012_EDA_Ratings.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exploratory Data Analysis: Rating and Revenue"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Libraries to import:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "sns.set_palette(\"muted\")\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load in our dataset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>url</th>\n",
       "      <th>title</th>\n",
       "      <th>year</th>\n",
       "      <th>genre</th>\n",
       "      <th>gross_usa</th>\n",
       "      <th>budget</th>\n",
       "      <th>worldwide_gross</th>\n",
       "      <th>pg_rated</th>\n",
       "      <th>rating</th>\n",
       "      <th>rating_pop</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>https://www.imdb.com/title/tt0054215/</td>\n",
       "      <td>Psycho</td>\n",
       "      <td>1960</td>\n",
       "      <td>['Horror', 'Mystery', 'Thriller']</td>\n",
       "      <td>$32,000,000</td>\n",
       "      <td>$806,947</td>\n",
       "      <td>$32,000,000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.5</td>\n",
       "      <td>580,863</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>https://www.imdb.com/title/tt0054047/</td>\n",
       "      <td>The Magnificent Seven</td>\n",
       "      <td>1960</td>\n",
       "      <td>['Action', 'Adventure', 'Western']</td>\n",
       "      <td>NaN</td>\n",
       "      <td>$2,000,000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.7</td>\n",
       "      <td>85,315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>https://www.imdb.com/title/tt0054331/</td>\n",
       "      <td>Spartacus</td>\n",
       "      <td>1960</td>\n",
       "      <td>['Adventure', 'Biography', 'Drama', 'History',...</td>\n",
       "      <td>$1,830,650</td>\n",
       "      <td>$12,000,000</td>\n",
       "      <td>$1,836,351</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.9</td>\n",
       "      <td>120,895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>https://www.imdb.com/title/tt0053604/</td>\n",
       "      <td>The Apartment</td>\n",
       "      <td>1960</td>\n",
       "      <td>['Comedy', 'Drama', 'Romance']</td>\n",
       "      <td>NaN</td>\n",
       "      <td>$3,000,000</td>\n",
       "      <td>$177,066</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.3</td>\n",
       "      <td>157,134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>https://www.imdb.com/title/tt0053917/</td>\n",
       "      <td>Home from the Hill</td>\n",
       "      <td>1960</td>\n",
       "      <td>['Drama', 'Romance']</td>\n",
       "      <td>NaN</td>\n",
       "      <td>$1,818,688</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.4</td>\n",
       "      <td>2,864</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18295</th>\n",
       "      <td>2995</td>\n",
       "      <td>https://www.imdb.com/title/tt7541720/</td>\n",
       "      <td>Music</td>\n",
       "      <td>2020</td>\n",
       "      <td>['Drama']</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>PG-13</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18296</th>\n",
       "      <td>2996</td>\n",
       "      <td>https://www.imdb.com/title/tt5867314/</td>\n",
       "      <td>The Empty Man</td>\n",
       "      <td>2020</td>\n",
       "      <td>['Crime', 'Drama', 'Horror', 'Mystery', 'Thril...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>R</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18297</th>\n",
       "      <td>2997</td>\n",
       "      <td>https://www.imdb.com/title/tt5714470/</td>\n",
       "      <td>Come Away</td>\n",
       "      <td>2020</td>\n",
       "      <td>['Adventure', 'Drama', 'Fantasy']</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.6</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18298</th>\n",
       "      <td>2998</td>\n",
       "      <td>https://www.imdb.com/title/tt6559576/</td>\n",
       "      <td>Selfie Dad</td>\n",
       "      <td>2020</td>\n",
       "      <td>['Comedy', 'Family']</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>PG</td>\n",
       "      <td>3.7</td>\n",
       "      <td>83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18299</th>\n",
       "      <td>2999</td>\n",
       "      <td>https://www.imdb.com/title/tt9614452/</td>\n",
       "      <td>The Big Bull</td>\n",
       "      <td>2020</td>\n",
       "      <td>['Crime', 'Drama']</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>18300 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Unnamed: 0                                    url  \\\n",
       "0               0  https://www.imdb.com/title/tt0054215/   \n",
       "1               1  https://www.imdb.com/title/tt0054047/   \n",
       "2               2  https://www.imdb.com/title/tt0054331/   \n",
       "3               3  https://www.imdb.com/title/tt0053604/   \n",
       "4               4  https://www.imdb.com/title/tt0053917/   \n",
       "...           ...                                    ...   \n",
       "18295        2995  https://www.imdb.com/title/tt7541720/   \n",
       "18296        2996  https://www.imdb.com/title/tt5867314/   \n",
       "18297        2997  https://www.imdb.com/title/tt5714470/   \n",
       "18298        2998  https://www.imdb.com/title/tt6559576/   \n",
       "18299        2999  https://www.imdb.com/title/tt9614452/   \n",
       "\n",
       "                       title  year  \\\n",
       "0                     Psycho  1960   \n",
       "1      The Magnificent Seven  1960   \n",
       "2                  Spartacus  1960   \n",
       "3              The Apartment  1960   \n",
       "4         Home from the Hill  1960   \n",
       "...                      ...   ...   \n",
       "18295                  Music  2020   \n",
       "18296          The Empty Man  2020   \n",
       "18297              Come Away  2020   \n",
       "18298             Selfie Dad  2020   \n",
       "18299           The Big Bull  2020   \n",
       "\n",
       "                                                   genre    gross_usa  \\\n",
       "0                      ['Horror', 'Mystery', 'Thriller']  $32,000,000   \n",
       "1                     ['Action', 'Adventure', 'Western']          NaN   \n",
       "2      ['Adventure', 'Biography', 'Drama', 'History',...   $1,830,650   \n",
       "3                         ['Comedy', 'Drama', 'Romance']          NaN   \n",
       "4                                   ['Drama', 'Romance']          NaN   \n",
       "...                                                  ...          ...   \n",
       "18295                                          ['Drama']          NaN   \n",
       "18296  ['Crime', 'Drama', 'Horror', 'Mystery', 'Thril...          NaN   \n",
       "18297                  ['Adventure', 'Drama', 'Fantasy']          NaN   \n",
       "18298                               ['Comedy', 'Family']          NaN   \n",
       "18299                                 ['Crime', 'Drama']          NaN   \n",
       "\n",
       "            budget worldwide_gross pg_rated  rating rating_pop  \n",
       "0         $806,947     $32,000,000      NaN     8.5    580,863  \n",
       "1       $2,000,000             NaN      NaN     7.7     85,315  \n",
       "2      $12,000,000      $1,836,351      NaN     7.9    120,895  \n",
       "3       $3,000,000        $177,066      NaN     8.3    157,134  \n",
       "4       $1,818,688             NaN      NaN     7.4      2,864  \n",
       "...            ...             ...      ...     ...        ...  \n",
       "18295          NaN             NaN    PG-13     NaN        NaN  \n",
       "18296          NaN             NaN        R     NaN        NaN  \n",
       "18297          NaN             NaN      NaN     6.6         27  \n",
       "18298          NaN             NaN       PG     3.7         83  \n",
       "18299          NaN             NaN      NaN     NaN        NaN  \n",
       "\n",
       "[18300 rows x 11 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined = pd.read_csv('../zippedData/combined.csv') # Load in our combined dataset.\n",
    "combined"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Cleaning:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We are concatenating several dataframes so their indexes are shared. We reset the index so they are unique."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "combined.reset_index(inplace=True) # Reset index due to concatenation."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we have to remove the extra column of old indexes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "combined = combined.drop('Unnamed: 0', axis=1)  # Remove extra index column from resetting index. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's make sure that our changes have been successful."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>url</th>\n",
       "      <th>title</th>\n",
       "      <th>year</th>\n",
       "      <th>genre</th>\n",
       "      <th>gross_usa</th>\n",
       "      <th>budget</th>\n",
       "      <th>worldwide_gross</th>\n",
       "      <th>pg_rated</th>\n",
       "      <th>rating</th>\n",
       "      <th>rating_pop</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>https://www.imdb.com/title/tt0054215/</td>\n",
       "      <td>Psycho</td>\n",
       "      <td>1960</td>\n",
       "      <td>['Horror', 'Mystery', 'Thriller']</td>\n",
       "      <td>$32,000,000</td>\n",
       "      <td>$806,947</td>\n",
       "      <td>$32,000,000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.5</td>\n",
       "      <td>580,863</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>https://www.imdb.com/title/tt0054047/</td>\n",
       "      <td>The Magnificent Seven</td>\n",
       "      <td>1960</td>\n",
       "      <td>['Action', 'Adventure', 'Western']</td>\n",
       "      <td>NaN</td>\n",
       "      <td>$2,000,000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.7</td>\n",
       "      <td>85,315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>https://www.imdb.com/title/tt0054331/</td>\n",
       "      <td>Spartacus</td>\n",
       "      <td>1960</td>\n",
       "      <td>['Adventure', 'Biography', 'Drama', 'History',...</td>\n",
       "      <td>$1,830,650</td>\n",
       "      <td>$12,000,000</td>\n",
       "      <td>$1,836,351</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.9</td>\n",
       "      <td>120,895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>https://www.imdb.com/title/tt0053604/</td>\n",
       "      <td>The Apartment</td>\n",
       "      <td>1960</td>\n",
       "      <td>['Comedy', 'Drama', 'Romance']</td>\n",
       "      <td>NaN</td>\n",
       "      <td>$3,000,000</td>\n",
       "      <td>$177,066</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.3</td>\n",
       "      <td>157,134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>https://www.imdb.com/title/tt0053917/</td>\n",
       "      <td>Home from the Hill</td>\n",
       "      <td>1960</td>\n",
       "      <td>['Drama', 'Romance']</td>\n",
       "      <td>NaN</td>\n",
       "      <td>$1,818,688</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.4</td>\n",
       "      <td>2,864</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18295</th>\n",
       "      <td>18295</td>\n",
       "      <td>https://www.imdb.com/title/tt7541720/</td>\n",
       "      <td>Music</td>\n",
       "      <td>2020</td>\n",
       "      <td>['Drama']</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>PG-13</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18296</th>\n",
       "      <td>18296</td>\n",
       "      <td>https://www.imdb.com/title/tt5867314/</td>\n",
       "      <td>The Empty Man</td>\n",
       "      <td>2020</td>\n",
       "      <td>['Crime', 'Drama', 'Horror', 'Mystery', 'Thril...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>R</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18297</th>\n",
       "      <td>18297</td>\n",
       "      <td>https://www.imdb.com/title/tt5714470/</td>\n",
       "      <td>Come Away</td>\n",
       "      <td>2020</td>\n",
       "      <td>['Adventure', 'Drama', 'Fantasy']</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.6</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18298</th>\n",
       "      <td>18298</td>\n",
       "      <td>https://www.imdb.com/title/tt6559576/</td>\n",
       "      <td>Selfie Dad</td>\n",
       "      <td>2020</td>\n",
       "      <td>['Comedy', 'Family']</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>PG</td>\n",
       "      <td>3.7</td>\n",
       "      <td>83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18299</th>\n",
       "      <td>18299</td>\n",
       "      <td>https://www.imdb.com/title/tt9614452/</td>\n",
       "      <td>The Big Bull</td>\n",
       "      <td>2020</td>\n",
       "      <td>['Crime', 'Drama']</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>18300 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       index                                    url                  title  \\\n",
       "0          0  https://www.imdb.com/title/tt0054215/                 Psycho   \n",
       "1          1  https://www.imdb.com/title/tt0054047/  The Magnificent Seven   \n",
       "2          2  https://www.imdb.com/title/tt0054331/              Spartacus   \n",
       "3          3  https://www.imdb.com/title/tt0053604/          The Apartment   \n",
       "4          4  https://www.imdb.com/title/tt0053917/     Home from the Hill   \n",
       "...      ...                                    ...                    ...   \n",
       "18295  18295  https://www.imdb.com/title/tt7541720/                  Music   \n",
       "18296  18296  https://www.imdb.com/title/tt5867314/          The Empty Man   \n",
       "18297  18297  https://www.imdb.com/title/tt5714470/              Come Away   \n",
       "18298  18298  https://www.imdb.com/title/tt6559576/             Selfie Dad   \n",
       "18299  18299  https://www.imdb.com/title/tt9614452/           The Big Bull   \n",
       "\n",
       "       year                                              genre    gross_usa  \\\n",
       "0      1960                  ['Horror', 'Mystery', 'Thriller']  $32,000,000   \n",
       "1      1960                 ['Action', 'Adventure', 'Western']          NaN   \n",
       "2      1960  ['Adventure', 'Biography', 'Drama', 'History',...   $1,830,650   \n",
       "3      1960                     ['Comedy', 'Drama', 'Romance']          NaN   \n",
       "4      1960                               ['Drama', 'Romance']          NaN   \n",
       "...     ...                                                ...          ...   \n",
       "18295  2020                                          ['Drama']          NaN   \n",
       "18296  2020  ['Crime', 'Drama', 'Horror', 'Mystery', 'Thril...          NaN   \n",
       "18297  2020                  ['Adventure', 'Drama', 'Fantasy']          NaN   \n",
       "18298  2020                               ['Comedy', 'Family']          NaN   \n",
       "18299  2020                                 ['Crime', 'Drama']          NaN   \n",
       "\n",
       "            budget worldwide_gross pg_rated  rating rating_pop  \n",
       "0         $806,947     $32,000,000      NaN     8.5    580,863  \n",
       "1       $2,000,000             NaN      NaN     7.7     85,315  \n",
       "2      $12,000,000      $1,836,351      NaN     7.9    120,895  \n",
       "3       $3,000,000        $177,066      NaN     8.3    157,134  \n",
       "4       $1,818,688             NaN      NaN     7.4      2,864  \n",
       "...            ...             ...      ...     ...        ...  \n",
       "18295          NaN             NaN    PG-13     NaN        NaN  \n",
       "18296          NaN             NaN        R     NaN        NaN  \n",
       "18297          NaN             NaN      NaN     6.6         27  \n",
       "18298          NaN             NaN       PG     3.7         83  \n",
       "18299          NaN             NaN      NaN     NaN        NaN  \n",
       "\n",
       "[18300 rows x 11 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined # Examine changes."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "For visualization we need our rating to be a numerical value so we will do an explicit type check to make sure."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('float64')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined.rating.dtype # Confirm the rating is numeric type."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6.219369740966871"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined.rating.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Cleaning our rating and rating_pop columns by removing commas and any missing values in either column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>index</th>\n",
       "      <th>url</th>\n",
       "      <th>title</th>\n",
       "      <th>year</th>\n",
       "      <th>genre</th>\n",
       "      <th>gross_usa</th>\n",
       "      <th>budget</th>\n",
       "      <th>worldwide_gross</th>\n",
       "      <th>pg_rated</th>\n",
       "      <th>rating</th>\n",
       "      <th>rating_pop</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>https://www.imdb.com/title/tt0054215/</td>\n",
       "      <td>Psycho</td>\n",
       "      <td>1960</td>\n",
       "      <td>['Horror', 'Mystery', 'Thriller']</td>\n",
       "      <td>$32,000,000</td>\n",
       "      <td>$806,947</td>\n",
       "      <td>$32,000,000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.5</td>\n",
       "      <td>580863</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>https://www.imdb.com/title/tt0054047/</td>\n",
       "      <td>The Magnificent Seven</td>\n",
       "      <td>1960</td>\n",
       "      <td>['Action', 'Adventure', 'Western']</td>\n",
       "      <td>NaN</td>\n",
       "      <td>$2,000,000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.7</td>\n",
       "      <td>85315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>https://www.imdb.com/title/tt0054331/</td>\n",
       "      <td>Spartacus</td>\n",
       "      <td>1960</td>\n",
       "      <td>['Adventure', 'Biography', 'Drama', 'History',...</td>\n",
       "      <td>$1,830,650</td>\n",
       "      <td>$12,000,000</td>\n",
       "      <td>$1,836,351</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.9</td>\n",
       "      <td>120895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>https://www.imdb.com/title/tt0053604/</td>\n",
       "      <td>The Apartment</td>\n",
       "      <td>1960</td>\n",
       "      <td>['Comedy', 'Drama', 'Romance']</td>\n",
       "      <td>NaN</td>\n",
       "      <td>$3,000,000</td>\n",
       "      <td>$177,066</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.3</td>\n",
       "      <td>157134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>https://www.imdb.com/title/tt0053917/</td>\n",
       "      <td>Home from the Hill</td>\n",
       "      <td>1960</td>\n",
       "      <td>['Drama', 'Romance']</td>\n",
       "      <td>NaN</td>\n",
       "      <td>$1,818,688</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.4</td>\n",
       "      <td>2864</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18291</th>\n",
       "      <td>18291</td>\n",
       "      <td>https://www.imdb.com/title/tt10370380/</td>\n",
       "      <td>The Windermere Children</td>\n",
       "      <td>2020</td>\n",
       "      <td>['Drama', 'War']</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.3</td>\n",
       "      <td>1577</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18292</th>\n",
       "      <td>18292</td>\n",
       "      <td>https://www.imdb.com/title/tt11127878/</td>\n",
       "      <td>Milea</td>\n",
       "      <td>2020</td>\n",
       "      <td>['Drama', 'Romance']</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.2</td>\n",
       "      <td>187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18293</th>\n",
       "      <td>18293</td>\n",
       "      <td>https://www.imdb.com/title/tt11358398/</td>\n",
       "      <td>The Man Standing Next</td>\n",
       "      <td>2020</td>\n",
       "      <td>['History', 'Thriller']</td>\n",
       "      <td>$113,527</td>\n",
       "      <td>NaN</td>\n",
       "      <td>$34,664,900</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.0</td>\n",
       "      <td>822</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18297</th>\n",
       "      <td>18297</td>\n",
       "      <td>https://www.imdb.com/title/tt5714470/</td>\n",
       "      <td>Come Away</td>\n",
       "      <td>2020</td>\n",
       "      <td>['Adventure', 'Drama', 'Fantasy']</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.6</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18298</th>\n",
       "      <td>18298</td>\n",
       "      <td>https://www.imdb.com/title/tt6559576/</td>\n",
       "      <td>Selfie Dad</td>\n",
       "      <td>2020</td>\n",
       "      <td>['Comedy', 'Family']</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>PG</td>\n",
       "      <td>3.7</td>\n",
       "      <td>83</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>18183 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       index                                     url                    title  \\\n",
       "0          0   https://www.imdb.com/title/tt0054215/                   Psycho   \n",
       "1          1   https://www.imdb.com/title/tt0054047/    The Magnificent Seven   \n",
       "2          2   https://www.imdb.com/title/tt0054331/                Spartacus   \n",
       "3          3   https://www.imdb.com/title/tt0053604/            The Apartment   \n",
       "4          4   https://www.imdb.com/title/tt0053917/       Home from the Hill   \n",
       "...      ...                                     ...                      ...   \n",
       "18291  18291  https://www.imdb.com/title/tt10370380/  The Windermere Children   \n",
       "18292  18292  https://www.imdb.com/title/tt11127878/                    Milea   \n",
       "18293  18293  https://www.imdb.com/title/tt11358398/    The Man Standing Next   \n",
       "18297  18297   https://www.imdb.com/title/tt5714470/                Come Away   \n",
       "18298  18298   https://www.imdb.com/title/tt6559576/               Selfie Dad   \n",
       "\n",
       "       year                                              genre    gross_usa  \\\n",
       "0      1960                  ['Horror', 'Mystery', 'Thriller']  $32,000,000   \n",
       "1      1960                 ['Action', 'Adventure', 'Western']          NaN   \n",
       "2      1960  ['Adventure', 'Biography', 'Drama', 'History',...   $1,830,650   \n",
       "3      1960                     ['Comedy', 'Drama', 'Romance']          NaN   \n",
       "4      1960                               ['Drama', 'Romance']          NaN   \n",
       "...     ...                                                ...          ...   \n",
       "18291  2020                                   ['Drama', 'War']          NaN   \n",
       "18292  2020                               ['Drama', 'Romance']          NaN   \n",
       "18293  2020                            ['History', 'Thriller']     $113,527   \n",
       "18297  2020                  ['Adventure', 'Drama', 'Fantasy']          NaN   \n",
       "18298  2020                               ['Comedy', 'Family']          NaN   \n",
       "\n",
       "            budget worldwide_gross pg_rated  rating rating_pop  \n",
       "0         $806,947     $32,000,000      NaN     8.5     580863  \n",
       "1       $2,000,000             NaN      NaN     7.7      85315  \n",
       "2      $12,000,000      $1,836,351      NaN     7.9     120895  \n",
       "3       $3,000,000        $177,066      NaN     8.3     157134  \n",
       "4       $1,818,688             NaN      NaN     7.4       2864  \n",
       "...            ...             ...      ...     ...        ...  \n",
       "18291          NaN             NaN      NaN     7.3       1577  \n",
       "18292          NaN             NaN      NaN     6.2        187  \n",
       "18293          NaN     $34,664,900      NaN     7.0        822  \n",
       "18297          NaN             NaN      NaN     6.6         27  \n",
       "18298          NaN             NaN       PG     3.7         83  \n",
       "\n",
       "[18183 rows x 11 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined['rating_pop'] = combined.rating_pop.str.replace(',', '') # Remove commas.\n",
    "combined.dropna(inplace = True, subset=['rating', 'rating_pop'])  # Remove missing values from rating and rating_pop.\n",
    "combined                                                          #"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Visualization for Rating Distribution:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we get to start visualizing. Let's plot the distribution of user ratings."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(6.55, 0.1, 'Breakeven Point')"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(combined.rating, bins = 20)                  # Plot movie rating distribution. \n",
    "plt.title('Distribution of User Rating')                  # Set title.\n",
    "plt.xlabel('User Rating')                                 # Set x-axis label.\n",
    "plt.axvline(x = 6.5)                                      # Create a vertical line at the breakeven point.\n",
    "plt.text(6.55,0.1,'Breakeven Point', color = 'royalblue') # Create text on graph."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see, the mean user rating is about a 6.25 out of 10. And there's a slight negative skew, possibly because users are more likely to go and rate movies they didn't like, and 10 out of 10 reviews are very rare."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Cleaning P.2: Incorporating Revenue"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's try to incorporate worldwide gross revenue and see how it relates to a movie's rating."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We need to drop missing values from the worldwide_gross column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "combined_gross = combined.dropna(subset=['worldwide_gross']) # Drop missing values from worldwide gross. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's clean the remaining entries by removing commas and stripping the dollar signs. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/arencarpenter/opt/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:1: 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",
      "  \"\"\"Entry point for launching an IPython kernel.\n",
      "/Users/arencarpenter/opt/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py: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",
      "  \n"
     ]
    }
   ],
   "source": [
    "combined_gross['worldwide_gross'] = combined_gross.worldwide_gross.str.replace(',', '') # Remove commas and dollar \n",
    "combined_gross['worldwide_gross'] = combined_gross.worldwide_gross.str.strip('$')       # signs. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Examine our changes. Looking good. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "       index                                     url  \\\n",
       "0          0   https://www.imdb.com/title/tt0054215/   \n",
       "2          2   https://www.imdb.com/title/tt0054331/   \n",
       "3          3   https://www.imdb.com/title/tt0053604/   \n",
       "5          5   https://www.imdb.com/title/tt0054357/   \n",
       "7          7   https://www.imdb.com/title/tt0053779/   \n",
       "...      ...                                     ...   \n",
       "18277  18277   https://www.imdb.com/title/tt9537292/   \n",
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       "18282  18282   https://www.imdb.com/title/tt8415352/   \n",
       "18290  18290   https://www.imdb.com/title/tt7095476/   \n",
       "18293  18293  https://www.imdb.com/title/tt11358398/   \n",
       "\n",
       "                                    title  year  \\\n",
       "0                                  Psycho  1960   \n",
       "2                               Spartacus  1960   \n",
       "3                           The Apartment  1960   \n",
       "5                   Swiss Family Robinson  1960   \n",
       "7                           La Dolce Vita  1960   \n",
       "...                                   ...   ...   \n",
       "18277             Ala Vaikunthapurramuloo  2020   \n",
       "18280                         18 Presents  2020   \n",
       "18282  The Murder of Nicole Brown Simpson  2020   \n",
       "18290                    Waiting for Anya  2020   \n",
       "18293               The Man Standing Next  2020   \n",
       "\n",
       "                                                   genre    gross_usa  \\\n",
       "0                      ['Horror', 'Mystery', 'Thriller']  $32,000,000   \n",
       "2      ['Adventure', 'Biography', 'Drama', 'History',...   $1,830,650   \n",
       "3                         ['Comedy', 'Drama', 'Romance']          NaN   \n",
       "5                                ['Adventure', 'Family']  $40,356,000   \n",
       "7                                    ['Comedy', 'Drama']          NaN   \n",
       "...                                                  ...          ...   \n",
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       "18280                                          ['Drama']          NaN   \n",
       "18282                              ['Crime', 'Thriller']          NaN   \n",
       "18290                       ['Drama', 'Thriller', 'War']          NaN   \n",
       "18293                            ['History', 'Thriller']     $113,527   \n",
       "\n",
       "             budget worldwide_gross pg_rated  rating rating_pop  \n",
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       "2       $12,000,000         1836351      NaN     7.9     120895  \n",
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       "5        $5,000,000        40356000      NaN     7.2      13620  \n",
       "7               NaN          171140      NaN     8.0      63887  \n",
       "...             ...             ...      ...     ...        ...  \n",
       "18277           NaN          631683      NaN     7.2       5734  \n",
       "18280  EUR2,500,000         3486375      NaN     6.7       1952  \n",
       "18282           NaN            8931        R     2.2        789  \n",
       "18290   $10,000,000           36404      NaN     5.6        431  \n",
       "18293           NaN        34664900      NaN     7.0        822  \n",
       "\n",
       "[10087 rows x 11 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined_gross # Examine changes."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we have removed special characters we need to recast the column from an object 'O' to an 'int64'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('O')"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined_gross.worldwide_gross.dtype # Worldwide gross is 'object' type. Need to recast as numeric. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We also had two non-USD entries that we removed manually. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/arencarpenter/opt/anaconda3/lib/python3.7/site-packages/pandas/core/generic.py:3939: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  self._update_inplace(obj)\n"
     ]
    }
   ],
   "source": [
    "combined_gross.worldwide_gross.drop([11895, 12556], axis=0, inplace=True) # This throws a warning because altering a\n",
    "                                                                          # copy of the original dataframe. But let the\n",
    "                                                                          # change go through."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
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       "      <td>['History', 'Thriller']</td>\n",
       "      <td>$113,527</td>\n",
       "      <td>NaN</td>\n",
       "      <td>34664900</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.0</td>\n",
       "      <td>822</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10087 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       index                                     url  \\\n",
       "0          0   https://www.imdb.com/title/tt0054215/   \n",
       "2          2   https://www.imdb.com/title/tt0054331/   \n",
       "3          3   https://www.imdb.com/title/tt0053604/   \n",
       "5          5   https://www.imdb.com/title/tt0054357/   \n",
       "7          7   https://www.imdb.com/title/tt0053779/   \n",
       "...      ...                                     ...   \n",
       "18277  18277   https://www.imdb.com/title/tt9537292/   \n",
       "18280  18280  https://www.imdb.com/title/tt10816484/   \n",
       "18282  18282   https://www.imdb.com/title/tt8415352/   \n",
       "18290  18290   https://www.imdb.com/title/tt7095476/   \n",
       "18293  18293  https://www.imdb.com/title/tt11358398/   \n",
       "\n",
       "                                    title  year  \\\n",
       "0                                  Psycho  1960   \n",
       "2                               Spartacus  1960   \n",
       "3                           The Apartment  1960   \n",
       "5                   Swiss Family Robinson  1960   \n",
       "7                           La Dolce Vita  1960   \n",
       "...                                   ...   ...   \n",
       "18277             Ala Vaikunthapurramuloo  2020   \n",
       "18280                         18 Presents  2020   \n",
       "18282  The Murder of Nicole Brown Simpson  2020   \n",
       "18290                    Waiting for Anya  2020   \n",
       "18293               The Man Standing Next  2020   \n",
       "\n",
       "                                                   genre    gross_usa  \\\n",
       "0                      ['Horror', 'Mystery', 'Thriller']  $32,000,000   \n",
       "2      ['Adventure', 'Biography', 'Drama', 'History',...   $1,830,650   \n",
       "3                         ['Comedy', 'Drama', 'Romance']          NaN   \n",
       "5                                ['Adventure', 'Family']  $40,356,000   \n",
       "7                                    ['Comedy', 'Drama']          NaN   \n",
       "...                                                  ...          ...   \n",
       "18277                                ['Action', 'Drama']          NaN   \n",
       "18280                                          ['Drama']          NaN   \n",
       "18282                              ['Crime', 'Thriller']          NaN   \n",
       "18290                       ['Drama', 'Thriller', 'War']          NaN   \n",
       "18293                            ['History', 'Thriller']     $113,527   \n",
       "\n",
       "             budget worldwide_gross pg_rated  rating rating_pop  \n",
       "0          $806,947        32000000      NaN     8.5     580863  \n",
       "2       $12,000,000         1836351      NaN     7.9     120895  \n",
       "3        $3,000,000          177066      NaN     8.3     157134  \n",
       "5        $5,000,000        40356000      NaN     7.2      13620  \n",
       "7               NaN          171140      NaN     8.0      63887  \n",
       "...             ...             ...      ...     ...        ...  \n",
       "18277           NaN          631683      NaN     7.2       5734  \n",
       "18280  EUR2,500,000         3486375      NaN     6.7       1952  \n",
       "18282           NaN            8931        R     2.2        789  \n",
       "18290   $10,000,000           36404      NaN     5.6        431  \n",
       "18293           NaN        34664900      NaN     7.0        822  \n",
       "\n",
       "[10087 rows x 11 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined_gross"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can recast the column. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/arencarpenter/opt/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:1: 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",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    }
   ],
   "source": [
    "combined_gross['worldwide_gross'] = combined_gross.worldwide_gross.astype('int64') # This throws a warning due to\n",
    "                                                                                   # altering a copy of the dataframe."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And we can confirm the data type of the column is now a numerical value. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('float64')"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined_gross.worldwide_gross.dtype # Confirm that worldwide_gross is numerical."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Visualization:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can visualize the average worldwide gross revenue for each user rating. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(48.5, 200000000, 'Breakeven Point')"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1800x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(25, 8))                                     # Set figure size. \n",
    "sns.barplot(x = 'rating', y = 'worldwide_gross', data = combined_gross, ci=None, palette='Blues_d')\n",
    "plt.yticks(np.arange(100000000, 600000000, 100000000))          # Setting axis labels and ticks. \n",
    "plt.title('Mean Gross Revenue by User Rating')                  # \n",
    "plt.ylabel('Worldwide Gross Revenue in hundred millions')       #\n",
    "plt.xlabel('Rating')                                            #\n",
    "sns.despine()                                                   #\n",
    "plt.axvline(x = 48)                                             # There are 76 x values, hence 48 aligning with a 6.5 \n",
    "plt.text(48.5,200000000,'Breakeven Point', color = 'royalblue') # rating (breakeven rating)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Movies that are rated poorly do not have high worldwide gross revenue. We also transposed our calculated breakeven point rating of 6.5 unto this graph. On average, movies that score a user rating above this threshold earn a profit. And at the extreme, movies that score a 8.4 or above have very high revenues. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Insights:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The average movie has a user rating on IMDB of about 6 out of 10. Comparing the respective budgets versus worldwide gross revenue suggests that movies with a user rating of 6.5 or greater have a positive return on investment. The key insights from this data are, that with an average rating of about 6 and the mean breakeven point being 6.5, about as many movies lose money as make money. Movies that make their budget back should have a rating greater than 6.5. That is, you should aim to make a highly rated film. While this seems obvious, there have been many examples of big summer blockbusters that flop at the box office due to poor reviews even with ample budget. "
   ]
  }
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