2018</td>\n",
       "      <td>A Quiet Place</td>\n",
       "      <td>17000000</td>\n",
       "      <td>188024361</td>\n",
       "      <td>334522294</td>\n",
       "      <td>1967.778200</td>\n",
       "      <td>tt6347308</td>\n",
       "      <td>A Quiet Place</td>\n",
       "      <td>2016</td>\n",
       "      <td>80.0</td>\n",
       "      <td>Documentary</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>79</td>\n",
       "      <td>Jul 22, 2016</td>\n",
       "      <td>Star Trek Beyond</td>\n",
       "      <td>185000000</td>\n",
       "      <td>158848340</td>\n",
       "      <td>335802233</td>\n",
       "      <td>181.514721</td>\n",
       "      <td>tt2660888</td>\n",
       "      <td>Star Trek Beyond</td>\n",
       "      <td>2016</td>\n",
       "      <td>122.0</td>\n",
       "      <td>Action,Adventure,Sci-Fi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>73</td>\n",
       "      <td>Nov 25, 2015</td>\n",
       "      <td>The Good Dinosaur</td>\n",
       "      <td>187500000</td>\n",
       "      <td>123087120</td>\n",
       "      <td>333771037</td>\n",
       "      <td>178.011220</td>\n",
       "      <td>tt1979388</td>\n",
       "      <td>The Good Dinosaur</td>\n",
       "      <td>2015</td>\n",
       "      <td>93.0</td>\n",
       "      <td>Adventure,Animation,Comedy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>25</td>\n",
       "      <td>May 31, 2019</td>\n",
       "      <td>Godzilla: King of the Monsters</td>\n",
       "      <td>170000000</td>\n",
       "      <td>85576941</td>\n",
       "      <td>299276941</td>\n",
       "      <td>176.045259</td>\n",
       "      <td>tt3741700</td>\n",
       "      <td>Godzilla: King of the Monsters</td>\n",
       "      <td>2019</td>\n",
       "      <td>132.0</td>\n",
       "      <td>Action,Adventure,Fantasy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>May 25, 2018</td>\n",
       "      <td>Solo: A Star Wars Story</td>\n",
       "      <td>275000000</td>\n",
       "      <td>213767512</td>\n",
       "      <td>393151347</td>\n",
       "      <td>142.964126</td>\n",
       "      <td>tt3778644</td>\n",
       "      <td>Solo: A Star Wars Story</td>\n",
       "      <td>2018</td>\n",
       "      <td>135.0</td>\n",
       "      <td>Action,Adventure,Fantasy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>32</td>\n",
       "      <td>May 18, 2012</td>\n",
       "      <td>Battleship</td>\n",
       "      <td>220000000</td>\n",
       "      <td>65233400</td>\n",
       "      <td>313477717</td>\n",
       "      <td>142.489871</td>\n",
       "      <td>tt1440129</td>\n",
       "      <td>Battleship</td>\n",
       "      <td>2012</td>\n",
       "      <td>131.0</td>\n",
       "      <td>Action,Adventure,Sci-Fi</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>213 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      id  release_date                           movie  production_budget  \\\n",
       "3342  43   Jun 6, 2014          The Fault in Our Stars           12000000   \n",
       "2491  92   Dec 9, 2016                      La La Land           20000000   \n",
       "3001   2  Aug 11, 2017             Annabelle: Creation           15000000   \n",
       "1623  24   Sep 8, 2017                              It           35000000   \n",
       "2865  66   Apr 6, 2018                   A Quiet Place           17000000   \n",
       "...   ..           ...                             ...                ...   \n",
       "78    79  Jul 22, 2016                Star Trek Beyond          185000000   \n",
       "72    73  Nov 25, 2015               The Good Dinosaur          187500000   \n",
       "124   25  May 31, 2019  Godzilla: King of the Monsters          170000000   \n",
       "11    12  May 25, 2018         Solo: A Star Wars Story          275000000   \n",
       "31    32  May 18, 2012                      Battleship          220000000   \n",
       "\n",
       "      domestic_gross  worldwide_gross          RoI   movie_id  \\\n",
       "3342       124872350        307166834  2559.723617  tt2582846   \n",
       "2491       151101803        426351163  2131.755815  tt3783958   \n",
       "3001       102092201        305384865  2035.899100  tt5140878   \n",
       "1623       327481748        697457969  1992.737054  tt1396484   \n",
       "2865       188024361        334522294  1967.778200  tt6347308   \n",
       "...              ...              ...          ...        ...   \n",
       "78         158848340        335802233   181.514721  tt2660888   \n",
       "72         123087120        333771037   178.011220  tt1979388   \n",
       "124         85576941        299276941   176.045259  tt3741700   \n",
       "11         213767512        393151347   142.964126  tt3778644   \n",
       "31          65233400        313477717   142.489871  tt1440129   \n",
       "\n",
       "                       primary_title  start_year  runtime_minutes  \\\n",
       "3342          The Fault in Our Stars        2014            126.0   \n",
       "2491                      La La Land        2016            128.0   \n",
       "3001             Annabelle: Creation        2017            109.0   \n",
       "1623                              It        2017            135.0   \n",
       "2865                   A Quiet Place        2016             80.0   \n",
       "...                              ...         ...              ...   \n",
       "78                  Star Trek Beyond        2016            122.0   \n",
       "72                 The Good Dinosaur        2015             93.0   \n",
       "124   Godzilla: King of the Monsters        2019            132.0   \n",
       "11           Solo: A Star Wars Story        2018            135.0   \n",
       "31                        Battleship        2012            131.0   \n",
       "\n",
       "                          genres  \n",
       "3342               Drama,Romance  \n",
       "2491          Comedy,Drama,Music  \n",
       "3001     Horror,Mystery,Thriller  \n",
       "1623             Horror,Thriller  \n",
       "2865                 Documentary  \n",
       "...                          ...  \n",
       "78       Action,Adventure,Sci-Fi  \n",
       "72    Adventure,Animation,Comedy  \n",
       "124     Action,Adventure,Fantasy  \n",
       "11      Action,Adventure,Fantasy  \n",
       "31       Action,Adventure,Sci-Fi  \n",
       "\n",
       "[213 rows x 12 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Join the 'movie_budgets_df' and 'movie_titles_df' DataFrames on the 'original_title' column and assign the resulting DataFrame to 'top_movies_df'\n",
    "top_movies_df = movie_budgets_df.join(movie_basics_df.set_index('original_title'), on='movie', how='inner')\n",
    "\n",
    "top_movies_df # Preview the updated 'top_movies_df' DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>release_date</th>\n",
       "      <th>movie</th>\n",
       "      <th>production_budget</th>\n",
       "      <th>domestic_gross</th>\n",
       "      <th>worldwide_gross</th>\n",
       "      <th>RoI</th>\n",
       "      <th>movie_id</th>\n",
       "      <th>primary_title</th>\n",
       "      <th>start_year</th>\n",
       "      <th>runtime_minutes</th>\n",
       "      <th>genres</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3342</th>\n",
       "      <td>43</td>\n",
       "      <td>Jun 6, 2014</td>\n",
       "      <td>The Fault in Our Stars</td>\n",
       "      <td>12000000</td>\n",
       "      <td>124872350</td>\n",
       "      <td>307166834</td>\n",
       "      <td>2559.723617</td>\n",
       "      <td>tt2582846</td>\n",
       "      <td>The Fault in Our Stars</td>\n",
       "      <td>2014</td>\n",
       "      <td>126.0</td>\n",
       "      <td>Drama,Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2491</th>\n",
       "      <td>92</td>\n",
       "      <td>Dec 9, 2016</td>\n",
       "      <td>La La Land</td>\n",
       "      <td>20000000</td>\n",
       "      <td>151101803</td>\n",
       "      <td>426351163</td>\n",
       "      <td>2131.755815</td>\n",
       "      <td>tt3783958</td>\n",
       "      <td>La La Land</td>\n",
       "      <td>2016</td>\n",
       "      <td>128.0</td>\n",
       "      <td>Comedy,Drama,Music</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3001</th>\n",
       "      <td>2</td>\n",
       "      <td>Aug 11, 2017</td>\n",
       "      <td>Annabelle: Creation</td>\n",
       "      <td>15000000</td>\n",
       "      <td>102092201</td>\n",
       "      <td>305384865</td>\n",
       "      <td>2035.899100</td>\n",
       "      <td>tt5140878</td>\n",
       "      <td>Annabelle: Creation</td>\n",
       "      <td>2017</td>\n",
       "      <td>109.0</td>\n",
       "      <td>Horror,Mystery,Thriller</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1623</th>\n",
       "      <td>24</td>\n",
       "      <td>Sep 8, 2017</td>\n",
       "      <td>It</td>\n",
       "      <td>35000000</td>\n",
       "      <td>327481748</td>\n",
       "      <td>697457969</td>\n",
       "      <td>1992.737054</td>\n",
       "      <td>tt1396484</td>\n",
       "      <td>It</td>\n",
       "      <td>2017</td>\n",
       "      <td>135.0</td>\n",
       "      <td>Horror,Thriller</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2865</th>\n",
       "      <td>66</td>\n",
       "      <td>Apr 6, 2018</td>\n",
       "      <td>A Quiet Place</td>\n",
       "      <td>17000000</td>\n",
       "      <td>188024361</td>\n",
       "      <td>334522294</td>\n",
       "      <td>1967.778200</td>\n",
       "      <td>tt6347308</td>\n",
       "      <td>A Quiet Place</td>\n",
       "      <td>2016</td>\n",
       "      <td>80.0</td>\n",
       "      <td>Documentary</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>79</td>\n",
       "      <td>Jul 22, 2016</td>\n",
       "      <td>Star Trek Beyond</td>\n",
       "      <td>185000000</td>\n",
       "      <td>158848340</td>\n",
       "      <td>335802233</td>\n",
       "      <td>181.514721</td>\n",
       "      <td>tt2660888</td>\n",
       "      <td>Star Trek Beyond</td>\n",
       "      <td>2016</td>\n",
       "      <td>122.0</td>\n",
       "      <td>Action,Adventure,Sci-Fi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>73</td>\n",
       "      <td>Nov 25, 2015</td>\n",
       "      <td>The Good Dinosaur</td>\n",
       "      <td>187500000</td>\n",
       "      <td>123087120</td>\n",
       "      <td>333771037</td>\n",
       "      <td>178.011220</td>\n",
       "      <td>tt1979388</td>\n",
       "      <td>The Good Dinosaur</td>\n",
       "      <td>2015</td>\n",
       "      <td>93.0</td>\n",
       "      <td>Adventure,Animation,Comedy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>25</td>\n",
       "      <td>May 31, 2019</td>\n",
       "      <td>Godzilla: King of the Monsters</td>\n",
       "      <td>170000000</td>\n",
       "      <td>85576941</td>\n",
       "      <td>299276941</td>\n",
       "      <td>176.045259</td>\n",
       "      <td>tt3741700</td>\n",
       "      <td>Godzilla: King of the Monsters</td>\n",
       "      <td>2019</td>\n",
       "      <td>132.0</td>\n",
       "      <td>Action,Adventure,Fantasy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>May 25, 2018</td>\n",
       "      <td>Solo: A Star Wars Story</td>\n",
       "      <td>275000000</td>\n",
       "      <td>213767512</td>\n",
       "      <td>393151347</td>\n",
       "      <td>142.964126</td>\n",
       "      <td>tt3778644</td>\n",
       "      <td>Solo: A Star Wars Story</td>\n",
       "      <td>2018</td>\n",
       "      <td>135.0</td>\n",
       "      <td>Action,Adventure,Fantasy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>32</td>\n",
       "      <td>May 18, 2012</td>\n",
       "      <td>Battleship</td>\n",
       "      <td>220000000</td>\n",
       "      <td>65233400</td>\n",
       "      <td>313477717</td>\n",
       "      <td>142.489871</td>\n",
       "      <td>tt1440129</td>\n",
       "      <td>Battleship</td>\n",
       "      <td>2012</td>\n",
       "      <td>131.0</td>\n",
       "      <td>Action,Adventure,Sci-Fi</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>204 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      id  release_date                           movie  production_budget  \\\n",
       "3342  43   Jun 6, 2014          The Fault in Our Stars           12000000   \n",
       "2491  92   Dec 9, 2016                      La La Land           20000000   \n",
       "3001   2  Aug 11, 2017             Annabelle: Creation           15000000   \n",
       "1623  24   Sep 8, 2017                              It           35000000   \n",
       "2865  66   Apr 6, 2018                   A Quiet Place           17000000   \n",
       "...   ..           ...                             ...                ...   \n",
       "78    79  Jul 22, 2016                Star Trek Beyond          185000000   \n",
       "72    73  Nov 25, 2015               The Good Dinosaur          187500000   \n",
       "124   25  May 31, 2019  Godzilla: King of the Monsters          170000000   \n",
       "11    12  May 25, 2018         Solo: A Star Wars Story          275000000   \n",
       "31    32  May 18, 2012                      Battleship          220000000   \n",
       "\n",
       "      domestic_gross  worldwide_gross          RoI   movie_id  \\\n",
       "3342       124872350        307166834  2559.723617  tt2582846   \n",
       "2491       151101803        426351163  2131.755815  tt3783958   \n",
       "3001       102092201        305384865  2035.899100  tt5140878   \n",
       "1623       327481748        697457969  1992.737054  tt1396484   \n",
       "2865       188024361        334522294  1967.778200  tt6347308   \n",
       "...              ...              ...          ...        ...   \n",
       "78         158848340        335802233   181.514721  tt2660888   \n",
       "72         123087120        333771037   178.011220  tt1979388   \n",
       "124         85576941        299276941   176.045259  tt3741700   \n",
       "11         213767512        393151347   142.964126  tt3778644   \n",
       "31          65233400        313477717   142.489871  tt1440129   \n",
       "\n",
       "                       primary_title  start_year  runtime_minutes  \\\n",
       "3342          The Fault in Our Stars        2014            126.0   \n",
       "2491                      La La Land        2016            128.0   \n",
       "3001             Annabelle: Creation        2017            109.0   \n",
       "1623                              It        2017            135.0   \n",
       "2865                   A Quiet Place        2016             80.0   \n",
       "...                              ...         ...              ...   \n",
       "78                  Star Trek Beyond        2016            122.0   \n",
       "72                 The Good Dinosaur        2015             93.0   \n",
       "124   Godzilla: King of the Monsters        2019            132.0   \n",
       "11           Solo: A Star Wars Story        2018            135.0   \n",
       "31                        Battleship        2012            131.0   \n",
       "\n",
       "                          genres  \n",
       "3342               Drama,Romance  \n",
       "2491          Comedy,Drama,Music  \n",
       "3001     Horror,Mystery,Thriller  \n",
       "1623             Horror,Thriller  \n",
       "2865                 Documentary  \n",
       "...                          ...  \n",
       "78       Action,Adventure,Sci-Fi  \n",
       "72    Adventure,Animation,Comedy  \n",
       "124     Action,Adventure,Fantasy  \n",
       "11      Action,Adventure,Fantasy  \n",
       "31       Action,Adventure,Sci-Fi  \n",
       "\n",
       "[204 rows x 12 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Drop records that are missing 'runtime_minutes' values\n",
    "top_movies_df = top_movies_df.dropna(subset=['runtime_minutes'])\n",
    "\n",
    "top_movies_df # Preview the updated 'top_movies_df' DataFrame"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The DataFrame has now been narrowed down to 204 records. In order to drop the illegitimate duplicate records, we will compare the year value in the 'release_date' column against the 'start_year' column. Records that return conflicting values will be dropped."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-28-21391e8ecdab>:2: 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",
      "  top_movies_df['release_year'] = top_movies_df['release_date'].apply(lambda x: int(x.split(\" \")[-1]))\n"
     ]
    },
    {
     "data": {
      "text/html": [
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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",
       "      <th>RoI</th>\n",
       "      <th>movie_id</th>\n",
       "      <th>primary_title</th>\n",
       "      <th>start_year</th>\n",
       "      <th>runtime_minutes</th>\n",
       "      <th>genres</th>\n",
       "      <th>release_year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3342</th>\n",
       "      <td>43</td>\n",
       "      <td>Jun 6, 2014</td>\n",
       "      <td>The Fault in Our Stars</td>\n",
       "      <td>12000000</td>\n",
       "      <td>124872350</td>\n",
       "      <td>307166834</td>\n",
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       "      <td>The Fault in Our Stars</td>\n",
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       "      <td>Drama,Romance</td>\n",
       "      <td>2014</td>\n",
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       "    <tr>\n",
       "      <th>2491</th>\n",
       "      <td>92</td>\n",
       "      <td>Dec 9, 2016</td>\n",
       "      <td>La La Land</td>\n",
       "      <td>20000000</td>\n",
       "      <td>151101803</td>\n",
       "      <td>426351163</td>\n",
       "      <td>2131.755815</td>\n",
       "      <td>tt3783958</td>\n",
       "      <td>La La Land</td>\n",
       "      <td>2016</td>\n",
       "      <td>128.0</td>\n",
       "      <td>Comedy,Drama,Music</td>\n",
       "      <td>2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3001</th>\n",
       "      <td>2</td>\n",
       "      <td>Aug 11, 2017</td>\n",
       "      <td>Annabelle: Creation</td>\n",
       "      <td>15000000</td>\n",
       "      <td>102092201</td>\n",
       "      <td>305384865</td>\n",
       "      <td>2035.899100</td>\n",
       "      <td>tt5140878</td>\n",
       "      <td>Annabelle: Creation</td>\n",
       "      <td>2017</td>\n",
       "      <td>109.0</td>\n",
       "      <td>Horror,Mystery,Thriller</td>\n",
       "      <td>2017</td>\n",
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       "    <tr>\n",
       "      <th>1623</th>\n",
       "      <td>24</td>\n",
       "      <td>Sep 8, 2017</td>\n",
       "      <td>It</td>\n",
       "      <td>35000000</td>\n",
       "      <td>327481748</td>\n",
       "      <td>697457969</td>\n",
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       "      <td>tt1396484</td>\n",
       "      <td>It</td>\n",
       "      <td>2017</td>\n",
       "      <td>135.0</td>\n",
       "      <td>Horror,Thriller</td>\n",
       "      <td>2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2865</th>\n",
       "      <td>66</td>\n",
       "      <td>Apr 6, 2018</td>\n",
       "      <td>A Quiet Place</td>\n",
       "      <td>17000000</td>\n",
       "      <td>188024361</td>\n",
       "      <td>334522294</td>\n",
       "      <td>1967.778200</td>\n",
       "      <td>tt6644200</td>\n",
       "      <td>A Quiet Place</td>\n",
       "      <td>2018</td>\n",
       "      <td>90.0</td>\n",
       "      <td>Drama,Horror,Sci-Fi</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>79</td>\n",
       "      <td>Jul 22, 2016</td>\n",
       "      <td>Star Trek Beyond</td>\n",
       "      <td>185000000</td>\n",
       "      <td>158848340</td>\n",
       "      <td>335802233</td>\n",
       "      <td>181.514721</td>\n",
       "      <td>tt2660888</td>\n",
       "      <td>Star Trek Beyond</td>\n",
       "      <td>2016</td>\n",
       "      <td>122.0</td>\n",
       "      <td>Action,Adventure,Sci-Fi</td>\n",
       "      <td>2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>73</td>\n",
       "      <td>Nov 25, 2015</td>\n",
       "      <td>The Good Dinosaur</td>\n",
       "      <td>187500000</td>\n",
       "      <td>123087120</td>\n",
       "      <td>333771037</td>\n",
       "      <td>178.011220</td>\n",
       "      <td>tt1979388</td>\n",
       "      <td>The Good Dinosaur</td>\n",
       "      <td>2015</td>\n",
       "      <td>93.0</td>\n",
       "      <td>Adventure,Animation,Comedy</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>25</td>\n",
       "      <td>May 31, 2019</td>\n",
       "      <td>Godzilla: King of the Monsters</td>\n",
       "      <td>170000000</td>\n",
       "      <td>85576941</td>\n",
       "      <td>299276941</td>\n",
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       "      <td>Godzilla: King of the Monsters</td>\n",
       "      <td>2019</td>\n",
       "      <td>132.0</td>\n",
       "      <td>Action,Adventure,Fantasy</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>May 25, 2018</td>\n",
       "      <td>Solo: A Star Wars Story</td>\n",
       "      <td>275000000</td>\n",
       "      <td>213767512</td>\n",
       "      <td>393151347</td>\n",
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       "      <td>Solo: A Star Wars Story</td>\n",
       "      <td>2018</td>\n",
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       "      <td>Action,Adventure,Fantasy</td>\n",
       "      <td>2018</td>\n",
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       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>32</td>\n",
       "      <td>May 18, 2012</td>\n",
       "      <td>Battleship</td>\n",
       "      <td>220000000</td>\n",
       "      <td>65233400</td>\n",
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       "      <td>Battleship</td>\n",
       "      <td>2012</td>\n",
       "      <td>131.0</td>\n",
       "      <td>Action,Adventure,Sci-Fi</td>\n",
       "      <td>2012</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>174 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      id  release_date                           movie  production_budget  \\\n",
       "3342  43   Jun 6, 2014          The Fault in Our Stars           12000000   \n",
       "2491  92   Dec 9, 2016                      La La Land           20000000   \n",
       "3001   2  Aug 11, 2017             Annabelle: Creation           15000000   \n",
       "1623  24   Sep 8, 2017                              It           35000000   \n",
       "2865  66   Apr 6, 2018                   A Quiet Place           17000000   \n",
       "...   ..           ...                             ...                ...   \n",
       "78    79  Jul 22, 2016                Star Trek Beyond          185000000   \n",
       "72    73  Nov 25, 2015               The Good Dinosaur          187500000   \n",
       "124   25  May 31, 2019  Godzilla: King of the Monsters          170000000   \n",
       "11    12  May 25, 2018         Solo: A Star Wars Story          275000000   \n",
       "31    32  May 18, 2012                      Battleship          220000000   \n",
       "\n",
       "      domestic_gross  worldwide_gross          RoI   movie_id  \\\n",
       "3342       124872350        307166834  2559.723617  tt2582846   \n",
       "2491       151101803        426351163  2131.755815  tt3783958   \n",
       "3001       102092201        305384865  2035.899100  tt5140878   \n",
       "1623       327481748        697457969  1992.737054  tt1396484   \n",
       "2865       188024361        334522294  1967.778200  tt6644200   \n",
       "...              ...              ...          ...        ...   \n",
       "78         158848340        335802233   181.514721  tt2660888   \n",
       "72         123087120        333771037   178.011220  tt1979388   \n",
       "124         85576941        299276941   176.045259  tt3741700   \n",
       "11         213767512        393151347   142.964126  tt3778644   \n",
       "31          65233400        313477717   142.489871  tt1440129   \n",
       "\n",
       "                       primary_title  start_year  runtime_minutes  \\\n",
       "3342          The Fault in Our Stars        2014            126.0   \n",
       "2491                      La La Land        2016            128.0   \n",
       "3001             Annabelle: Creation        2017            109.0   \n",
       "1623                              It        2017            135.0   \n",
       "2865                   A Quiet Place        2018             90.0   \n",
       "...                              ...         ...              ...   \n",
       "78                  Star Trek Beyond        2016            122.0   \n",
       "72                 The Good Dinosaur        2015             93.0   \n",
       "124   Godzilla: King of the Monsters        2019            132.0   \n",
       "11           Solo: A Star Wars Story        2018            135.0   \n",
       "31                        Battleship        2012            131.0   \n",
       "\n",
       "                          genres  release_year  \n",
       "3342               Drama,Romance          2014  \n",
       "2491          Comedy,Drama,Music          2016  \n",
       "3001     Horror,Mystery,Thriller          2017  \n",
       "1623             Horror,Thriller          2017  \n",
       "2865         Drama,Horror,Sci-Fi          2018  \n",
       "...                          ...           ...  \n",
       "78       Action,Adventure,Sci-Fi          2016  \n",
       "72    Adventure,Animation,Comedy          2015  \n",
       "124     Action,Adventure,Fantasy          2019  \n",
       "11      Action,Adventure,Fantasy          2018  \n",
       "31       Action,Adventure,Sci-Fi          2012  \n",
       "\n",
       "[174 rows x 13 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a new column called 'release_year' that extracts the year from the 'release_date' column\n",
    "top_movies_df['release_year'] = top_movies_df['release_date'].apply(lambda x: int(x.split(\" \")[-1]))\n",
    "\n",
    "\n",
    "#Compare the 'release_year' column to the 'start_year' column. Drop the conflicting records\n",
    "top_movies_df = top_movies_df.loc[top_movies_df['release_year'] == top_movies_df['start_year']]\n",
    "\n",
    "top_movies_df # Preview the updated 'top_movies_df' DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "170"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(top_movies_df['movie'].unique()) # Identify the number of movie titles in the 'top_movies_df' DataFrame"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The length of the DataFrame has been significantly reduced. This means that we have possibly gotten rid of invalid records. With the DataFrame down to 174 records and the number of unique movie titles now at 170, this means that there could be up to 4 duplicate records that need to be checked."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3 duplicate title(s)\n",
      "0 duplicate id(s)\n"
     ]
    }
   ],
   "source": [
    "# Identify the titles of the duplicate movie titles\n",
    "duplicate_movie_titles = top_movies_df[top_movies_df.duplicated(subset='movie')].movie.unique()\n",
    "print(f\"{len(duplicate_movie_titles)} duplicate title(s)\") # identify number of duplicate movie titles\n",
    "\n",
    "# Identify the movie ids of the unique movie titles\n",
    "duplicate_movie_ids = top_movies_df[top_movies_df.duplicated(subset='movie_id')].movie_id.unique()\n",
    "print(f\"{len(duplicate_movie_ids)} duplicate id(s)\") # identify number of duplicate movie ids"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
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       "      <td>15.0</td>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     id  release_date       movie  production_budget  domestic_gross  \\\n",
       "412  13  Mar 13, 2015  Cinderella           95000000       201151353   \n",
       "412  13  Mar 13, 2015  Cinderella           95000000       201151353   \n",
       "101   2  Nov 22, 2017        Coco          175000000       209726015   \n",
       "101   2  Nov 22, 2017        Coco          175000000       209726015   \n",
       "243  44  Mar 27, 2015        Home          130000000       177397510   \n",
       "243  44  Mar 27, 2015        Home          130000000       177397510   \n",
       "243  44  Mar 27, 2015        Home          130000000       177397510   \n",
       "\n",
       "     worldwide_gross         RoI   movie_id primary_title  start_year  \\\n",
       "412        534551353  562.685635  tt1661199    Cinderella        2015   \n",
       "412        534551353  562.685635  tt5089556    Cinderella        2015   \n",
       "101        798008101  456.004629  tt2380307          Coco        2017   \n",
       "101        798008101  456.004629  tt7002100          Coco        2017   \n",
       "243        385997896  296.921458  tt2224026          Home        2015   \n",
       "243        385997896  296.921458  tt5121236          Home        2015   \n",
       "243        385997896  296.921458  tt5884960          Home        2015   \n",
       "\n",
       "     runtime_minutes                      genres  release_year  \n",
       "412            105.0        Drama,Family,Fantasy          2015  \n",
       "412            113.0                     Fantasy          2015  \n",
       "101            105.0  Adventure,Animation,Comedy          2017  \n",
       "101             98.0                      Horror          2017  \n",
       "243             94.0  Adventure,Animation,Comedy          2015  \n",
       "243             15.0                 Documentary          2015  \n",
       "243             58.0                 Documentary          2015  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Examine the records that belong to the specified set of movies\n",
    "top_movies_df[top_movies_df.movie.isin(duplicate_movie_titles)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have identified the records that are duplicated. Before we can decide which records to drop, we will need to integrate more features into our top_movies_df DataFrame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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",
       "      <th>RoI</th>\n",
       "      <th>movie_id</th>\n",
       "      <th>primary_title</th>\n",
       "      <th>start_year</th>\n",
       "      <th>runtime_minutes</th>\n",
       "      <th>genres</th>\n",
       "      <th>release_year</th>\n",
       "      <th>averagerating</th>\n",
       "      <th>numvotes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3342</th>\n",
       "      <td>43</td>\n",
       "      <td>Jun 6, 2014</td>\n",
       "      <td>The Fault in Our Stars</td>\n",
       "      <td>12000000</td>\n",
       "      <td>124872350</td>\n",
       "      <td>307166834</td>\n",
       "      <td>2559.723617</td>\n",
       "      <td>tt2582846</td>\n",
       "      <td>The Fault in Our Stars</td>\n",
       "      <td>2014</td>\n",
       "      <td>126.0</td>\n",
       "      <td>Drama,Romance</td>\n",
       "      <td>2014</td>\n",
       "      <td>7.7</td>\n",
       "      <td>315135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2491</th>\n",
       "      <td>92</td>\n",
       "      <td>Dec 9, 2016</td>\n",
       "      <td>La La Land</td>\n",
       "      <td>20000000</td>\n",
       "      <td>151101803</td>\n",
       "      <td>426351163</td>\n",
       "      <td>2131.755815</td>\n",
       "      <td>tt3783958</td>\n",
       "      <td>La La Land</td>\n",
       "      <td>2016</td>\n",
       "      <td>128.0</td>\n",
       "      <td>Comedy,Drama,Music</td>\n",
       "      <td>2016</td>\n",
       "      <td>8.0</td>\n",
       "      <td>436070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3001</th>\n",
       "      <td>2</td>\n",
       "      <td>Aug 11, 2017</td>\n",
       "      <td>Annabelle: Creation</td>\n",
       "      <td>15000000</td>\n",
       "      <td>102092201</td>\n",
       "      <td>305384865</td>\n",
       "      <td>2035.899100</td>\n",
       "      <td>tt5140878</td>\n",
       "      <td>Annabelle: Creation</td>\n",
       "      <td>2017</td>\n",
       "      <td>109.0</td>\n",
       "      <td>Horror,Mystery,Thriller</td>\n",
       "      <td>2017</td>\n",
       "      <td>6.5</td>\n",
       "      <td>90402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1623</th>\n",
       "      <td>24</td>\n",
       "      <td>Sep 8, 2017</td>\n",
       "      <td>It</td>\n",
       "      <td>35000000</td>\n",
       "      <td>327481748</td>\n",
       "      <td>697457969</td>\n",
       "      <td>1992.737054</td>\n",
       "      <td>tt1396484</td>\n",
       "      <td>It</td>\n",
       "      <td>2017</td>\n",
       "      <td>135.0</td>\n",
       "      <td>Horror,Thriller</td>\n",
       "      <td>2017</td>\n",
       "      <td>7.4</td>\n",
       "      <td>359123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2865</th>\n",
       "      <td>66</td>\n",
       "      <td>Apr 6, 2018</td>\n",
       "      <td>A Quiet Place</td>\n",
       "      <td>17000000</td>\n",
       "      <td>188024361</td>\n",
       "      <td>334522294</td>\n",
       "      <td>1967.778200</td>\n",
       "      <td>tt6644200</td>\n",
       "      <td>A Quiet Place</td>\n",
       "      <td>2018</td>\n",
       "      <td>90.0</td>\n",
       "      <td>Drama,Horror,Sci-Fi</td>\n",
       "      <td>2018</td>\n",
       "      <td>7.6</td>\n",
       "      <td>305031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>79</td>\n",
       "      <td>Jul 22, 2016</td>\n",
       "      <td>Star Trek Beyond</td>\n",
       "      <td>185000000</td>\n",
       "      <td>158848340</td>\n",
       "      <td>335802233</td>\n",
       "      <td>181.514721</td>\n",
       "      <td>tt2660888</td>\n",
       "      <td>Star Trek Beyond</td>\n",
       "      <td>2016</td>\n",
       "      <td>122.0</td>\n",
       "      <td>Action,Adventure,Sci-Fi</td>\n",
       "      <td>2016</td>\n",
       "      <td>7.1</td>\n",
       "      <td>209844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>73</td>\n",
       "      <td>Nov 25, 2015</td>\n",
       "      <td>The Good Dinosaur</td>\n",
       "      <td>187500000</td>\n",
       "      <td>123087120</td>\n",
       "      <td>333771037</td>\n",
       "      <td>178.011220</td>\n",
       "      <td>tt1979388</td>\n",
       "      <td>The Good Dinosaur</td>\n",
       "      <td>2015</td>\n",
       "      <td>93.0</td>\n",
       "      <td>Adventure,Animation,Comedy</td>\n",
       "      <td>2015</td>\n",
       "      <td>6.7</td>\n",
       "      <td>91465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>25</td>\n",
       "      <td>May 31, 2019</td>\n",
       "      <td>Godzilla: King of the Monsters</td>\n",
       "      <td>170000000</td>\n",
       "      <td>85576941</td>\n",
       "      <td>299276941</td>\n",
       "      <td>176.045259</td>\n",
       "      <td>tt3741700</td>\n",
       "      <td>Godzilla: King of the Monsters</td>\n",
       "      <td>2019</td>\n",
       "      <td>132.0</td>\n",
       "      <td>Action,Adventure,Fantasy</td>\n",
       "      <td>2019</td>\n",
       "      <td>6.7</td>\n",
       "      <td>36479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>May 25, 2018</td>\n",
       "      <td>Solo: A Star Wars Story</td>\n",
       "      <td>275000000</td>\n",
       "      <td>213767512</td>\n",
       "      <td>393151347</td>\n",
       "      <td>142.964126</td>\n",
       "      <td>tt3778644</td>\n",
       "      <td>Solo: A Star Wars Story</td>\n",
       "      <td>2018</td>\n",
       "      <td>135.0</td>\n",
       "      <td>Action,Adventure,Fantasy</td>\n",
       "      <td>2018</td>\n",
       "      <td>7.0</td>\n",
       "      <td>226243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>32</td>\n",
       "      <td>May 18, 2012</td>\n",
       "      <td>Battleship</td>\n",
       "      <td>220000000</td>\n",
       "      <td>65233400</td>\n",
       "      <td>313477717</td>\n",
       "      <td>142.489871</td>\n",
       "      <td>tt1440129</td>\n",
       "      <td>Battleship</td>\n",
       "      <td>2012</td>\n",
       "      <td>131.0</td>\n",
       "      <td>Action,Adventure,Sci-Fi</td>\n",
       "      <td>2012</td>\n",
       "      <td>5.8</td>\n",
       "      <td>225342</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>171 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      id  release_date                           movie  production_budget  \\\n",
       "3342  43   Jun 6, 2014          The Fault in Our Stars           12000000   \n",
       "2491  92   Dec 9, 2016                      La La Land           20000000   \n",
       "3001   2  Aug 11, 2017             Annabelle: Creation           15000000   \n",
       "1623  24   Sep 8, 2017                              It           35000000   \n",
       "2865  66   Apr 6, 2018                   A Quiet Place           17000000   \n",
       "...   ..           ...                             ...                ...   \n",
       "78    79  Jul 22, 2016                Star Trek Beyond          185000000   \n",
       "72    73  Nov 25, 2015               The Good Dinosaur          187500000   \n",
       "124   25  May 31, 2019  Godzilla: King of the Monsters          170000000   \n",
       "11    12  May 25, 2018         Solo: A Star Wars Story          275000000   \n",
       "31    32  May 18, 2012                      Battleship          220000000   \n",
       "\n",
       "      domestic_gross  worldwide_gross          RoI   movie_id  \\\n",
       "3342       124872350        307166834  2559.723617  tt2582846   \n",
       "2491       151101803        426351163  2131.755815  tt3783958   \n",
       "3001       102092201        305384865  2035.899100  tt5140878   \n",
       "1623       327481748        697457969  1992.737054  tt1396484   \n",
       "2865       188024361        334522294  1967.778200  tt6644200   \n",
       "...              ...              ...          ...        ...   \n",
       "78         158848340        335802233   181.514721  tt2660888   \n",
       "72         123087120        333771037   178.011220  tt1979388   \n",
       "124         85576941        299276941   176.045259  tt3741700   \n",
       "11         213767512        393151347   142.964126  tt3778644   \n",
       "31          65233400        313477717   142.489871  tt1440129   \n",
       "\n",
       "                       primary_title  start_year  runtime_minutes  \\\n",
       "3342          The Fault in Our Stars        2014            126.0   \n",
       "2491                      La La Land        2016            128.0   \n",
       "3001             Annabelle: Creation        2017            109.0   \n",
       "1623                              It        2017            135.0   \n",
       "2865                   A Quiet Place        2018             90.0   \n",
       "...                              ...         ...              ...   \n",
       "78                  Star Trek Beyond        2016            122.0   \n",
       "72                 The Good Dinosaur        2015             93.0   \n",
       "124   Godzilla: King of the Monsters        2019            132.0   \n",
       "11           Solo: A Star Wars Story        2018            135.0   \n",
       "31                        Battleship        2012            131.0   \n",
       "\n",
       "                          genres  release_year  averagerating  numvotes  \n",
       "3342               Drama,Romance          2014            7.7    315135  \n",
       "2491          Comedy,Drama,Music          2016            8.0    436070  \n",
       "3001     Horror,Mystery,Thriller          2017            6.5     90402  \n",
       "1623             Horror,Thriller          2017            7.4    359123  \n",
       "2865         Drama,Horror,Sci-Fi          2018            7.6    305031  \n",
       "...                          ...           ...            ...       ...  \n",
       "78       Action,Adventure,Sci-Fi          2016            7.1    209844  \n",
       "72    Adventure,Animation,Comedy          2015            6.7     91465  \n",
       "124     Action,Adventure,Fantasy          2019            6.7     36479  \n",
       "11      Action,Adventure,Fantasy          2018            7.0    226243  \n",
       "31       Action,Adventure,Sci-Fi          2012            5.8    225342  \n",
       "\n",
       "[171 rows x 15 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Join the 'movie_ratings_df' to the 'top_movies_df' DataFrame on the 'movie_id' column\n",
    "top_movies_df = top_movies_df.join(movie_ratings_df.set_index('movie_id'), on='movie_id', how='inner')\n",
    "\n",
    "top_movies_df # Preview the updated 'top_movies_df' DataFrame"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we check whether we still have dupplicate records."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 duplicate movie title(s)\n"
     ]
    }
   ],
   "source": [
    "# Identify the titles of the duplicate movie titles\n",
    "duplicate_movie_titles = set(top_movies_df[top_movies_df.duplicated(subset='movie')].movie)\n",
    "print(f\"{len(duplicate_movie_titles)} duplicate movie title(s)\") # identify number of duplicate movie titles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>101</th>\n",
       "      <td>2</td>\n",
       "      <td>Nov 22, 2017</td>\n",
       "      <td>Coco</td>\n",
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       "      <th>101</th>\n",
       "      <td>2</td>\n",
       "      <td>Nov 22, 2017</td>\n",
       "      <td>Coco</td>\n",
       "      <td>175000000</td>\n",
       "      <td>209726015</td>\n",
       "      <td>798008101</td>\n",
       "      <td>456.004629</td>\n",
       "      <td>tt7002100</td>\n",
       "      <td>Coco</td>\n",
       "      <td>2017</td>\n",
       "      <td>98.0</td>\n",
       "      <td>Horror</td>\n",
       "      <td>2017</td>\n",
       "      <td>7.4</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
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       "</table>\n",
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      ],
      "text/plain": [
       "     id  release_date movie  production_budget  domestic_gross  \\\n",
       "101   2  Nov 22, 2017  Coco          175000000       209726015   \n",
       "101   2  Nov 22, 2017  Coco          175000000       209726015   \n",
       "\n",
       "     worldwide_gross         RoI   movie_id primary_title  start_year  \\\n",
       "101        798008101  456.004629  tt2380307          Coco        2017   \n",
       "101        798008101  456.004629  tt7002100          Coco        2017   \n",
       "\n",
       "     runtime_minutes                      genres  release_year  averagerating  \\\n",
       "101            105.0  Adventure,Animation,Comedy          2017            8.4   \n",
       "101             98.0                      Horror          2017            7.4   \n",
       "\n",
       "     numvotes  \n",
       "101    277194  \n",
       "101        35  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Examine the records that belong to the specified set of movies\n",
    "top_movies_df[top_movies_df.movie.isin(duplicate_movie_titles)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since there is no definate way of justifying which data is correct between both records, it is best to drop them both than risk having incorrect data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>primary_title</th>\n",
       "      <th>start_year</th>\n",
       "      <th>runtime_minutes</th>\n",
       "      <th>genres</th>\n",
       "      <th>release_year</th>\n",
       "      <th>averagerating</th>\n",
       "      <th>numvotes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3342</th>\n",
       "      <td>43</td>\n",
       "      <td>Jun 6, 2014</td>\n",
       "      <td>The Fault in Our Stars</td>\n",
       "      <td>12000000</td>\n",
       "      <td>124872350</td>\n",
       "      <td>307166834</td>\n",
       "      <td>2559.723617</td>\n",
       "      <td>tt2582846</td>\n",
       "      <td>The Fault in Our Stars</td>\n",
       "      <td>2014</td>\n",
       "      <td>126.0</td>\n",
       "      <td>Drama,Romance</td>\n",
       "      <td>2014</td>\n",
       "      <td>7.7</td>\n",
       "      <td>315135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2491</th>\n",
       "      <td>92</td>\n",
       "      <td>Dec 9, 2016</td>\n",
       "      <td>La La Land</td>\n",
       "      <td>20000000</td>\n",
       "      <td>151101803</td>\n",
       "      <td>426351163</td>\n",
       "      <td>2131.755815</td>\n",
       "      <td>tt3783958</td>\n",
       "      <td>La La Land</td>\n",
       "      <td>2016</td>\n",
       "      <td>128.0</td>\n",
       "      <td>Comedy,Drama,Music</td>\n",
       "      <td>2016</td>\n",
       "      <td>8.0</td>\n",
       "      <td>436070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3001</th>\n",
       "      <td>2</td>\n",
       "      <td>Aug 11, 2017</td>\n",
       "      <td>Annabelle: Creation</td>\n",
       "      <td>15000000</td>\n",
       "      <td>102092201</td>\n",
       "      <td>305384865</td>\n",
       "      <td>2035.899100</td>\n",
       "      <td>tt5140878</td>\n",
       "      <td>Annabelle: Creation</td>\n",
       "      <td>2017</td>\n",
       "      <td>109.0</td>\n",
       "      <td>Horror,Mystery,Thriller</td>\n",
       "      <td>2017</td>\n",
       "      <td>6.5</td>\n",
       "      <td>90402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1623</th>\n",
       "      <td>24</td>\n",
       "      <td>Sep 8, 2017</td>\n",
       "      <td>It</td>\n",
       "      <td>35000000</td>\n",
       "      <td>327481748</td>\n",
       "      <td>697457969</td>\n",
       "      <td>1992.737054</td>\n",
       "      <td>tt1396484</td>\n",
       "      <td>It</td>\n",
       "      <td>2017</td>\n",
       "      <td>135.0</td>\n",
       "      <td>Horror,Thriller</td>\n",
       "      <td>2017</td>\n",
       "      <td>7.4</td>\n",
       "      <td>359123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2865</th>\n",
       "      <td>66</td>\n",
       "      <td>Apr 6, 2018</td>\n",
       "      <td>A Quiet Place</td>\n",
       "      <td>17000000</td>\n",
       "      <td>188024361</td>\n",
       "      <td>334522294</td>\n",
       "      <td>1967.778200</td>\n",
       "      <td>tt6644200</td>\n",
       "      <td>A Quiet Place</td>\n",
       "      <td>2018</td>\n",
       "      <td>90.0</td>\n",
       "      <td>Drama,Horror,Sci-Fi</td>\n",
       "      <td>2018</td>\n",
       "      <td>7.6</td>\n",
       "      <td>305031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>79</td>\n",
       "      <td>Jul 22, 2016</td>\n",
       "      <td>Star Trek Beyond</td>\n",
       "      <td>185000000</td>\n",
       "      <td>158848340</td>\n",
       "      <td>335802233</td>\n",
       "      <td>181.514721</td>\n",
       "      <td>tt2660888</td>\n",
       "      <td>Star Trek Beyond</td>\n",
       "      <td>2016</td>\n",
       "      <td>122.0</td>\n",
       "      <td>Action,Adventure,Sci-Fi</td>\n",
       "      <td>2016</td>\n",
       "      <td>7.1</td>\n",
       "      <td>209844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>73</td>\n",
       "      <td>Nov 25, 2015</td>\n",
       "      <td>The Good Dinosaur</td>\n",
       "      <td>187500000</td>\n",
       "      <td>123087120</td>\n",
       "      <td>333771037</td>\n",
       "      <td>178.011220</td>\n",
       "      <td>tt1979388</td>\n",
       "      <td>The Good Dinosaur</td>\n",
       "      <td>2015</td>\n",
       "      <td>93.0</td>\n",
       "      <td>Adventure,Animation,Comedy</td>\n",
       "      <td>2015</td>\n",
       "      <td>6.7</td>\n",
       "      <td>91465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>25</td>\n",
       "      <td>May 31, 2019</td>\n",
       "      <td>Godzilla: King of the Monsters</td>\n",
       "      <td>170000000</td>\n",
       "      <td>85576941</td>\n",
       "      <td>299276941</td>\n",
       "      <td>176.045259</td>\n",
       "      <td>tt3741700</td>\n",
       "      <td>Godzilla: King of the Monsters</td>\n",
       "      <td>2019</td>\n",
       "      <td>132.0</td>\n",
       "      <td>Action,Adventure,Fantasy</td>\n",
       "      <td>2019</td>\n",
       "      <td>6.7</td>\n",
       "      <td>36479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>May 25, 2018</td>\n",
       "      <td>Solo: A Star Wars Story</td>\n",
       "      <td>275000000</td>\n",
       "      <td>213767512</td>\n",
       "      <td>393151347</td>\n",
       "      <td>142.964126</td>\n",
       "      <td>tt3778644</td>\n",
       "      <td>Solo: A Star Wars Story</td>\n",
       "      <td>2018</td>\n",
       "      <td>135.0</td>\n",
       "      <td>Action,Adventure,Fantasy</td>\n",
       "      <td>2018</td>\n",
       "      <td>7.0</td>\n",
       "      <td>226243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>32</td>\n",
       "      <td>May 18, 2012</td>\n",
       "      <td>Battleship</td>\n",
       "      <td>220000000</td>\n",
       "      <td>65233400</td>\n",
       "      <td>313477717</td>\n",
       "      <td>142.489871</td>\n",
       "      <td>tt1440129</td>\n",
       "      <td>Battleship</td>\n",
       "      <td>2012</td>\n",
       "      <td>131.0</td>\n",
       "      <td>Action,Adventure,Sci-Fi</td>\n",
       "      <td>2012</td>\n",
       "      <td>5.8</td>\n",
       "      <td>225342</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>169 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      id  release_date                           movie  production_budget  \\\n",
       "3342  43   Jun 6, 2014          The Fault in Our Stars           12000000   \n",
       "2491  92   Dec 9, 2016                      La La Land           20000000   \n",
       "3001   2  Aug 11, 2017             Annabelle: Creation           15000000   \n",
       "1623  24   Sep 8, 2017                              It           35000000   \n",
       "2865  66   Apr 6, 2018                   A Quiet Place           17000000   \n",
       "...   ..           ...                             ...                ...   \n",
       "78    79  Jul 22, 2016                Star Trek Beyond          185000000   \n",
       "72    73  Nov 25, 2015               The Good Dinosaur          187500000   \n",
       "124   25  May 31, 2019  Godzilla: King of the Monsters          170000000   \n",
       "11    12  May 25, 2018         Solo: A Star Wars Story          275000000   \n",
       "31    32  May 18, 2012                      Battleship          220000000   \n",
       "\n",
       "      domestic_gross  worldwide_gross          RoI   movie_id  \\\n",
       "3342       124872350        307166834  2559.723617  tt2582846   \n",
       "2491       151101803        426351163  2131.755815  tt3783958   \n",
       "3001       102092201        305384865  2035.899100  tt5140878   \n",
       "1623       327481748        697457969  1992.737054  tt1396484   \n",
       "2865       188024361        334522294  1967.778200  tt6644200   \n",
       "...              ...              ...          ...        ...   \n",
       "78         158848340        335802233   181.514721  tt2660888   \n",
       "72         123087120        333771037   178.011220  tt1979388   \n",
       "124         85576941        299276941   176.045259  tt3741700   \n",
       "11         213767512        393151347   142.964126  tt3778644   \n",
       "31          65233400        313477717   142.489871  tt1440129   \n",
       "\n",
       "                       primary_title  start_year  runtime_minutes  \\\n",
       "3342          The Fault in Our Stars        2014            126.0   \n",
       "2491                      La La Land        2016            128.0   \n",
       "3001             Annabelle: Creation        2017            109.0   \n",
       "1623                              It        2017            135.0   \n",
       "2865                   A Quiet Place        2018             90.0   \n",
       "...                              ...         ...              ...   \n",
       "78                  Star Trek Beyond        2016            122.0   \n",
       "72                 The Good Dinosaur        2015             93.0   \n",
       "124   Godzilla: King of the Monsters        2019            132.0   \n",
       "11           Solo: A Star Wars Story        2018            135.0   \n",
       "31                        Battleship        2012            131.0   \n",
       "\n",
       "                          genres  release_year  averagerating  numvotes  \n",
       "3342               Drama,Romance          2014            7.7    315135  \n",
       "2491          Comedy,Drama,Music          2016            8.0    436070  \n",
       "3001     Horror,Mystery,Thriller          2017            6.5     90402  \n",
       "1623             Horror,Thriller          2017            7.4    359123  \n",
       "2865         Drama,Horror,Sci-Fi          2018            7.6    305031  \n",
       "...                          ...           ...            ...       ...  \n",
       "78       Action,Adventure,Sci-Fi          2016            7.1    209844  \n",
       "72    Adventure,Animation,Comedy          2015            6.7     91465  \n",
       "124     Action,Adventure,Fantasy          2019            6.7     36479  \n",
       "11      Action,Adventure,Fantasy          2018            7.0    226243  \n",
       "31       Action,Adventure,Sci-Fi          2012            5.8    225342  \n",
       "\n",
       "[169 rows x 15 columns]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Drop records that have 'Coco' as the movie title\n",
    "top_movies_df.drop(top_movies_df[top_movies_df['movie'] == 'Coco'].index, inplace = True)\n",
    "\n",
    "top_movies_df # Preview the updated 'top_movies_df' DataFrame"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we no longer have duplicate data, we can start cleaning the DataFrame by dropping the irrelevant columns and organizing the DataFrame. The columns that shall be dropped are: `'id'`, `'domestic_gross'`, `'primary_title'`, `'start_year'`, `'release_year'`, and `'numvotes'`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "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>release_date</th>\n",
       "      <th>movie</th>\n",
       "      <th>production_budget</th>\n",
       "      <th>worldwide_gross</th>\n",
       "      <th>RoI</th>\n",
       "      <th>movie_id</th>\n",
       "      <th>runtime_minutes</th>\n",
       "      <th>genres</th>\n",
       "      <th>averagerating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3342</th>\n",
       "      <td>Jun 6, 2014</td>\n",
       "      <td>The Fault in Our Stars</td>\n",
       "      <td>12000000</td>\n",
       "      <td>307166834</td>\n",
       "      <td>2559.723617</td>\n",
       "      <td>tt2582846</td>\n",
       "      <td>126.0</td>\n",
       "      <td>Drama,Romance</td>\n",
       "      <td>7.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2491</th>\n",
       "      <td>Dec 9, 2016</td>\n",
       "      <td>La La Land</td>\n",
       "      <td>20000000</td>\n",
       "      <td>426351163</td>\n",
       "      <td>2131.755815</td>\n",
       "      <td>tt3783958</td>\n",
       "      <td>128.0</td>\n",
       "      <td>Comedy,Drama,Music</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3001</th>\n",
       "      <td>Aug 11, 2017</td>\n",
       "      <td>Annabelle: Creation</td>\n",
       "      <td>15000000</td>\n",
       "      <td>305384865</td>\n",
       "      <td>2035.899100</td>\n",
       "      <td>tt5140878</td>\n",
       "      <td>109.0</td>\n",
       "      <td>Horror,Mystery,Thriller</td>\n",
       "      <td>6.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1623</th>\n",
       "      <td>Sep 8, 2017</td>\n",
       "      <td>It</td>\n",
       "      <td>35000000</td>\n",
       "      <td>697457969</td>\n",
       "      <td>1992.737054</td>\n",
       "      <td>tt1396484</td>\n",
       "      <td>135.0</td>\n",
       "      <td>Horror,Thriller</td>\n",
       "      <td>7.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2865</th>\n",
       "      <td>Apr 6, 2018</td>\n",
       "      <td>A Quiet Place</td>\n",
       "      <td>17000000</td>\n",
       "      <td>334522294</td>\n",
       "      <td>1967.778200</td>\n",
       "      <td>tt6644200</td>\n",
       "      <td>90.0</td>\n",
       "      <td>Drama,Horror,Sci-Fi</td>\n",
       "      <td>7.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>Jul 22, 2016</td>\n",
       "      <td>Star Trek Beyond</td>\n",
       "      <td>185000000</td>\n",
       "      <td>335802233</td>\n",
       "      <td>181.514721</td>\n",
       "      <td>tt2660888</td>\n",
       "      <td>122.0</td>\n",
       "      <td>Action,Adventure,Sci-Fi</td>\n",
       "      <td>7.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>Nov 25, 2015</td>\n",
       "      <td>The Good Dinosaur</td>\n",
       "      <td>187500000</td>\n",
       "      <td>333771037</td>\n",
       "      <td>178.011220</td>\n",
       "      <td>tt1979388</td>\n",
       "      <td>93.0</td>\n",
       "      <td>Adventure,Animation,Comedy</td>\n",
       "      <td>6.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>May 31, 2019</td>\n",
       "      <td>Godzilla: King of the Monsters</td>\n",
       "      <td>170000000</td>\n",
       "      <td>299276941</td>\n",
       "      <td>176.045259</td>\n",
       "      <td>tt3741700</td>\n",
       "      <td>132.0</td>\n",
       "      <td>Action,Adventure,Fantasy</td>\n",
       "      <td>6.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>May 25, 2018</td>\n",
       "      <td>Solo: A Star Wars Story</td>\n",
       "      <td>275000000</td>\n",
       "      <td>393151347</td>\n",
       "      <td>142.964126</td>\n",
       "      <td>tt3778644</td>\n",
       "      <td>135.0</td>\n",
       "      <td>Action,Adventure,Fantasy</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>May 18, 2012</td>\n",
       "      <td>Battleship</td>\n",
       "      <td>220000000</td>\n",
       "      <td>313477717</td>\n",
       "      <td>142.489871</td>\n",
       "      <td>tt1440129</td>\n",
       "      <td>131.0</td>\n",
       "      <td>Action,Adventure,Sci-Fi</td>\n",
       "      <td>5.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>169 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      release_date                           movie  production_budget  \\\n",
       "3342   Jun 6, 2014          The Fault in Our Stars           12000000   \n",
       "2491   Dec 9, 2016                      La La Land           20000000   \n",
       "3001  Aug 11, 2017             Annabelle: Creation           15000000   \n",
       "1623   Sep 8, 2017                              It           35000000   \n",
       "2865   Apr 6, 2018                   A Quiet Place           17000000   \n",
       "...            ...                             ...                ...   \n",
       "78    Jul 22, 2016                Star Trek Beyond          185000000   \n",
       "72    Nov 25, 2015               The Good Dinosaur          187500000   \n",
       "124   May 31, 2019  Godzilla: King of the Monsters          170000000   \n",
       "11    May 25, 2018         Solo: A Star Wars Story          275000000   \n",
       "31    May 18, 2012                      Battleship          220000000   \n",
       "\n",
       "      worldwide_gross          RoI   movie_id  runtime_minutes  \\\n",
       "3342        307166834  2559.723617  tt2582846            126.0   \n",
       "2491        426351163  2131.755815  tt3783958            128.0   \n",
       "3001        305384865  2035.899100  tt5140878            109.0   \n",
       "1623        697457969  1992.737054  tt1396484            135.0   \n",
       "2865        334522294  1967.778200  tt6644200             90.0   \n",
       "...               ...          ...        ...              ...   \n",
       "78          335802233   181.514721  tt2660888            122.0   \n",
       "72          333771037   178.011220  tt1979388             93.0   \n",
       "124         299276941   176.045259  tt3741700            132.0   \n",
       "11          393151347   142.964126  tt3778644            135.0   \n",
       "31          313477717   142.489871  tt1440129            131.0   \n",
       "\n",
       "                          genres  averagerating  \n",
       "3342               Drama,Romance            7.7  \n",
       "2491          Comedy,Drama,Music            8.0  \n",
       "3001     Horror,Mystery,Thriller            6.5  \n",
       "1623             Horror,Thriller            7.4  \n",
       "2865         Drama,Horror,Sci-Fi            7.6  \n",
       "...                          ...            ...  \n",
       "78       Action,Adventure,Sci-Fi            7.1  \n",
       "72    Adventure,Animation,Comedy            6.7  \n",
       "124     Action,Adventure,Fantasy            6.7  \n",
       "11      Action,Adventure,Fantasy            7.0  \n",
       "31       Action,Adventure,Sci-Fi            5.8  \n",
       "\n",
       "[169 rows x 9 columns]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Drop the 'id', 'domestic_gross', 'primary_title', 'start_year', 'release_year', and 'numvotes' column from the 'top_movies_df' DataFrame\n",
    "top_movies_df.drop(columns=['id', 'domestic_gross', 'primary_title', 'start_year', 'release_year', 'numvotes'], inplace=True)\n",
    "\n",
    "top_movies_df # Preview the updated 'top_movies_df' DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>movie_id</th>\n",
       "      <th>release_date</th>\n",
       "      <th>movie</th>\n",
       "      <th>production_budget</th>\n",
       "      <th>worldwide_gross</th>\n",
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       "      <th>genres</th>\n",
       "      <th>averagerating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>tt2582846</td>\n",
       "      <td>Jun 6, 2014</td>\n",
       "      <td>The Fault in Our Stars</td>\n",
       "      <td>12000000</td>\n",
       "      <td>307166834</td>\n",
       "      <td>2559.723617</td>\n",
       "      <td>126.0</td>\n",
       "      <td>Drama,Romance</td>\n",
       "      <td>7.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>tt3783958</td>\n",
       "      <td>Dec 9, 2016</td>\n",
       "      <td>La La Land</td>\n",
       "      <td>20000000</td>\n",
       "      <td>426351163</td>\n",
       "      <td>2131.755815</td>\n",
       "      <td>128.0</td>\n",
       "      <td>Comedy,Drama,Music</td>\n",
       "      <td>8.0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>tt5140878</td>\n",
       "      <td>Aug 11, 2017</td>\n",
       "      <td>Annabelle: Creation</td>\n",
       "      <td>15000000</td>\n",
       "      <td>305384865</td>\n",
       "      <td>2035.899100</td>\n",
       "      <td>109.0</td>\n",
       "      <td>Horror,Mystery,Thriller</td>\n",
       "      <td>6.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>tt1396484</td>\n",
       "      <td>Sep 8, 2017</td>\n",
       "      <td>It</td>\n",
       "      <td>35000000</td>\n",
       "      <td>697457969</td>\n",
       "      <td>1992.737054</td>\n",
       "      <td>135.0</td>\n",
       "      <td>Horror,Thriller</td>\n",
       "      <td>7.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>tt6644200</td>\n",
       "      <td>Apr 6, 2018</td>\n",
       "      <td>A Quiet Place</td>\n",
       "      <td>17000000</td>\n",
       "      <td>334522294</td>\n",
       "      <td>1967.778200</td>\n",
       "      <td>90.0</td>\n",
       "      <td>Drama,Horror,Sci-Fi</td>\n",
       "      <td>7.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>164</th>\n",
       "      <td>tt2660888</td>\n",
       "      <td>Jul 22, 2016</td>\n",
       "      <td>Star Trek Beyond</td>\n",
       "      <td>185000000</td>\n",
       "      <td>335802233</td>\n",
       "      <td>181.514721</td>\n",
       "      <td>122.0</td>\n",
       "      <td>Action,Adventure,Sci-Fi</td>\n",
       "      <td>7.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>165</th>\n",
       "      <td>tt1979388</td>\n",
       "      <td>Nov 25, 2015</td>\n",
       "      <td>The Good Dinosaur</td>\n",
       "      <td>187500000</td>\n",
       "      <td>333771037</td>\n",
       "      <td>178.011220</td>\n",
       "      <td>93.0</td>\n",
       "      <td>Adventure,Animation,Comedy</td>\n",
       "      <td>6.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>166</th>\n",
       "      <td>tt3741700</td>\n",
       "      <td>May 31, 2019</td>\n",
       "      <td>Godzilla: King of the Monsters</td>\n",
       "      <td>170000000</td>\n",
       "      <td>299276941</td>\n",
       "      <td>176.045259</td>\n",
       "      <td>132.0</td>\n",
       "      <td>Action,Adventure,Fantasy</td>\n",
       "      <td>6.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>167</th>\n",
       "      <td>tt3778644</td>\n",
       "      <td>May 25, 2018</td>\n",
       "      <td>Solo: A Star Wars Story</td>\n",
       "      <td>275000000</td>\n",
       "      <td>393151347</td>\n",
       "      <td>142.964126</td>\n",
       "      <td>135.0</td>\n",
       "      <td>Action,Adventure,Fantasy</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>168</th>\n",
       "      <td>tt1440129</td>\n",
       "      <td>May 18, 2012</td>\n",
       "      <td>Battleship</td>\n",
       "      <td>220000000</td>\n",
       "      <td>313477717</td>\n",
       "      <td>142.489871</td>\n",
       "      <td>131.0</td>\n",
       "      <td>Action,Adventure,Sci-Fi</td>\n",
       "      <td>5.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>169 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      movie_id  release_date                           movie  \\\n",
       "0    tt2582846   Jun 6, 2014          The Fault in Our Stars   \n",
       "1    tt3783958   Dec 9, 2016                      La La Land   \n",
       "2    tt5140878  Aug 11, 2017             Annabelle: Creation   \n",
       "3    tt1396484   Sep 8, 2017                              It   \n",
       "4    tt6644200   Apr 6, 2018                   A Quiet Place   \n",
       "..         ...           ...                             ...   \n",
       "164  tt2660888  Jul 22, 2016                Star Trek Beyond   \n",
       "165  tt1979388  Nov 25, 2015               The Good Dinosaur   \n",
       "166  tt3741700  May 31, 2019  Godzilla: King of the Monsters   \n",
       "167  tt3778644  May 25, 2018         Solo: A Star Wars Story   \n",
       "168  tt1440129  May 18, 2012                      Battleship   \n",
       "\n",
       "     production_budget  worldwide_gross          RoI  runtime_minutes  \\\n",
       "0             12000000        307166834  2559.723617            126.0   \n",
       "1             20000000        426351163  2131.755815            128.0   \n",
       "2             15000000        305384865  2035.899100            109.0   \n",
       "3             35000000        697457969  1992.737054            135.0   \n",
       "4             17000000        334522294  1967.778200             90.0   \n",
       "..                 ...              ...          ...              ...   \n",
       "164          185000000        335802233   181.514721            122.0   \n",
       "165          187500000        333771037   178.011220             93.0   \n",
       "166          170000000        299276941   176.045259            132.0   \n",
       "167          275000000        393151347   142.964126            135.0   \n",
       "168          220000000        313477717   142.489871            131.0   \n",
       "\n",
       "                         genres  averagerating  \n",
       "0                 Drama,Romance            7.7  \n",
       "1            Comedy,Drama,Music            8.0  \n",
       "2       Horror,Mystery,Thriller            6.5  \n",
       "3               Horror,Thriller            7.4  \n",
       "4           Drama,Horror,Sci-Fi            7.6  \n",
       "..                          ...            ...  \n",
       "164     Action,Adventure,Sci-Fi            7.1  \n",
       "165  Adventure,Animation,Comedy            6.7  \n",
       "166    Action,Adventure,Fantasy            6.7  \n",
       "167    Action,Adventure,Fantasy            7.0  \n",
       "168     Action,Adventure,Sci-Fi            5.8  \n",
       "\n",
       "[169 rows x 9 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Reset the index of the 'top_movies_df' DataFrame\n",
    "top_movies_df.set_index('movie_id', inplace=True)\n",
    "top_movies_df.reset_index(inplace=True)\n",
    "top_movies_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert the 'release_date' column to a datetime object\n",
    "top_movies_df['release_date'] = pd.to_datetime(top_movies_df['release_date'], format='%b %d, %Y')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>tt2582846</td>\n",
       "      <td>2014-06-06</td>\n",
       "      <td>The Fault in Our Stars</td>\n",
       "      <td>12000000</td>\n",
       "      <td>307166834</td>\n",
       "      <td>2559.723617</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
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       "      <td>2131.755815</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>tt5140878</td>\n",
       "      <td>2017-08-11</td>\n",
       "      <td>Annabelle: Creation</td>\n",
       "      <td>15000000</td>\n",
       "      <td>305384865</td>\n",
       "      <td>2035.899100</td>\n",
       "      <td>109.0</td>\n",
       "      <td>Horror,Mystery,Thriller</td>\n",
       "      <td>6.5</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>tt1396484</td>\n",
       "      <td>2017-09-08</td>\n",
       "      <td>It</td>\n",
       "      <td>35000000</td>\n",
       "      <td>697457969</td>\n",
       "      <td>1992.737054</td>\n",
       "      <td>135.0</td>\n",
       "      <td>Horror,Thriller</td>\n",
       "      <td>7.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>tt6644200</td>\n",
       "      <td>2018-04-06</td>\n",
       "      <td>A Quiet Place</td>\n",
       "      <td>17000000</td>\n",
       "      <td>334522294</td>\n",
       "      <td>1967.778200</td>\n",
       "      <td>90.0</td>\n",
       "      <td>Drama,Horror,Sci-Fi</td>\n",
       "      <td>7.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>164</th>\n",
       "      <td>tt2660888</td>\n",
       "      <td>2016-07-22</td>\n",
       "      <td>Star Trek Beyond</td>\n",
       "      <td>185000000</td>\n",
       "      <td>335802233</td>\n",
       "      <td>181.514721</td>\n",
       "      <td>122.0</td>\n",
       "      <td>Action,Adventure,Sci-Fi</td>\n",
       "      <td>7.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>165</th>\n",
       "      <td>tt1979388</td>\n",
       "      <td>2015-11-25</td>\n",
       "      <td>The Good Dinosaur</td>\n",
       "      <td>187500000</td>\n",
       "      <td>333771037</td>\n",
       "      <td>178.011220</td>\n",
       "      <td>93.0</td>\n",
       "      <td>Adventure,Animation,Comedy</td>\n",
       "      <td>6.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>166</th>\n",
       "      <td>tt3741700</td>\n",
       "      <td>2019-05-31</td>\n",
       "      <td>Godzilla: King of the Monsters</td>\n",
       "      <td>170000000</td>\n",
       "      <td>299276941</td>\n",
       "      <td>176.045259</td>\n",
       "      <td>132.0</td>\n",
       "      <td>Action,Adventure,Fantasy</td>\n",
       "      <td>6.7</td>\n",
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       "    <tr>\n",
       "      <th>167</th>\n",
       "      <td>tt3778644</td>\n",
       "      <td>2018-05-25</td>\n",
       "      <td>Solo: A Star Wars Story</td>\n",
       "      <td>275000000</td>\n",
       "      <td>393151347</td>\n",
       "      <td>142.964126</td>\n",
       "      <td>135.0</td>\n",
       "      <td>Action,Adventure,Fantasy</td>\n",
       "      <td>7.0</td>\n",
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       "    <tr>\n",
       "      <th>168</th>\n",
       "      <td>tt1440129</td>\n",
       "      <td>2012-05-18</td>\n",
       "      <td>Battleship</td>\n",
       "      <td>220000000</td>\n",
       "      <td>313477717</td>\n",
       "      <td>142.489871</td>\n",
       "      <td>131.0</td>\n",
       "      <td>Action,Adventure,Sci-Fi</td>\n",
       "      <td>5.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>169 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      movie_id release_date                           movie  \\\n",
       "0    tt2582846   2014-06-06          The Fault in Our Stars   \n",
       "1    tt3783958   2016-12-09                      La La Land   \n",
       "2    tt5140878   2017-08-11             Annabelle: Creation   \n",
       "3    tt1396484   2017-09-08                              It   \n",
       "4    tt6644200   2018-04-06                   A Quiet Place   \n",
       "..         ...          ...                             ...   \n",
       "164  tt2660888   2016-07-22                Star Trek Beyond   \n",
       "165  tt1979388   2015-11-25               The Good Dinosaur   \n",
       "166  tt3741700   2019-05-31  Godzilla: King of the Monsters   \n",
       "167  tt3778644   2018-05-25         Solo: A Star Wars Story   \n",
       "168  tt1440129   2012-05-18                      Battleship   \n",
       "\n",
       "     production_budget  worldwide_gross          RoI  runtime_minutes  \\\n",
       "0             12000000        307166834  2559.723617            126.0   \n",
       "1             20000000        426351163  2131.755815            128.0   \n",
       "2             15000000        305384865  2035.899100            109.0   \n",
       "3             35000000        697457969  1992.737054            135.0   \n",
       "4             17000000        334522294  1967.778200             90.0   \n",
       "..                 ...              ...          ...              ...   \n",
       "164          185000000        335802233   181.514721            122.0   \n",
       "165          187500000        333771037   178.011220             93.0   \n",
       "166          170000000        299276941   176.045259            132.0   \n",
       "167          275000000        393151347   142.964126            135.0   \n",
       "168          220000000        313477717   142.489871            131.0   \n",
       "\n",
       "                         genres  averagerating  \n",
       "0                 Drama,Romance            7.7  \n",
       "1            Comedy,Drama,Music            8.0  \n",
       "2       Horror,Mystery,Thriller            6.5  \n",
       "3               Horror,Thriller            7.4  \n",
       "4           Drama,Horror,Sci-Fi            7.6  \n",
       "..                          ...            ...  \n",
       "164     Action,Adventure,Sci-Fi            7.1  \n",
       "165  Adventure,Animation,Comedy            6.7  \n",
       "166    Action,Adventure,Fantasy            6.7  \n",
       "167    Action,Adventure,Fantasy            7.0  \n",
       "168     Action,Adventure,Sci-Fi            5.8  \n",
       "\n",
       "[169 rows x 9 columns]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_movies_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>production_budget</th>\n",
       "      <th>worldwide_gross</th>\n",
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       "      <th>count</th>\n",
       "      <td>1.690000e+02</td>\n",
       "      <td>1.690000e+02</td>\n",
       "      <td>169.000000</td>\n",
       "      <td>169.000000</td>\n",
       "      <td>169.000000</td>\n",
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       "      <th>mean</th>\n",
       "      <td>1.402728e+08</td>\n",
       "      <td>6.186634e+08</td>\n",
       "      <td>555.407647</td>\n",
       "      <td>119.224852</td>\n",
       "      <td>6.966864</td>\n",
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       "      <th>std</th>\n",
       "      <td>6.460577e+07</td>\n",
       "      <td>3.102610e+08</td>\n",
       "      <td>415.318136</td>\n",
       "      <td>20.310309</td>\n",
       "      <td>0.825323</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.200000e+07</td>\n",
       "      <td>2.992769e+08</td>\n",
       "      <td>142.489871</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>4.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>9.200000e+07</td>\n",
       "      <td>3.713506e+08</td>\n",
       "      <td>304.796374</td>\n",
       "      <td>102.000000</td>\n",
       "      <td>6.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.450000e+08</td>\n",
       "      <td>5.295307e+08</td>\n",
       "      <td>414.780490</td>\n",
       "      <td>118.000000</td>\n",
       "      <td>7.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.800000e+08</td>\n",
       "      <td>7.665751e+08</td>\n",
       "      <td>641.749457</td>\n",
       "      <td>133.000000</td>\n",
       "      <td>7.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>3.306000e+08</td>\n",
       "      <td>2.048134e+09</td>\n",
       "      <td>2559.723617</td>\n",
       "      <td>180.000000</td>\n",
       "      <td>8.600000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       production_budget  worldwide_gross          RoI  runtime_minutes  \\\n",
       "count       1.690000e+02     1.690000e+02   169.000000       169.000000   \n",
       "mean        1.402728e+08     6.186634e+08   555.407647       119.224852   \n",
       "std         6.460577e+07     3.102610e+08   415.318136        20.310309   \n",
       "min         1.200000e+07     2.992769e+08   142.489871        72.000000   \n",
       "25%         9.200000e+07     3.713506e+08   304.796374       102.000000   \n",
       "50%         1.450000e+08     5.295307e+08   414.780490       118.000000   \n",
       "75%         1.800000e+08     7.665751e+08   641.749457       133.000000   \n",
       "max         3.306000e+08     2.048134e+09  2559.723617       180.000000   \n",
       "\n",
       "       averagerating  \n",
       "count     169.000000  \n",
       "mean        6.966864  \n",
       "std         0.825323  \n",
       "min         4.100000  \n",
       "25%         6.500000  \n",
       "50%         7.000000  \n",
       "75%         7.600000  \n",
       "max         8.600000  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# General overview of the numeric data in the 'top_movies_df' DataFrame\n",
    "top_movies_df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now have a reletively clean DataFrame that can be used to obtain relevant and reliable insights."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Analysis\n",
    "> The analysis phase focuses on exploring the relationships in the previously prepared data to determine.\n",
    "\n",
    "We shall be looking at the relationsips the different features within and the RoI of a film."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. **Runtime:**\n",
    "    * The runtime of the film is the length of the film in minutes. We are going to plot a scatter plot to visualize the relationship between the 'runtime_minutes' and 'RoI'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ylucsxt8CTwf+OiLOz8x7M/OfI+KrwIuBX6IkpTZRrh52DrvWn/oHypDKpwG/SFUHDPhvgMw8t6ob9ReUGlaTlB4qrwI+WtP7WLTMfHlEfIuSYHwu5e/yPeDVwBvn+Ps02XmUpNMuBepbZeb7IuLblGL1TwB+lZJwuxn4CPChlsU/SBm29wuUK/7tS9kfLqBsoytb1jsZEf+Xsm/9PvCAatb76EJtrMxcGxHHUfav36hecwelCPy3qjjumHsNbXkXJel6KiVBuRfwJcCkkyRpQbH4K71KkiRJkiRJs7OmkyRJkiRJkmpn0kmSJEmSJEm1M+kkSZIkSZKk2pl0kiRJkiRJUu1G6up1Bx10UB5++OH9DkOSJEmSJGloXHbZZXdk5oNmto9U0unwww9nzZo1/Q5DkiRJkiRpaETEjbO1O7xOkiRJkiRJtTPpJEmSJEmSpNqZdJIkSZIkSVLtTDpJkiRJkiSpdiadJEmSJEmSVDuTTpIkSZIkSaqdSSdJkiRJkiTVzqSTJEmSJEmSamfSSZIkSZIkSbUz6SRJkiRJkqTamXSSJEmSJElS7Uw6SZIkSZIkqXYmnSRJkiRJklS7vfodgCRJklpMroUNq2Hzeli2AsZXwdjKfkclSZLUMXs6SZIkNcXkWlh3NmydhH0PK9N1Z5d2SZKkAWPSSZIkqSk2rIalY+UWe0zf37C635FJkiR1zKSTJElSU2xeD0uW79y2ZHlplyRJGjAmnSRJkppi2QrYtmnntm2bSrskSdKAMekkSZLUFOOrSh2nrZOQO6bvj6/qd2SSJEkdM+kkSZLUFGMr4ZgzSx2nLRvL9JgzvXqdJEkaSHv1OwBJkiS1GFtpkkmSJA2FnvZ0iojxiPhCRKyLiKsi4s+q9rMi4qaIuLy6PbXlOa+MiOsi4uqIeHJL+6Mj4opq3lsiInr5XiRJkiRJkjS3Xvd02g78ZWZ+MyIeCFwWERdX896UmWe3LhwRjwROBR4FPBT4XEQcnZn3A/8OnAFcClwEPAX4VI/ehyRJkiRJkubR055OmXlLZn6zun8PsA44dJ6nnAJckJn3Zeb1wHXACRFxCLB/Zn4tMxN4L/CM7kYvSZIkSZKkdvWtkHhEHA78LPD1qulFEbE2Is6NiLGq7VBgQ8vTNlZth1b3Z7bP9jpnRMSaiFhz++231/kWJEmSJEmSNIe+JJ0iYj/go8CfZ+bdlKFyRwLHA7cAb5xadJan5zztuzZmnpOZE5k58aAHPWh3Q5ckSZIkSVIbep50iogllITT+zNzNUBm3pqZ92fmDuCdwAnV4huB8ZanHwbcXLUfNku7JEmSJEmSGqDXV68L4N3Ausz8l5b2Q1oWeyZwZXX/QuDUiNg7Io4AjgK+kZm3APdExInVOp8LfLwnb0KSJEmSJEkL6vXV634R+D3gioi4vGp7FfBbEXE8ZYjcDcAfAWTmVRHxYeA7lCvfvbC6ch3AC4DzgH0pV63zynWSJEmSJEkNEeXib6NhYmIi16xZ0+8wJEmSJEmShkZEXJaZEzPb+3b1OkmSJEmSJA0vk06SJEmSJEmqnUknSZIkSZIk1c6kkyRJkiRJkmpn0kmSJEmSJEm1M+kkSZIkSZKk2pl0kiRJkiRJUu1MOkmSJEmSJKl2Jp0kSZIkSZJUO5NOkiRJkiRJqp1JJ0mSJEmSJNXOpJMkSZIkSZJqZ9JJkiRJkiRJtTPpJEmSJEmSpNqZdJIkSZIkSVLtTDpJkiRJkiSpdiadJEmSJEmSVDuTTpIkSZIkSaqdSSdJkiRJkiTVzqSTJEmSJEmSamfSSZIkSZIkSbUz6SRJkiRJkqTamXSSJEmSJElS7Uw6SZIkSZIkqXYmnSRJkiRJklQ7k06SJEmSJEmqnUknSZIkSZIk1c6kkyRJkiRJkmpn0kmSJEmSJEm1M+kkSZIkSZKk2pl0kiRJkiRJUu1MOkmSJEmSJKl2Jp0kSZIkSZJUO5NOkiRJkiRJqp1JJ0mSJEmSJNXOpJMkSZIkSZJqZ9JJkiRJkiRJtTPpJEmSJEmSpNqZdJIkSZIkSVLtTDpJkiRJkiSpdiadJEmSJEmSVDuTTpIkSZIkSaqdSSdJkiRJkiTVzqSTJEmSJEmSardXvwOQJElSF0yuhQ2rYfN6WLYCxlfB2Mp+RyVJkkaIPZ0kSZKGzeRaWHc2bJ2EfQ8r03Vnl3ZJkqQeMekkSZI0bDashqVj5RZ7TN/fsLrfkUmSpBFi0kmSJGnYbF4PS5bv3LZkeWmXJEnqEZNOkiRJw2bZCti2aee2bZtKuyRJUo+YdJIkSRo246tKHaetk5A7pu+Pr+p3ZJIkaYSYdJIkSRo2YyvhmDNLHactG8v0mDO9ep0kSeqpvfodgCRJkrpgbKVJJkmS1Ff2dJIkSZIkSVLtTDpJkiRJkiSpdiadJEmSJEmSVDuTTpIkSZIkSaqdSSdJkiRJkiTVzqSTJEmSJEmSardXvwOQJKlxJtfChtWweT0sWwHjq7z0vCRJktQhezpJktRqci2sOxu2TsK+h5XpurNLuyRJkqS2mXSSJKnVhtWwdKzcYo/p+xtW9zsySZIkaaCYdJIkqdXm9bBk+c5tS5aXdkmSJEltM+kkSVKrZStg26ad27ZtKu2SJEmS2tbTpFNEjEfEFyJiXURcFRF/VrUfGBEXR8S11XSs5TmvjIjrIuLqiHhyS/ujI+KKat5bIiJ6+V4kSUNqfFWp47R1EnLH9P3xVf2OTJIkSRooi046RcSei3jaduAvM/MY4ETghRHxSOAVwCWZeRRwSfWYat6pwKOApwBva3ndfwfOAI6qbk9Z7HuRJOknxlbCMWeWOk5bNpbpMWd69TppUE2uhbVnwaXPL1MvCiBJUs/s1c5CVc+jU4EnAScABwN7RcSPgGuArwIfycyvzLeezLwFuKW6f09ErAMOBU4BTq4WOx/4IvDyqv2CzLwPuD4irgNOiIgbgP0z82tVfO8FngF8qp33I0nSvMZWmmSShsHU1SiXju18NUoTyZIk9cS8SaeIOBz4W0rCaRK4FHgXcAdwH3AAcDjTvZa+D/wD8L7MzDbW/bPA14EHVwkpMvOWiDi4WuzQ6jWnbKzatlX3Z7bP9jpnUHpEsWKF9TgkSZJGRuvVKGF6umG1SSdJknpgoZ5OVwAXAE/KzK/Ot2BE/BTwLMrQuMOA182z7H7AR4E/z8y75ynHNNuMnKd918bMc4BzACYmJuZNhEmqweTa8mV+8/pSeHl8lV/sJUn9sXl96eHUyqtRSpLUMwvVdHpEZv7hQgkngMz8YWa+IzMfBbxnruUiYgkl4fT+zFxdNd8aEYdU8w8BbqvaNwLjLU8/DLi5aj9slnZJ/TQ1jGHr5M7DGKyfIUnqB69GKUlSX82bdMrMRSVyMvMHs7VXV5h7N7AuM/+lZdaFwGnV/dOAj7e0nxoRe0fEEZSC4d+ohuLdExEnVut8bstzJPVL6zCG2GP6/obVCz9XkqS6eTVKSZL6qq1C4rOJiKcCT6AMdftCZn6yjaf9IvB7wBURcXnV9irgn4APR8TpwHrg2QCZeVVEfBj4DuXKdy/MzPur570AOA/Yl1JA3CLiUr85jEGS1CRTV6NsHfZ95OkO+5YkqUcWlXSKiL8DfpcyTG4Z8L6I+LfMfPV8z6uubjdXAacnzvGc1wKvnaV9DXBsJ3FL6rJlK8ovyFOFWsFhDJKk/vJqlJIk9c28w+si4qFzzPoD4Bcy86WZ+SeUq8P9Ud3BSRowDmOQJEmSJFUWKiR+ZUT8VUTsPaP9R8DhLY8fVrVJGmVTwxiWjsGWjWV6zJn+wixJkiRJI2ih4XUnAGcD342Il2bmR6r2VwCfiYirKcPrjgCe17UoJQ0OhzFIkiRJklgg6ZSZ1wHPiIgnAW+KiBcDL87M1RHxFeDEatGvZ+atXY5VkiRJkiRJA2Kh4XUAZObngOOAD1F6OJ1TmvPC6mbCSZIkSZIkST/RVtIJIDN3ZOa/AY8A7gXWRcSZEbGka9FJkiRJkiRpIC109bqDI+J9EXFrRNwVERcDKzLzxcBjgScCV0XE03oRrCRJkiRJkgbDQj2dzgceDDyNkmT6HvDpiNgrM9dl5q8BfwG8oUpISZIkSZIkSQsmnX4B+OfM/EZmXkG5at1DKFerAyAzPwEcC1zUtSglSZIkSZI0UOa9eh3wDeClEXEnpY7TC4HbgBtaF8rM7cCbuhGgJEmSJEmSBs9CPZ1+D5gEPgN8DTgGeGpmbut2YJIkSZIkSRpc8/Z0yswfAKf2KBZJkiRJkiQNiYV6OkmSJEmSJEkdmzfpFBGXRMST2l1ZRBwcEX8XEX+6+6FJkiRJkiRpUC1USPwi4P0RcS+wGvgf4ErgDuA+4ADKleweDfwa8HhK/ae/7FK8kiRJkiRJGgAL1XR6Y0S8Hfht4LmUq9ftOWOxAG6hJKVempmXdyFOSZIkSZIkDZCFejqRmT8G3gm8MyKWAccBDwH2Ae4Ers7MG7oZpCRJkiRJkgbLgkmnVpm5Gfhal2KRJEmSJEnSkPDqdZIkSZIkSaqdSSdJkiRJkiTVzqSTJEmSJEmSamfSSZIkSZIkSbVrO+kUEY+LiP3mmLdfRDyuvrAkSZIkSZI0yDrp6fQF4JFzzHtENV+SJEmSJEnqKOkU88zbD9i8m7FIkiRJkiRpSOw138xqyNzJLU1/EBFPmbHYPsCvA1fUG5okSZIkSZIG1bxJJ+DngT+t7ifwbGD7jGW2At8FXlpvaJIkSZIkSRpU8yadMvMNwBsAIuJ64BmZ+e1eBCZJkiRJkqTBtVBPp5/IzCO6GYgkSZIkSZKGR9tJJ4CI2Ad4HHAYpZZTq8zMf68rMEmSJEmSJA2utpNOEfFLwGrgoDkWScCkkyRJkiRJktijg2XfAnwP+Flg78zcY8Ztz+6EKEmSJEmSpEHTyfC6RwCrLCQuSZIkSZKkhXTS02kt8JBuBSJJkiRJkqTh0UnS6QXASyLi8d0KRpIkSZIkScOhk+F1FwPLgM9HxDbg7pkLZObBdQUmSZIkSZKkwdVJ0unfKFeokyRJkiRJkubVdtIpM8/qYhySJEmSJEkaIp30dAIgIsaAY4Fx4FOZORkR+wBbM3NH3QFKkiRJkiRp8LRdSDwi9oqI1wMbgS8B/wEcUc3+KPC39YcnSZIkSZKkQdTJ1eteC/wh8CLgp4Fomfdx4Gk1xiVJkiRJkqQB1snwuucCr8jM90TEnjPmfY+SiJIkSZIkSZI66ul0ACW5NJulwMxElCRJkiRJkkZUJ0mnK4FT5pj3a8A3dz8cSZIkSZIkDYNOhtf9A/DRiNgX+E8ggeMj4pnAHwFP70J8kiRJ0uwm18KG1bB5PSxbAeOrYGxlv6OSJEmVtns6ZebHgd8GngR8ilJI/F3A84Dfy8zPdCNASZIkaReTa2Hd2bB1EvY9rEzXnV3aJUlSI3TS04nM/DDw4Yg4GjgIuBO4OjOzG8FJkiRJs9qwGpaOlRtMTzestreTJEkN0VHSaUpmXgNcU3MskiRJUns2ry89nFotWV7aJUlSI3SUdIqIhwJPAw4F9pkxOzPz5XUFJkmSJM1p2YoypG6qhxPAtk2lXZIkNULbSaeIOBU4n1LL6XZg64xFEjDpJElSpyyGLHVufFWp4QSlh9O2TSUJdeTp/Y1LkiT9RNuFxIHXAh8FDsrMQzPziBm3n+5SjJIkDS+LIUuLM7YSjjmz9HTasrFMjznThK0kSQ3SyfC6nwLenZl3dysYSZJGjsWQpcUbW+nnRJKkBusk6bQaOBm4pDuhSBoZDiWSplkMWZIkSUOqk6TTi4B3R8S7gM8Dd81cIDMvqikuScNqaijR0rGdhxI5JEKjymLIkiRJGlKdJJ2OBk4AjgCeP8v8BPasIyhJQ8yhRNLOLIYsSZKkIdVJ0uk9wN3ArwPXsevV6yRpYQ4lknY2VQy5dcjpkaebhJUkSdLA67Sn06rM/Ey3gpE0AhxKJO3KYsiSJEkaQnt0sOw3AM8KJe2e8VUl6bR1EnLH9P3xVf2OTJIkSZJUo056Ov0FcF5EbGHuQuKba4pL0rByKJEkSZIkjYROkk6XVdPz51nGQuKSFuZQImlxJtfunLAdX+VnSZIkSY3VSdLp+ZQr1EmSpF6bXFuucrd0rBTj3zpZHh9zpoknSZIkNVLbSafMPK+LcUiSpPlsWF0STlNF+KemG1abdJIkSVIjtV1IPCK+HxHHzTHv2Ij4fhvrODcibouIK1vazoqImyLi8ur21JZ5r4yI6yLi6oh4ckv7oyPiimreWyIi2n0fkiQNpM3rYcnynduWLC/tkiRJUgN1cvW6w4G955i3DDisjXWcBzxllvY3Zebx1e0igIh4JHAq8KjqOW+LiKmaUf8OnAEcVd1mW6ckScNj2QrYtmnntm2bSrskSZLUQPMmnSJi/4hYERFT32gfMvW45XY0JTl000IvlplfBu5sM7ZTgAsy877MvB64DjghIg4B9s/Mr2VmAu8FntHmOiVJGkzjq0odp62TkDum74+v6ndkkiRJ0qwW6un0EuAG4HpKEfGPVfdbb+uAPwfeshtxvCgi1lbD76oiFRwKbGhZZmPVdmh1f2b7rCLijIhYExFrbr/99t0IUZKkPhpbWYqGLx2DLRvL1CLikiRJarCFCol/AFgDBHAhcCZw9YxltgJXZ+Zii0r8O/D3lKTW3wNvpFwpb7Y6TTlP+6wy8xzgHICJiQmvvidJGlxjK00ySZIkaWDMm3TKzGuBawEi4gnAZZn5ozoDyMxbp+5HxDuBT1QPNwLjLYseBtxctR82S7skSZIkSZIaopNC4uuAB009iOKMiHhzRDxtsQFUNZqmPBOYurLdhcCpEbF3RBxBKRj+jcy8BbgnIk6srlr3XODji319SZIkSZIk1W+h4XWtzqMU835x9fg1wKuqthdFxB9k5nnzrSAiPgicDBwUERuBvwVOjojjKUPkbgD+CCAzr4qIDwPfAbYDL8zM+6tVvaCKZ1/gU9VNkiRJkiRJDRHlAnBtLBjxA+CPM/O/ImIP4FbgDZn5+oh4DXBKZh7fvVB338TERK5Zs6bfYUiSJEmSJA2NiLgsMydmtncyvG458MPq/qOBA4H3V48/Dzx8tyKUJEmSJEnS0Ogk6bQReGR1/9eB72bmTdXj5cC9dQYmSZIkSZKkwdVJTadzgddHxJMoSadXtsw7kVJoXJIkSbOZXAsbVsPm9bBsBYyvgrGV/Y5KkiSpa9ru6ZSZrwP+FPhBNX1Ly+wDgXfVG5okSdKQmFwL686GrZOw72Fluu7s0i5JkjSkOunpRGa+F3jvLO1/XFtEkiRJw2bDalg6Vm4wPd2w2t5OkiRpaHVS04mI2DsiXhAR746Iz0bEUVX7cyLimO6EKEmSNOA2r4cly3duW7K8tEuSJA2ptns6RcTRwMWUouGXAScDD6xmP5ZS5+m5NccnSZI0+JatKEPqpno4AWzbVNoHgfWoJEnSInTS0+ktwHrgcODJQLTM+xLwS/WFJUmSNETGV5Wk09ZJyB3T98dX9TuyhVmPSpIkLVInNZ0eCzw7M++KiD1nzLsVOKS+sCQ1jr9ya1i4L6sfxlbCMWfuvO8defpg7HvWo5IkSYvUSdLpXmDfOeYdCty129FIaqapX7mXju38K/cxZ3rCocHivqx+Gls5mPvZ5vXl89LKelSSJKkNnQyvuxh4VUS0VsHMiNgb+FPgolojk9Qcrb9yxx7T9zes7ndkUmfcl6XOLVtR6k+1GqR6VJIkqW86STq9FHgQcB3wH0ACfwNcATwU+Kvao5PUDF51ScPCfVnq3CDXo5IkSX3VdtIpMzcAxwFvpxQT/x6ljtN/Ao/OzB90I0BJDeCv3BoW7stS56bqUS0dgy0by9QhqZIkqQ2d1HQiMyeBv65ukkbF+KpS9wZKr5Btm8qv3Eee3t+4pE65L0uLM6j1qCRJUl+13dMpIs6PiF+LiI4SVZKGgL9ya1i4L0uSJEk900kC6VHAJ4E7I+JjwAeBL2RmdiUySc3ir9waFu7LkiRJUk90UtNpAjgSeCMwAXwOuCUi3hoRj+1SfJIkSZIkSRpAnVy9jsy8PjNfl5k/CxwDvA04GfhiRHjpH0mSJEmSJAEdJp1aZebVwLnAe4AfAIfWFZQkSZIkSZIGW8dFwSPiIcCzgecAJwKTwH8BF9QamSRJkiRJkgZW20mniPhjSqLpl4AfAx8H/hH4bGZu7054kiRJkiRJGkSd9HQ6G/gEpZfTpzLzvu6EJEmSJEmSpEHXSdLp4Mzc3LVIJGnUTK6FDath83pYtgLGV8HYyn5HJUmSJEm1aLuQeGvCKSL2iohlM2/dCVGShtDkWlh3NmydhH0PK9N1Z5d2SZIkSRoCbSedImL/iHhrRNwM3AvcM8tNktSODath6Vi5xR7T9zes7ndkkiRJklSLTobXvQP4DeBdwHeArV2JSJJGweb1pYdTqyXLS7skSZIkDYFOkk5PBl6Sme/qVjCSNDKWrShD6paOTbdt21TaJUmSJGkItD28DvgxsLFbgUjSSBlfVZJOWychd0zfH1/V78gkSZIkqRadJJ3eCPxJRHTyHEnSbMZWwjFnlp5OWzaW6TFnevU6SZIkSUOjk+F1hwLHAVdHxBeAu2bMz8x8eV2BSdLQG1tpkkmSJEnS0Ook6fQsYEf1nF+ZZX4CJp0kSZIkSZLUftIpM4/oZiCSJEmSJEkaHtZnkiRJkiRJUu3m7ekUEU/tZGWZedHuhSNJkiRJkqRhsNDwuk9QajVFG+tKYM/djkiSJEmSJEkDb6Gkk3WcJEmSJEmS1LF5k06ZeWOvApEkSZIkSdLwaPvqdZIG0ORa2LAaNq+HZStgfBWMrex3VJIkSZKkEeDV66RhNbkW1p0NWydh38PKdN3ZpV2SJEmSpC4z6SQNqw2rYelYucUe0/c3rO53ZJIkSZKkEWDSSRpWm9fDkuU7ty1ZXtolSZIkSeoyk07SsFq2ArZt2rlt26bSLkmSJElSl3VcSDwijgYOA/aZOS8zL6ojKEk1GF9VajhB6eG0bVOp63Tk6Ytbn0XJJUmSJEkdaDvpFBGPBD4EPBKIWRZJYM+a4pK0u8ZWwjFn7pwoOvL0xSWKpoqSLx3buSj5MWeaeJIkSZIkzaqTnk7vAJYCq4DvAFu7EpGk+oytrCcp1FqUHKanG1abdJIkSZIkzaqTpNPPAqdm5ie6FYykhtq8vvRwamVRckmSJEnSPDopJP49ZqnjJGkEWJRckiRJktShTno6/SXw+oj4ZmZ+v1sBSWqguouSS52ykL0kSZI0cDrp6fQ64FDguxFxTUR8Y+atSzFK6repouRLx2DLxjK1iLh6ZaqQ/dbJnQvZT67td2SSJEmS5tFJT6crq5ukUVRXUXKpUxaylyRJkgZS20mnzPz9bgYiSdKsLGQvSZIkDaROhtf9REQcFBFHRcRP1R2QJEk7sZC9JEmSNJA6SjpFxHMiYh1wK/Bd4LaIWBcRz+5KdJIkja8qdZy2TkLumL4/vqrfkUmSJEmaR9vD6yLit4D3A5+iFBW/FXgw8BzggojYMzMv6EqUkqTRNVXIvvXqdUeebj2nYeRVCiVJkoZKZGZ7C0ZcCXwlM/94lnlvB34pM4+tOb5aTUxM5Jo1a/odhiRJmmnqKoVLx0rNrm2bSo82r5QpSZLUeBFxWWZOzGzvZHjdw4GPzjHvo9V8SZKkzrVepTD2mL6/YXW/I5MkSdIidZJ0uhXYJWtVmajmS5IkdW7z+tLDqZVXKZQkSRpobdd0At4DnBURewIfoSSZDgaeDbyaUudJkiSpc8tWlOF0S8em27xKoSRJ0kDrJOn0d8AS4BXAa1ratwBnV/MlSZI6N76q1HSCnWs6HXl6f+OSJEnSorWddMrMHcBfRcTZwLHAIcAtwJWZOdml+CRJ0pRhvrqbVymUJEkaOp30dAKgSjD9f12IRZIkzaX16m77HlZ6Aa07e7iu7ja2cnjeiyRJkjoqJC5JkvrFq7tJkiRpwJh0kiRpEHh1N0mSJA2YniadIuLciLgtIq5saTswIi6OiGur6VjLvFdGxHURcXVEPLml/dERcUU17y0REb18H5Ik9dyyFaW4diuv7iZJkqQG63VPp/OAp8xoewVwSWYeBVxSPSYiHgmcCjyqes7bImLP6jn/DpwBHFXdZq5TkqThMr6q1HHaOgm5Y/r++Kp+RyZJkiTNqqdJp8z8MnDnjOZTgPOr++cDz2hpvyAz78vM64HrgBMi4hBg/8z8WmYm8N6W50iSNJymru62dAy2bCzTYSoiLkmSpKHT0dXrImICWAUcBuwzY3Zm5nMWEcODM/OWagW3RMTBVfuhwKUty22s2rZV92e2zxXzGZReUaxY4RAESdIA8+pukiRJGiBtJ50i4gXAW4EfAtcCW7sV1NRLztKW87TPKjPPAc4BmJiYmHM5SZIkSZIk1aeTnk5nAu8B/jgzt9cYw60RcUjVy+kQ4LaqfSMw3rLcYcDNVfths7RLkiRJkiSpITqp6XQw8MGaE04AFwKnVfdPAz7e0n5qROwdEUdQCoZ/oxqKd09EnFhdte65Lc+RJEmSJElSA3SSdPoU8PO782IR8UHga8AjImJjRJwO/BPwKxFxLfAr1WMy8yrgw8B3gE8DL8zM+6tVvQB4F6W4+Peq2CRJkiRJktQQUS4A18aCEY+n1Eb6AHAxcNfMZTLzO3UGV7eJiYlcs2ZNv8OQJEnSfCbXwobVsHk9LFsB46ssoi9JUoNFxGWZObFLewdJpx0tD2c+KShXr9tz8SF2n0knSZKkhptcC+vOhqVjsGQ5bNsEWyfhmDNNPEmS1FBzJZ06KST+hBrjkdQk/qIsSWqKDatLwmnpWHk8Nd2w2v9NkiQNmLaSThGxD/C7wLsz89LuhiSpp1p/Ud73sPJr8rqz/UVZktQfm9eX/0etliwv7ZIkaaC0VUg8M+8FTgX26W44knqu9Rfl2GP6/obV/Y5MkjSKlq0oQ+pabdtU2iVJ0kDp5Op1n8chdtLw2by+/ILcyl+UJUn9Mr6q9LrdOgm5Y/r++Kp+RyZJkjrUSU2nfwPeFREPAC4CbmVGQfGmX71O0iyWrShf5qdqZoC/KEuS+mdsZRni3Vpr8MjTHfItSdIA6iTp9Olq+hfVrTXhFNXjRl+9TtIsxleVGk6w81WCjjy9v3FJkkbX2EqTTJIkDQGvXieNOn9RliRJkiR1QdtJp8z8UjcDkdRH/qIsSZIkSapZ20mniFi20DKZuXn3wpEkSZIkSdIw6GR43Y+YUTh8FtZ0kiRJkiRJUkdJp+eza9LpQOBXgUcCf19XUJIkSZIkSRpsndR0Om+OWW+KiLcBj6olIklS/0yu3bmo/Pgq631JkiRJWpROejrNZzXwIeAlNa1PktRrk2th3dmwdAz2PQy2TpbHx5w5XIknE2uSRpHHPklSH+xR03oeA9xX07okqXw5XnsWXPr8Mp1c2994RsGG1SXhtHQMYo/p+xtW9zuy+kwl1rZO7pxYc/+SNMw89kmS+qSTq9e9fpbmpcAxwBOBN9cUk6RRNyo9bppm8/qyvVstWV7ah0VrYg2mpxtWu29JGl4e+yRJfdLJ8LrfZNdC4vcCG4EXA+fUFZSkEeeX4/5YtqIk+Ka2N8C2TaV9WIxCYk2SZvLYJ0nqk04KiR/exTgkaZpfjvtjfFXpUQZle2/bVJJQR57e37jqNAqJNUmayWOfJKlP2q7pFBF/ExEPnWPeIRHxN/WFJWmkLVtRvgy3GqQvx4Naj2psZRnCuHQMtmws02Eb0ji+qpx4bZ2E3DF9f3xVvyOTpO7x2CdJ6pPInDlibo4FI+4HTsrMb8wy79HANzJzz5rjq9XExESuWbOm32FIWkhrTafWHjeDkAAZ5NhHhVdwkjSKPPZJkrooIi7LzImZ7Z3UdAp2rek05TBgcjGBSdIupnrctH45PvL0wfhybD2q5htb6d9C0ujx2CdJ6oN5k04RcRpwWvUwgX+PiLtnLLYP8DPAZ+sPT9LIGtQvx9ajkjRo7AEjSZK6ZKGaTpuBH1a3ADa1PJ66XQ+8Hjije2FK0oAY9HpUkkbL1JDgrZMlYb51sjwelFp0kiSp0ebt6ZSZ/wn8J0BEvAf4+8z8fi8Ck6SBNApXgJM0PBwSLEmSuqjtmk6Z+fsAERGUGk7jwLcz88ddik2SBs8g16OStHhNHKLWTkwOCZYkSV3USSFxIuJPgFcDD6HUeHoM8M2IWA18OTPfXHuEkjRoel2Pqoknu9Ioab1qZesQtX5etbLdmJatKPOmejiBQ4IlSVJtFqrp9BMR8VLgX4B3Ar9MqfE05YvAc2qNTJK0MOuxSP3XOkQt9pi+v2F182MaX1WOG1snIXdM3x9f1Z+4JUnSUGk76QS8EPibzPxb4P+bMe9q4OjaopIktaeJJ7vSqNm8vgxJa9XvIWrtxjQ1JHjpGGzZWKb97KElSZKGSifD6x4CXDbHvB3APrsfjiSpI9ZjkfqviUPUOomp10OCJUnSyOikp9N1wOPnmPc44Du7H44kqSPLVpQTyVb9PtlV5ybXwtqz4NLnl6nDIwdLE4eoNTEmSZI0cjpJOr0ZeEVEvBo4qmo7OCJOB/4CeFPNsUmSFuKJ5eCzLtfga+IQtSbGJEmSRk5kZvsLl2LifwMsY7qQ+GbgNZn5hvrDq9fExESuWbOm32FIUr28et1gW3vWrsOgph6vPKtPQUkaSf4/kSQtUkRclpkTM9s7qelEZr4hIt4OnAQcBNwJfC0zN83/TElS11iPZbBZl2uaJ7xS/0z1ulw6tnOvS3vISZJ2QyfD6wDIzHsy87OZ+YHM/HRmboqIJ0TEp7oRoCRJQ826XIXDDKX+8mqokqQuWDDpFBEHRMSpEfHSiHhWRCxpmffsiFgDXAIc0c1AJUkaStblKjzhlfpr8/rSy7LVqPa6lCTVZt7hdRHxM8BngQe3NH8zIv4v8AHKMLurgN8BPtStICWp6xzWo36ZKvjcuv8defrw7X8LfcYcZij117IVu9aXG8Vel5KkWi1U0+kfgbuBZwDfBh4G/Cvwv8DewHMz833dDFCSus46Fuq3Ya/L1c5nzBNeqb/GV5XPJZSE77ZN5TN55On9jatu/sgkST21UNJpAvizzPx69fjqiHgBcC1whgknSUOhdVgPTE83rPaLaLf148u/Jxy9185nbFROeFUvP8/1GVsJhz4drnkrbLkJ9j0Ujn7RcG1Pf2SSpJ5bqKbTg4EbZrRNPf523cFIUl9Yx6I/+lE42mLV/dHOZ2xqmOHSMdiysUw9EdR8/DzXa3It3HQhjB0HD/utMr3pwuHantaOk6SeW6inE0DO0b69zkAkqW8c1tMf/ehh1tRebcPeW6Pdz9iwDzNUvZr6eR5U/dqevTz+WTtOknpuwavXAZ+JiNumbsAtVfslre3VPEkaPF49rD/60cOsib3aRqG3hp8xdUMTP8+DrB/bs9fHv2UrSsK7lT8ySVJXLdTT6TU9iUKS+mlUrh7WNP3oYdbEXm2j0FvDz5i6oYmf50HWj+3Z6+OfteMkqefmTTplpkknSaPBYT29148v/0084RiV4R6D/Bkb9uGPg6qJn+dB1o/t2evjnwlwSeq5dobXSZJUv34Ujm5isWqHezTbKAx/HFRN/DwPsn5sz34c/8ZWwsqz4MRzy9T9RZK6qp1C4pJUL3sNaEo/er80rcdN3b0L/HzVaxSGPw6ypn2eB12vt6e91SRp6NnTSVJv2WtA2lmdvQua/PmaXAtrz4JLn1+mTYipHRarlrrH3mqSNPTs6SSpt+w1IO2qrt4FTf18TSXDlo7tnAwbhJPLZSvg7mtgy82lF8aS5bDvQ2H/o/sdmTQc7K0mSUPNpJOk3hqVoslSPzT189XUZFg7lh8L178X9tofluwPW+8q23N8Vb8jk/pnkIfxDnLskjSAHF4nqbcsmix1T1M/X4M8RG3TlXDQSbD0ANh+T5kedFJpl0ZRk4fxLmSQY5ekAWXSSVJvja8qX/K2TkLumL5vrwFp9zX189XUZFg7Nq+H/Y6EB58Mh51SpvsdORgJM6kbWnsuxh7T9zes7ndkCxvk2CVpQJl0ktSeuooAWzRU6p6mfr6amgxrxyAnzKRuGOSei4McuyQNKGs6SVpY3UWALRoqdU8TP19TybDWOipHnt68OGfjJd2lnS1bUT4DU7XZYHASsYMcuyQNKJNOkhY2yEWAJTVDE5Nh7RjkhJnUDYOciB3k2CVpQJl0krSwpl4RS4PPqwhpEAxqwkzqhkFOxA5y7JI0oEw6SVqY3dHVDXUP25Qk7Z52fwgY5ETsIMcuSQPIQuKSFjbIRYDVXJ1cRaiuQvaSpNlN/RCwdXLnHwI83kqSdoNJJ0kLa+oVsTTY2r2KkCdCkjS/OhLznfwQIElSmxxeJ6k9dkdX3dodtmkhe0maW11Dla3fKEnqAns6SZL6o91hm+32iJKkUVRXD6VlK0riv5X1GyVJu8mkkySpP9odtumJkCTNra7EvPUbpcWz9qQ0J4fXSRps7V5pR83UzrDN8VVlqAiUE6ltm8qJ0JGndz8+qak89mlKXVeYnfohoHW/OvJ09ytpIV6NV5pXZGa/Y+iZiYmJXLNmTb/DkFSX1n/yrcmIfv+T92Swfm5TaVpTj31TsflZ7a0m7w/SKFh71q6J36nHK8/qU1BS70XEZZk5MbO9MT2dIuIG4B7gfmB7Zk5ExIHAh4DDgRuA38zMyWr5VwKnV8u/ODM/04ewJfVTEwtM+2tXd1jIXprWxGMf1Hv8M3nVPnsoSf1lEX5pXo1JOlWekJl3tDx+BXBJZv5TRLyievzyiHgkcCrwKOChwOci4ujMvL/3IUvqmyb+k2/qyaCk4dHEYx/Ud/zrV/J+kBNdJual/qlriKs0pJpeSPwU4Pzq/vnAM1raL8jM+zLzeuA64ITehyepr+osMF1XAUivtCap25paXL+u419dV2PrxFSia+vkzokuiwFLWohF+KV5NSnplMBnI+KyiDijantwZt4CUE0PrtoPBTa0PHdj1baLiDgjItZExJrbb7+9S6FL6ou6/snXebLR1JNBqd+8sk99mnqCU9fxrx/J+3YTXe7HkmZq92q80ohqUtLpFzPz54BfA14YEY+bZ9mYpW3WiuiZeU5mTmTmxIMe9KA64pTUFHX9k6/zV/WmngxK/WQvkno19QSnruNfP5L37SS63I8lzWVsZSkafuK5Zdrv47HUII2p6ZSZN1fT2yLiY5ThcrdGxCGZeUtEHALcVi2+ERhvefphwM09DVhSM9RRx6LO+igWdJV2Za2z+vW6hk879Y7qOv6NryrJHNj5amxHnl7Pe5lNOzVZ3I8lSepYI5JOEfEAYI/MvKe6/6vA3wEXAqcB/1RNP1495ULgAxHxL5RC4kcB3+h54JKGQ90FIC3o2r5BLtw7yHq93Zta+Frt6aSwdx3Hv34k79tJdLkfS5LUsUYknYAHAx+LCCgxfSAzPx0R/wt8OCJOB9YDzwbIzKsi4sPAd4DtwAu9cp2kRevHr+rq3xWqht1CCaV+bPd+XNnHhGZ9+tHDp9fJ+3YSXV6hqn/8PEvSwIrMWUshDaWJiYlcs2ZNv8OQ1MQvj02MaditPWvXE7ipxyvP6lNQA641odSaQG1NKK09q/fbvZ24Bvn1ht2lzy8JymgpBZo7Sj2pE8/tfH2Derx1v+oPt7sGwaAe16QaRcRlmTkxs71JhcQljYKmFmK1AGTv9eMKVcOunaL4/djuvS58XefFAVRvYe+m/g9oR1MLuA87P89qukE+rkk90JThdZJGhYVYNcWhKtPq+oW0nZoz/druvRwuZe2detU5BHnQ/wdYs6/3/Dyr6Qb9uCZ1mUknSb3ll8dpTeyK3cuY6q6l1cTt2Y46ayy1k1AahRpmdSfWBnXfqkudhb03rweWwOQXy99kyXJ44CNg+wj+D6jTMO+j/kChpvO7rTQvh9dJ6q12h2lMri21Zy59fpkOUhfldmJvYlfsXsdU51CVJm7PdtU5dGR8VXnvWydLzZ2p++OrppcZhSFC7WyHdg3yvtVEsRRu/zLcvwX22r9Mb/9yae+3Qf2/M+z7aJ2fZ6kb6hyCLA0hk06SequdL4+D/AV6ci1c/iq4+VNw5zfL9PJX7Rp7E2tU9COmumppNXF7tqvOGktjK+HQp8Pkt+HGD5bpoU8froRSO+pMrA3yvlWXOo/J5UrF1f052vthkP/vDPs+OgqJcg02E6PSvBxeJ6m32hmmUffY+F4OO7jmbfCj78GS/WGv5bDj3vL4mrfBz799erkmdsWuO6ZebvdBHrJT59CRybVw04Uwdhwc/LiynpsuhP2Pnt72dQ7na7K6au808bPaa3Uek3fcV/bNu6+e/qyOHV/a+6nd99jEYWyjsI9aS0tNVucQ5Lrd+BG45q2w5SbY91A4+kXwsGf1OyqNGJNOknpvoS+PdX6B7vUJ9g8vhSUPhD33LY/33BfI0t6qiTUqlq2Au6+BLTdPnwzu+9CSsOhUr7d7LIXbvlQl+1qG7Bz8+Ppfq269LtI8yEndfmjiZ7XX6jwmT23PB5883bZ1EpYeslsh7rZ23mNTE7buo1qMYT9216mdbdXExOiNH4HLX1a+F+1zCGy9qzwGE0/qKYfXSWqeOsfG93zYQUDOaMqqvVU/umIvVK9k+bElObb1LtjrgWX6w0tLe6c2rIYd28vwrpv+u0x3bO/edm/qkJ121Dl0pJ2henUO5xvkIUntcthEvcfkpm7Pdt5jU4exja+Ce75XhnNv/K8yved7/d+maq5ROHbXZZC31TVvLQmnpQdUx6wDyuNr3trvyASDW0dwEUw6SWqeOk9KNq+H7ffCrV+EjR8v0+33dm/YwUEnwvZ7Sk8bsky331PaW/W6RkU7X5o2XQkHnVS+lGy/p0wPOqm0d/x6l5fntRYL3nRlae+GqSE7e+4L2+4u04Mf1/8hO+2qq7ZVOyfOA53U7QPrydR7TG7q9mznPdaZsG1XuyclUwn2nPFYms0gH7t7faI+yNtqy02lB3irJfuXdvXXICczF8HhdZKap86x8b0ednXUC2DzRrj3Nti6CfbcGx748NI+Uy+7Yrf2PGodOtc6pGrzetjvSHjgUdPPyx2LO6Haehewx87DDO+/r2rvgqYO2em1dobq1TmcbxRqyUAzh030UpPrldSlnffY62Fs7Q7n27Aa9vtpOPDR021bJxc/ZLbd2ByaNbgG9djdjyGug7qtoNRw2npX+RFxyra7S7v6q+5SBw1n0klSM9V1ktfusKu6vkCPrYTjXtu8L+OTl8OPvl+SP609j+7fPL1MnSdUSw+AH98E990OeT/EnrDHMtjv8N18I3OoM5EyyNo5ca4zgWAtmdFR1zG5qXWRYOH32OvjTLsnJb0+KW7y31DtGdRjdz9O1Ad1W0EpGj5Vw2nJ/iXhtP1uOPbV/Y1Lg53MXASTTpKGWztXSqr7C3QTe0W00/OozhOqfR4C8e1yP7Mk/GJ7ae+GUeiJ0a529r+69lGTfd3R614kvXy9Qf51t9fHmXZPSnp9UjzIf0MVg3rs7seJ+qBuK5guFt569bpjX20R8SYY5GTmIph0kjTc2hl2NQpfoJceAPfdWXo47bkP3H9vGTrX2uW61mGNAXvsA3sfPP162+62zsiwMdlXv173Iun16w36r7u9/FGh3ZOSdk+K60ouDvrfUIN77O7HifqgbqspD3uWSaYmGuRk5iKYdJI03No5qI/CF+ix42GvB8Dmm6Z7fD3w4TvXb4L6Tqja6WFWJ4d79E8Te/bVbZh7AvX69Ubs193d0u5JSTsnxXUeI/0bThvk2laDeOzu14n6IG4rNdugJzM7ZNJJ0nBrYnHYfpj6ojZ23M5f1Lp1Se1eF/Yehd5q6o9B7gnUzgnx5vXAEpj8YktC+hGwvUtJ9yb/utu0BEInJyULnRTXeYxs8t+wl/yxo/dG7ERdQ26EkpkmnSQNv6YVh+2HXn9Rq3ubLnQyOAq91dQfg9oTqN0T4l5f4bOpJ41NTSDUdVJS5zGyqX/DXvPHjv4YoRN1aViYdJKkfn2B7vWv6r38olbnNm3nZHAUequpPwa1J1C7J8TtXuGzTnUei+o6jg57AqHuY6Qn/v7YIUltMukkSdD7L9BN/VW9TnVt03ZOBkeht5r6o92eQHUlP+pK2LZ7Qtzr+mt1qvM42tQEQl37lcfI+vljhyS1xaSTJPXDoP+qXteJULs1ZxY6GXS4h7qlnZ5AdSeR60jYtntC3Ov6a3Wq8zjaxARCnfuVx8j6mciTpLaYdJKkfmjqr+rtqOtEqN31LFsBd18DW26e7omx70Nh/6N3Xp/DPZqtaUWa29VOT6AmJpHbPSEe5BPnOo+jTdwOde9XTRzWOMhM5ElSW0w6SRps7XzxbeKX4378ql7XtqrrRKjd9Sw/Fq5/bxnatGR/2HpXia9bV95rsibuy+0Y5OGk7fQEamISud0T4kE+ca7zONrE7dCP/ard/xOD+nmumz92SNKCTDpJGlztfPFt6pfjXv+qXue2qutEqN31bLoSDjoJNt9UttPSA+CAR5V2ntXZaw6ypu7L7WhiT6B2tfNZbeLQLGj/hHhQT5zrPo42bTv0er9q9xjT1M/zoCblJWnI7dHvACRp0Vq/+MYe0/c3rO5smX6Y+lV96Rhs2Vim3Uwe1Lmtlq0oJz6tFnMi1O56Nq+H/Y4sPU0OO6VM9ztyMIYi1qmp+3I7Nq8vSYFW/e4J1K52Pqvjq8oJ+tZJyB3T90exN14v9fo42mu93q/aPcY08fM8lTDbOrlzwmxybf9ikiQB9nSS1FQ3fgSueStsuQn2PRSOfhE8bEavlnZ6yjRx2Es/1Lmt6upd0O56mtqLpNeaOtSmHYP+N1yoB0wTh2aNiqb1TqpTr/erdo8xTfw8N7X3Vd3szSVpAJl0ktQ8N34ELn9ZqeGzzyGlhs/lLyvzWhNP7XzxbeKXY+j9UKk6t1VdJ0LtrqeJBX6h91/++zHU5vJXwX23l6LZd10FP1wDx/9j5++zqX/DOg1z8kOdqfPY0Mv9qt1jTBM/z6PwA9MgD7GWNNIcXiepea55a0k4LT2g6uJ/QHl8zVt3Xq6doQdNHfbS66FSdW+rsZWw8iw48dwy3Z0TqoXW08QhNP0YytHrffmat8GPvlfu71UNpfnR90p7p5r4N+zE5FpYexZc+vwyHcUhO26D9gzyMK92jzFN/DzXNey7yQZ5iLWkkRaZ2e8YemZiYiLXrFnT7zAkLeTCo0oPp2jJi+cOuPcWePq1Oy87qFevu/T55YRk5nvcsrEkYLphULdVE609a9ceAVOPV57Vvdft5d/nouNhj6Ww57Lptvs3w46t8NTLu/OaTdTau6C1V0e/T7J7yW3QvrVn9efYUJdB/R8wCvtoP743SFIHIuKyzJyY2e7wOknNs++hZUjd0gOm27bdXdpnamfoQROHvfRj2N+gbqsm6tdQjp7+fQJm/i6VVfsoGZVaMfNxG7Rv0Id5Der/gFGoq9bUcgGStACTTpKa5+gXTddwWrJ/SThtvxuOfXV/46pTE2tiwOD+yt1ro/Dl/6AT4dYvQgTsuQ/cfy9sv6dcPbCfer2PDnoSoQ6b1wNLYPKLZT9fshwe+AjYvshtMMzHmVE4NjTVoCbM2tXU7w2StABrOklqnoc9C45/fenpdO8tZXr863e9et0ga2JNjEGuRdJrTa0VVqejXgAPfHi5v7WqlfLAh5f2fql7H22nTlFTa8X0ssZSLIXbvwz3byn19e7fUh7H0s7XNezHmVE4Nqg/mvi9QZLaYE8nSc30sGcNV5JpNk37VdYhNO0bhaEcYyvhuNc2q0fKhtWwYztMfnu6x82+D13cPtrulaCa2Lug11exipYhlTFHe7uG/TgzCscG9U/TvjdIUhtMOg2SYe6OLqn/HEbUmVH48t+09zh5Ofzo+7DnvtM9bjZdWQqcd6rd5EcTkwi9TtzsuA8OfhzcffV0sm/s+NLeqVE4zjTtcyNJUh+ZdBoUvf5VU9LoWbYC7r4Gtty8cy+S/Y/ud2RSsfUu2H4vbL0bdtwLe+xTrrC39a7O19VJ8qNpSYReJ26m6hS11vPaOglLD9l12YV+ILPmkSRJI8WaToOi9VfN2GP6/obV/Y5M0ky9rLVSp+XHwg8vLSfwez2wTH94aWnXrvrxdx7Ufas2AVtvLz2c9lhapltvZ1FX1GtqraZ29Dr2dusUtVOvyZpHkiSNFJNOg2Lz+vIrZqth644uDYN+FDquy6Yr4aCTSuH27feU6UEnlXbtrB9/52EvwNyWhH0Ohr32hR1by3Sfg0t7pwY5+dHr2NstYNzOD2QWQ5YkaaQ4vG5Q2B1dGgx11lrp9bDazethvyPhgUdNt+UOk9uz6UdB62EvwNyOpQfAfXfCPgfAnvvA/ffC9i2lvVNNrNXUrn7E3s4Qw3aH/TVtuKIkSeoak06DoolXz5G0qzprrfQ6yWByu339KGg9CgWYFzJ2POz1ANh803Sy74EP3zlR2tH6Bjj50cTYPYYMPi9aI0mqmcPrBoXd0aXZNa3GTZ21Vno9rHaQhxv12ta7gD1K0imiTNlj8QWt2/k7D3INorqMr4LYE8aOg0OfVqax5+L30aYdPwadx5DB5hBeSVIXmHQaJGMrYeVZcOK5ZWrCSaOuiV+Q6zzp6nWSweR2+5YeUP6+928Bskxzx+KGebX7d/aEvt59tInHj0HX7t/HZF8zedEaSVIXOLxO0uBqYo2bOmut9GNYbROH7DRRncO82v07D3INojrVtY828fgxDBb6+/S6Vp3a5xBeSVIXmHSSNLia+gW5rpNikwzNNZUoGjtu50TRYnoddfJ3NilYn6YeP4adyb7msiaXJKkLTDpJGlyj8AXZJEMz1Z0Q9O/ce6Nw/Ggik33N5UVrJEldYNJJ0uDyC7L6yUTRYPP40R/9SPZ5Rbb22LtWktQFkZn9jqFnJiYmcs2aNf0OQ1KdPJmQtFgeP+q30DZtrenUmuzrVk2nXr+eJEkjKiIuy8yJXdpNOkmSJGm3tZvg6WWyb+1Zu/asmnq88qzuvKYkSSNorqSTw+skSZK0+9otEt7LoanWkJIkqa/26HcAkiRJGgKb15eETqt+J3iWrSg9rlpZMF6SpJ4x6SRJkqTd18QEz/iqMpxu6yTkjun746v6F5MkSSPEpJMkSZJ2XxMTPFNXZFs6Bls2lqlFxCVJ6hlrOkmSJGn3TSV4WouEH3l6/xM8vawhJUmSdmLSSZIkSfUwwSNJklo4vE6SJEmSJEm1M+kkSZIkSZKk2pl0kiRJkiRJUu1MOkmSJEmSJKl2Jp0kSZIkSZJUO5NOkiRJkiRJqp1JJ0mSJEmSJNXOpJMkSZIkSZJqZ9JJkiRJkiRJtTPpJEmSJEmSpNqZdJIkSZIkSVLtTDpJkiRJkiSpdpGZ/Y6hZyLiduDGfsehnjsIuKPfQUg94L6uUeL+rlHi/q5R4v6uUTJM+/vDMvNBMxtHKumk0RQRazJzot9xSN3mvq5R4v6uUeL+rlHi/q5RMgr7u8PrJEmSJEmSVDuTTpIkSZIkSaqdSSeNgnP6HYDUI+7rGiXu7xol7u8aJe7vGiVDv79b00mSJEmSJEm1s6eTJEmSJEmSamfSSZIkSZIkSbUz6aShEhEviYirIuLKiPhgROwTEQdGxMURcW01Het3nNJiRMS5EXFbRFzZ0jbn/h0Rr4yI6yLi6oh4cn+ilhZnjv39DRHx3YhYGxEfi4gDWua5v2tgzba/t8w7MyIyIg5qaXN/18Caa3+PiD+t9umrIuL1Le3u7xpYc3yfOT4iLo2IyyNiTUSc0DJv6PZ3k04aGhFxKPBiYCIzjwX2BE4FXgFckplHAZdUj6VBdB7wlBlts+7fEfFIyv7/qOo5b4uIPXsXqrTbzmPX/f1i4NjMXAlcA7wS3N81FM5j1/2diBgHfgVY39Lm/q5Bdx4z9veIeAJwCrAyMx8FnF21u79r0J3Hrsf31wOvyczjgb+pHg/t/m7SScNmL2DfiNgLWAbcTPkHdn41/3zgGf0JTdo9mfll4M4ZzXPt36cAF2TmfZl5PXAdcALSgJhtf8/Mz2bm9urhpcBh1X33dw20OY7vAG8CXga0XvnH/V0DbY79/QXAP2XmfdUyt1Xt7u8aaHPs7wnsX91fTjlnhSHd3006aWhk5k2UX0XWA7cAmzLzs8CDM/OWaplbgIP7F6VUu7n270OBDS3LbazapGHxfOBT1X33dw2diHg6cFNmfnvGLPd3DaOjgcdGxNcj4ksR8Ziq3f1dw+jPgTdExAbK+esrq/ah3N9NOmloVLVsTgGOAB4KPCAifre/UUl9E7O05Sxt0sCJiL8CtgPvn2qaZTH3dw2siFgG/BVl2MUus2dpc3/XoNsLGANOBF4KfDgiAvd3DacXAC/JzHHgJcC7q/ah3N9NOmmYPAm4PjNvz8xtwGrgF4BbI+IQgGp62zzrkAbNXPv3RmC8ZbnDmO66Kw2siDgN+A3gdzJz6ouY+7uGzZGUH9G+HRE3UPbpb0bEQ3B/13DaCKzO4hvADuAg3N81nE6jnKsC/CfTQ+iGcn836aRhsh44MSKWVb+MPBFYB1xI+WBTTT/ep/ikbphr/74QODUi9o6II4CjgG/0IT6pNhHxFODlwNMzc3PLLPd3DZXMvCIzD87MwzPzcMqJyM9l5g9wf9dw+i/glwEi4mhgKXAH7u8aTjcDj6/u/zJwbXV/KPf3vfodgFSXzPx6RHwE+CZl2MW3gHOA/ShddE+nJKae3b8opcWLiA8CJwMHRcRG4G+Bf2KW/Tszr4qIDwPfoXweXpiZ9/clcGkR5tjfXwnsDVxcflvg0sz8Y/d3DbrZ9vfMfPdsy7q/a9DNcXw/Fzi3uqz8VuC0qjer+7sG2hz7+x8C/6+6+NW9wBkwvMf3mO6ZLkmSJEmSJNXD4XWSJEmSJEmqnUknSZIkSZIk1c6kkyRJkiRJkmpn0kmSJEmSJEm1M+kkSZIkSZKk2pl0kiRJfRcRZ0VEttx+EBGfiIiVXXzNMyLiGbO03xARZ3frddsVEY+OiMmI2L+GdZ0XEWvqiGuWdR9d/f0O6MK6JyLihxGxvO51S5Kk7jPpJEmSmmITcFJ1+3PgaODiiDiwS693BvCMWdqfCbylS6/ZiX8A3p6Zd9ewrr8HnlfDemZzNPC3wAF1rzgz1wCXAy+pe92SJKn7TDpJkqSm2J6Zl1a3C4DnAgcDT+llEJn5rcxc38vXnCkijqK873PrWF9mfi8zr6xjXX3wHuCPI2KvfgciSZI6Y9JJkiQ11ber6fhUQzX07kWtC1VDu+5oefy8armfiYiLI+LHEfHdiFjVsswXgUcDp7UM6XteNW+n4XVTQ9Mi4tcj4jsRsTkiPhkRB0bEwyPiC9VrrJk5HDAi9oiIV0TEdRFxX0RcExGntfHeTwPWZua1Les6uYrziRHx8eo1r42IX42IPSPiDRFxR0TcFBF/MSOOnYbXtbONZtsWM567X0ScDPx3Nev6qv2GlmVXRMQFEXFntd0+ExGPmLG+V1bb596IuDUiPh0RD2lZ5ELgQODJbWw3SZLUICadJElSU62optcv8vkfoCQsnglcC1wQEYdV8/4E+C5wEdND+j65QCx/B7yaMizvF4BzgAuq27OAvarXiJbn/Wv1nHOAXwc+BpwbEb+xQOxPBP5njnnvAL5Sva8bgY8AbwUeCPx29fiNEXHiAq8B82+jdnwTOLO6v4qyHZ8JUA2L/ArwCOCPgd8EHgB8LiL2rZZ5LvAq4F8oSaUXANdVywFQDS+8CnhSB3FJkqQGsJuyJElqjJYhVA+jJFIuBz6+yNW9KTPPrdZ7GXAr8BuUOknfiYgfA7dn5qVtrOtA4KTM/F61vpXAS4HTMvO9VVtQElf/B1gXEQ+nJFF+PzPPr9bzuYg4hFID6ROzvVC1np8F3jdHLP+RmW+olt1IScg8IjN/uWr7HPAcSvJnofc25zZa4HlASQhFxNXVw29l5g0ts19CSR4dn5l3Vq/xVeAG4PnAvwEnAJ/NzLe1PG/1LC/17WpZSZI0QOzpJEmSmuKngG3V7TpK4mVVZt63yPV9dupOZv4QuA3opBdPqxumEk6V66rp52dpO7SaPhHYAXwsIvaaugGXAMdHxJ5zvNYYsDdwxxzzL5kvjszcAXy/JY751LmNZnoScDFwd8t7vwe4DJiolrkceGpEvCYiTphnm9wBPGSOeZIkqaFMOkmSpKbYBDwGOBH4I2Ap8IGIWOz3lbtmPN4K7FPjuma2T7VNvcZBwJ6U97Wt5XYepbf5IXO81tTz50q2/eQ1M3O2OKZiaee9LvZ57TiI0uNq24zbE5iu03UuZXjdbwJfB26NiL+fJfl0X41xSZKkHnF4nSRJaortmTlV7PrrEbEFeC/wbOBDVft9lGRUqwN7FF+n7gS2A79I6fE0021zPO+H1fSALsTUqXtZ/Pa+k1Iv6u9nmXcP/KRX1puAN0XEOPA7wGuBm9h5iN8B1fokSdIAMekkSZKa6n3Ay6vbVNJpI3DM1AJVL6hfXuT66+zVM5vPU3o6Lc/Mi9t9UmbeFxHrgSO6Fln7dtrelV+Z8XhmD68pl1B6MF2VmVsWeqHM3AD8U0T8PvDIGbMPB65pJ2BJktQcJp0kSVIjZWZGxD8C74+IJ2bmJZSrv70wIr5FqVv0B8D+i3yJ7wJPjognU3oXXV/VNapFZl4dEW+nXBHu9cAaSmLmUcDRmfkH8zz9q8Cj64plN3wM+NeIeBXwv5Qr1D1qxjJThcT/KCIuADZn5hWUK9L9LvD5iPhXSu+lBwOPB76SmR+MiHdQejBdShmG+ATgKEqisdUE8M91vzlJktRd1nSSJElN9iHgWuBl1ePXAP8J/AOlNtLllLpAi/EPwDrgw5SEytN2I865vJAyvOy5wEWUmH8d+PICz1sNPD4i9u1CTJ04B3gz8GLKdtpK2W4/kZk3AmdSElJfBf67ar+DUp/ru5QhdJ8FXg8sB9ZWT/8a8DjgPZTt80zgDzPzv6bWHxE/CzyI2a9qJ0mSGiwys98xSJIkqUVELKUMbXthZv5nv+Ppp4h4HfCYzHxSv2ORJEmdMekkSZLUQBHxUuCUzPylfsfSLxHxAOBG4FmZ+cU+hyNJkjpkTSdJkqRmeiuwLCKWZ+amfgfTJyuAvzPhJEnSYLKnkyRJkiRJkmpnIXFJkiRJkiTVzqSTJEmSJEmSamfSSZIkSZIkSbUz6SRJkiRJkqTamXSSJEmSJElS7f5/xSYDbi94F1oAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1440x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(20,7))\n",
    "\n",
    "ax.scatter(top_movies_df['runtime_minutes'], top_movies_df['RoI'], c='orange', alpha=0.5)\n",
    "ax.set_xlabel('Runtime (minutes)', fontsize=15)\n",
    "ax.set_ylabel('Return on Investment (%)', fontsize=15)\n",
    "ax.set_title('Runtime vs Return on Investment', fontsize=20);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.096733990756916"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Evalulate the Pearson correlation coefficient between the 'runtime_minutes' and 'RoI' columns\n",
    "np.corrcoef(top_movies_df['runtime_minutes'], top_movies_df['RoI'])[0,1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the scatter plot above, we can see that there is no distinct relationship between the runtime and the RoI. Furthermore, looking at the pearson correlation coefficient, we also see that the correlation coefficient is closer to 0. This therefore suggests that the length of a film has not impact on its RoI."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2. **Production Budget**\n",
    "   * The production budget of the film is the amount of money that the studio invests into the making of the film. We are going to plot a scatter plot to visualize the relationship between the 'production_budget' and 'RoI'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1440x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(20,7))\n",
    "\n",
    "ax.scatter(top_movies_df['production_budget']*(10**(-6)), top_movies_df['RoI'], c='lightgreen', alpha=0.7)\n",
    "ax.set_xlabel('Production Budget (Million Dollars)', fontsize=15)\n",
    "ax.set_ylabel('Return on Investment (%)', fontsize=15)\n",
    "ax.set_title('Production Budget vs Return on Investment', fontsize=20);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.6014369562247763"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Evaluate the Pearson correlation coefficient between the 'production_budget' and 'RoI' columns\n",
    "np.corrcoef(top_movies_df['production_budget'], top_movies_df['RoI'])[0,1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the scatter plot above, we see that there is a negative correlation between the production budget and the RoI. However, this relationship is not linear. From 0 to 100 million dollars, the correlation is strongly negative. However, from 100 to 300 million dollars, there is no distict correlation between the return on investment and the budget. Looking at the Pearson correlation coefficient, we see that the correlation coefficient (-0.6) suggests a moderately negative relationship between the return on investment and the production budget. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3. **Worldwide Gross**\n",
    "    * The worldwide gross of the film is the amount of money that the film made worldwide. We are going to plot a scatter plot to visualize the relationship between the 'worldwide_gross' and 'RoI'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1440x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a scatter plot to visualize worldwide gross vs RoI\n",
    "fig, ax = plt.subplots(figsize=(20,7))\n",
    "\n",
    "ax.scatter(top_movies_df['worldwide_gross']*(10**(-6)), top_movies_df['RoI'], c='orange', alpha=0.5)\n",
    "ax.set_xlabel('Worldwide Gross (Million Dollars)', fontsize=15)\n",
    "ax.set_ylabel('Return on Investment (%)', fontsize=15)\n",
    "ax.set_title('Worldwide Gross vs Return on Investment', fontsize=20);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.1511294766315754"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We can also look at the Pearson correlation coefficient between the 'worldwide_gross' and 'RoI' columns\n",
    "np.corrcoef(top_movies_df['worldwide_gross'], top_movies_df['RoI'])[0,1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the scatter plot above, there is no distinct relationship between the worldwide gross and the RoI. Looking at the Pearson correlation coefficient, we see that the correlation coefficient (0.15) is closer to 0 therefore suggesting that the worldwide gross of a film has no impact on its RoI."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "4. **Release Date**\n",
    "    * The release time of the film is the time that the film was released. We shall be looking to see if there is a relationship between the release date and the RoI.\n",
    "\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Month**\n",
    "* We are first going to be focusing on the month that the films were released and plot a bar plot to visualize the relationship between the month of release and the average RoI."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "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>movie_id</th>\n",
       "      <th>release_date</th>\n",
       "      <th>movie</th>\n",
       "      <th>production_budget</th>\n",
       "      <th>worldwide_gross</th>\n",
       "      <th>RoI</th>\n",
       "      <th>runtime_minutes</th>\n",
       "      <th>genres</th>\n",
       "      <th>averagerating</th>\n",
       "      <th>release_month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>tt2582846</td>\n",
       "      <td>2014-06-06</td>\n",
       "      <td>The Fault in Our Stars</td>\n",
       "      <td>12000000</td>\n",
       "      <td>307166834</td>\n",
       "      <td>2559.723617</td>\n",
       "      <td>126.0</td>\n",
       "      <td>Drama,Romance</td>\n",
       "      <td>7.7</td>\n",
       "      <td>June</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>tt3783958</td>\n",
       "      <td>2016-12-09</td>\n",
       "      <td>La La Land</td>\n",
       "      <td>20000000</td>\n",
       "      <td>426351163</td>\n",
       "      <td>2131.755815</td>\n",
       "      <td>128.0</td>\n",
       "      <td>Comedy,Drama,Music</td>\n",
       "      <td>8.0</td>\n",
       "      <td>December</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>tt5140878</td>\n",
       "      <td>2017-08-11</td>\n",
       "      <td>Annabelle: Creation</td>\n",
       "      <td>15000000</td>\n",
       "      <td>305384865</td>\n",
       "      <td>2035.899100</td>\n",
       "      <td>109.0</td>\n",
       "      <td>Horror,Mystery,Thriller</td>\n",
       "      <td>6.5</td>\n",
       "      <td>August</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>tt1396484</td>\n",
       "      <td>2017-09-08</td>\n",
       "      <td>It</td>\n",
       "      <td>35000000</td>\n",
       "      <td>697457969</td>\n",
       "      <td>1992.737054</td>\n",
       "      <td>135.0</td>\n",
       "      <td>Horror,Thriller</td>\n",
       "      <td>7.4</td>\n",
       "      <td>September</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>tt6644200</td>\n",
       "      <td>2018-04-06</td>\n",
       "      <td>A Quiet Place</td>\n",
       "      <td>17000000</td>\n",
       "      <td>334522294</td>\n",
       "      <td>1967.778200</td>\n",
       "      <td>90.0</td>\n",
       "      <td>Drama,Horror,Sci-Fi</td>\n",
       "      <td>7.6</td>\n",
       "      <td>April</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    movie_id release_date                   movie  production_budget  \\\n",
       "0  tt2582846   2014-06-06  The Fault in Our Stars           12000000   \n",
       "1  tt3783958   2016-12-09              La La Land           20000000   \n",
       "2  tt5140878   2017-08-11     Annabelle: Creation           15000000   \n",
       "3  tt1396484   2017-09-08                      It           35000000   \n",
       "4  tt6644200   2018-04-06           A Quiet Place           17000000   \n",
       "\n",
       "   worldwide_gross          RoI  runtime_minutes                   genres  \\\n",
       "0        307166834  2559.723617            126.0            Drama,Romance   \n",
       "1        426351163  2131.755815            128.0       Comedy,Drama,Music   \n",
       "2        305384865  2035.899100            109.0  Horror,Mystery,Thriller   \n",
       "3        697457969  1992.737054            135.0          Horror,Thriller   \n",
       "4        334522294  1967.778200             90.0      Drama,Horror,Sci-Fi   \n",
       "\n",
       "   averagerating release_month  \n",
       "0            7.7          June  \n",
       "1            8.0      December  \n",
       "2            6.5        August  \n",
       "3            7.4     September  \n",
       "4            7.6         April  "
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a new column that contains the month that the movies were released\n",
    "release_time_df = top_movies_df.copy()\n",
    "release_time_df['release_month'] = release_time_df[\"release_date\"].dt.strftime('%B').astype(str)\n",
    "\n",
    "release_time_df.head() # Preview the updated 'release_time_df' DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1440x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a plot that shows average return on investment (RoI) by month\n",
    "fig, ax = plt.subplots(figsize=(20,7))\n",
    "release_time_df.groupby('release_month')['RoI'].mean().sort_values().plot(kind='bar', color='lightblue')\n",
    "ax.set_xlabel('Month', fontsize=15)\n",
    "plt.xticks(rotation=0)\n",
    "ax.set_ylabel('Average Return on Investment (%)', fontsize=15)\n",
    "ax.set_title('Average Return on Investment per Month of Release', fontsize=20);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see that films that are released in September and October (Fall Season) seem to be having a higher average return on investment than films that are released in the other months. However, we also need to take into account the distribution of the films released per month."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1440x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a plot that shows the number of films released each month\n",
    "fig, ax = plt.subplots(figsize=(20,7))\n",
    "release_time_df.groupby('release_month')['movie'].count().sort_values().plot(kind='bar', color='lightgreen')\n",
    "ax.set_xlabel('Month', fontsize=15)\n",
    "plt.xticks(rotation=0)\n",
    "ax.set_ylabel('Number of Films', fontsize=15)\n",
    "ax.set_title('Distribution Of Films Released per Month', fontsize=20);\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the 'Number of Films Released per Month' plot above, we can see that the distribution of the number of films released per month is not the most even. This may therefore suggest why months such as September and October which have fewer number of films classified under them are having a higher average return on investment than the others."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Season**\n",
    "* We are now going to look at the seasons that the films were released and plot a bar plot to visualize the average RoI per season."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "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>movie_id</th>\n",
       "      <th>release_date</th>\n",
       "      <th>movie</th>\n",
       "      <th>production_budget</th>\n",
       "      <th>worldwide_gross</th>\n",
       "      <th>RoI</th>\n",
       "      <th>runtime_minutes</th>\n",
       "      <th>genres</th>\n",
       "      <th>averagerating</th>\n",
       "      <th>release_month</th>\n",
       "      <th>release_season</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>tt2582846</td>\n",
       "      <td>2014-06-06</td>\n",
       "      <td>The Fault in Our Stars</td>\n",
       "      <td>12000000</td>\n",
       "      <td>307166834</td>\n",
       "      <td>2559.723617</td>\n",
       "      <td>126.0</td>\n",
       "      <td>Drama,Romance</td>\n",
       "      <td>7.7</td>\n",
       "      <td>June</td>\n",
       "      <td>Summer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>tt3783958</td>\n",
       "      <td>2016-12-09</td>\n",
       "      <td>La La Land</td>\n",
       "      <td>20000000</td>\n",
       "      <td>426351163</td>\n",
       "      <td>2131.755815</td>\n",
       "      <td>128.0</td>\n",
       "      <td>Comedy,Drama,Music</td>\n",
       "      <td>8.0</td>\n",
       "      <td>December</td>\n",
       "      <td>Winter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>tt5140878</td>\n",
       "      <td>2017-08-11</td>\n",
       "      <td>Annabelle: Creation</td>\n",
       "      <td>15000000</td>\n",
       "      <td>305384865</td>\n",
       "      <td>2035.899100</td>\n",
       "      <td>109.0</td>\n",
       "      <td>Horror,Mystery,Thriller</td>\n",
       "      <td>6.5</td>\n",
       "      <td>August</td>\n",
       "      <td>Summer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>tt1396484</td>\n",
       "      <td>2017-09-08</td>\n",
       "      <td>It</td>\n",
       "      <td>35000000</td>\n",
       "      <td>697457969</td>\n",
       "      <td>1992.737054</td>\n",
       "      <td>135.0</td>\n",
       "      <td>Horror,Thriller</td>\n",
       "      <td>7.4</td>\n",
       "      <td>September</td>\n",
       "      <td>Fall</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>tt6644200</td>\n",
       "      <td>2018-04-06</td>\n",
       "      <td>A Quiet Place</td>\n",
       "      <td>17000000</td>\n",
       "      <td>334522294</td>\n",
       "      <td>1967.778200</td>\n",
       "      <td>90.0</td>\n",
       "      <td>Drama,Horror,Sci-Fi</td>\n",
       "      <td>7.6</td>\n",
       "      <td>April</td>\n",
       "      <td>Spring</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    movie_id release_date                   movie  production_budget  \\\n",
       "0  tt2582846   2014-06-06  The Fault in Our Stars           12000000   \n",
       "1  tt3783958   2016-12-09              La La Land           20000000   \n",
       "2  tt5140878   2017-08-11     Annabelle: Creation           15000000   \n",
       "3  tt1396484   2017-09-08                      It           35000000   \n",
       "4  tt6644200   2018-04-06           A Quiet Place           17000000   \n",
       "\n",
       "   worldwide_gross          RoI  runtime_minutes                   genres  \\\n",
       "0        307166834  2559.723617            126.0            Drama,Romance   \n",
       "1        426351163  2131.755815            128.0       Comedy,Drama,Music   \n",
       "2        305384865  2035.899100            109.0  Horror,Mystery,Thriller   \n",
       "3        697457969  1992.737054            135.0          Horror,Thriller   \n",
       "4        334522294  1967.778200             90.0      Drama,Horror,Sci-Fi   \n",
       "\n",
       "   averagerating release_month release_season  \n",
       "0            7.7          June         Summer  \n",
       "1            8.0      December         Winter  \n",
       "2            6.5        August         Summer  \n",
       "3            7.4     September           Fall  \n",
       "4            7.6         April         Spring  "
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a dictionary that maps months to their season\n",
    "seasons = {'January':'Winter',\n",
    "           'February':'Winter',\n",
    "           'March':'Spring',\n",
    "           'April':'Spring',\n",
    "           'May':'Spring',\n",
    "           'June':'Summer',\n",
    "           'July':'Summer',\n",
    "           'August':'Summer',\n",
    "           'September':'Fall',\n",
    "           'October':'Fall',\n",
    "           'November':'Fall',\n",
    "           'December':'Winter'}\n",
    "\n",
    "# Create a new column that contains the season that the movie was released in\n",
    "release_time_df['release_season'] = release_time_df['release_month'].map(seasons)\n",
    "\n",
    "release_time_df.head() # Preview the updated 'release_time_df' DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1440x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a plot that shows average return on investment (RoI) by season\n",
    "fig, ax = plt.subplots(figsize=(20,7))\n",
    "\n",
    "release_time_df.groupby('release_season')['RoI'].mean().sort_values().plot(kind='bar', color='orange')\n",
    "ax.set_xlabel('Season', fontsize=15)\n",
    "plt.xticks(rotation=0)\n",
    "ax.set_ylabel('Average Return on Investment (%)', fontsize=15)\n",
    "ax.set_title('Average Return on Investment per Season of Release', fontsize=20);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "According to the bar plot above, we see that the average return on investment for films released in the Fall is the highest. It is closely followed by Winter, then Summer, and lastly Spring. However, before making any conclusions, we need to take into account the distribution of the films released per season."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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V0NZYt1Z63f2hqvq9DINY32Vm1lJ9e67SEvFixhZOvzpu596z3zE1/Orek1bY948yjk9WVdfO0JrokRm6r23J0PVutpafSfKZFXZ/wLLlANjBGBMJgHUxXnw8bXz6sq1Zt7tP7+7XdPeDM3SD+NkkPz+zyE+ycf8TfYtVxgo6ZLz/2My0pe4j115h+dXG7FgKYbam/o9l+Df6jivMu+O4rZV+CWo9vDPDYLbXyMXHhbmYqrpJhgvRC5L83w2qZSF19w+6++3d/QdJ/iJD96t7beM235Tk+Axdlx42M+sDSfYeX+9Lu+1zMlzQ36Sq5g1ubjDev3qFeWt2HevBZ7r77zO0skqGXylcsrXHtHSuX2K/40/G32zO7Sy30nHcIUNIPPu5/8DMvM221B11tk/i1ta39N4etzykTnLrJFdYa+XuPqW7/y1Da7MvJrn92PUxueh1O2T5emPrtpslOTfJ55bPB2DHIEQCYJuNLYn+PcOFw9cyXFivtfzlq+rQWjY4yzhOx9JF7uxYG9/OMEbKmhc3l9KeGVpKzNZxcIaBfb+b5LUzs5bGN/nfY4ugpeWvvXwbM5a6GM3TPWzJS8f7vxzHs1nazxUz/Ox6Mgy4u+7Glke/k6HV1dFVdYmBxavqxhkG671chsGJT96IWhbJeL6udP4ttY5ZPjbMpbHUUucZM+fXc8f7f6qqSwxgXlVXGruRTXlOhrDrpePF/PLt7D2OZbTk5PH+kGXL3TwXDT49O/3nq2rLCvtd6fXZ2mN6fYYA92HjZ3PWUbn0XU7/uKp+OtbO2B33L8ens8Ho/83QVfTpY5ey5fXuUlWHXMoalm/r1lX1yJXOtfH7cWlg/XfNzHp+hjGmnltVN1xhvd2rajZgOnm8P2TZcvsl+YcV1t+3qm6zQrlXSnKVDEHyUtfbfx1reUJV3WDZ8s9MctUk/7qVrUwBWCC6swGwVWYGlt0lQxeum2ToirZ7hpDl4XP8WtUVMnSlOLmqPpjkqxl+XvtuGQanPW72F7OSnJDkVkmOr6p3ZRi74xPd/YZ1OKR3JXnMeJH03gzdLR4yHt/jZ7uHdPcHx/3fMcmHqurtGS6S75vkzVm5hdIJGcYq+adx/JBzkpzd3c9fraDufllVHZbkwRkGHn9dhm4x98/QxeiVY0uADdHdb62qh2cIs15TVR/K8Nqcn+H9vkeGAOmvM1wYXhb8bZItVXVihovw8zO0GrpLhvP337d1B919UlW9PsOvkT06yYu6+4SqOjJDuPHFccywr2QY3+a6GVrTvCfJPSe2/dKqumWGgPB/qurNGQLffTKcU3fMEJb81rjKv2Q4b59XVXfO0OLkwAxd+F6T4TMy665JnlNV70vy+SSnZxgz67AMgeRfz9SyVcfU3edU1eMyjOPz7hoGNj81w/fOz2f4DK/Uam/K5zJ8vl6VIfg4LEMryP9M8v9m6v12VT0wQ6D8gao6IUPLrgszhMO3zdAFbo9LUcNy18jwPjy/qt6TYaDuczN8L90zQzexL2XmFyW7+/NV9agMn9fPVNXxSb6Q4TN6nQwtlM7IMJh1Mox19d4kDxjfr/dk+B67V5L/zkWDzi+55njcn8vQKuyUDGHQfcZ6/m7pxwm6++SqOiJDGPXRqnrluO87ZXidPp+LgjAAdkTd7ebm5ubmNnnLEGLM3s7L8GtPH8nwE9f3zDDOyUrrnjj8k/PT55fLMO7Gf2W4kD03w4XGBzJcxO6+bP0rJfnHJF/P8L/eneSYZbWduEbtx4zLbJmZtmVpOxmCq6XWDj/McIF1j1W2tdd4vKePr8GnkzxudnsrrPMHGS5YzxuXOXm112Zm+i4ZLvhPGmv64fha/+5Kr/Nar8FKxz/ne36tDBf/n8rQjebcDAHKsUluvcZ6Kx7TxL6OGms8amK5Nd/LeY97Zn+HrDDvkeO8R85Me3CSl2cIU87JMGbOp5P8eZJ95zzGpTpPXmOZm2YIJ76eZI+Z6bdP8soMF/jnZ/i8fDxDC6ODl23j5NX2keHC/43j+Xt+hgGVP5Tkz5LcaNmyB2VocXZ6kh+M599jVnq9M3yGnjOer2eM5/rJSV6V5Har1DL3MY3L3y1D4PHDDJ/V12cIRrbq/F46PzMMHv1nGQKs8zIMMv30JJdf4/17/ngOnDueA5/PEDjdf50+c1dJ8tAMQdInM3zHXpBhUPP3ZfgBgqussu4vjPv96ng8Z43n6IuS3GXZsvskecH4Hp2b4UcO/iLJFZefPxm+8/4kQ1fjb4zbPnV8HR+apFao5e5J3jK+T+dlCL6enWSvrfm+yAqfRTc3Nze3zb1V93r+Ki8AACyusTXZnbq7ppYFAC7OmEgAAAAATBIiAQAAADBJiAQAAADAJGMiAQAAADBpt80u4NK6+tWv3lu2bNnsMgAAAAB2Gh/5yEfO7O59V5q3w4ZIW7ZsyUknnbTZZQAAAADsNKrqq6vNMyYSAAAAAJOESAAAAABMEiIBAAAAMEmIBAAAAMAkIRIAAAAAk4RIAAAAAEwSIgEAAAAwSYgEAAAAwCQhEgAAAACThEgAAAAATBIiAQAAADBJiAQAAADAJCESAAAAAJOESAAAAABMEiIBAAAAMEmIBAAAAMCk3Ta7AAAAAFgvR3/n6M0ugR3E4Xsfvtkl7HC0RAIAAABgkhAJAAAAgElCJAAAAAAmCZEAAAAAmCREAgAAAGCSEAkAAACASUIkAAAAACYJkQAAAACYJEQCAAAAYJIQCQAAAIBJQiQAAAAAJgmRAAAAAJgkRAIAAABgkhAJAAAAgElCJAAAAAAmCZEAAAAAmCREAgAAAGCSEAkAAACASUIkAAAAACYJkQAAAACYJEQCAAAAYJIQCQAAAIBJQiQAAAAAJgmRAAAAAJgkRAIAAABgkhAJAAAAgElCJAAAAAAmCZEAAAAAmCREAgAAAGDSbtt7h1V1cpLvJ/lJkgu6++Cq2ifJK5JsSXJykgd393e2d20AAAAArGyzWiLdubtv1t0Hj8+PTHJCdx+Y5ITxOQAAAAALYlG6sx2W5Njx8bFJ7r95pQAAAACw3GaESJ3kLVX1kap63Dht/+4+NUnG+/1WWrGqHldVJ1XVSWecccZ2KhcAAACA7T4mUpJf7u5vVtV+Sd5aVZ+fd8XufnGSFyfJwQcf3BtVIAAAAAAXt91bInX3N8f705O8Nsmtk5xWVQckyXh/+vauCwAAAIDVbdcQqaquVFVXWXqc5O5JPp3kuCSPGBd7RJLXb8+6AAAAAFjb9u7Otn+S11bV0r5f1t3HV9WHk7yyqh6d5GtJHrSd6wIAAABgDds1ROruLye56QrTv53k0O1ZCwAAAADz24xfZwMAAABgByNEAgAAAGCSEAkAAACASUIkAAAAACYJkQAAAACYJEQCAAAAYJIQCQAAAIBJQiQAAAAAJgmRAAAAAJgkRAIAAABgkhAJAAAAgElCJAAAAAAmCZEAAAAAmCREAgAAAGCSEAkAAACASUIkAAAAACYJkQAAAACYJEQCAAAAYJIQCQAAAIBJQiQAAAAAJgmRAAAAAJi022YXAADAZdPR3zl6s0tgB3H43odvdgkAREskAAAAAOYgRAIAAABgkhAJAAAAgElCJAAAAAAmCZEAAAAAmCREAgAAAGCSEAkAAACASUIkAAAAACYJkQAAAACYJEQCAAAAYJIQCQAAAIBJQiQAAAAAJgmRAAAAAJgkRAIAAABgkhAJAAAAgElCJAAAAAAmCZEAAAAAmCREAgAAAGCSEAkAAACASUIkAAAAACYJkQAAAACYJEQCAAAAYJIQCQAAAIBJQiQAAAAAJgmRAAAAAJgkRAIAAABgkhAJAAAAgElCJAAAAAAmCZEAAAAAmCREAgAAAGCSEAkAAACASUIkAAAAACYJkQAAAACYJEQCAAAAYJIQCQAAAIBJmxIiVdWuVfWxqnrj+HyfqnprVX1xvN97M+oCAAAAYGWb1RLp8CSfm3l+ZJITuvvAJCeMzwEAAABYENs9RKqqayX5lSQvmZl8WJJjx8fHJrn/di4LAAAAgDVsRkuk5yV5UpILZ6bt392nJsl4v99KK1bV46rqpKo66YwzztjwQgEAAAAYbNcQqaruk+T07v7IpVm/u1/c3Qd398H77rvvOlcHAAAAwGp22877++Uk96uqeyfZI8lVq+pfk5xWVQd096lVdUCS07dzXQAAAACsYbu2ROrup3T3tbp7S5JfT/L27v6NJMclecS42COSvH571gUAAADA2jbr19mWe1aSu1XVF5PcbXwOAAAAwILY3t3Zfqq7T0xy4vj420kO3axaAAAAAFjborREAgAAAGCBCZEAAAAAmCREAgAAAGCSEAkAAACASUIkAAAAACYJkQAAAACYJEQCAAAAYJIQCQAAAIBJlzpEqqobVdX9q+oa61kQAAAAAItnrhCpql5UVS+cef6QJJ9K8pokn6+q221QfQAAAAAsgHlbIt0zybtmnj8zycuTXCPJm8fnAAAAAOyk5g2R9ktySpJU1YFJbpDk2d39rSQvTnLzjSkPAAAAgEUwb4h0VpL9x8d3TfKt7v70+LyS7LrehQEAAACwOHabc7n/SvKnVbV/kicleeXMvJ9PcvI61wUAAADAApm3JdITk3wgyW9lGBvpT2bm/WqS49e5LgAAAAAWyFwtkbr7u0ketcq8O6xrRQAAAAAsnHlbIgEAAABwGTZXS6Sq2j3JERm6rl0zyR7Ll+nu/da1MgAAAAAWxrwDa/9jkocneX2Styc5f8MqAgAAAGDhzBsiPSDJEd39wo0sBgAAAIDFNO+YSGcl+dpGFgIAAADA4pq3JdKfJnliVb2zu3+wkQUBsHiO/s7Rm10CO4jD9z58s0sAAGCDzBUidfexVXVQkq9V1UeSnH3JRfoh610cAAAAAIth3l9ne2KSP0ryrSRXSnK5jSwKAAAAgMUyb3e2I5P8XZLf7+7ewHoAAAAAWEDzDqxdSd4oQAIAAAC4bJo3RDomya9tYB0AAAAALLB5u7N9PckfVNXbkrw9Kw+s/Y/rWRgAAAAAi2PeEOk54/21ktxlhfmdRIgEAAAAsJOaK0Tq7nm7vQEAAACwExIOAQAAADBp1ZZIVXXQ1myouz+77eUAAAAAsIjW6s726QxjHU2pcbld16UiAAAAABbOWiHSnbdbFQAAAAAstFVDpO5+5/YsBAAAAIDFZWBtAAAAACatNbD26Unu0d0fq6ozMjE+Unfvt97FAQAAALAY1hoT6R+SnDbzeJ5BtgEAAADYCa0VIn0xyblJ0t1HbZdqAAAAAFhIa42J9P+S3GDpSVXtUlVfq6qf3/iyAAAAAFgka4VItcLzayXZfePKAQAAAGAR+XU2AAAAACYJkQAAAACYtNbA2knyhKo6dXy81L3t8Ko6bdly3d1PXt/SAAAAAFgUa4VIX0ty+2XTvprkjiss20mESAAAAAA7qVVDpO7esh3rAAAAAGCBGRMJAAAAgElTYyKxgzn6O0dvdgnsIA7f+/DNLgEAAIAdiJZIAAAAAEwSIgEAAAAwadUQqaquU1WX257FAAAAALCY1mqJ9JUkN0+Sqnp7Vd1o+5QEAAAAwKJZK0T6UZIrjo8PSXLVDa8GAAAAgIW01q+zfSzJ0VX11vH5E6rq1FWW7e5+8vqWBgAAAMCiWCtEemySv05yWJJOcmiS81ZZtpMIkQAAAAB2UquGSN39+ST3TZKqujDJ/bv7Q9urMAAAAAAWx1otkWZdL8lqXdkAAAAA2MnNFSJ191erareqekiS2yfZJ8lZSd6d5DXdfcE826mqPZK8K8nlx32/qrufXlX7JHlFki1JTk7y4O7+zlYeCwAAAAAbZK1fZ/upqtovyUlJXp7kV5Jcf7z/9yQfrqp959zfeUnu0t03TXKzJPesql9KcmSSE7r7wCQnjM8BAAAAWBBzhUhJnpPkaklu093X7+7bdvf1k9xmnP6ceTbSg3PGp5cbb51h8O5jx+nHJrn/nHUBAAAAsB3MGyLdO8mTu/vDsxPH50/J0CppLlW1a1V9PMnpSd7a3R9Msn93nzpu89Qk+62y7uOq6qSqOumMM86Yd5cAAAAAbKN5Q6TLJ/n+KvO+n2T3eXfY3T/p7psluVaSW1fVz2/Fui/u7oO7++B99523Bx0AAAAA22reEOkDSZ5cVVeanTg+f/I4f6t099lJTkxyzySnVdUB4zYPyNBKCQAAAIAFMdevsyV5YpJ3JDmlqt6S5LQMXc7ukaSSHDLPRsYBuH/c3WdX1RWS3DXJXyU5LskjkjxrvH/9VhwDAAAAABtsrhCpuz9eVQcm+cMkt0ryi0lOTfLCJM/p7jPn3N8BSY6tql0ztIJ6ZXe/saren+SVVfXoJF9L8qCtPA4AAAAANtC8LZEyBkVHbsvOuvuTSW6+wvRvJzl0W7YNAAAAwMaZd0wkAAAAAC7DhEgAAAAATBIiAQAAADBJiAQAAADApMkQqaouX1VPq6qbbo+CAAAAAFg8kyFSd5+X5GlJ9trwagAAAABYSPN2Z/tgkltuZCEAAAAALK7d5lzuSUleVlXnJ3lTktOS9OwC3f3Dda4NAAAAgAUxb4j0wfH+75Icvcoyu257OQAAAAAsonlDpEdlWcsjAAAAAC475gqRuvuYDa4DAAAAgAU2b0ukJElVHZRhgO1rJ3lpd3+rqm6Q5LTu/v5GFAgAAADA5psrRKqqKyd5aZIHJvnxuN7xSb6V5C+SfC3JH25QjQAAAABssl3mXO45SW6X5NAkV0lSM/PelOSe61wXAAAAAAtk3u5sD0hyeHe/o6qW/wrbV5Ncd33LAgAAAGCRzNsS6QpJvr3KvKsk+cn6lAMAAADAIpo3RPpwkv+1yrwHJnnf+pQDAAAAwCKatzvb/0nytqp6W5L/SNJJ7l1Vv58hRLrjBtUHAAAAwAKYqyVSd78nw6Dal0/y/AwDaz8jyfWT3LW7P7xhFQIAAACw6eZtiZTufm+SO1TVFZLsneTs7v7hhlUGAAAAwMKYd0ykWecm+XGSH61zLQAAAAAsqLlDpKq6d1W9L0OI9K0k51bV+6rqVzasOgAAAAAWwlwhUlU9PskbkpyT5PAkDxrvz0ly3DgfAAAAgJ3UvGMiPTXJi7v7t5dNf2FVvTDJ05K8aF0rAwAAAGBhzNud7WpJXrPKvFcn2Wd9ygEAAABgEc0bIr0jyZ1WmXenJO9an3IAAAAAWESrdmerqoNmnv5dkpdU1dWSvC7J6Un2S/KrSe6V5DEbWCMAAAAAm2ytMZE+naRnnleSx4+3Hp8vOT7JruteHQAAAAALYa0Q6c7brQoAAAAAFtqqIVJ3v3N7FgIAAADA4lqrJdKKqmq3JLsvn97dP1yXigAAAABYOHP9OltV7VlVL6iqU5Ocm+T7K9wAAAAA2EnN2xLpmCR3SvJPSb6U5PyNKggAAACAxTNviHRoksd398s3shgAAAAAFtNc3dmSfC2JMY8AAAAALqPmDZGelOT/VNV1NrIYAAAAABbTXN3ZuvtNVXXXJF+qqpOTnL3CMrde39IAAAAAWBRzhUhV9TdJjkjy4RhYGwAAAOAyZ96BtR+T5Gnd/ZcbWQwAAAAAi2neMZF+mOQjG1kIAAAAAItr3hDp6CSPq6rayGIAAAAAWEzzdme7epLbJPnvqjoxlxxYu7v7yetYFwAAAAALZN4Q6YFJLkhyuSR3W2F+JxEiAQAAAOyk5gqRuvt6G10IAAAAAItr3jGRAAAAALgMm6slUlX9ztQy3f2CbS8HAAAAgEU075hIz19jXo/3QiQAAACAndRc3dm6e5fltyT7JHlokk8kOWgjiwQAAABgc83bEukSuvvsJK+oqj2TvCjJIetUEwAAAAALZj0G1v5KkoPXYTsAAAAALKhtCpGq6oAkT8wQJAEAAACwk5r319nOyEUDaC/ZPclVkpyb5AHrXBcAAAAAC2TeMZH+IZcMkc5N8vUkx3f3t9e1KgAAAAAWylwhUncftcF1AAAAALDA1mNgbQAAAAB2cqu2RKqqt2/Fdrq7D12HegAAAABYQGt1Z5tnnKMDktwulxwvCQAAAICdyKohUnc/aLV5VXWdJE9Ocp8kZyZ57jw7q6prJ/mXJD+T5MIkL+7uo6tqnySvSLIlyclJHtzd35nvEAAAAADYaFs1JlJV3aCq/jnJF5PcL8lTkly3u/9yzk1ckOSJ3X3jJL+U5Her6qAkRyY5obsPTHLC+BwAAACABTFXiFRVN6mqlyX5XJI7Jzk8yc929/O6+0fz7qy7T+3uj46Pvz9u75pJDkty7LjYsUnuP/cRAAAAALDh1gyRquqWVfWaJJ9McvMkj0lyYHe/sLvP35YdV9WWcZsfTLJ/d5+aDEFTkv1WWedxVXVSVZ10xhlnbMvuAQAAANgKq4ZIVfVfST6U5HpJfr27b9zdx3b3T7Z1p1V15SSvTnJEd39v3vW6+8XdfXB3H7zvvvtuaxkAAAAAzGmtX2e7x3h/7ST/UFX/sNaGunvF1kPLVdXlMgRI/9bdrxknn1ZVB3T3qVV1QJLT59kWAAAAANvHWiHSM9Z7Z1VVSf45yee6+zkzs45L8ogkzxrvX7/e+wYAAADg0ls1ROrudQ+Rkvxykt9M8qmq+vg47akZwqNXVtWjk3wtyYM2YN8AAAAAXEprtURad939niS1yuxDt2ctAAAAAMxvzV9nAwAAAIBEiAQAAADAHIRIAAAAAEwSIgEAAAAwSYgEAAAAwCQhEgAAAACThEgAAAAATBIiAQAAADBJiAQAAADAJCESAAAAAJOESAAAAABMEiIBAAAAMEmIBAAAAMAkIRIAAAAAk4RIAAAAAEwSIgEAAAAwSYgEAAAAwCQhEgAAAACThEgAAAAATBIiAQAAADBJiAQAAADAJCESAAAAAJOESAAAAABMEiIBAAAAMEmIBAAAAMAkIRIAAAAAk4RIAAAAAEwSIgEAAAAwSYgEAAAAwCQhEgAAAACThEgAAAAATBIiAQAAADBJiAQAAADAJCESAAAAAJOESAAAAABMEiIBAAAAMEmIBAAAAMAkIRIAAAAAk4RIAAAAAEwSIgEAAAAwSYgEAAAAwCQhEgAAAACThEgAAAAATBIiAQAAADBJiAQAAADAJCESAAAAAJOESAAAAABMEiIBAAAAMEmIBAAAAMAkIRIAAAAAk4RIAAAAAEwSIgEAAAAwSYgEAAAAwCQhEgAAAACThEgAAAAATBIiAQAAADBJiAQAAADApO0aIlXVS6vq9Kr69My0farqrVX1xfF+7+1ZEwAAAADTtndLpGOS3HPZtCOTnNDdByY5YXwOAAAAwALZriFSd78ryVnLJh+W5Njx8bFJ7r89awIAAABg2iKMibR/d5+aJOP9fqstWFWPq6qTquqkM844Y7sVCAAAAHBZtwgh0ty6+8XdfXB3H7zvvvtudjkAAAAAlxmLECKdVlUHJMl4f/om1wMAAADAMosQIh2X5BHj40ckef0m1gIAAADACrZriFRVL0/y/iQ/V1Vfr6pHJ3lWkrtV1ReT3G18DgAAAMAC2W177qy7H7rKrEO3Zx0AAAAAbJ1F6M4GAAAAwIITIgEAAAAwSYgEAAAAwCQhEgAAAACThEgAAAAATBIiAQAAADBJiAQAAADAJCESAAAAAJOESAAAAABMEiIBAAAAMEmIBAAAAMAkIRIAAAAAk4RIAAAAAEwSIgEAAAAwSYgEAAAAwCQhEgAAAACThEgAAAAATBIiAQAAADBJiAQAAADAJCESAAAAAJOESAAAAABMEiIBAAAAMEmIBAAAAMAkIRIAAAAAk4RIAAAAAEwSIgEAAAAwSYgEAAAAwCQhEgAAAACThEgAAAAATBIiAQAAADBJiAQAAADAJCESAAAAAJOESAAAAABMEiIBAAAAMEmIBAAAAMAkIRIAAAAAk4RIAAAAAEwSIgEAAAAwSYgEAAAAwCQhEgAAAACThEgAAAAATBIiAQAAADBJiAQAAADAJCESAAAAAJOESAAAAABMEiIBAAAAMEmIBAAAAMAkIRIAAAAAk4RIAAAAAEwSIgEAAAAwSYgEAAAAwCQhEgAAAACThEgAAAAATBIiAQAAADBJiAQAAADAJCESAAAAAJOESAAAAABMWpgQqaruWVX/XVVfqqojN7seAAAAAC6yECFSVe2a5B+S3CvJQUkeWlUHbW5VAAAAACxZiBApya2TfKm7v9zd5yf59ySHbXJNAAAAAIyquze7hlTVA5Pcs7sfMz7/zSS36e7fW7bc45I8bnz6c0n+e7sWyo7q6knO3OwigJ2O7xZgI/huATaC7xa2xnW7e9+VZuy2vStZRa0w7RLpVne/OMmLN74cdiZVdVJ3H7zZdQA7F98twEbw3QJsBN8trJdF6c729STXnnl+rSTf3KRaAAAAAFhmUUKkDyc5sKquV1W7J/n1JMdtck0AAAAAjBaiO1t3X1BVv5fkzUl2TfLS7v7MJpfFzkMXSGAj+G4BNoLvFmAj+G5hXSzEwNoAAAAALLZF6c4GAAAAwAITIgEAAAAwSYjEwquq51bVETPP31xVL5l5/rdV9SdVdeTEdrZU1cM2sFRgJ1NVP6mqj8/ctqyx7Dnj/Zaq+vR2KxJYSFX1tKr6TFV9cvz+uM1WrHu/qb9rgJ3ftnyPwEZZiIG1YcL7kjwoyfOqapckV09y1Zn5t0tyRHd/cGI7W5I8LMnL5t1xVe3a3T/ZunKBnciPuvtmm10EsGOpqtsmuU+SW3T3eVV19SS7z7nubt19XPxSMVymbcv3yPZQVZVhjOULN7sWti8tkdgRvDdDUJQkN0ny6STfr6q9q+rySW6c5KZV9fwkqapjqurvqup9VfXlqnrguO6zktxhTPF/v6p2raq/rqoPj+n+48f1D6mqd1TVy5J8arseKbDQqurKVXVCVX20qj5VVYdtdk3AQjogyZndfV6SdPeZ3f3Nqjq5qv6qqj403m6Q/PRvl+dU1TuS/FVVPXLq75qq2qWqXjC2UnhjVb1p5m8eYMe31vfI1ZOkqg6uqhPHx0dV1bFV9ZZxmQdU1bPHv1eOr6rLjcudXFV/UVXvr6qTquoWY0+P/6mq31raeVX90cx10jPGaVuq6nNV9YIkH01y7e37krAIhEgsvO7+ZpILquo6GcKk9yf5YJLbJjk4ySeTnL9stQOS3D5Dev+scdqRSd7d3Tfr7ucmeXSS73b3rZLcKsljq+p647K3TvK07j5o444M2AFcYaYr22uTnJvkV7v7FknunORvx/+JA5j1liTXrqovjEHPnWbmfa+7b53k+UmeNzP9hknu2t1PXGF7K/1d84AMrax/IcljMvxdBOw81voeWc3PJvmVJIcl+dck7+juX0jyo3H6klO6+7ZJ3p3kmCQPTPJLSf40Sarq7kkOzHBNdLMkt6yqO47r/lySf+num3f3V7ftENkR6c7GjmKpNdLtkjwnyTXHx9/N0N1tudeNTSs/W1X7r7LNuyf5xZn/tdszw5fl+Uk+1N1fWcf6gR3Txbqzjf+L9xfjH1IXZvgu2j/JtzanPGARdfc5VXXLJHfIEDi/YmaMo5fP3D93ZrX/WKML/Up/19x+XOfCJN8aWzEBO4mJ75HV/Fd3/7iqPpVk1yTHj9M/lSF0XnLczPQrd/f3M/T0OLeq9spwnXT3JB8bl7tyhuukryX5and/YJsOjh2aEIkdxfsyhEa/kKE72ylJnpjke0lemuRqy5Y/b+bxaq0EKskTuvvNF5tYdUiSH2xzxcDO6OFJ9k1yy/GPtJOT7LG5JQGLaAyETkxy4nhB94ilWbOLzTxe62+Plf6u0QoSdnKrfI9ckIt6FC3/G2Sp69uFVfXj7l76jrkwF7/2P29m+uz3y9JyleQvu/tFsxuv4QdGXCddxunOxo7ivRmacJ/V3T/p7rOS7JWh6fb759zG95NcZeb5m5P89kz/4BtW1ZXWr2RgJ7RnktPHAOnOSa672QUBi6eqfq6qDpyZdLMkS90+HjJzP+/fMCt5T5JfG8dG2j/JIduwLWDBrPE9cnKSW47Tfm2Ddv/mJI+qqiuPtVyzqvbboH2xg9ESiR3FpzL8KtvLlk27cnefOeeQJJ/MMLbSJzL0/T06Q7POj45jmpyR5P7rVzKwE/q3JG+oqpOSfDzJ5ze3HGBBXTnJ34/dQi5I8qUkj8vwH2KXr6oPZvjP3Iduwz5eneTQDC20v5BhvMjvbsP2gMWy2vfIjZP8c1U9NcPnft1191uq6sZJ3j9eZ52T5DeS+NVqUhe1cAMAADbK2AX24O4+c522d+Vx3JSrJflQkl/ubmO0AbBhtEQCAIAd0xvHVgq7J3mmAAmAjaYlEgAAAACTDKwNAAAAwCQhEgAAAACThEgAAAAATBIiAQCXaVX1yKr6SFV9v6q+U1Ufq6rnbHZdAACLxsDaAMBlVlU9Jckzkzw7yTuS7JHklkl+o7tvsJm1AQAsGiESAHCZVVXfSPK67v7dZdOr/ZEEAHAxurMBAJdleyX51vKJywOkqtqjqp5dVadU1XlV9YmquveyZf5XVb2nqs4au8W9o6oOXrbMTarq+HGZH1TV56pqeYD1e1X1xXE/X6qq3182/6iqOrOqbl5VH6iqH45d8O6wrS8GAMBahEgAwGXZR5M8oaoeUVVXW2O5VyV5ZJK/SHLfJB9OclxV3WxmmS1J/iXJg5I8LMnXk7yrqq4/s8xxSX6S5DeS3C/J3ye5ytLMqnrsOO24cT//keRvq+rIZfVcMcmxSV6U5NeSnJfktVV1xTmPGwBgq+nOBgBcZlXVLyZ5XZLrJekkn0vy6iR/093fG5c5NMnbkhzS3e+cWfddSU7r7getsN1dMvxn3aeTvKy7/7Sqrp7kjCS/2N2fWmWdU5K8pbv/98z0FyR5eJL9u/vcqjoqydOTHNrdbx+XuVmSjyW5V3cfv00vCgDAKrREAgAus7r7k0lunKFV0AuSVJI/TnJSVV15XOyuGbq8vbeqdlu6JTkhyU+7q1XVjavqtVV1WobWRj9O8nNJbjguclaGkOiFVfWQqtpvWTnXSnKNDK2PZr0iyVWT/MLMtB8nOXHm+WdntgEAsCGESADAZVp3n9fdb+ju3+vug5I8JsmBSR49LnL1JD+TIbiZvR2V5NpJUlVXSfKW8fkfJLlDklsl+USGX3xLd1+Y5O4ZAqmXJvlWVb27qm4+7ueA8f60ZSUuPd9nZtr3xu0tHcP548M9LsVLAAAwl902uwAAgEXS3f9cVc9OcqNx0llJvpHk/musdtsMrYDu1t2fX5pYVXsu2/bnk/xaVV0uQ9D0V0n+s6quleTUcbHlLZT2n6kDAGDTaIkEAFxmrdClLFW1b5I9c1ELoBMytEQ6p7tPWn4bl7nCeH/ezHZul2Gw7Uvo7h+P4xk9J0MLpL0yDMT9zQwDc896cJLvJbnEOEoAANuTlkgAwGXZp6rq9Rm6op2e5LpJ/jDJDzP8+lmSvDXJm5O8tar+KslnMoxRdLMke3T3U5J8IMk5Sf5pbMV0rQzd3b6xtKNxEO+/yTDG0ZeT7J3kyUk+0d1njcscleRFVfXtcb93SvLbSZ7a3eduyCsAADAnIRIAcFn2p0kOS/J3GcYc+laS9yV5SHd/JUm6u6vqAUmemuSIJNfJ0LXs40n+flzmtKp6UIaQ6PVJvpjkt5I8aWZf38rQuulpGQbQPjvJOzIESRm3809VdflxP4dnaJ30xO5+7nofOADA1qru3uwaAAAAAFhwxkQCAAAAYJIQCQAAAIBJQiQAAAAAJgmRAAAAAJgkRAIAAABgkhAJAAAAgElCJAAAAAAmCZEAAAAAmPT/A/6IW0qEYZRPAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1440x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a plot that shows the number of films released each season\n",
    "fig, ax = plt.subplots(figsize=(20,7))\n",
    "\n",
    "release_time_df.groupby('release_season')['movie'].count().sort_values().plot(kind='bar', color='lightgreen')\n",
    "ax.set_xlabel('Season', fontsize=15)\n",
    "plt.xticks(rotation=0)\n",
    "ax.set_ylabel('Number of Films', fontsize=15)\n",
    "ax.set_title('Distribution Of Films Released per Season', fontsize=20);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Based on the 'Number of Films Released per Season' plot above, we can see that the distribution of the number of films released per season is fairly even. We see that despite Summer having the most number of films released, the average return on investment is not the lowest, in fact, it is comparable to the average return on investment for the the top (Fall) season. This suggests that Summer could be best season to release a film."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "5. **Genre**\n",
    "    * The genre of the film is the category within which the film is classified. We are going to plot a barchart to visualize the relationship between the 'genre' and average 'RoI' for that genre. However, before we can plot, the genre column needs to normalized."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "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>movie_id</th>\n",
       "      <th>release_date</th>\n",
       "      <th>movie</th>\n",
       "      <th>production_budget</th>\n",
       "      <th>worldwide_gross</th>\n",
       "      <th>RoI</th>\n",
       "      <th>runtime_minutes</th>\n",
       "      <th>genres</th>\n",
       "      <th>averagerating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>tt2582846</td>\n",
       "      <td>2014-06-06</td>\n",
       "      <td>The Fault in Our Stars</td>\n",
       "      <td>12000000</td>\n",
       "      <td>307166834</td>\n",
       "      <td>2559.723617</td>\n",
       "      <td>126.0</td>\n",
       "      <td>Drama</td>\n",
       "      <td>7.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>tt2582846</td>\n",
       "      <td>2014-06-06</td>\n",
       "      <td>The Fault in Our Stars</td>\n",
       "      <td>12000000</td>\n",
       "      <td>307166834</td>\n",
       "      <td>2559.723617</td>\n",
       "      <td>126.0</td>\n",
       "      <td>Romance</td>\n",
       "      <td>7.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>tt3783958</td>\n",
       "      <td>2016-12-09</td>\n",
       "      <td>La La Land</td>\n",
       "      <td>20000000</td>\n",
       "      <td>426351163</td>\n",
       "      <td>2131.755815</td>\n",
       "      <td>128.0</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>tt3783958</td>\n",
       "      <td>2016-12-09</td>\n",
       "      <td>La La Land</td>\n",
       "      <td>20000000</td>\n",
       "      <td>426351163</td>\n",
       "      <td>2131.755815</td>\n",
       "      <td>128.0</td>\n",
       "      <td>Drama</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>tt3783958</td>\n",
       "      <td>2016-12-09</td>\n",
       "      <td>La La Land</td>\n",
       "      <td>20000000</td>\n",
       "      <td>426351163</td>\n",
       "      <td>2131.755815</td>\n",
       "      <td>128.0</td>\n",
       "      <td>Music</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    movie_id release_date                   movie  production_budget  \\\n",
       "0  tt2582846   2014-06-06  The Fault in Our Stars           12000000   \n",
       "0  tt2582846   2014-06-06  The Fault in Our Stars           12000000   \n",
       "1  tt3783958   2016-12-09              La La Land           20000000   \n",
       "1  tt3783958   2016-12-09              La La Land           20000000   \n",
       "1  tt3783958   2016-12-09              La La Land           20000000   \n",
       "\n",
       "   worldwide_gross          RoI  runtime_minutes   genres  averagerating  \n",
       "0        307166834  2559.723617            126.0    Drama            7.7  \n",
       "0        307166834  2559.723617            126.0  Romance            7.7  \n",
       "1        426351163  2131.755815            128.0   Comedy            8.0  \n",
       "1        426351163  2131.755815            128.0    Drama            8.0  \n",
       "1        426351163  2131.755815            128.0    Music            8.0  "
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Assign the top_movies_df DataFrame to a new variable called 'genre_analysis_df'. It shall be used for genre analysis\n",
    "genre_analysis_df = top_movies_df.copy()\n",
    "genre_analysis_df['genres'] = genre_analysis_df.genres.apply(lambda x: x.split(','))\n",
    "\n",
    "genre_analysis_df = genre_analysis_df.explode('genres') # Normalize the 'genres' column by exploding the list of genres\n",
    "\n",
    "genre_analysis_df.head() # Preview the first few records of the 'genre_analysis_df' DataFrame\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The 'genres' columns have been normalised successfully. Now we need to identify the different genres within the 'genres' column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Drama', 'Romance', 'Comedy', 'Music', 'Horror', 'Mystery',\n",
       "       'Thriller', 'Sci-Fi', 'Biography', 'Adventure', 'Animation',\n",
       "       'Family', 'Action', 'Fantasy', 'Musical', 'Crime', 'Western',\n",
       "       'History', 'War', 'Documentary'], dtype=object)"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "genre_analysis_df.genres.unique() # Identify the unique genres in the 'genre_analysis_df' DataFrame"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " There is both a 'Music' and 'Musical' genre which is most probably an error. Therefore, we need to combine the two genres into one that we shall call 'Music'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Drama', 'Romance', 'Comedy', 'Music', 'Horror', 'Mystery',\n",
       "       'Thriller', 'Sci-Fi', 'Biography', 'Adventure', 'Animation',\n",
       "       'Family', 'Action', 'Fantasy', 'Crime', 'Western', 'History',\n",
       "       'War', 'Documentary'], dtype=object)"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Identify the records that belong to the 'Musical' genre an convert them to 'Music' genre\n",
    "genre_analysis_df.loc[genre_analysis_df['genres'] == 'Musical', 'genres'] = 'Music'\n",
    "\n",
    "genre_analysis_df.genres.unique() # Check if the unique genres have been updated"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1440x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a plot that shows average return on investment (RoI) by genre\n",
    "fig, ax = plt.subplots(figsize=(20,7))\n",
    "\n",
    "genre_analysis_df.groupby('genres')['RoI'].mean().sort_values().plot(kind='bar',color='yellow')\n",
    "ax.set_xlabel('Genre', fontsize=15)\n",
    "plt.xticks(rotation=0)\n",
    "ax.set_ylabel('Average Return on Investment (%)', fontsize=15)\n",
    "ax.set_title('Average Return on Investment per Genre', fontsize=20);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see that 'Music' and 'Horror' genres have a higher average return on investment than other genres. However, before any conclusion can be made, we need to first understand the number of films released in each genre and gauge the distribution per genre."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1440x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a plot that counts the number of movies by genre\n",
    "fig, ax = plt.subplots(figsize=(13,7))\n",
    "\n",
    "genre_analysis_df.groupby('genres')['movie'].count().sort_values().plot(kind='bar', figsize=(20,7), color='orange')\n",
    "ax.set_xlabel('Genre', fontsize=15)\n",
    "plt.xticks(rotation=0)\n",
    "ax.set_ylabel('Number of Films', fontsize=15)\n",
    "ax.set_title('Distribution Of Films per Genre', fontsize=20);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " From the plot above, we can see that the distribution of the genre of films in the dataset is not evenly distributed. Therefore, we need to take this into account that the reason that the 'Horror' and 'Music' genre have a higher average return on investment is because there are fewer films classified under them. It is however important to note that of the Top 200 Highest Grossing Films that were originally selected, majority of were Adventure and Action. This suggests that Adventure and Action films averagely make the most money."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "6. **Directors**\n",
    "   * We are going to plot a barchart to visualize the relationship between directors and average RoI for the films. We first need to create a new DataFrame that contains the movies and their director information."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "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>movie_id</th>\n",
       "      <th>release_date</th>\n",
       "      <th>movie</th>\n",
       "      <th>production_budget</th>\n",
       "      <th>worldwide_gross</th>\n",
       "      <th>RoI</th>\n",
       "      <th>runtime_minutes</th>\n",
       "      <th>genres</th>\n",
       "      <th>averagerating</th>\n",
       "      <th>person_id</th>\n",
       "      <th>primary_name</th>\n",
       "      <th>birth_year</th>\n",
       "      <th>death_year</th>\n",
       "      <th>primary_profession</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>tt2582846</td>\n",
       "      <td>2014-06-06</td>\n",
       "      <td>The Fault in Our Stars</td>\n",
       "      <td>12000000</td>\n",
       "      <td>307166834</td>\n",
       "      <td>2559.723617</td>\n",
       "      <td>126.0</td>\n",
       "      <td>Drama,Romance</td>\n",
       "      <td>7.7</td>\n",
       "      <td>nm1837748</td>\n",
       "      <td>Josh Boone</td>\n",
       "      <td>1979.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>writer,director,producer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>tt3783958</td>\n",
       "      <td>2016-12-09</td>\n",
       "      <td>La La Land</td>\n",
       "      <td>20000000</td>\n",
       "      <td>426351163</td>\n",
       "      <td>2131.755815</td>\n",
       "      <td>128.0</td>\n",
       "      <td>Comedy,Drama,Music</td>\n",
       "      <td>8.0</td>\n",
       "      <td>nm3227090</td>\n",
       "      <td>Damien Chazelle</td>\n",
       "      <td>1985.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>producer,writer,director</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>tt5140878</td>\n",
       "      <td>2017-08-11</td>\n",
       "      <td>Annabelle: Creation</td>\n",
       "      <td>15000000</td>\n",
       "      <td>305384865</td>\n",
       "      <td>2035.899100</td>\n",
       "      <td>109.0</td>\n",
       "      <td>Horror,Mystery,Thriller</td>\n",
       "      <td>6.5</td>\n",
       "      <td>nm2497546</td>\n",
       "      <td>David F. Sandberg</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>director,composer,writer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>tt0448115</td>\n",
       "      <td>2019-04-05</td>\n",
       "      <td>Shazam!</td>\n",
       "      <td>85000000</td>\n",
       "      <td>362899733</td>\n",
       "      <td>426.940862</td>\n",
       "      <td>132.0</td>\n",
       "      <td>Action,Adventure,Comedy</td>\n",
       "      <td>7.4</td>\n",
       "      <td>nm2497546</td>\n",
       "      <td>David F. Sandberg</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>director,composer,writer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>tt1396484</td>\n",
       "      <td>2017-09-08</td>\n",
       "      <td>It</td>\n",
       "      <td>35000000</td>\n",
       "      <td>697457969</td>\n",
       "      <td>1992.737054</td>\n",
       "      <td>135.0</td>\n",
       "      <td>Horror,Thriller</td>\n",
       "      <td>7.4</td>\n",
       "      <td>nm0615592</td>\n",
       "      <td>Andy Muschietti</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>director,writer,producer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>164</th>\n",
       "      <td>tt2660888</td>\n",
       "      <td>2016-07-22</td>\n",
       "      <td>Star Trek Beyond</td>\n",
       "      <td>185000000</td>\n",
       "      <td>335802233</td>\n",
       "      <td>181.514721</td>\n",
       "      <td>122.0</td>\n",
       "      <td>Action,Adventure,Sci-Fi</td>\n",
       "      <td>7.1</td>\n",
       "      <td>nm0510912</td>\n",
       "      <td>Justin Lin</td>\n",
       "      <td>1973.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>producer,director,writer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>165</th>\n",
       "      <td>tt1979388</td>\n",
       "      <td>2015-11-25</td>\n",
       "      <td>The Good Dinosaur</td>\n",
       "      <td>187500000</td>\n",
       "      <td>333771037</td>\n",
       "      <td>178.011220</td>\n",
       "      <td>93.0</td>\n",
       "      <td>Adventure,Animation,Comedy</td>\n",
       "      <td>6.7</td>\n",
       "      <td>nm0812307</td>\n",
       "      <td>Peter Sohn</td>\n",
       "      <td>1977.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>actor,animation_department,art_department</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>166</th>\n",
       "      <td>tt3741700</td>\n",
       "      <td>2019-05-31</td>\n",
       "      <td>Godzilla: King of the Monsters</td>\n",
       "      <td>170000000</td>\n",
       "      <td>299276941</td>\n",
       "      <td>176.045259</td>\n",
       "      <td>132.0</td>\n",
       "      <td>Action,Adventure,Fantasy</td>\n",
       "      <td>6.7</td>\n",
       "      <td>nm1002424</td>\n",
       "      <td>Michael Dougherty</td>\n",
       "      <td>1974.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>writer,director,actor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>167</th>\n",
       "      <td>tt3778644</td>\n",
       "      <td>2018-05-25</td>\n",
       "      <td>Solo: A Star Wars Story</td>\n",
       "      <td>275000000</td>\n",
       "      <td>393151347</td>\n",
       "      <td>142.964126</td>\n",
       "      <td>135.0</td>\n",
       "      <td>Action,Adventure,Fantasy</td>\n",
       "      <td>7.0</td>\n",
       "      <td>nm0000165</td>\n",
       "      <td>Ron Howard</td>\n",
       "      <td>1954.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>actor,producer,director</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>168</th>\n",
       "      <td>tt1440129</td>\n",
       "      <td>2012-05-18</td>\n",
       "      <td>Battleship</td>\n",
       "      <td>220000000</td>\n",
       "      <td>313477717</td>\n",
       "      <td>142.489871</td>\n",
       "      <td>131.0</td>\n",
       "      <td>Action,Adventure,Sci-Fi</td>\n",
       "      <td>5.8</td>\n",
       "      <td>nm0000916</td>\n",
       "      <td>Peter Berg</td>\n",
       "      <td>1964.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>producer,actor,writer</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>202 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      movie_id release_date                           movie  \\\n",
       "0    tt2582846   2014-06-06          The Fault in Our Stars   \n",
       "1    tt3783958   2016-12-09                      La La Land   \n",
       "2    tt5140878   2017-08-11             Annabelle: Creation   \n",
       "74   tt0448115   2019-04-05                         Shazam!   \n",
       "3    tt1396484   2017-09-08                              It   \n",
       "..         ...          ...                             ...   \n",
       "164  tt2660888   2016-07-22                Star Trek Beyond   \n",
       "165  tt1979388   2015-11-25               The Good Dinosaur   \n",
       "166  tt3741700   2019-05-31  Godzilla: King of the Monsters   \n",
       "167  tt3778644   2018-05-25         Solo: A Star Wars Story   \n",
       "168  tt1440129   2012-05-18                      Battleship   \n",
       "\n",
       "     production_budget  worldwide_gross          RoI  runtime_minutes  \\\n",
       "0             12000000        307166834  2559.723617            126.0   \n",
       "1             20000000        426351163  2131.755815            128.0   \n",
       "2             15000000        305384865  2035.899100            109.0   \n",
       "74            85000000        362899733   426.940862            132.0   \n",
       "3             35000000        697457969  1992.737054            135.0   \n",
       "..                 ...              ...          ...              ...   \n",
       "164          185000000        335802233   181.514721            122.0   \n",
       "165          187500000        333771037   178.011220             93.0   \n",
       "166          170000000        299276941   176.045259            132.0   \n",
       "167          275000000        393151347   142.964126            135.0   \n",
       "168          220000000        313477717   142.489871            131.0   \n",
       "\n",
       "                         genres  averagerating  person_id       primary_name  \\\n",
       "0                 Drama,Romance            7.7  nm1837748         Josh Boone   \n",
       "1            Comedy,Drama,Music            8.0  nm3227090    Damien Chazelle   \n",
       "2       Horror,Mystery,Thriller            6.5  nm2497546  David F. Sandberg   \n",
       "74      Action,Adventure,Comedy            7.4  nm2497546  David F. Sandberg   \n",
       "3               Horror,Thriller            7.4  nm0615592    Andy Muschietti   \n",
       "..                          ...            ...        ...                ...   \n",
       "164     Action,Adventure,Sci-Fi            7.1  nm0510912         Justin Lin   \n",
       "165  Adventure,Animation,Comedy            6.7  nm0812307         Peter Sohn   \n",
       "166    Action,Adventure,Fantasy            6.7  nm1002424  Michael Dougherty   \n",
       "167    Action,Adventure,Fantasy            7.0  nm0000165         Ron Howard   \n",
       "168     Action,Adventure,Sci-Fi            5.8  nm0000916         Peter Berg   \n",
       "\n",
       "     birth_year  death_year                         primary_profession  \n",
       "0        1979.0         NaN                   writer,director,producer  \n",
       "1        1985.0         NaN                   producer,writer,director  \n",
       "2           NaN         NaN                   director,composer,writer  \n",
       "74          NaN         NaN                   director,composer,writer  \n",
       "3           NaN         NaN                   director,writer,producer  \n",
       "..          ...         ...                                        ...  \n",
       "164      1973.0         NaN                   producer,director,writer  \n",
       "165      1977.0         NaN  actor,animation_department,art_department  \n",
       "166      1974.0         NaN                      writer,director,actor  \n",
       "167      1954.0         NaN                    actor,producer,director  \n",
       "168      1964.0         NaN                      producer,actor,writer  \n",
       "\n",
       "[202 rows x 14 columns]"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a copy of the 'top_movies_df' DataFrame to a new variable called 'director_analysis_df'\n",
    "director_analysis_df = top_movies_df.copy()\n",
    "\n",
    "# Join the directors_df DataFrame to the top_movies_df DataFrame and assign it to a new DataFrame called 'director_analysis_df'\n",
    "director_analysis_df = director_analysis_df.join(directors_df.set_index('movie_id'), on='movie_id', how='left')\n",
    "\n",
    "# Join the persons_df DataFrame to the director_analysis_df DataFrame so that we can identify their names and know whether they are still alive or not\n",
    "director_analysis_df = director_analysis_df.join(persons_df.set_index('person_id'), on='person_id', how='inner')\n",
    "director_analysis_df # Preview the updated 'director_analysis_df' DataFrame"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next we need to check if any of the directors are still alive. If that is not the case, we will need to drop their records from the DataFrame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "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",
       "    .dataframe tbody tr th {\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>movie_id</th>\n",
       "      <th>release_date</th>\n",
       "      <th>movie</th>\n",
       "      <th>production_budget</th>\n",
       "      <th>worldwide_gross</th>\n",
       "      <th>RoI</th>\n",
       "      <th>runtime_minutes</th>\n",
       "      <th>genres</th>\n",
       "      <th>averagerating</th>\n",
       "      <th>person_id</th>\n",
       "      <th>primary_name</th>\n",
       "      <th>birth_year</th>\n",
       "      <th>death_year</th>\n",
       "      <th>primary_profession</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [movie_id, release_date, movie, production_budget, worldwide_gross, RoI, runtime_minutes, genres, averagerating, person_id, primary_name, birth_year, death_year, primary_profession]\n",
       "Index: []"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check if any records have directors that have passed on. If so, drop the records from the 'director_analysis_df' DataFrame\n",
    "director_analysis_df[director_analysis_df['death_year'].notna()]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we are looking for directors with a proven track record, we need to identify directors that have more than one film in our DataFrame. Thereafter, we will be plot a barchart to visualize the relationship between directors and average RoI for their films."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>movie_id</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>tt5140878</td>\n",
       "      <td>2017-08-11</td>\n",
       "      <td>Annabelle: Creation</td>\n",
       "      <td>15000000</td>\n",
       "      <td>305384865</td>\n",
       "      <td>2035.899100</td>\n",
       "      <td>109.0</td>\n",
       "      <td>Horror,Mystery,Thriller</td>\n",
       "      <td>6.5</td>\n",
       "      <td>nm2497546</td>\n",
       "      <td>David F. Sandberg</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>director,composer,writer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>tt0448115</td>\n",
       "      <td>2019-04-05</td>\n",
       "      <td>Shazam!</td>\n",
       "      <td>85000000</td>\n",
       "      <td>362899733</td>\n",
       "      <td>426.940862</td>\n",
       "      <td>132.0</td>\n",
       "      <td>Action,Adventure,Comedy</td>\n",
       "      <td>7.4</td>\n",
       "      <td>nm2497546</td>\n",
       "      <td>David F. Sandberg</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>director,composer,writer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>tt1727824</td>\n",
       "      <td>2018-11-02</td>\n",
       "      <td>Bohemian Rhapsody</td>\n",
       "      <td>55000000</td>\n",
       "      <td>894985342</td>\n",
       "      <td>1627.246076</td>\n",
       "      <td>134.0</td>\n",
       "      <td>Biography,Drama,Music</td>\n",
       "      <td>8.0</td>\n",
       "      <td>nm0001741</td>\n",
       "      <td>Bryan Singer</td>\n",
       "      <td>1965.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>producer,director,writer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>tt1877832</td>\n",
       "      <td>2014-05-23</td>\n",
       "      <td>X-Men: Days of Future Past</td>\n",
       "      <td>200000000</td>\n",
       "      <td>747862775</td>\n",
       "      <td>373.931388</td>\n",
       "      <td>132.0</td>\n",
       "      <td>Action,Adventure,Sci-Fi</td>\n",
       "      <td>8.0</td>\n",
       "      <td>nm0001741</td>\n",
       "      <td>Bryan Singer</td>\n",
       "      <td>1965.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>producer,director,writer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>tt3385516</td>\n",
       "      <td>2016-05-27</td>\n",
       "      <td>X-Men: Apocalypse</td>\n",
       "      <td>178000000</td>\n",
       "      <td>542537546</td>\n",
       "      <td>304.796374</td>\n",
       "      <td>144.0</td>\n",
       "      <td>Action,Adventure,Sci-Fi</td>\n",
       "      <td>7.0</td>\n",
       "      <td>nm0001741</td>\n",
       "      <td>Bryan Singer</td>\n",
       "      <td>1965.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>producer,director,writer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>112</th>\n",
       "      <td>tt1872181</td>\n",
       "      <td>2014-05-02</td>\n",
       "      <td>The Amazing Spider-Man 2</td>\n",
       "      <td>200000000</td>\n",
       "      <td>708996336</td>\n",
       "      <td>354.498168</td>\n",
       "      <td>142.0</td>\n",
       "      <td>Action,Adventure,Sci-Fi</td>\n",
       "      <td>6.6</td>\n",
       "      <td>nm1989536</td>\n",
       "      <td>Marc Webb</td>\n",
       "      <td>1974.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>producer,director,writer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115</th>\n",
       "      <td>tt0948470</td>\n",
       "      <td>2012-07-03</td>\n",
       "      <td>The Amazing Spider-Man</td>\n",
       "      <td>220000000</td>\n",
       "      <td>757890267</td>\n",
       "      <td>344.495576</td>\n",
       "      <td>136.0</td>\n",
       "      <td>Action,Adventure,Sci-Fi</td>\n",
       "      <td>7.0</td>\n",
       "      <td>nm1989536</td>\n",
       "      <td>Marc Webb</td>\n",
       "      <td>1974.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>producer,director,writer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113</th>\n",
       "      <td>tt2975590</td>\n",
       "      <td>2016-03-25</td>\n",
       "      <td>Batman v Superman: Dawn of Justice</td>\n",
       "      <td>250000000</td>\n",
       "      <td>867500281</td>\n",
       "      <td>347.000112</td>\n",
       "      <td>151.0</td>\n",
       "      <td>Action,Adventure,Fantasy</td>\n",
       "      <td>6.5</td>\n",
       "      <td>nm0811583</td>\n",
       "      <td>Zack Snyder</td>\n",
       "      <td>1966.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>producer,director,writer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134</th>\n",
       "      <td>tt0770828</td>\n",
       "      <td>2013-06-14</td>\n",
       "      <td>Man of Steel</td>\n",
       "      <td>225000000</td>\n",
       "      <td>667999518</td>\n",
       "      <td>296.888675</td>\n",
       "      <td>143.0</td>\n",
       "      <td>Action,Adventure,Sci-Fi</td>\n",
       "      <td>7.1</td>\n",
       "      <td>nm0811583</td>\n",
       "      <td>Zack Snyder</td>\n",
       "      <td>1966.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>producer,director,writer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>155</th>\n",
       "      <td>tt0974015</td>\n",
       "      <td>2017-11-17</td>\n",
       "      <td>Justice League</td>\n",
       "      <td>300000000</td>\n",
       "      <td>655945209</td>\n",
       "      <td>218.648403</td>\n",
       "      <td>120.0</td>\n",
       "      <td>Action,Adventure,Fantasy</td>\n",
       "      <td>6.5</td>\n",
       "      <td>nm0811583</td>\n",
       "      <td>Zack Snyder</td>\n",
       "      <td>1966.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>producer,director,writer</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>87 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      movie_id release_date                               movie  \\\n",
       "2    tt5140878   2017-08-11                 Annabelle: Creation   \n",
       "74   tt0448115   2019-04-05                             Shazam!   \n",
       "5    tt1727824   2018-11-02                   Bohemian Rhapsody   \n",
       "106  tt1877832   2014-05-23          X-Men: Days of Future Past   \n",
       "126  tt3385516   2016-05-27                   X-Men: Apocalypse   \n",
       "..         ...          ...                                 ...   \n",
       "112  tt1872181   2014-05-02            The Amazing Spider-Man 2   \n",
       "115  tt0948470   2012-07-03              The Amazing Spider-Man   \n",
       "113  tt2975590   2016-03-25  Batman v Superman: Dawn of Justice   \n",
       "134  tt0770828   2013-06-14                        Man of Steel   \n",
       "155  tt0974015   2017-11-17                      Justice League   \n",
       "\n",
       "     production_budget  worldwide_gross          RoI  runtime_minutes  \\\n",
       "2             15000000        305384865  2035.899100            109.0   \n",
       "74            85000000        362899733   426.940862            132.0   \n",
       "5             55000000        894985342  1627.246076            134.0   \n",
       "106          200000000        747862775   373.931388            132.0   \n",
       "126          178000000        542537546   304.796374            144.0   \n",
       "..                 ...              ...          ...              ...   \n",
       "112          200000000        708996336   354.498168            142.0   \n",
       "115          220000000        757890267   344.495576            136.0   \n",
       "113          250000000        867500281   347.000112            151.0   \n",
       "134          225000000        667999518   296.888675            143.0   \n",
       "155          300000000        655945209   218.648403            120.0   \n",
       "\n",
       "                       genres  averagerating  person_id       primary_name  \\\n",
       "2     Horror,Mystery,Thriller            6.5  nm2497546  David F. Sandberg   \n",
       "74    Action,Adventure,Comedy            7.4  nm2497546  David F. Sandberg   \n",
       "5       Biography,Drama,Music            8.0  nm0001741       Bryan Singer   \n",
       "106   Action,Adventure,Sci-Fi            8.0  nm0001741       Bryan Singer   \n",
       "126   Action,Adventure,Sci-Fi            7.0  nm0001741       Bryan Singer   \n",
       "..                        ...            ...        ...                ...   \n",
       "112   Action,Adventure,Sci-Fi            6.6  nm1989536          Marc Webb   \n",
       "115   Action,Adventure,Sci-Fi            7.0  nm1989536          Marc Webb   \n",
       "113  Action,Adventure,Fantasy            6.5  nm0811583        Zack Snyder   \n",
       "134   Action,Adventure,Sci-Fi            7.1  nm0811583        Zack Snyder   \n",
       "155  Action,Adventure,Fantasy            6.5  nm0811583        Zack Snyder   \n",
       "\n",
       "     birth_year  death_year        primary_profession  \n",
       "2           NaN         NaN  director,composer,writer  \n",
       "74          NaN         NaN  director,composer,writer  \n",
       "5        1965.0         NaN  producer,director,writer  \n",
       "106      1965.0         NaN  producer,director,writer  \n",
       "126      1965.0         NaN  producer,director,writer  \n",
       "..          ...         ...                       ...  \n",
       "112      1974.0         NaN  producer,director,writer  \n",
       "115      1974.0         NaN  producer,director,writer  \n",
       "113      1966.0         NaN  producer,director,writer  \n",
       "134      1966.0         NaN  producer,director,writer  \n",
       "155      1966.0         NaN  producer,director,writer  \n",
       "\n",
       "[87 rows x 14 columns]"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Directors with more than one film will give the 'person_id' column duplicates. Therefore, we need to identify the duplicate person_id values\n",
    "director_ids = director_analysis_df[director_analysis_df.duplicated(subset='person_id')]['person_id'].unique()\n",
    "\n",
    "# Update the 'director_analysis_df' DataFrame to only include directors with multiple films\n",
    "director_analysis_df = director_analysis_df[director_analysis_df['person_id'].isin(director_ids)]\n",
    "director_analysis_df # Preview the updated 'director_analysis_df' DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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W6yl0ib039+3zfB54/zmAH9PdOP5tSV44Stxb9416m1//RZd8ek/PD9av0I24+kKSJ/bvkOTBSfp/XN/Y3h8zSj/nAY9J8ry+8g/RPUV1mL5Ld7zvSLJB37aPAw8FvjvGvfAWJ0fRjdx8Ld0TLaFvCnnzLbrRSfsmecADEZI8KMl2PesbJ5k6oJ2RkWH/6Ckb63qYNAvpez7Igh7Tc5K8uK/s7XT3nzu5qv4AUFXX0z1oZHqSDyd5wMjTJI/v+58WC02SZ9M9OfbBwNeqqv8+jBMyn5/Xl9r7N5Ks1lf3QUnW7Ska9XOqqrvpzu0qwMf62nk83YjQOdz3oBJJ0mLAe9BJkrQQtR/+GwKXVNW8br79Tbonae6VZN/qnmAJ3XTWHYFP9qwP8hq6ZN43k+xDNz3rFrpRY5vQTdPbGrh+nKEfQndvtv9Nsj3wG7qHNryYLjGy24B93kZ3M/2vtoTbxXQPl3g5XTJuJ7oRMwBU1ZwkuwDHA8clOQuYRZcAeTTdfakeRzcdqzcpMmFVdW2Sb9AlOt9H92TDK9oP54OAy5L8nO7poMvT/ejdlm5k40Y9TZ1IN8LmqCQ/pXvS5x+qauSH7ufo7ln24/YE2ZuAbeimC5/CgNFri0pVXZ3kXcD/ARck+QHd8T2b7tq4gu7JmUuEqrozyQ+BNwJvpUtY9I8uHbnP467A0cA5SU4ELqO7Fh9Dd+wPo3uiJnTft8+36/EKuu/Mo7jv+v1sT/NX0t0z7FVJ7qZ7qm4B3xlJRE2yyf6eD3Ii3Xf/gCRH0D0J9Jaq+so49/8JcHSSo+me6rwp3YNmbqL7nHq9ne7vysfoniB8Bt097B5J9/CELelGDl/F5JnW89CGh9AlXp8GPJnu8/083d+IyTDRz+tAupGGrwd+k+THdN/RR9I9aOggYCT2s+n+Lr6rPc145N5/X67uCbDvp/sb9vb2EJyTgbWAVwKrAm+vvifPSpKGywSdJEkL18iN6Q+cV6WWPPkl3TS6l9AlE6BLhv0f3eimS6tq4JSkqromyRbAO+gSYrvTTX36C92TKb8MXDLeoKvqz2302KfofjD+C12y4q3ALxmQoKuqy5NsTZdMfE57XQzsTPdjeyfuu1fdyD4XJ9kU+He65N9edD+Sr6ObCrcv3bTeyfDfdJ/HPkn+t6r+WlXfTXIR8P/opn09D7iD7gmbR/DA++0dSDcS7lV0P+Kn0D218jvteE5M8jK6+5S9qrX1C7rzNV/30ptMVfXVJL8F3kN3naxE9zTPzwKfHOVhEIuzg+kSdMsDh7WRQw/QPpdN6I77X+gSF3fTfc4ncf+p3McD/0v3RNud6L5719F9jp+vqrN62p2bZGe678lI4iN0Ty2e9ATdZH/PR+nj+CT/j+678m66EWV/oBtxOh5HAfvT/Q+HF9GN1DqKLin+676+/t5Gru1Nl8x6OV2i9K90/1Pg3XTnfTJt2l7QJbhupvvbdgRdYvW3k9XRRD+vqiq6Kdsn0J2TV9IlEa+jG413TE/dm5O8nO5v5F7c95Tt7wK3VtVN7e/xB4Bd6P7G3kk3yvezVXXCZB2nJGlypPvvgCRJ0sKT5FC6H+AbVdWVw45H0uRKsifddOK9qurg4UYjSdKSx3vQSZKkSdHukfSApwYm2YFuBNnlJuckSZKkB3KKqyRJmiwPBv6U5GS6KWP3AE+hm7Z7N9096iRJkiT1MUEnSZImyxzg63T3nnsa3f3NbgB+CHyqqi4cYmySJEnSYst70EmSJEmSJElD5Ag6DbTWWmvV1KlThx2GJEmSJEnSUmPmzJk3VNXa/eUm6DTQ1KlTmTFjxrDDkCRJkiRJWmok+cOgcp/iKkmSJEmSJA2RCTpJkiRJkiRpiEzQSZIkSZIkSUNkgk6SJEmSJEkaIh8SoYFumT2Ho668bthhSJIkSZKkZdAuG6477BAWKUfQSZIkSZIkSUNkgm4Cktzes/zCJL9J8phR6u6Z5CsTaHu7JLcmmZXk4iS/TPLwcexz7Cjbrk6y1nj7lyRJkiRJ0nCYoJsPSXYAvgw8v6r+OIlNn15V06pqE+B84G2T2LYkSZIkSZIWQyboJijJtsABwIuq6ndJTk8yrWf7mUk26dtn7SRHJjm/vZ4xRh8BVgVubutbJTkryYXtfcMB+zwsyQmtzjeA9Gz7UZKZSS5LsveCHL8kSZIkSZImlwm6iXkI8GPgZVV1RSs7ENgTIMkTgYdU1cV9+30R+EJVbQm8vO0zyLZJZgF/BHYEDmrlVwDPqqrNgI8Anxyw777AGa3OMUDv1Ns3VNUWwHRgnyQPG9R5kr2TzEgy49abbxwlREmSJEmSJE0mE3QTMwc4C3hjT9kPgRcnWR54A3DwgP12BL7Skm/HAA9NsuqAeiNTXB8NfAv4TCtfDfhhkkuBLwBPGbDvs4DvAlTVcbTRd80+SS4CzgEeDTxh0MFV1f5VNb2qpq+2xsAcniRJkiRJkiaZCbqJuRd4JbBlkv8EqKp/AL8AdmrbvjdgvwcBW7fk27SqWq+qbhujr2Pokm4AHwdOrqqNgZcAK4yyT/UXJNmOLkG4dVVtClw4j/0lSZIkSZK0iJmgm6CWkHsxsHuSkZF0BwJfAs6vqpsG7HYC8PaRld571s3DM4HfteXVgGvb8p6j1D8N2L21/wJgjZ59b66qfyTZCHj6OPqWJEmSJEnSIjJl2AEsiarqpiTPB05LckNV/TjJ3+mmpQ6yD/B/SS6mO+enAW8ZUG/kHnQBbgX+tZV/Bvh2kn8HThqlj48ChyW5ADiV7j52AD8H3tL6vpJumqskSZIkSZIWE6l6wKxITVCSRwKnABtV1b1DDmdSTJ8+vWbMmDHsMCRJkiRJkpYaSWZW1fT+cqe4LqAkrwfOBT64tCTnJEmSJEmStOg4xXUBVdUhwCHDjkOSJEmSJElLJkfQSZIkSZIkSUNkgk6SJEmSJEkaIhN0kiRJkiRJ0hCZoJMkSZIkSZKGyASdJEmSJEmSNEQm6CRJkiRJkqQhMkEnSZIkSZIkDZEJOkmSJEmSJGmIpgw7AC2ebpk9h6OuvG7YYUiSJEmSpAW0y4brDjsEjcERdJIkSZIkSdIQmaBrksxNMivJpUl+mGSlJNOTfGkR9f/EJD9N8tskv0rygyTrjLHPYUkuTvLuJBu1+C9M8vgkZy2KuCVJkiRJkrRgnOJ6nzurahpAkkOBt1TV54EZ420gyZSqume09XnstwJwHPDvVfWTVrY9sDbw11H2eQSwTVWt39bfD/y4qvZtVbYZb9ySJEmSJEkaHkfQDXY6sEGS7ZIcC5Bk5SQHJTm/jVLbqZXv2Ubc/QQ4YcD6wP36vAY4eyQ5B1BVJ1fVpUlWSPKtJJe0/bdvVU4AHt5Gze0LvAv41yQnt7hub+/bJTklyRFJrkhyaJIsnNMmSZIkSZKkiXIEXZ8kU4AXAD/v2/RB4KSqekOS1YHzkvyybdsa2KSqbkqyZ9/6JwftV1V39LS9MTBzlJDeBlBVT02yEV3S74nAS4Fje0b9Bbi9qj43oI3NgKcAfwbOBJ4BnDHg2PcG9gZY65HrjRKOJEmSJEmSJpMj6O6zYpJZdFNa/wh8s2/784D3tzqnACsAj2nbflFVN/XU7V2f137j8UzgOwBVdQXwB+CJE9gf4Lyquqaq7gVmAVMHVaqq/atqelVNX22Nh02wC0mSJEmSJM0PR9Dd55/3oBvRNxM0wMur6sq+Ok8DekfD0bc+cL8+lwHPHmXbZExHvatneS5+7pIkSZIkSYsNR9CN3/HAO0bu35Zks0nc73vANkleNFKQ5PlJngqcBuzeyp5IN/puXsk+SZIkSZIkLUFM0I3fx4HlgYuTXNrWJ2W/qroTeDFdIu83SS4H9gSuB74KLJfkEuD7wJ5VdVd/G5IkSZIkSVoypaqGHYMWQ9OnT68ZM2YMOwxJkiRJkqSlRpKZVTW9v9wRdJIkSZIkSdIQmaCTJEmSJEmShsgEnSRJkiRJkjREJugkSZIkSZKkITJBJ0mSJEmSJA2RCTpJkiRJkiRpiEzQSZIkSZIkSUNkgk6SJEmSJEkaIhN0kiRJkiRJ0hCZoJMkSZIkSZKGaMqwA9Di6ZbZczjqyuuGHYYkSZIkaRLssuG6ww5B0jw4gk6SJEmSJEkaomUyQZfkEUkOT/K7JJcn+WmSJybZLsmx42zjY0l2HGfdPZP8LcmsJFckefeCHcH8S7JfkvcMq39JkiRJkiTd3zI3xTVJgKOBb1fVq1rZNGCdCbSxXFV9ZIJdf7+q3p7kYcCVSY6oqj9NsA1JkiRJkiQtZZbFEXTbA3Oq6usjBVU1q6pOb6urJDmijXQ7tCX0SHJ1ko8kOQN4RZKDk+zatn2qjcS7OMnn5tV5Vd0I/BZYt+372iTntdF130iyXCu/PcknklyU5Jwk67TylyQ5N8mFSX7ZU36/kXFJLk0ytS1/MMmVSX4JbDgJ51CSJEmSJEmTZFlM0G0MzJzH9s2AdwFPBh4HPKNn2+yqemZVHT5SkGRNYGfgKVW1CfBf8+o8yWOAFYCLkzwJ2A14RlVNA+YCu7eqKwPnVNWmwGnAm1r5GcDTq2oz4HDgfWP0twXwqnZcuwBbzqPu3klmJJlx6803zqtZSZIkSZIkTZJlborrOJxXVdcAJJkFTKVLigF8f0D9vwOzgQOTHAeMdg+73ZJsTzeC7U1VNTvJDsAWwPltoN6KwPWt/t09bc0EntuWHwV8P8m6wIOBq8Y4nm2Bo6vqH+2YjhmtYlXtD+wPsMHGm9YY7UqSJEmSJGkSLIsj6C6jS4qN5q6e5bncP4l5R3/lqroH2Ao4EngZ8PNR2v1+VT2FLmH2P0keAYTuXnjT2mvDqtqv1Z9TVSNJst44vgx8paqeCryZbjQewD3c//NcoWfZZJskSZIkSdJiallM0J0EPCTJyJRRkmyZ5Nnz01iSVYDVquqndFNjp82rflWdDXwHeCdwIrBrkoe3ttZMsv4YXa4GXNuW9+gpvxrYvLWzOfDYVn4asHOSFZOsCrxkXAcmSZIkSZKkRWKZS9C1UWk7A89N8rsklwH7AX+ezyZXBY5NcjFwKvDucezzaWAv4E/Ah4AT2v6/oD08Yh72A36Y5HTghp7yI4E127TcfwN+DVBVF9BNzZ3V6pyOJEmSJEmSFhu5bxaldJ/p06fXjBkzhh2GJEmSJEnSUiPJzKqa3l++zI2gkyRJkiRJkhYnJugkSZIkSZKkITJBJ0mSJEmSJA2RCTpJkiRJkiRpiEzQSZIkSZIkSUNkgk6SJEmSJEkaIhN0kiRJkiRJ0hCZoJMkSZIkSZKGyASdJEmSJEmSNEQm6CRJkiRJkqQhmjLsALR4umX2HI668rphhyFJkiRJWkC7bLjusEOQNAZH0EmSJEmSJElDtFgm6JLMTTIryWVJLkry70nmK9Yk05N8aZRtVydZa5TyS1oMs5JsM8r+6yQ5tsV4eZKfzk+MA9qdmuTStrxnkq9MRruSJEmSJEla/CyuU1zvrKppAEkeDnwPWA3Yd6INVdUMYMZ8xLB9Vd0wRp2PAb+oqi8CJNlkPvpZqJIsV1Vzhx2HJEmSJEmSBlssR9D1qqrrgb2Bt6czNcnpSS5or20Aknw/yQtH9ktycJKXJ9kuybGt7GFJTkhyYZJvAFnA8NYFrumJ9eLWzypJTmzxXZJkp1Y+NcmvkhzQRgeekGTFtm2LNhLvbOBtff08OsnPk1yZ5J9JyiSvTXJeG+X3jSTLtfLbk3wsybnA1knemOTXSU5pfTsiT5IkSZIkaTGx2CfoAKrq93SxPhy4HnhuVW0O7AaMTF89vK2T5MHADkD/lNN9gTOqajPgGOAx8+j25Jb4Oncedf4P+GaSk5N8MMkjW/lsYOcW4/bA/yQZSQY+Afi/qnoKcAvw8lb+LWCfqtp6QD9bAbsD04BXtGm7T2rH+4w22nBuqwOwMnBpVT0N+D3wYeDpwHOBjUY7mCR7J5mRZMatN984j8OWJEmSJEnSZFlcp7gOMpLgWh74SpJpdEmpJ7bynwFfSvIQ4PnAaVV15315MQCeBewCUFXHJbl5Hv2NOcW1qo5P8rjW3wuAC5NsTJd4+2SSZwH3AusB67TdrqqqWW15JjA1yWrA6lV1aiv/TmtvxC+q6kaAJEcBzwTuAbYAzm/HuCJd8pJ2Xo5sy1sBp1bVTW3/H3LfOes/nv2B/QE22HjTmtexS5IkSZIkaXIsEQm6lgSbS5eA2hf4K7Ap3ai62QBVNTvJKcC/0I0sO2yU5iY18dQSX98Dvtem0j4LWBVYG9iiquYkuRpYoe1yV8/uc+kSaxkjrv5t1fb5dlV9YED92T33nVvQabySJEmSJElaiBb7Ka5J1ga+DnylqoruYRHXVdW9wOuA5XqqHw7sBWwLHD+gudNo00CTvABYYwFje06SldryqsDjgT+2GK9vybntgfXn1U5V3QLcmuSZrWj3virPTbJmu1/dy4AzgROBXdtDNGjbB/VzHvDsJGskmcJ9U2olSZIkSZK0GFhcR9CtmGQW3XTWe+imfH6+bfsqcGSSVwAnA3f07HcCcAhwTFXdPaDdjwKHJbkAOJUumTamJB8DZlTVMX2btqCbbnsPXbLzwKo6P8lVwE+SzABmAVeMo5u9gIOS/IMHJhfPoDsHGwDfa0+mJcmHgBOSPAiYQ/dwiT/07lhV1yb5JHAu8GfgcuDW8Ry3JEmSJEmSFr50g9K0NEuySlXd3kbQHQ0cVFVHz2ufDTbetD5z5M8XTYCSJEmSpIVmlw3XHXYIkpokM6tqen/54jqCTpNrvyQ70t0H7wTgR2PtsPoKy/tHXJIkSZIkaREwQbcMqKr3DDsGSZIkSZIkDbbYPyRCkiRJkiRJWpqZoJMkSZIkSZKGyASdJEmSJEmSNEQm6CRJkiRJkqQhMkEnSZIkSZIkDZEJOkmSJEmSJGmITNBJkiRJkiRJQ2SCTpIkSZIkSRqiKcMOQIunW2bP4agrrxt2GJIkSZK0UO2y4brDDkGSHEEnSZIkSZIkDZMJunFIcvsQ+35nkv/tWf9Gkl/2rL8jyZeGEpwkSZIkSZIWmAm6xd9ZwDY969OA1ZIs19a3Ac5c1EFJkiRJkiRpcpigG6ckqyQ5MckFSS5JslMrn5rkiiQHJrk0yaFJdkxyZpLfJNmq1Vs5yUFJzk9yYc/+T0lyXpJZSS5O8oS+ri8EnphkxSSrAf8AZgFPbdu3Ac5K8qbW9kVJjkyyUmv/4CRfSnJWkt8n2XXhny1JkiRJkiSNlwm68ZsN7FxVmwPbA/+TJG3bBsAXgU2AjYDXAM8E3gP8Z6vzQeCkqtqy7f/ZJCsDbwG+WFXTgOnANb2dVtU9dAm5LYGnA+cC5wDbJHkkkKr6E3BUVW1ZVZsCvwLe2NPMui2eFwOfGu0Ak+ydZEaSGbfefOMET48kSZIkSZLmh09xHb8An0zyLOBeYD1gnbbtqqq6BCDJZcCJVVVJLgGmtjrPA16a5D1tfQXgMcDZwAeTPIouyfabAX2fSTdSbsVW/zd0ib+/0U2BBdg4yX8BqwOrAMf37P+jqroXuDzJOoyiqvYH9gfYYONNa8wzIkmSJEmSpAVmgm78dgfWBraoqjlJrqZLsgHc1VPv3p71e7nvHAd4eVVd2dfur5KcC7wIOD7Jv1bVSX11zgLe3Pr7P7rE3JPb+8j95w4GXlZVFyXZE9iuZ//e+IIkSZIkSZIWG05xHb/VgOtbcm57YP0J7n888I6RabFJNmvvjwN+X1VfAo6hmybb7yy66a1rV9X1VVV0ybmduG8E3arAdUmWp0smSpIkSZIkaQlggm4MSabQjUA7FJieZAZdAuyKCTb1cWB54OIkl7Z1gN2AS5PMort/3SH9O1bVzXQJuct6is8GHg5c1NY/THd/ul/MR2ySJEmSJEkaknSDsTSaJJsCB1TVVsOOZVHaYONN6zNH/nzYYUiSJEnSQrXLhusOOwRJy5AkM6tqen+596CbhyRvAfYB3jXkUBa51VdY3v9QSZIkSZIkLQIm6Oahqr4OfH3YcUiSJEmSJGnp5T3oJEmSJEmSpCEyQSdJkiRJkiQNkQk6SZIkSZIkaYhM0EmSJEmSJElDZIJOkiRJkiRJGiITdJIkSZIkSdIQmaCTJEmSJEmShsgEnSRJkiRJkjREU4YdgBZPt8yew1FXXjfsMCRJkh5glw3XHXYIkiRJk8oRdJIkSZIkSdIQLdMJuiRzk8xKclGSC5Jss4j73zDJKS2GXyXZv5VPT/KlRRmLJEmSJEmShmNZn+J6Z1VNA0jyL8B/A8/urZBkuaqau5D6/xLwhar6cevrqQBVNQOYsZD6pPU1paruWZh9SJIkSZIkaWzL9Ai6Pg8FbgZIsl2Sk5N8D7gkyceTvHOkYpJPJNknySpJTmyj7y5JslPbPrWNiDsgyWVJTkiy4oA+1wWuGVmpqkt6+j+2Le+X5KA20u73SfbpiePDSa5I8oskhyV5Tyt/fJKfJ5mZ5PQkG7Xyg5N8PsnJwKcn+fxJkiRJkiRpPizrI+hWTDILWIEuWfacnm1bARtX1VVJpgJHAV9M8iDgVW37bGDnqvp7krWAc5Ic0/Z/AvDqqnpTkh8ALwe+29f/F4CTkpwFnAB8q6puGRDnRsD2wKrAlUm+Bmza2tyM7nO8AJjZ6u8PvKWqfpPkacBXe47ticCOC3FUoCRJkiRJkiZgWU/Q9U5x3Ro4JMnGbdt5VXUVQFVdneTGJJsB6wAXVtWNSZYHPpnkWcC9wHptO8BVVTWrLc8EpvZ3XlXfSnI88HxgJ+DNSTYdEOdxVXUXcFeS61sfzwR+XFV3tvh/0t5XAbYBfphkZP+H9LT1w9GSc0n2BvYGWOuR6w2qIkmSJEmSpEm2rCfo/qmqzm6j4NZuRXf0VTkQ2BN4BHBQK9u91d+iquYkuZpuNB7AXT37zgUGTXGlqv7c2jsoyaXAxgOq9bc1BciAetBNW75lJPE4QP9x9cayP93oOzbYeNMarZ4kSZIkSZImj/ega9p92pYDbhylytF0I922BI5vZasB17fk3PbA+hPs8/ltFB5JHgE8DLh2nLufAbwkyQpt1NyLAKrq78BVSV7R2s0oo/IkSZIkSZK0GFjWR9CN3IMOuhFpe1TV3J6pof9UVXe3hyvc0jNF9FDgJ0lmALOAKybY//Po7ms3u62/t6r+MvJQh3mpqvPb/e4uAv5A99TXW9vm3YGvJfkQsDxweKsnSZIkSZKkxUyqnMk4Hu3hEBcAr6iq3ww7HujuN1dVtydZCTgN2LuqLpiMtjfYeNP6zJE/n4ymJEmSJtUuG6477BAkSZLmS5KZVTW9v3xZH0E3LkmeDBwLHL24JOea/VtsKwDfnqzkHMDqKyzvP34lSZIkSZIWARN041BVlwOPG3Yc/arqNcOOQZIkSZIkSQvGh0RIkiRJkiRJQ2SCTpIkSZIkSRoiE3SSJEmSJEnSEJmgkyRJkiRJkobIBJ0kSZIkSZI0RCboJEmSJEmSpCEyQSdJkiRJkiQNkQk6SZIkSZIkaYimDDsALZ5umT2Ho668bthhSJIk3c8uG6477BAkSZImnSPoJEmSJEmSpCEyQSdJkiRJkiQNkQm6SZbkC0ne1bN+fJIDe9b/J8m/L0D72yW5NcmsJBcn+WWSh49jn2Pb8p5JvjK//UuSJEmSJGlymaCbfGcB2wAkeRCwFvCUnu3bAGcuYB+nV9W0qtoEOB942wK2J0mSJEmSpCExQTf5zqQl6OgSc5cCtyVZI8lDgCcBFybZIsmpSWa2UXbrAiTZJ8nlbXTc4fPqKEmAVYGb2/pWSc5KcmF733BhHaQkSZIkSZImh09xnWRV9eck9yR5DF2i7mxgPWBr4FbgYqCALwM7VdXfkuwGfAJ4A/B+4LFVdVeS1UfpZtsks4CHAXcA/9nKrwCeVVX3JNkR+CTw8vHGnmRvYG+AtR653vgPWpIkSZIkSfPNBN3CMTKKbhvg83QJum3oEnRnARsCGwO/6AbBsRxwXdv3YuDQJD8CfjRK+6dX1YsBkvwH8BngLcBqwLeTPIEuCbj8RIKuqv2B/QE22HjTmsi+kiRJkiRJmj9OcV04Ru5D91S6Ka7n0I2gG7n/XIDL2n3kplXVU6vqeW3fFwH/B2wBzEwyVhL1GOBZbfnjwMlVtTHwEmCFSTwmSZIkSZIkLQQm6BaOM4EXAzdV1dyquglYnS5JdzZwJbB2kq0Bkiyf5CntoRKPrqqTgfe1fVYZo69nAr9ry6sB17blPSftaCRJkiRJkrTQOMV14biE7umt3+srW6WqbgBIsivwpSSr0X0O/wv8GvhuKwvwhaq6ZUD7I/egC9202X9t5Z+hm+L678BJk3xMkiRJkiRJWghS5a3G9EDTp0+vGTNmDDsMSZIkSZKkpUaSmVU1vb/cKa6SJEmSJEnSEJmgkyRJkiRJkobIBJ0kSZIkSZI0RCboJEmSJEmSpCEyQSdJkiRJkiQNkQk6SZIkSZIkaYhM0EmSJEmSJElDZIJOkiRJkiRJGiITdJIkSZIkSdIQmaCTJEmSJEmShmjKsAPQ4umW2XM46srrhh2GJEkagl02XHfYIUiSJC1THEEnSZIkSZIkDdFinaBL8ogkhyf5XZLLk/w0yROTbJfk2HG28bEkO85j+8uSPHkBYtwvyXvmd/8B7R2c5B9JVu0p+2KSSrJWWz+rvU9NcmlbHvc5kSRJkiRJ0uJjsU3QJQlwNHBKVT2+qp4M/CewzgTaWK6qPlJVv5xHtZcB852gW1BJlhtQ/Ftgp7b9QcD2wLUjG6tqm0no1+nNkiRJkiRJi4HFNkFHl5SaU1VfHymoqllVdXpbXSXJEUmuSHJoS+iR5OokH0lyBvCKNiJt17btU20k3sVJPpdkG+ClwGeTzEry+CTTkpzT6hydZI227ylJ/jfJWUkuTbJVT6xPbtt/n2SfkcIkr01yXmv7GyPJuCS3t5F95wJbDzj2w4Dd2vJ2wJnAPT3t3j6vE5dk5SQHJTk/yYVJRpJ9eyb5YZKfACfM+/RLkiRJkiRpUVicE3QbAzPnsX0z4F10o98eBzyjZ9vsqnpmVR0+UpBkTWBn4ClVtQnwX1V1FnAM8N6qmlZVvwMOAf6j1bkE2Len3ZXb6LW3Agf1lG8E/AuwFbBvkuWTPIkuyfaMqpoGzAV2H2kHuLSqnlZVZww4tt8Aa7fk4KuBwwfUmZcPAidV1ZZ0ic7PJlm5bdsa2KOqntO/U5K9k8xIMuPWm2+cYJeSJEmSJEmaH4tzgm4s51XVNVV1LzALmNqz7fsD6v8dmA0cmGQX4B/9FZKsBqxeVae2om8Dz+qpchhAVZ0GPDTJ6q38uKq6q6puAK6nm4a7A7AFcH6SWW39ca3+XODIMY7vKOBVwNOA08eo2+95wPtbv6cAKwCPadt+UVU3DdqpqvavqulVNX21NR42wS4lSZIkSZI0Pxbn+5BdBuw6j+139SzP5f7Hckd/5aq6p01L3YEu8fV24AGjyMZQo6wPiiXAt6vqAwPamV1Vc8fo63DggtbGvW0G73gFeHlVXXm/wuRpDDg3kiRJkiRJGp7FeQTdScBDkrxppCDJlkmePT+NJVkFWK2qfko3NXZa23QbsCpAVd0K3Jxk27btdcCpPc3s1tp6JnBrqz+aE4Fdkzy87bNmkvXHG29V/ZFuqupXx7tPj+OBd/Tcl2+z+WhDkiRJkiRJi8BiO4KuqirJzsD/Jnk/3fTUq+mSa+vNR5OrAj9OsgLdCLN3t/LDgQPawx12BfYAvp5kJeD3wF49bdyc5CzgocAbxoj/8iQfAk5oT2KdA7wN+MN4A66qb4y3bp+PA/8LXNySdFcDL57PtiRJkiRJkrQQpap/1uYoFZOHAO8Bjq2qixZqVIuhJKcA76mqGcOOZVGYPn16zZixTByqJEmSJEnSIpFkZlVN7y8f9xTXqrqLbsrl6pMYlyRJkiRJkrRMm+g96M6lezLpMqeqtltWRs9JkiRJkiRp0ZnoPejeB3wvyd3AT4G/0vdk06r6xyTFJkmSJEmSJC31JpqgO7e9fwn44ih1lpv/cCRJkiRJkqRly0QTdG+gb8ScJEmSJEmSpPk3oQRdVR28kOKQJEmSJEmSlkkTHUEHQJJHAlsDawI3AWdX1Z8nMzBJkiRJkiRpWTChBF2S5YAvA2/i/veam5tkf+AdVXXvJMYnSZIkSZIkLdUeNMH6H6W7D91/AlOBFdv7f7by/SYvNEmSJEmSJGnpN9Eprq8HPlRVn+sp+yPw2SQF7AN8ZLKCkyRJkiRJkpZ2E03QPRy4eJRtF7ftWgrcMnsOR1153bDDkCRJQ7DLhusOOwRJkqRlykSnuP4aeNUo214FXLlg4UiSJEmSJEnLlokm6P4L2DPJL5O8JcnOSd6c5JfAHm37Mi/J3CSzklya5IdJVkoyNcmlo9T/WJId2/IpSaYPqDOwfD7juzrJWpPRliRJkiRJkhbMhKa4VtUPktxC97CILwLLA3OAmcDzq+oXkx7hkunOqpoGkORQ4C3AUaNVrqqFdt++JFOq6p6F1b4kSZIkSZIWzETvQUdVnQCckORBwFrADVV176RHtvQ4HdikLS+X5ABgG+BaYKequjPJwcCxVXXERBpOsiZwEPA44B/A3lV1cZL9gEfSPWH3hiTvAA4D1gbOA7KgByVJkiRJkqTJMaEprkkOSvJYgKq6t6quH0nOJVk/yUELI8glVZIpwAuAS1rRE4D/q6qnALcAL1/ALj4KXFhVmwD/CRzSs20LugTga4B9gTOqajPgGOAxo8S7d5IZSWbcevONCxiaJEmSJEmSxmOi96Dbk24U1iBr0d2HTrBiklnADOCPwDdb+VVVNastz6Qb4bYgngl8B6CqTgIelmS1tu2YqrqzLT8L+G6rdxxw86DGqmr/qppeVdNXW+NhCxiaJEmSJEmSxmPCU1yBGqV8Y+BvCxDL0uSf96AbkQTgrp6iucCKC9jPoKmqI5/PHaOUS5IkSZIkaTEy5gi6JO9M8vskv6dL8vxoZL3n9We6e6Edt7AD1v2cBuwOkGQ7uvsB/n2Mei8A1lhE8UmSJEmSJGkM4xlBdzlwJN1orX8HTgau66tzN3AF8INJjU79jksypy2fDbwZ+FaSi+keEjHaFOOPAocluQA4lW7arSRJkiRJkhYDqRr/zMck+wIHVNWfF15IWhxMnz69ZsyYMewwJEmSJEmSlhpJZlbV9P7yiT4k4iDgEaN0sHmSR89PcJIkSZIkSdKyaqIJuq8Brx1l22uAry5YOJIkSZIkSdKyZaIJuqcDJ42y7eS2XZIkSZIkSdI4TTRBtxLdk1xHs/ICxCJJkiRJkiQtcyaaoLsEePUo214NXLZg4UiSJEmSJEnLlikTrP8p4MgkDwEOBq4D1gX2AF7eXpIkSZIkSZLGaUIJuqo6OskewH/TJeMKCHAt8Nqq+tGkRyhJkiRJkiQtxSY6go6q+k6S7wIbAWsCNwJXVtW87k0nSZIkSZIkaYAJJ+gAqqqSXEE3vfV6k3OSJEmSJEnS/JnoQyJI8sIk5wKzgT8Cm7Ty/ZO8dpLjkyRJkiRJkpZqExpBl+T1wEHAocBXgW/1bP4N8Ebgu5MWnYbmltlzOOrK64YdhiRJGsMuG6477BAkSZK0gCY6gu6DwGerag8emIi7DHjypEQlSZIkSZIkLSMmeg+69YFfjLJtNvDQBQtn8ZDk9qpaZYj9Xw3cBsxtRW+tqrNGqbsfcHtVfW7RRCdJkiRJkqTJNNEE3Z+AzYCTBmybDvx2gSPSiO2r6oZhByFJkiRJkqSFa6JTXL8J7NseBrFiK0uSHYD3AQdMZnDDlGSVJCcmuSDJJUl2auVTk1yR5MAklyY5NMmOSc5M8pskW7V6Kyc5KMn5SS7s2f8pSc5LMivJxUmeMM541m/xXNzeHzOgzuOT/DzJzCSnJ9koyapJrkqyfKvz0CRXj6xLkiRJkiRpuCaaoPs08B3g28BNrews4Hjg+1X1pUmMbdhmAztX1ebA9sD/JEnbtgHwRbon2G4EvAZ4JvAe4D9bnQ8CJ1XVlm3/zyZZGXgL8MWqmkY36vCaUfo/uSXxzm3rXwEOqapN6B7SMehc7w+8o6q2aLF8tapuA04BXtTqvAo4sqrm9O+cZO8kM5LMuPXmG+d9diRJkiRJkjQpJjTFtaoKeFuSzwM7AGvRJepOqqpfL4T4hinAJ5M8C7gXWA9Yp227qqouAUhyGXBiVVWSS4Cprc7zgJcmeU9bXwF4DHA28MEkjwKOqqrfjNJ//xTXrYFd2vJ3gM/cL9hkFWAb4If35RF5SHs/kG6E44+AvYA3DeqwqvanS/Kxwcab1ihxSZIkSZIkaRKNO0GXZAXgVmC3qvoR8LuFFdRiYndgbWCLqprTHtywQtt2V0+9e3vW7+W+cxrg5VV1ZV+7v2qj4l4EHJ/kX6tq0D39xtKfQHsQcEsbmXf/ilVntqm5zwaWq6pL56M/SZIkSZIkLQTjnuJaVbOB64F7Fl44i5XVgOtbcm57uifYTsTxwDtGpsUm2ay9Pw74fZsOfAzdNNnxOItueip0ycMzejdW1d+Bq5K8ovWTJJv2VDkEOAz41gSPQ5IkSZIkSQvRRO9B9w1gn6X5AQNJptCNiDsUmJ5kBl1C7IoJNvVxYHng4iSXtnWA3YBLk8yiu3/dIeNsbx9gryQXA68D3jmgzu7AG5NcBFwG7NSz7VBgDboknSRJkiRJkhYT6W4rN87KyefoHohQwInAX7n/VMuqqv+Y1AgXsTbq7ICq2mrYsUymJLsCO1XV68ZTf/r06TVjxoyFHJUkSZIkSdKyI8nMqpreXz6hh0QAL+e++61tO2B7AUtsgi7JW+hGqr1ryKFMqiRfBl4AvHDYsUiSJEmSJOn+JvoU18curEAWB1X1deDrw45jslXVO4YdgyRJkiRJkgab6D3oJEmSJEmSJE2iMUfQJXkhcEZV/b0tz1NV/XRSIpMkSZIkSZKWAeOZ4nos8HTgvLY8LwUst6BBSZIkSZIkScuK8SToHgtc17MsSZIkSZIkaZKMmaCrqj8AJAmwId1ounXa5r8CZwEnVlUtrCAlSZIkSZKkpdW4nuKaZDPg+8AGwD3ADUCAh7U2fp3kVVU1ayHFKUmSJEmSJC2VxnyKa5J1gOOBO4EXAKtU1SOral1gVeBFwN3A8UkevjCDlSRJkiRJkpY2YybogHfQJee2rarjq+rukQ1VdVdV/Qx4Vqvz9oUTpiRJkiRJkrR0Gs8U1+cBX62qv49WoapuSfI14OXARyYrOA3PLbPncNSV141dUZIkjdsuG6477BAkSZK0GBrPCLoNgAvGUW9mqytJkiRJkiRpnMaToFsNuHUc9W4DHrpg4cy/JJXkOz3rU5L8Lcmxbf2lSd7flg9Osut89HFukllJ/tjantVeU5PcPnlHM3G9xydJkiRJkqQlx3imuAaocbaXBYhlQd0BbJxkxaq6E3gucO3Ixqo6BjhmQTqoqqcBJNkTmF5V/7znXjL/h55kuaqau4CxTej4kkypqnsWpE9JkiRJkiQtuPGMoIPuCa3Xz+sF/GxhBjpOP6N7qizAq4HDRjYk2TPJV/p3SPLxNqLuQUnem+T8JBcn+ehEO0/yiSQXJTmnPf32AaP1RkbaJdkuyclJvgdc0tZPTfKDJL9O8qkkuyc5L8klSR7f9ls7yZEtzvOTPKP/+OZRZ78k+yc5AThkoscnSZIkSZKkyTeeEXQTTlQN0eHAR9q01k2Ag4BtR6uc5DN0U3j3ohtx9wRgK7qRgMckeVZVnTbOvlcGzqmqD7Z23wT81xj7bAVsXFVXJdkO2BR4EnAT8HvgwKraKsk76Z6m+y7gi8AXquqMJI8Bjm/79JpXnS2AZ7ZRhv3nY29gb4C1HrneOA9bkiRJkiRJC2LMBF1VLTEJuqq6OMlUutFzPx2j+oeBc6tqb4Akz6N7Yu2FbfsqdAm78Sbo7gaObcsz6RJ+Yzmvqq7qWT+/qq5r8fwOOKGVXwJs35Z3BJ7cM6X2oUlW7Wt3XnWOGZScA6iq/YH9ATbYeNPxTmuWJEmSJEnSAhjPCLolzTHA54DtgIfNo975wBZJ1qyqm+hGzf13VX1jPvudU1UjSa253Hdu76FNJU6XMXtwzz539LVxV8/yvT3r9/a09yBg6/4kW9898OZVp79PSZIkSZIkDdF470G3JDkI+FhVXTJGvZ8DnwKOa6PLjgfekGQVgCTrJXn4JMRzNd20UoCdgOUXsL0TgN6HU0ybzzqSJEmSJElaDCx1CbqquqaqvjjOuj8EDqAbdXc68D3g7CSXAEcA/VNH58cBwLOTnAc8jQUfwbYPML09yOJy4C3zWUeSJEmSJEmLgdw3K1O6zwYbb1qfOfLnww5DkqSlyi4brjvsECRJkjRESWZW1fT+8qXxHnSaBKuvsLw/IiRJkiRJkhaBpW6KqyRJkiRJkrQkMUEnSZIkSZIkDZEJOkmSJEmSJGmITNBJkiRJkiRJQ2SCTpIkSZIkSRoiE3SSJEmSJEnSEJmgkyRJkiRJkobIBJ0kSZIkSZI0RCboJEmSJEmSpCGaMuwAtHi6ZfYcjrryumGHIUnSQrPLhusOOwRJkiQJcASdJEmSJEmSNFSLNEGXZJ0k30vy+yQzk5ydZOdF0O/VSdYaR72HJZnVXn9Jcm3P+oPHsf92SbYZR739krxnvPFPRJLbF0a7kiRJkiRJWjgW2RTXJAF+BHy7ql7TytYHXrqoYhhLVd0ITIMuiQbcXlWfG8++SaYA2wG3A2ctnAglSZIkSZK0tFmUI+ieA9xdVV8fKaiqP1TVlwGSLJfks0nOT3Jxkje38u2SnJLkiCRXJDm0JftGRsZ9NMkFSS5JslErf1iSE5JcmOQbwEj9jyd550j/ST6RZJ+xAk/yphbXRUmOTLJSKz84yeeTnAx8H3gL8O424m7bJC9Jcm6L45dJ1hml7Z8lWTHJvye5tL3e1bZ/Oslbe+rvl+T/JVk3yWmtr0uTbNvX7lpthOJOSa5Ksnwrf2g7b8uP50OTJEmSJEnSwrUoE3RPAS6Yx/Y3ArdW1ZbAlsCbkjy2bdsMeBfwZOBxwDN69ruhqjYHvgaMTBvdFzijqjYDjgEe08q/CewBkORBwKuAQ8cR+1FVtWVVbQr8qsU64onAjlX1cuDrwBeqalpVnQ6cATy9xXE48L7eRpO8HXgJ8LJ2bHsBTwOe3o5/ZL/denZ7JfBD4DXA8VU1DdgUmNXT7jrAccBHqurHwCnAi9rmVwFHVtWc/oNMsneSGUlm3HrzjeM4LZIkSZIkSVpQQ3uKa5L/A55JN6puS+B5wCZJdm1VVgOeANwNnFdV17T9ZgFT6ZJfAEe195nALm35WSPLVXVckpvb8tVJbmyJr3WAC9u01rFsnOS/gNWBVYDje7b9sKrmjrLfo4DvJ1kXeDBwVc+21wHXAC+rqjlJngkcXVV3tOM8Cti2qr6U5OFJHgmsDdxcVX9Mcj5wUBsJ96OqmtXaXR44EXhbVZ3ayg6kSw7+iC4J+KZBwVbV/sD+ABtsvGmN47xIkiRJkiRpAS3KEXSXAZuPrFTV24Ad6JJO0E1DfUcbfTatqh5bVSe0bXf1tDOX+ycW7xqlfLQE04HAnnSJqoPGGfvBwNur6qnAR4EVerbdMY/9vgx8pe335r79LqVLND6qrWce7RwB7Eo3ku5wgKo6jS4ReS3wnSSvb3XvoUtW/svIzlV1JjA1ybOB5arq0nn0JUmSJEmSpEVoUSboTgJWSPJvPWUr9SwfD/xbz73Snphk5fns6zRg99bOC4A1erYdDTyfbhrt8Q/cdaBVgetabLvPo95tre6I1egSaNCm1va4kC5pd0wbHXca8LIkK7Xj3hk4vdU9nG5q6q50ybqRB2xcX1UH0E3dHUl+FvAGYKMk7+/p7xDgMOBb4zpiSZIkSZIkLRKLLEFXVUV3r7Vnt4cWnAd8G/iPVuVA4HLggiSXAt9g/qfgfhR4VpIL6KbO/rEnjruBk4EfzGNqar8PA+cCvwCumEe9nwA7jzwkAtgP+GGS04Eb+itX1Rl09807rsV4MHBe6+vAqrqw1buMLvF3bVVd13bfDpiV5ELg5cAXe9qdS5fQ277nAROH0iUqDxvnMUuSJEmSJGkRSJc3W3a0h0NcALyiqn4z7HgWlXZvv52q6nXjqb/BxpvWZ478+UKOSpKk4dllw3WHHYIkSZKWMUlmVtX0/vKhPSRiGJI8GTiW7mEMy1Jy7svAC4AXjnef1VdY3h8ukiRJkiRJi8AylaCrqsuBxw07jkWtqt4x7BgkSZIkSZI02KJ8SIQkSZIkSZKkPiboJEmSJEmSpCEyQSdJkiRJkiQNkQk6SZIkSZIkaYhM0EmSJEmSJElDZIJOkiRJkiRJGiITdJIkSZIkSdIQmaCTJEmSJEmShmjKsAPQ4umW2XM46srrhh2GJElj2mXDdYcdgiRJkrRAHEEnSZIkSZIkDdESkaBLcnvP8guT/CbJYxZBv6sneWvP+nZJjh3HfgcnuSrJrCRXJNl34UY6ZjynJJk+zBgkSZIkSZI02BKRoBuRZAfgy8Dzq+qPi6DL1YG3jlVpFO+tqmnANGCPJI/tr5BkufmOTJIkSZIkSUuFJSZBl2Rb4ADgRVX1u1b22iTntZFq3xhJeCW5PcknklyU5Jwk67Tyg5N8KclZSX6fZNee9t+b5PwkFyf5aCv+FPD41v5nW9kqSY5oI+MOTZIxQl+hvd/R+rk6yUeSnAG8IsmbWr8XJTkyyUrzijXJKklOTHJBkkuS7NRzDB9ucf0iyWFJ3tMTxyvaufp1O5eSJEmSJElaDCwpCbqHAD8GXlZVVwAkeRKwG/CMNlJtLrB7q78ycE5VbQqcBrypp611gWcCL6ZLwJHkecATgK3oRrxtkeRZwPuB31XVtKp6b9t/M+BdwJOBxwHPGCXmzyaZBVwDHF5V1/dsm11Vz6yqw4GjqmrLFuuvgDfOK1ZgNrBzVW0ObA/8TzrTgZe3+HYB+qe0TqmqrVrsQ51yK0mSJEmSpPssKQm6OcBZ3D95tQOwBXB+S4TtQJcwA7gbGLlX3Exgas9+P6qqe6vqcmCdVva89roQuADYiC5hN8h5VXVNVd0LzOpru9fIFNdHADsk2aZn2/d7ljdOcnqSS+gSjE8ZI9YAn0xyMfBLYL227ZnAj6vqzqq6DfhJXzxHtff+8/FPSfZOMiPJjFtvvnGUw5IkSZIkSdJkmjLsAMbpXuCVwC+T/GdVfZIuUfXtqvrAgPpzqqra8lzuf5x39Syn5/2/q+obvY0kmTqg7d79+9t+gKq6PckpdAm0s1rxHT1VDqYbGXhRkj2B7caIdXdgbWCLqpqT5Gq6abRjTbUdaWvUmKtqf2B/gA023rQG1ZEkSZIkSdLkWlJG0FFV/6Cb6rl7kjcCJwK7Jnk4QJI1k6w/n80fD7whySqtrfVau7cBqy5I3EmmAE8DfjdKlVWB65Isz31TdOdlNeD6lpzbHhg55jOAlyRZoR3HixYkbkmSJEmSJC0aS8oIOgCq6qYkz6e7r9y7gA8BJyR5EN002LcBf5iPdk9o97Q7uz3z4XbgtVX1uyRnJrkU+Blw3ASa/WySDwEPpksmHjVKvQ8D57a4L2HshOChwE+SzKCbYntFO4bzkxwDXNTamgHcOoF4JUmSJEmSNAS5byaolnRJVmlTaleiS2LuXVUXzE9bG2y8aX3myJ9PboCSJC0Eu2y47rBDkCRJksYlycyq6n+w55I1gk5j2j/Jk+nuSfft+U3OAay+wvL+4JEkSZIkSVoETNAtRarqNcOOQZIkSZIkSROzxDwkQpIkSZIkSVoamaCTJEmSJEmShsgEnSRJkiRJkjREJugkSZIkSZKkITJBJ0mSJEmSJA2RCTpJkiRJkiRpiEzQSZIkSZIkSUNkgk6SJEmSJEkaoinDDkCLp1tmz+GoK68bdhiSpCXcLhuuO+wQJEmSpMWeI+gkSZIkSZKkITJBJ0mSJEmSJA3REpGgSzI3yaye19SF2NdPk6w+zroHJ9l1YcUiSZIkSZKkpd+Scg+6O6tq2qANSQKkqu6djI6q6oWT0c6CSrJcVc0ddhySJEmSJElauJaIEXT9kkxN8qskXwUuAB6d5GtJZiS5LMlHe+peneSjSS5IckmSjVr5Kkm+1couTvLynvprJVk5yXFJLkpyaZLdxhnbKklO7Olvp1b+viT7tOUvJDmpLe+Q5Ltt+fYkH0tyLrB1ktcmOa+NGvxGkuV66n2ixXZOknVa+TpJjm7lFyXZppUPbEeSJEmSJEnDt6Qk6Fbsmd56dCvbEDikqjarqj8AH6yq6cAmwLOTbNKz/w1VtTnwNeA9rezDwK1V9dSq2gQ4qa/P5wN/rqpNq2pj4OfjjHU2sHPrb3vgf9oov9OAbVud6cAqSZYHngmc3spXBi6tqqcBNwK7Ac9oowfnArv31DunqjZt7b6plX8JOLWVbw5cluRJ82jnfpLs3ZKcM269+cZxHq4kSZIkSZIWxBI5xbXdg+4PVXVOT51XJtmb7pjWBZ4MXNy2HdXeZwK7tOUdgVeN7FxVN/f1eQnwuSSfBo6tqtMZnwCfTPIs4F5gPWCd1vcWSVYF7qIb+TedLmm3T9t3LnBkW94B2AI4v8vvsSJwfdt2N3BszzE9ty0/B3h9O565wK1JXjePdu6nqvYH9gfYYONNa5zHK0mSJEmSpAWwpCToBrljZCHJY+lGxm1ZVTcnORhYoafuXe19Lvcdc4BRk1BV9eskWwAvBP47yQlV9bFxxLU7sDawRVXNSXI1sELP8l7AWXTJw+2BxwO/avvO7rnvXIBvV9UHBvQxp6pGYu89pkHm1Y4kSZIkSZKGbEmZ4jqWh9Il7G5t92N7wTj2OQF4+8hKkjV6NyZ5JPCPqvou8Dm6KaPjsRpwfUvIbQ+s37PtNLpE4ml001rfAszqSbb1OhHYNcnDWzxrJll/QL3+ff6t1V8uyUPnsx1JkiRJkiQtIktFgq6qLgIuBC4DDgLOHMdu/wWs0R4AcRHdaLZeTwXOSzIL+GCrP8g3klzTXmcDhwLTk8ygG013RU/d0+mm355dVX+lu1/dwKmzVXU58CHghCQXA79o+87LO4Htk1xCN/X1KfPZjiRJkiRJkhaRDB68pWXd9OnTa8aMGcMOQ5IkSZIkaamRZGZ7yOn9LBUj6CRJkiRJkqQllQk6SZIkSZIkaYhM0EmSJEmSJElDZIJOkiRJkiRJGiITdJIkSZIkSdIQmaCTJEmSJEmShsgEnSRJkiRJkjREJugkSZIkSZKkITJBJ0mSJEmSJA2RCTpJkiRJkiRpiKYMOwAtnm6ZPYejrrxu2GFIkhZzu2y47rBDkCRJkpZ4jqCTJEmSJEmShmiZT9AluX2S21s9yY1J0ta3TlJJHtXWV0tyU5IHJTklyfQF6OvgJLtOVuySJEmSJEla9Jb5BN1kq6pbgL8AT2pF2wAXtneApwPnVtW9iz46SZIkSZIkLW5M0AHpfDbJpUkuSbJbK183yWlJZrVt2yZZro1cG6n77gFNnsl9CbltgC/0rZ/VU/cVSc5L8usk27Z+l2vxnJ/k4iRv7onzK0kuT3Ic8PCeY9ghyYUtpoOSPKSVX53ko0kuaNs2msRTJ0mSJEmSpAVkgq6zCzAN2BTYEfhsknWB1wDHV9XItlmt3npVtXFVPRX41oD2zuK+hNzjgB8CI1NZt6FL4I2YUlVbAe8C9m1lbwRuraotgS2BNyV5LLAzsCHwVOBNI30kWQE4GNitxTQF+LeePm6oqs2BrwHvGe0kJNk7yYwkM269+cbRqkmSJEmSJGkSmaDrPBM4rKrmVtVfgVPpEmPnA3sl2Q94alXdBvweeFySLyd5PvD3Ae2dCWzTkmpXV9VsugFwqwBbAOf11D2qvc8Eprbl5wGvTzILOBd4GPAE4Fk9cf4ZOKnV3xC4qqp+3da/3erOq48HqKr9q2p6VU1fbY2HjVZNkiRJkiRJk8gEXSeDCqvqNLpE17XAd5K8vqpuphtNdwrwNuDAAfv9BlgDeAlwdiueCexFl0jrfTDFXe19Lt3It5F43lFV09rrsVV1wkjz441/jD4kSZIkSZK0GDBB1zkN2K3d+21tuqTceUnWB66vqgOAbwKbJ1kLeFBVHQl8GNh8lDbPBt7JfQm6s+mmsZ41Sv1exwP/lmR5gCRPTLJyi/NVLc51ge1b/SuAqUk2aOuvoxsFKEmSJEmSpMXcMj2aKskUutFlRwNbAxfRjVB7X1X9JckewHuTzAFuB14PrAd8K8lIcvMDozR/JvBCYEZbP5vufnTjSdAdSDcV9YIkAf4GvKzF+RzgEuDXtCRcVc1Oshfww3ZM5wNfH0c/kiRJkiRJGrJUDZoxuWxIsilwQHtIg3pMnz69ZsyYMXZFSZIkSZIkjUuSmVU1vb98mZ3imuQtwGHAh4YdiyRJkiRJkpZdy+wU16r6Ok4DlSRJkiRJ0pAtsyPoJEmSJEmSpMWBCTpJkiRJkiRpiEzQSZIkSZIkSUNkgk6SJEmSJEkaIhN0kiRJkiRJ0hCZoJMkSZIkSZKGyASdJEmSJEmSNEQm6CRJkiRJkqQhmjLsALR4umX2HI668rphhyFJmiS7bLjusEOQJEmSNApH0EmSJEmSJElDtNQm6JLsnKSSbDTO+u9KslLP+u0LL7oH9D01yZ1JZiW5PMkhSZZfVP1LkiRJkiRpeJbaBB3wauAM4FXjrP8uYKWxKi1Ev6uqacBTgUcBrxxiLJIkSZIkSVpElsoEXZJVgGcAb6QnQZdkuySnJDkiyRVJDk1nH+CRwMlJTu6p/4kkFyU5J8k6rWz9JCcmubi9P6aVH5zkS0nOSvL7JLu28u8k2amnzUOTvHS02KtqLnAesF6rf3WStdry9CSntOVntxF3s5JcmGTVJOsmOa2VXZpk21b31UkuaWWfnoxzLEmSJEmSpMmxVCbogJcBP6+qXwM3Jdm8Z9tmdKPlngw8DnhGVX0J+DOwfVVt3+qtDJxTVZsCpwFvauVfAQ6pqk2AQ4Ev9bS9LvBM4MXAp1rZgcBeAElWA7YBfjpa4ElWAJ4G/HyMY3wP8LY26m5b4E7gNcDxrWxTYFaSRwKfBp4DTAO2TPKyUfreO8mMJDNuvfnGMbqXJEmSJEnSZFhaE3SvBg5vy4e39RHnVdU1VXUvMAuYOkobdwPHtuWZPfW2Br7Xlr9Dl5Ab8aOqureqLgfWAaiqU4ENkjy8xXFkVd0zoL/HJ5kF3Aj8saouHuMYzwQ+30b/rd7aPB/YK8l+wFOr6jZgS+CUqvpbq3Mo8KxBDVbV/lU1vaqmr7bGw8boXpIkSZIkSZNhqUvQJXkY3WixA5NcDbwX2C1JWpW7eqrPBaaM0tScqqpx1Kue5d6207P8HWB3upF03xqlnZF70G0APL1nGuw93Pc5rfDPTqs+BfwrsCJwTpKNquo0uuTbtcB3kry+Lw5JkiRJkiQtZpa6BB2wK90U1PWrampVPRq4ivuPdBvkNmDVcbR/Fvfd1253ugdRjOVgumm1VNVl86pYVdcB7wc+0IquBrZoyy8fqZfk8VV1SVV9GpgBbJRkfeD6qjoA+CawOXAu8OwkayVZjm4U36njiFmSJEmSJEmLwNKYoHs1cHRf2ZF092ebl/2Bn/U+JGIU+9BNI70YeB3wzrECqqq/Ar9i9NFz/X4ErNQe8vBR4ItJTqcbyTfiXe2hDxfR3X/uZ8B2dPedu5AumffFlvD7AHAycBFwQVX9eJxxSJIkSZIkaSHLfbM4tbAkWQm4BNi8qm4ddjzjMX369JoxY8aww5AkSZIkSVpqJJlZVdP7y5fGEXSLlSQ7AlcAX15SknOSJEmSJEladEZ78IEmSVX9EnjMsOOQJEmSJEnS4skRdJIkSZIkSdIQmaCTJEmSJEmShsgEnSRJkiRJkjREJugkSZIkSZKkITJBJ0mSJEmSJA2RCTpJkiRJkiRpiEzQSZIkSZIkSUNkgk6SJEmSJEkaoinDDkCLp1tmz+GoK68bdhiSpEmyy4brDjsESZIkSaNwBJ0kSZIkSZI0RI6gmwRJbq+qVRawjauB24ACbgZeX1V/mITwJEmSJEmStBhzBN3iZfuq2gQ4BfjQkGORJEmSJEnSImCCbpKk89kklya5JMluPdvem+T8JBcn+eg4mjsbWK/te3CSXXvaur29r5vktCSzWp/bJlmu1R+J4d2t7rQk57T+j06yxuQevSRJkiRJkuaXCbrJswswDdgU2BH4bEuiPQ94ArBV275FkmeN0dbzgR+NUec1wPFVNdLnrNb+elW1cVU9FfhWq3sI8B9tdN4lwL6DGkyyd5IZSWbcevONY3QvSZIkSZKkyWCCbvI8EzisquZW1V+BU4Etgee114XABcBGdAm7QU5Ocj1dgu97Y/R3PrBXkv2Ap1bVbcDvgccl+XKS5wN/T7IasHpVndr2+zYwMEFYVftX1fSqmr7aGg8b31FLkiRJkiRpgZigmzyZR/l/V9W09tqgqr45St3tgfWBy4CPtbJ7aJ9TkgAPBqiq0+gSbdcC30ny+qq6mW403SnA24ADF/ioJEmSJEmStFCZoJs8pwG7tfvArU2XPDsPOB54Q5JVAJKsl+ThozVSVXcC7wJen2RN4Gpgi7Z5J2D51s76wPVVdQDwTWDzJGsBD6qqI4EPA5tX1a3AzUm2bW28jm50nyRJkiRJkhYDU4YdwJIuyRTgLuBoYGvgIqCA91XVX4C/JHkScHY3AI7bgdcC14/WZlVdl+QwulFw+wM/TnIecCJwR6u2HfDeJHNam6+ne7DEt5KMJF4/0N73AL6eZCW6abB7TcKhS5IkSZIkaRKkqoYdwxItyabAAVW11bBjmUzTp0+vGTNmDDsMSZIkSZKkpUaSmVU1vb/cKa4LIMlbgMOADw07FkmSJEmSJC2ZnOK6AKrq68DXhx2HJEmSJEmSllyOoJMkSZIkSZKGyASdJEmSJEmSNEQm6CRJkiRJkqQhMkEnSZIkSZIkDZEJOkmSJEmSJGmITNBJkiRJkiRJQ2SCTpIkSZIkSRoiE3SSJEmSJEnSEE0ZdgBaPN0yew5HXXndsMOQpGXSLhuuO+wQJEmSJC1CjqCTJEmSJEmShsgE3RiSzE0yK8mlSX6SZPVW/sgkR4yx79VJ1hqjznZJKskbe8o2a2XvmZSDkCRJkiRJ0mLLBN3Y7qyqaVW1MXAT8DaAqvpzVe06SX1cAuzWs/4q4KLJaDiJ05glSZIkSZIWYyboJuZsYD2AJFOTXNqWl0vyuSSXJLk4yTt69nlHkgvato1GafePwApJ1kkS4PnAz0Y2JpmW5JzW9tFJ1hij/JQkn0xyKvDOJFskOTXJzCTHJ/HmRpIkSZIkSYsJE3TjlGQ5YAfgmAGb9wYeC2xWVZsAh/Zsu6GqNge+BsxryuoRwCuAbYALgLt6th0C/Edr+xJg3zHKAVavqmcDXwK+DOxaVVsABwGfGOUY904yI8mMW2++cR6hSpIkSZIkabKYoBvbiklmATcCawK/GFBnR+DrVXUPQFXd1LPtqPY+E5g6j35+QJegezVw2EhhktXokm2ntqJvA88arbynve+39w2BjYFftOP4EPCoQQFU1f5VNb2qpq+2xsPmEaokSZIkSZImiwm6sd1ZVdOA9YEH0+5B1ydAjbL/yEi4ucCo94Orqr8Ac4DnAifOb7A97uiJ7bJ2H71pVfXUqnreJLQvSZIkSZKkSWCCbpyq6lZgH+A9SZbv23wC8JaRBzIkWXM+u/kI3ZTVuX393pxk21b0OuDU0coHtHklsHaSrVtsyyd5ynzGJ0mSJEmSpEnmEz4noKouTHIR3VNWT+/ZdCDwRODiJHOAA4CvzEf7Z42yaQ/g60lWAn4P7DVGeW+bdyfZFfhSmxY7Bfhf4LKJxidJkiRJkqTJl6rRZmZqWbbBxpvWZ478+bDDkKRl0i4b+rBtSZIkaWmUZGZVTe8vdwSdBlp9heX9gShJkiRJkrQIeA86SZIkSZIkaYhM0EmSJEmSJElDZIJOkiRJkiRJGiITdJIkSZIkSdIQ+RRXDZTkNuDKYcehJdZawA3DDkJLLK8fLQivHy0Irx8tKK8hLQivHy0Ir58lx/pVtXZ/oU9x1WiuHPTYX2k8kszw+tH88vrRgvD60YLw+tGC8hrSgvD60YLw+lnyOcVVkiRJkiRJGiITdJIkSZIkSdIQmaDTaPYfdgBaonn9aEF4/WhBeP1oQXj9aEF5DWlBeP1oQXj9LOF8SIQkSZIkSZI0RI6gkyRJkiRJkobIBJ0kSZIkSZI0RCbodD9Jnp/kyiS/TfL+YcejxU+SRyc5OcmvklyW5J2tfM0kv0jym/a+Rs8+H2jX1JVJ/mV40WtxkWS5JBcmObate/1oXJKsnuSIJFe0v0Nbe/1oIpK8u/3369IkhyVZwWtIo0lyUJLrk1zaUzbh6yXJFkkuadu+lCSL+li06I1y/Xy2/Tfs4iRHJ1m9Z5vXj+5n0DXUs+09SSrJWj1lXkNLMBN0+qckywH/B7wAeDLw6iRPHm5UWgzdA/y/qnoS8HTgbe06eT9wYlU9ATixrdO2vQp4CvB84KvtWtOy7Z3Ar3rWvX40Xl8Efl5VGwGb0l1HXj8alyTrAfsA06tqY2A5umvEa0ijOZjus+81P9fL14C9gSe0V3+bWjodzAM/618AG1fVJsCvgQ+A149GdTADPu8kjwaeC/yxp8xraAlngk69tgJ+W1W/r6q7gcOBnYYckxYzVXVdVV3Qlm+j+3G8Ht218u1W7dvAy9ryTsDhVXVXVV0F/JbuWtMyKsmjgBcBB/YUe/1oTEkeCjwL+CZAVd1dVbfg9aOJmQKsmGQKsBLwZ7yGNIqqOg24qa94QtdLknWBh1bV2dU9oe+Qnn20FBt0/VTVCVV1T1s9B3hUW/b60QOM8jcI4AvA+4Dep356DS3hTNCp13rAn3rWr2ll0kBJpgKbAecC61TVddAl8YCHt2peV+r3v3T/oLi3p8zrR+PxOOBvwLfaFOkDk6yM14/GqaquBT5HN+LgOuDWqjoBryFNzESvl/Xacn+59AbgZ23Z60fjkuSlwLVVdVHfJq+hJZwJOvUaNA+9BpRJJFkFOBJ4V1X9fV5VB5R5XS2jkrwYuL6qZo53lwFlXj/LrinA5sDXqmoz4A7a1LJReP3oftq9wnYCHgs8Elg5yWvntcuAMq8hjWa068XrSA+Q5IN0t445dKRoQDWvH91PkpWADwIfGbR5QJnX0BLEBJ16XQM8umf9UXTTPqT7SbI8XXLu0Ko6qhX/tQ2fpr1f38q9rtTrGcBLk1xNN43+OUm+i9ePxuca4JqqOretH0GXsPP60XjtCFxVVX+rqjnAUcA2eA1pYiZ6vVzDfdMYe8u1jEqyB/BiYPc25RC8fjQ+j6f7n0wXtX9PPwq4IMkj8Bpa4pmgU6/zgSckeWySB9PdYPKYIcekxUx74s83gV9V1ed7Nh0D7NGW9wB+3FP+qiQPSfJYupuSnreo4tXipao+UFWPqqqpdH9jTqqq1+L1o3Goqr8Af0qyYSvaAbgcrx+N3x+BpydZqf33bAe6e6l6DWkiJnS9tGmwtyV5ervuXt+zj5YxSZ4P/Afw0qr6R88mrx+NqaouqaqHV9XU9u/pa4DN27+RvIaWcFOGHYAWH1V1T5K3A8fTPdXsoKq6bMhhafHzDOB1wCVJZrWy/wQ+BfwgyRvpfgC9AqCqLkvyA7of0fcAb6uquYs8ai3uvH40Xu8ADm3/I+n3wF50/8PR60djqqpzkxwBXEB3TVwI7A+sgteQBkhyGLAdsFaSa4B9mb//Zv0b3dMYV6S759jP0FJvlOvnA8BDgF90uRLOqaq3eP1okEHXUFV9c1Bdr6ElX+4bUStJkiRJkiRpUXOKqyRJkiRJkjREJugkSZIkSZKkITJBJ0mSJEmSJA2RCTpJkiRJkiRpiEzQSZIkSZIkSUNkgk6SJC3x0rkqSSXZYNjxDEOSq9vxV5K7k/wmyaeTrDzBdh6cZL8k0xZSqIuVdqw3DDuO0SR5ZZI9hx3HIEme2M7f6uOsnyQXJdmjp+xlSX6f5Pok+w7Y5yNJfjyg/L1JTlygA5AkaTFigk6SJC0NtgamtuVXDTGOYfse3bnYETgEeDfwxQm28WBgX2DapEam+fVKYM9hBzGKJ9JdK6uPs/4rgTXorlOSrAV8F/gG8Hbg35M8b6RykvWAdwH/b0BbXwc2T7LdfEUuSdJixgSdJElaGrwauAM4ty0vEklWXFR9jdN1VXVOVZ1WVR8HDgJem2Ro/+ZLslySBw+rfy1W9gG+U1Vz2vrTgT9U1aer6gd0ibvn9tT/DPDNqvptf0NVdRtwJPCOhRyzJEmLhAk6SZK0REuyHPAK4Bi6hNSTk2zStq2c5I4kbx2w34wk3+lZf0ySw5PclOQfSY5PsmHP9qlt+ujuSQ5Jcgvwk7bt9UnOaPvenOTkJNMH9Pn2JH9qMf0oyQ6tze166jwoyfuT/DbJXUl+3TslcIIuAh4CrN3T/ppJvpHkr0lmJzkrydN69rmtvX+rZ8rs1CTbteWN+47plCRH9Kwf3M7ty5JcBswGntZT/twkF7dzcEaSp4x1EEke287X35PcluQn/VOZW2zvTPLJJH9rUyb/L8lDJnLCeo5zuyQ/THJ7m4L51p46e7XPZvW+fZ/S9t2hp2yndtyzk/wlyWeSLN+z/VFJftDivTPJ75J8fORcAi8Hnt3zWezXe95bLFe1OL+T5CFJtkpyXis7Jclj+uJcocXxp3YcFyV5YV+dq5N8Lsm7k1zTruvDR465XbM/adVHppdfPY/zugGwDXBET/GDgTt71v/RykjydGAH4OOjtUmXoHtxkjXnUUeSpCWCCTpJkrSkew6wDnA43Y//ObRRdFV1B3AssFvvDkkeB2wBfL+trwmcAWwIvIVuKt7KwC/zwFFyn6NLYr0C+GQrm0o3pfQVwGuAa4DTWj8jfe4MfJkukbgzcDHwzQHH82XgQ8D+wIuAo4GDkrx43GfkPo9psd7QYngI8Eu6UUrvBV4G/K0d5yPaPs9p7/9FN112a+C6CfY7lW70038DLwSu6onns8An6D6jhwM/SJLRGmoxnwg8CXgT3XTPxwKnDkjM/D/gkcBrWz9vBt45wdhHHECX4NwZOAX4vyRbtW1Htfed+/bZDbi+1SfJK1vd84CXAh8F9qY7LyMOAR7dyl9Ad25GkoofB04GLuS+z+LAnn2fDuxBN4rsfXTX7Zdb7F+kOw+Po7uWeh1Bdx4/CbwEOB84Jg+87+Ar6ZJkewP/AbyY+675C4D3tOVdWmz956PXDnSjXC/qKbsQeGqS7ZM8li4ZOaNdD18EPlRVf59Hm2cBywPbzqOOJElLhqry5cuXL1++fPlaYl90o+ZuBh7c1o+jSwilre8MzAUe2bPPB4Cbevb5OHAjsGZPnTWAW4G3tfWpQAFHjxHPg4ApwBXAR3rKzweO66v71dbmdm19A+BeYI++eocA54/R79XA/7S+VwKe387Lf/TUeSNwN/CEnrIpwO+Az7b1VVpMe/a1v10r37iv/BTgiJ71g1u9aX31Dgbu6ev7Za3uRvM4rre0/R7XU/aodhwf6Ckr4LS+fX8EnDPGedsPuGHAcX6sp2x5ukTmp3rKfgz8vK+tK4GvtOUAfwC+1VfnDXSjxh7W1m8HXjKP+I4AThlQfgpwC7BaT9kPWuzP6il7aytbqa3v0Naf3dfeacAP+66n3wFTesr+F/hLz/qLW1tTx/E93Z8B1zDwkdZG0X13p9AlHS8AHjSOdq8GPjFWPV++fPny5WtxfzmCTpIkLbHa6Kqd6ZJmd7fiw+iSaU9v6z+jS4K8omfX3fr22RH4BfD3JFOSTKEbeTYT6J+qetyAOJ6U5Ogkf6VLBs6hG433xLZ9ObqHLhzTt2v/+g50CbqjR+JosZwITGvtzMu/t77vaMd9clV9umf7ju2YruppG+DUAce5IK6tqlkDyq+uqt/0rF/e3h81j7a2Ai6oqt+PFFTVNcCZwDP76p7Qt375GG3Pyz/bqu6eab/pa+v7wA7pHnRAG332xFZOW34M3QjB3s/yJGAFYGSq8Czgv5Ps2T8VdRxmVNWtPeu/pUtcntFXBt3IQuiugb8AZw64xvqvgZOr6p6e9cuBh2f+7in4CNpIzl5V9TG6KdhTq+pFdOfmk3QPh5iS5OttyvIlSfo/b1qbjxhQLknSEsUEnSRJWpK9gO4Jkj9Nsnq7P9YpwF3cN811Nt1op90A0t1XblO6KbEj1mrb5/S9tqebftjrr70rSValS+Y8mi5Bti2wJd1UvhVatbXpRgb9ra+t/vW1gOXoRu71xnFw23/d0U8F0D0Rc0u6UWDfAnZO8m997T99wHHuNeA4F8RfRym/pW99JEG6AqNbd5T2/gr0T3Ed1P682p6Xsdo6hu7c7dLWdwOu5b7k2Frt/afc/1yPTPd9dM9+M4AvAH9IMqv3HnbzEeNtVXVvXxk9sa9Fl9Dqvwb244HXwKD2Q7tP3AStQPe9fICquqGq/tBWPwCcWVWn0Y2e3JQu2fkJ4PsD7il4F/P/GUuStNiYMnYVSZKkxdbIE1t/OGDbK5O8u6rm0o1q+kkbobQbXWLspJ66N9ElXAbdkP62vvXqW9+abmTVc6vqipHCJKv11Pkb3TTNtfv27V+/qdV7Bt1Iun7XDyjr9deqmtGWT02yPvCxJIdUdz++m+iSQf82YN+ByZMes9t7f3JmTR44Mqr/HC2I64BBD5JYh+54hqKqbk9yHN31tD/d/dp+UFUjxz4S295091rrd1Vr51pgz3RP2t2KLlF2TJLHVNWNCyH0m+gSiS9bCG2P1e88R7olmUo3JXezVrQ9cGhV3QwcnuQrdMm6S3p2W50hXgeSJE0WE3SSJGmJlGQVuntgHcYDb4K/GfB5uh/4v6Qb4XYzXRJlN7p7ps3tqX9i23ZZVd3JxIw8ROKfCa4k29BNs50JUFVzk8wCdgK+0bPvS/vaOoluBN1qVfWLCcYxyAeAc+nuPfcluuN8HvDHqhot2TfaqLZr2vuT6O4PRpJH003l/fUkxDqac4HXJ3lsVV3V+l2P7omg+y3EfsfjcLpRXS+hexhD76jMK+kSYVOr6oCxGmqj3s5J8lG6hx+sT3dfxAUZBTjIiXQP07i9N6E8n8YzAnLElXTJ7Hn5HN09/K7uKVsJ/jlN/CF0I/hoZQ+im0a8MK8/SZIWCRN0kiRpSbUT3Y/3L1bVub0bkpwJfJBuhN0vq2pOkqPppqCuSzdKp9fn6Z54eVKSL9MlVtYBng2cUVWHzSOOc+jucXdAks/Qjabbr7XR65P/v707eLGyjOI4/j07F2KgCP0BJS7bhQupNiG0SEQCW9giUSEwwzZFiZUEmWAuTFsoii7UhYoiohNkRQQTtLC0mZQkiVGhhRgNNgtPi/OOXi86mne6rxe/n80s3vc+95n7vqsf5zkHONRUAR2lquReaq7dBMjM0YjYQVULbaKq3WZQFWTzMnPFfX+VDpk5HBFDwFsRsY0aNrEaOB0Rm4HfgDlU5daVzNySmRMRcZGqQPyZqpw7k5l/RMQPwEcRMU61SnmX/796aTc1QfRERKynevxtoKr2vrj3x/riODDe7ONiZg5PXsjMmxGxDtgbEbOonoATVJC3GFhKDZ84ST2XX6kAah3VI+6XZqkR4OWIWEyFpGOZOdbDnoea7xyKiE+As8AsqkfijMx85z+sNdr8XRUR+4HxzPzpHvd+B6yPiLmZ2X20m4h4jttTaSd9DayNiHPUdOG/Or4TKhye2awtSdJAswedJEkaVMuA893hHNxq6n8QWNLRs2o/Fc6NAd923f8nFQ6MUL3ATgGbgCeAM1NtIjOvUgMonqR63a2lQrALXfcdBtZQ4cwRqlfc283l6x23vkEdtV1O9S/bTQV530y1jylspKr5Xmn68b1AhTQfUP/nVuBpYLjjM6upXmVfUtNnJwcMvApconrdfQx8yJ2BybTLzH+owQYjwE5gDzUd9fnMbPVoY/N7HqXeqwN3uX6ACpKfoY5hH6LC4R+psO4GdVzzzWadPVTg92JHJefn1HPaRT2LlT3uOam+ebuod/UkFTAu4M7hEg+y1u/UO7yECsmOTXH7aSrMXdR9oamE+4yayvt3x6XtVMXfPqrf5LLmfZi0iDoqfLcjxJIkDZS43SZDkiRJ/RQR71GVfrMf4mitNFAiYivwVDOtdTrW+x44npkbp2M9SZLa5BFXSZKkPoiIuVRPuK+oKqmF1NHNnYZzekx8CoxGxLzM7KlvXEQ8C8ynKuskSRp4BnSSJEn9MUEFCsupo7OXqeOl77e5Kalfmj6Gr1NHgnsd7DAbeC0zr/W8MUmSHgEecZUkSZIkSZJa5JAISZIkSZIkqUUGdJIkSZIkSVKLDOgkSZIkSZKkFhnQSZIkSZIkSS0yoJMkSZIkSZJa9C8WPUmuN+BKYQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1440x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot a bar chart that shows the average film (RoI) per director for the top 20 directors\n",
    "fig, ax = plt.subplots(figsize=(20,7))\n",
    "\n",
    "director_analysis_df.groupby('primary_name')['RoI'].mean().sort_values(ascending=True)[20:].plot(kind='barh', color='lightblue')\n",
    "ax.set_ylabel('Director', fontsize=15)\n",
    "ax.set_xlabel('Average Return on Investment (%)', fontsize=15)\n",
    "ax.set_title('Average Return on Investment per Director', fontsize=20);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see that the Top 5 directors with a highest average return on investment are: Kyle Balda, Pierre Coffin, Chris Rennaud, David F. Sandberg, and James Wan."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "7. **Writers**\n",
    "    * We are going to plot a barchart to visualize the relationship between writers and average RoI for the films. We first need to create a new DataFrame that contains the movies and their actor information."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "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>movie_id</th>\n",
       "      <th>release_date</th>\n",
       "      <th>movie</th>\n",
       "      <th>production_budget</th>\n",
       "      <th>worldwide_gross</th>\n",
       "      <th>RoI</th>\n",
       "      <th>runtime_minutes</th>\n",
       "      <th>genres</th>\n",
       "      <th>averagerating</th>\n",
       "      <th>person_id</th>\n",
       "      <th>primary_name</th>\n",
       "      <th>birth_year</th>\n",
       "      <th>death_year</th>\n",
       "      <th>primary_profession</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>tt2582846</td>\n",
       "      <td>2014-06-06</td>\n",
       "      <td>The Fault in Our Stars</td>\n",
       "      <td>12000000</td>\n",
       "      <td>307166834</td>\n",
       "      <td>2559.723617</td>\n",
       "      <td>126.0</td>\n",
       "      <td>Drama,Romance</td>\n",
       "      <td>7.7</td>\n",
       "      <td>nm2354099</td>\n",
       "      <td>Scott Neustadter</td>\n",
       "      <td>1977.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>writer,producer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>tt2582846</td>\n",
       "      <td>2014-06-06</td>\n",
       "      <td>The Fault in Our Stars</td>\n",
       "      <td>12000000</td>\n",
       "      <td>307166834</td>\n",
       "      <td>2559.723617</td>\n",
       "      <td>126.0</td>\n",
       "      <td>Drama,Romance</td>\n",
       "      <td>7.7</td>\n",
       "      <td>nm2352210</td>\n",
       "      <td>Michael H. Weber</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>writer,producer,miscellaneous</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>tt2582846</td>\n",
       "      <td>2014-06-06</td>\n",
       "      <td>The Fault in Our Stars</td>\n",
       "      <td>12000000</td>\n",
       "      <td>307166834</td>\n",
       "      <td>2559.723617</td>\n",
       "      <td>126.0</td>\n",
       "      <td>Drama,Romance</td>\n",
       "      <td>7.7</td>\n",
       "      <td>nm1981261</td>\n",
       "      <td>John Green</td>\n",
       "      <td>1977.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>producer,writer,director</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>tt3783958</td>\n",
       "      <td>2016-12-09</td>\n",
       "      <td>La La Land</td>\n",
       "      <td>20000000</td>\n",
       "      <td>426351163</td>\n",
       "      <td>2131.755815</td>\n",
       "      <td>128.0</td>\n",
       "      <td>Comedy,Drama,Music</td>\n",
       "      <td>8.0</td>\n",
       "      <td>nm3227090</td>\n",
       "      <td>Damien Chazelle</td>\n",
       "      <td>1985.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>producer,writer,director</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>tt5140878</td>\n",
       "      <td>2017-08-11</td>\n",
       "      <td>Annabelle: Creation</td>\n",
       "      <td>15000000</td>\n",
       "      <td>305384865</td>\n",
       "      <td>2035.899100</td>\n",
       "      <td>109.0</td>\n",
       "      <td>Horror,Mystery,Thriller</td>\n",
       "      <td>6.5</td>\n",
       "      <td>nm2477891</td>\n",
       "      <td>Gary Dauberman</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>writer,producer,director</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>165</th>\n",
       "      <td>tt1979388</td>\n",
       "      <td>2015-11-25</td>\n",
       "      <td>The Good Dinosaur</td>\n",
       "      <td>187500000</td>\n",
       "      <td>333771037</td>\n",
       "      <td>178.011220</td>\n",
       "      <td>93.0</td>\n",
       "      <td>Adventure,Animation,Comedy</td>\n",
       "      <td>6.7</td>\n",
       "      <td>nm0373282</td>\n",
       "      <td>Peter Hedges</td>\n",
       "      <td>1962.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>writer,director,actor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>166</th>\n",
       "      <td>tt3741700</td>\n",
       "      <td>2019-05-31</td>\n",
       "      <td>Godzilla: King of the Monsters</td>\n",
       "      <td>170000000</td>\n",
       "      <td>299276941</td>\n",
       "      <td>176.045259</td>\n",
       "      <td>132.0</td>\n",
       "      <td>Action,Adventure,Fantasy</td>\n",
       "      <td>6.7</td>\n",
       "      <td>nm1296461</td>\n",
       "      <td>Zach Shields</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>producer,writer,soundtrack</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>167</th>\n",
       "      <td>tt3778644</td>\n",
       "      <td>2018-05-25</td>\n",
       "      <td>Solo: A Star Wars Story</td>\n",
       "      <td>275000000</td>\n",
       "      <td>393151347</td>\n",
       "      <td>142.964126</td>\n",
       "      <td>135.0</td>\n",
       "      <td>Action,Adventure,Fantasy</td>\n",
       "      <td>7.0</td>\n",
       "      <td>nm0440459</td>\n",
       "      <td>Jonathan Kasdan</td>\n",
       "      <td>1979.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>actor,writer,director</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>167</th>\n",
       "      <td>tt3778644</td>\n",
       "      <td>2018-05-25</td>\n",
       "      <td>Solo: A Star Wars Story</td>\n",
       "      <td>275000000</td>\n",
       "      <td>393151347</td>\n",
       "      <td>142.964126</td>\n",
       "      <td>135.0</td>\n",
       "      <td>Action,Adventure,Fantasy</td>\n",
       "      <td>7.0</td>\n",
       "      <td>nm0001410</td>\n",
       "      <td>Lawrence Kasdan</td>\n",
       "      <td>1949.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>writer,producer,director</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>167</th>\n",
       "      <td>tt3778644</td>\n",
       "      <td>2018-05-25</td>\n",
       "      <td>Solo: A Star Wars Story</td>\n",
       "      <td>275000000</td>\n",
       "      <td>393151347</td>\n",
       "      <td>142.964126</td>\n",
       "      <td>135.0</td>\n",
       "      <td>Action,Adventure,Fantasy</td>\n",
       "      <td>7.0</td>\n",
       "      <td>nm0000184</td>\n",
       "      <td>George Lucas</td>\n",
       "      <td>1944.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>writer,producer,director</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>862 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      movie_id release_date                           movie  \\\n",
       "0    tt2582846   2014-06-06          The Fault in Our Stars   \n",
       "0    tt2582846   2014-06-06          The Fault in Our Stars   \n",
       "0    tt2582846   2014-06-06          The Fault in Our Stars   \n",
       "1    tt3783958   2016-12-09                      La La Land   \n",
       "2    tt5140878   2017-08-11             Annabelle: Creation   \n",
       "..         ...          ...                             ...   \n",
       "165  tt1979388   2015-11-25               The Good Dinosaur   \n",
       "166  tt3741700   2019-05-31  Godzilla: King of the Monsters   \n",
       "167  tt3778644   2018-05-25         Solo: A Star Wars Story   \n",
       "167  tt3778644   2018-05-25         Solo: A Star Wars Story   \n",
       "167  tt3778644   2018-05-25         Solo: A Star Wars Story   \n",
       "\n",
       "     production_budget  worldwide_gross          RoI  runtime_minutes  \\\n",
       "0             12000000        307166834  2559.723617            126.0   \n",
       "0             12000000        307166834  2559.723617            126.0   \n",
       "0             12000000        307166834  2559.723617            126.0   \n",
       "1             20000000        426351163  2131.755815            128.0   \n",
       "2             15000000        305384865  2035.899100            109.0   \n",
       "..                 ...              ...          ...              ...   \n",
       "165          187500000        333771037   178.011220             93.0   \n",
       "166          170000000        299276941   176.045259            132.0   \n",
       "167          275000000        393151347   142.964126            135.0   \n",
       "167          275000000        393151347   142.964126            135.0   \n",
       "167          275000000        393151347   142.964126            135.0   \n",
       "\n",
       "                         genres  averagerating  person_id      primary_name  \\\n",
       "0                 Drama,Romance            7.7  nm2354099  Scott Neustadter   \n",
       "0                 Drama,Romance            7.7  nm2352210  Michael H. Weber   \n",
       "0                 Drama,Romance            7.7  nm1981261        John Green   \n",
       "1            Comedy,Drama,Music            8.0  nm3227090   Damien Chazelle   \n",
       "2       Horror,Mystery,Thriller            6.5  nm2477891    Gary Dauberman   \n",
       "..                          ...            ...        ...               ...   \n",
       "165  Adventure,Animation,Comedy            6.7  nm0373282      Peter Hedges   \n",
       "166    Action,Adventure,Fantasy            6.7  nm1296461      Zach Shields   \n",
       "167    Action,Adventure,Fantasy            7.0  nm0440459   Jonathan Kasdan   \n",
       "167    Action,Adventure,Fantasy            7.0  nm0001410   Lawrence Kasdan   \n",
       "167    Action,Adventure,Fantasy            7.0  nm0000184      George Lucas   \n",
       "\n",
       "     birth_year  death_year             primary_profession  \n",
       "0        1977.0         NaN                writer,producer  \n",
       "0           NaN         NaN  writer,producer,miscellaneous  \n",
       "0        1977.0         NaN       producer,writer,director  \n",
       "1        1985.0         NaN       producer,writer,director  \n",
       "2           NaN         NaN       writer,producer,director  \n",
       "..          ...         ...                            ...  \n",
       "165      1962.0         NaN          writer,director,actor  \n",
       "166         NaN         NaN     producer,writer,soundtrack  \n",
       "167      1979.0         NaN          actor,writer,director  \n",
       "167      1949.0         NaN       writer,producer,director  \n",
       "167      1944.0         NaN       writer,producer,director  \n",
       "\n",
       "[862 rows x 14 columns]"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a copy of the 'top_movies_df' DataFrame to a new variable called 'writer_analysis_df'\n",
    "writer_analysis_df = top_movies_df.copy()\n",
    "\n",
    "# Join the top_movies_df DataFrame to the writer_analysis_df DataFrame\n",
    "writer_analysis_df = writer_analysis_df.join(writers_df.set_index('movie_id'), on='movie_id', how='left')\n",
    "\n",
    "# Join the persons_df DataFrame to the writer_analysis_df DataFrame so that we can identify their names\n",
    "writer_analysis_df = writer_analysis_df.join(persons_df.set_index('person_id'), on='person_id', how='inner')\n",
    "writer_analysis_df # Preview the updated 'writer_analysis_df' DataFrame"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we are once again looking for writers with a proven track record, we need to identify directors that have more than one film in our DataFrame. Thereafter, we will be plot a barchart to visualize the relationship between these writers and the average RoI for their films."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>movie_id</th>\n",
       "      <th>release_date</th>\n",
       "      <th>movie</th>\n",
       "      <th>production_budget</th>\n",
       "      <th>worldwide_gross</th>\n",
       "      <th>RoI</th>\n",
       "      <th>runtime_minutes</th>\n",
       "      <th>genres</th>\n",
       "      <th>averagerating</th>\n",
       "      <th>person_id</th>\n",
       "      <th>primary_name</th>\n",
       "      <th>birth_year</th>\n",
       "      <th>death_year</th>\n",
       "      <th>primary_profession</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>tt5140878</td>\n",
       "      <td>2017-08-11</td>\n",
       "      <td>Annabelle: Creation</td>\n",
       "      <td>15000000</td>\n",
       "      <td>305384865</td>\n",
       "      <td>2035.899100</td>\n",
       "      <td>109.0</td>\n",
       "      <td>Horror,Mystery,Thriller</td>\n",
       "      <td>6.5</td>\n",
       "      <td>nm2477891</td>\n",
       "      <td>Gary Dauberman</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>writer,producer,director</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>tt1396484</td>\n",
       "      <td>2017-09-08</td>\n",
       "      <td>It</td>\n",
       "      <td>35000000</td>\n",
       "      <td>697457969</td>\n",
       "      <td>1992.737054</td>\n",
       "      <td>135.0</td>\n",
       "      <td>Horror,Thriller</td>\n",
       "      <td>7.4</td>\n",
       "      <td>nm2477891</td>\n",
       "      <td>Gary Dauberman</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>writer,producer,director</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>tt2293640</td>\n",
       "      <td>2015-07-10</td>\n",
       "      <td>Minions</td>\n",
       "      <td>74000000</td>\n",
       "      <td>1160336173</td>\n",
       "      <td>1568.021855</td>\n",
       "      <td>91.0</td>\n",
       "      <td>Adventure,Animation,Comedy</td>\n",
       "      <td>6.4</td>\n",
       "      <td>nm0528244</td>\n",
       "      <td>Brian Lynch</td>\n",
       "      <td>1973.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>writer,actor,director</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>tt2293640</td>\n",
       "      <td>2015-07-10</td>\n",
       "      <td>Minions</td>\n",
       "      <td>74000000</td>\n",
       "      <td>1160336173</td>\n",
       "      <td>1568.021855</td>\n",
       "      <td>91.0</td>\n",
       "      <td>Adventure,Animation,Comedy</td>\n",
       "      <td>6.4</td>\n",
       "      <td>nm0528244</td>\n",
       "      <td>Brian Lynch</td>\n",
       "      <td>1973.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>writer,actor,director</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>tt2709768</td>\n",
       "      <td>2016-07-08</td>\n",
       "      <td>The Secret Life of Pets</td>\n",
       "      <td>75000000</td>\n",
       "      <td>886750534</td>\n",
       "      <td>1182.334045</td>\n",
       "      <td>87.0</td>\n",
       "      <td>Adventure,Animation,Comedy</td>\n",
       "      <td>6.5</td>\n",
       "      <td>nm0528244</td>\n",
       "      <td>Brian Lynch</td>\n",
       "      <td>1973.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>writer,actor,director</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>164</th>\n",
       "      <td>tt2660888</td>\n",
       "      <td>2016-07-22</td>\n",
       "      <td>Star Trek Beyond</td>\n",
       "      <td>185000000</td>\n",
       "      <td>335802233</td>\n",
       "      <td>181.514721</td>\n",
       "      <td>122.0</td>\n",
       "      <td>Action,Adventure,Sci-Fi</td>\n",
       "      <td>7.1</td>\n",
       "      <td>nm0734472</td>\n",
       "      <td>Gene Roddenberry</td>\n",
       "      <td>1921.0</td>\n",
       "      <td>1991.0</td>\n",
       "      <td>writer,producer,miscellaneous</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>tt1623205</td>\n",
       "      <td>2013-03-08</td>\n",
       "      <td>Oz the Great and Powerful</td>\n",
       "      <td>200000000</td>\n",
       "      <td>490359051</td>\n",
       "      <td>245.179525</td>\n",
       "      <td>130.0</td>\n",
       "      <td>Adventure,Family,Fantasy</td>\n",
       "      <td>6.3</td>\n",
       "      <td>nm1865755</td>\n",
       "      <td>David Lindsay-Abaire</td>\n",
       "      <td>1969.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>writer,soundtrack,music_department</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>157</th>\n",
       "      <td>tt1446192</td>\n",
       "      <td>2012-11-21</td>\n",
       "      <td>Rise of the Guardians</td>\n",
       "      <td>145000000</td>\n",
       "      <td>306900902</td>\n",
       "      <td>211.655794</td>\n",
       "      <td>97.0</td>\n",
       "      <td>Action,Adventure,Animation</td>\n",
       "      <td>7.3</td>\n",
       "      <td>nm1865755</td>\n",
       "      <td>David Lindsay-Abaire</td>\n",
       "      <td>1969.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>writer,soundtrack,music_department</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>158</th>\n",
       "      <td>tt2345759</td>\n",
       "      <td>2017-06-09</td>\n",
       "      <td>The Mummy</td>\n",
       "      <td>195000000</td>\n",
       "      <td>409953905</td>\n",
       "      <td>210.232772</td>\n",
       "      <td>110.0</td>\n",
       "      <td>Action,Adventure,Fantasy</td>\n",
       "      <td>5.5</td>\n",
       "      <td>nm0003160</td>\n",
       "      <td>Christopher McQuarrie</td>\n",
       "      <td>1968.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>writer,producer,director</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>159</th>\n",
       "      <td>tt1631867</td>\n",
       "      <td>2014-06-06</td>\n",
       "      <td>Edge of Tomorrow</td>\n",
       "      <td>178000000</td>\n",
       "      <td>370541256</td>\n",
       "      <td>208.169245</td>\n",
       "      <td>113.0</td>\n",
       "      <td>Action,Sci-Fi</td>\n",
       "      <td>7.9</td>\n",
       "      <td>nm0003160</td>\n",
       "      <td>Christopher McQuarrie</td>\n",
       "      <td>1968.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>writer,producer,director</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>601 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      movie_id release_date                      movie  production_budget  \\\n",
       "2    tt5140878   2017-08-11        Annabelle: Creation           15000000   \n",
       "3    tt1396484   2017-09-08                         It           35000000   \n",
       "7    tt2293640   2015-07-10                    Minions           74000000   \n",
       "7    tt2293640   2015-07-10                    Minions           74000000   \n",
       "14   tt2709768   2016-07-08    The Secret Life of Pets           75000000   \n",
       "..         ...          ...                        ...                ...   \n",
       "164  tt2660888   2016-07-22           Star Trek Beyond          185000000   \n",
       "148  tt1623205   2013-03-08  Oz the Great and Powerful          200000000   \n",
       "157  tt1446192   2012-11-21      Rise of the Guardians          145000000   \n",
       "158  tt2345759   2017-06-09                  The Mummy          195000000   \n",
       "159  tt1631867   2014-06-06           Edge of Tomorrow          178000000   \n",
       "\n",
       "     worldwide_gross          RoI  runtime_minutes  \\\n",
       "2          305384865  2035.899100            109.0   \n",
       "3          697457969  1992.737054            135.0   \n",
       "7         1160336173  1568.021855             91.0   \n",
       "7         1160336173  1568.021855             91.0   \n",
       "14         886750534  1182.334045             87.0   \n",
       "..               ...          ...              ...   \n",
       "164        335802233   181.514721            122.0   \n",
       "148        490359051   245.179525            130.0   \n",
       "157        306900902   211.655794             97.0   \n",
       "158        409953905   210.232772            110.0   \n",
       "159        370541256   208.169245            113.0   \n",
       "\n",
       "                         genres  averagerating  person_id  \\\n",
       "2       Horror,Mystery,Thriller            6.5  nm2477891   \n",
       "3               Horror,Thriller            7.4  nm2477891   \n",
       "7    Adventure,Animation,Comedy            6.4  nm0528244   \n",
       "7    Adventure,Animation,Comedy            6.4  nm0528244   \n",
       "14   Adventure,Animation,Comedy            6.5  nm0528244   \n",
       "..                          ...            ...        ...   \n",
       "164     Action,Adventure,Sci-Fi            7.1  nm0734472   \n",
       "148    Adventure,Family,Fantasy            6.3  nm1865755   \n",
       "157  Action,Adventure,Animation            7.3  nm1865755   \n",
       "158    Action,Adventure,Fantasy            5.5  nm0003160   \n",
       "159               Action,Sci-Fi            7.9  nm0003160   \n",
       "\n",
       "              primary_name  birth_year  death_year  \\\n",
       "2           Gary Dauberman         NaN         NaN   \n",
       "3           Gary Dauberman         NaN         NaN   \n",
       "7              Brian Lynch      1973.0         NaN   \n",
       "7              Brian Lynch      1973.0         NaN   \n",
       "14             Brian Lynch      1973.0         NaN   \n",
       "..                     ...         ...         ...   \n",
       "164       Gene Roddenberry      1921.0      1991.0   \n",
       "148   David Lindsay-Abaire      1969.0         NaN   \n",
       "157   David Lindsay-Abaire      1969.0         NaN   \n",
       "158  Christopher McQuarrie      1968.0         NaN   \n",
       "159  Christopher McQuarrie      1968.0         NaN   \n",
       "\n",
       "                     primary_profession  \n",
       "2              writer,producer,director  \n",
       "3              writer,producer,director  \n",
       "7                 writer,actor,director  \n",
       "7                 writer,actor,director  \n",
       "14                writer,actor,director  \n",
       "..                                  ...  \n",
       "164       writer,producer,miscellaneous  \n",
       "148  writer,soundtrack,music_department  \n",
       "157  writer,soundtrack,music_department  \n",
       "158            writer,producer,director  \n",
       "159            writer,producer,director  \n",
       "\n",
       "[601 rows x 14 columns]"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Writers with more than one film will give the 'person_id' column duplicates. Therefore, we need to identify the duplicate person_id values\n",
    "writer_ids = writer_analysis_df[writer_analysis_df.duplicated(subset='person_id')]['person_id'].unique()\n",
    "\n",
    "# Update the 'writer_analysis_df' DataFrame to only include writers with multiple films\n",
    "writer_analysis_df = writer_analysis_df[writer_analysis_df['person_id'].isin(writer_ids)]\n",
    "writer_analysis_df # Preview the updated 'director_analysis_df' DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1440x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot a bar chart that shows the average film (RoI) per writer for the top 20 writers\n",
    "fig, ax = plt.subplots(figsize=(20,7))\n",
    "\n",
    "writer_analysis_df.groupby('primary_name')['RoI'].mean().sort_values(ascending=False)[:20].plot(kind='barh', color='lightgreen')\n",
    "ax.set_ylabel('Writer', fontsize=15)\n",
    "ax.set_xlabel('Average Return on Investment (%)', fontsize=15)\n",
    "ax.set_title('Average Return on Investment per Writer', fontsize=20);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see that Gary Dauberman has the highest average RoI amongst the writers with an average RoI that's nearly 50% better than the second highest writer, Brian Lynch."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "8. **Rating**\n",
    "   * The rating of the film is the rank on a scale of 0-10 that the film was given by the people. We are going to plot a scatter plot to visualize the relationship between the 'rating' and 'RoI'. From this we want to see what people think of these top performing films and whether being a top performing film means that it is a good film."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1440x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot a scatter plot that shows the relationship between RoI and rating \n",
    "fig, ax = plt.subplots(figsize=(20,7))\n",
    "\n",
    "ax.scatter(top_movies_df['averagerating'], top_movies_df['RoI'], alpha=0.5, color='orange')\n",
    "ax.set_xlabel('Rating', fontsize=15)\n",
    "ax.set_ylabel('Return on Investment (%)', fontsize=15)\n",
    "ax.set_title('Average Rating vs Return on Investment', fontsize=20);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.1519738142877462"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Evaluate the Pearson correlation coefficient between the 'averagerating' and 'RoI' columns\n",
    "np.corrcoef(top_movies_df['averagerating'], top_movies_df['RoI'])[0,1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Based on the scatter plot, it is difficult to see any distinct relationship between the rating and the RoI. However, looking at the Pearson correlation coefficient (0.15), we see that it is closer to 0 therefore confirming that there is indeed no relationship between the rating of a film and the RoI."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusion\n",
    "This analysis leads to the following reccommendations for the types of films that are the best performing in the box office:\n",
    "\n",
    "1. **The length of a film (Runtime) of a film has no impact on its box office performance.**\n",
    "2. **The Production Budget of a film has a modereately negative correlation with its return on investment.**\n",
    "3. **The Worldwide Gross of a film has no impact on its return on investment.**\n",
    "4. **Movies released in the Summer are more likely to yield a higher return on investment**. Though September (Fall) had the highest average return on investment, the number of films released was significantly less than other months. Therefore, it would not have been an accurate measure to determine the optimal time to release a film.\n",
    "5. **'Horror' and 'Music' genres are more likely to have a higher return on investment.**. It is however important to note that 'Action' and 'Adventure' films are the top most grossing.\n",
    "6. **Films directed by Kyle Balda, Pierre Coffin, Chris Rennaud, David F. Sandberg, and James Wan produce the highest return on investment.**\n",
    "7. **Films written by Gary Dauberman produce the highest return on investment.**\n",
    "8. **Success of a film is not determinant on the films rating**"
   ]
  }
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