FinalProject_group068.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "view-in-github"
   },
   "source": [
    "<a href=\"https://colab.research.google.com/github/COGS108/group068_fa20/blob/main/Cogs%20108%20Group(068)%20Project%20%20(1).ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "0MCgO8RAgq4u"
   },
   "source": [
    "# COGS 108 - Final Project Group 068 -- Women in Film"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "o13BuGqdgq4w"
   },
   "source": [
    "  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "IhQFOCt3gq4w"
   },
   "source": [
    "## OVERVIEW\n",
    "\n",
    "The objective of this project was to be able to decipher whether or not women’s representation in movie cinema has an effect on its success.To do this, we analyzed the gender ratio, ratings, revenue for each movie from 1990 to 2020 using IMDb movie data and casting data. We found that women's representation does play a part in the success of movies. We also discovered that overall more women in the main cast is increasing. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "qayxfQqagq4x"
   },
   "source": [
    "   "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "d4p8-vfNgq4x"
   },
   "source": [
    "## NAMES & IDS\n",
    "Jalyn Metra-Lopez -- XXXXXXX\n",
    "\n",
    "Jocelyn Alvarez -- XXXXXXX\n",
    "\n",
    "Tabassum Alam -- XXXXXXX\n",
    "\n",
    "Richard Duong -- XXXXXXX\n",
    "\n",
    "Winnie Chen -- XXXXXXX\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "eGPcwZBbgq4y"
   },
   "source": [
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "HuBIK66fgq4y"
   },
   "source": [
    "## RESEARCH QUESTION\n",
    "\n",
    "How has female representation in different genres of movies affected the influence of success for movies over time? (1990-2010 vs 2020) Success as defined by gross revenue and IMDb ratings.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "lgqvF5asgq4z"
   },
   "source": [
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "dm2EDp_bgq4z"
   },
   "source": [
    "## BACKGROUND AND PRIOR WORK\n",
    "\n",
    "As young adults, we can attest to the fact that movies and cinema play a big part in American pop culture. In today’s society, movies are so much more accessible than they have ever been. Especially, with our current ongoing global pandemic, people no longer have to physically go to a movie theater, or wait until a movie comes out on DVD in order to watch it. With the many streaming platforms available now, movies are so easily obtainable. Almost every household has Netflix, Hulu, etc. With that being said, movies in America should represent Americans. Not only have films with casts that went from 21% to 30% minority acquired the highest median for box office receipts [2], but women hold 51% of moviegoers [7]. The entire cinema industry and Hollywood altogether are doing our society a disservice by inaccurately representing Americans.\n",
    "    \n",
    "According to a report from San Diego State University and the Center for the Study of Women in Television and Film [4], it was found that in 2019, women reached “historic highs” by partaking in 37% of major characters. While this may sound like great news, it is not enough. Time and time again in our society, women are always given the short end of the stick and given less to that compared to males. As being in the year of now, 2020, and having media play such a big role in our everyday lives, women deserve to be equally represented on the big screen. With our given political climate in the past 10 years, it has given much rise to the women and feminists movements. And with the rise of social media giving platforms for women to air their concerns and grievances, companies today are more easily scrutinized and consumers are quick to pull their spending dollar. \n",
    "\n",
    "When attempting to form a question, we as a group put a great deal of effort into finding a topic that is not only something that every group did not mind to analyze, but more importantly something we felt strongly about. It seems as though most movies' leading character and cast tends to be that of men; and having that our group is primarily made up of women, we wish to see more women representation within movies’ cast. We discovered that movies with more women representation were able to attract new groups of moviegoers, meaning that this representation has a positive influence on the financial success of films. We found that there is a clear disparity in gender representation in movies and see that there are pervasive and harmful ideas held about casting more female roles. In addition, we are also interested in seeing which genres employ the most women representation over time. The studies we found mostly focused on representation in general but not by each genre and it’s success. Since we are analyzing movies from the time period of 1990-2020 we decided to split this time frame and compare 1990-2010 (pre 2010) with 2010-present (post 2010). This time frame is valuable to us because it is relevant to our lifespan as young adults. We decided to split the time frame into pre 2010 and post 2010 because 2010 marked a significant increase of more gender and race inclusion in Hollywood [8]. For this study we are measuring success through box office revenue and average ratings from IMDb.\n",
    "\n",
    "References:\n",
    "1. https://www.theguardian.com/film/2019/sep/04/diversity-films-gender-race\n",
    "2. https://womenandhollywood.com/2020-diversity-report-women-and-poc-make-strides-in-film-remain-underrepresented/\n",
    "3. https://variety.com/2020/film/news/diversity-hollywood-behind-the-scenes-ucla-study-1203494631/\n",
    "4. https://www.nywift.org/status-of-women-in-the-industry/\n",
    "5. https://www.elon.edu/u/academics/communications/journal/wp-content/uploads/sites/153/2019/12/03-Kunsey.pdf\n",
    "6. https://www.bbc.com/culture/article/20180508-the-data-that-reveals-the-film-industrys-woman-problem\n",
    "7. https://womenandhollywood.com/resources/statistics/ \n",
    "8. https://www.masslive.com/entertainment/2019/12/pop-culture-in-2010s-marked-gains-in-diversity-inclusion.html \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "X0zVzGg1gq40"
   },
   "source": [
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "-UB1Iv95gq40"
   },
   "source": [
    "## HYPOTHESIS\n",
    "\n",
    "We hypothesize that since there has been an increased demand for more women representation since 2010, movies with an equal female-male ratio cast are just as successful, if not more, than movies with males as the lead and/or males of the leading role.\n",
    "We believe that this hypothesis will hold true due to the changing demographics and increase in gender awareness in the US and due to previous data that has shown positive effects from having more women on screen.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Kyaa2Eysgq41"
   },
   "source": [
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "5aXK9tW3gq41"
   },
   "source": [
    "## DATASETS\n",
    "1. IMDb Movie Data\\\n",
    "https://www.kaggle.com/stefanoleone992/imdb-extensive-dataset \\\n",
    "\\\n",
    "This dataset contains 4 .csv files, all of which we utilized in our analysis. The owner of this dataset has explained that all his data has been webscraped directly from the IMDb site, and that he includes all the movies that have more than 100 votes on IMDb. \n",
    "\n",
    " * The “IMDb movies.csv” file contains 85,855 movies and 22 attributes. We used the attributes “imdb_title_id”, “original_title”, “year of release”, “language”, “actors”, “budget”, “genre”, “movie_country”, and “usa_gross_income”  in our analysis.\n",
    " * The “IMDb names.csv” file contains 297,705 cast members and 20 attributes. We used the attributes “imdb_name_id”, “name”, and “birth_name” in our analysis.\n",
    " * The “IMDb ratings.csv” file contains 85,855 movies with 49 attributes. We used the attributes “imdb_title_id” and “weighted_average_vote” in our analysis.\n",
    " * The “IMDb title_principals.csv” file contains 835,513 cast members roles in movies with 6 attributes. We used the attributes “imdb_title_id”, “imdb_name_id”, and “category” in our analysis. \\\n",
    "\\\n",
    "IMDb is a very popular website where movie data is stored. They list movie ratings, actors/actresses, directors, writers, location, year, and much more. Their data is provided by the filmmakers and site contributors. Ratings and reviews are submitted by the public, which are then weighted by a measure that they did not disclose. \n",
    "\n",
    "\n",
    "2. Budget.csv\\\n",
    "We webscraped this data from the website: \\\n",
    "https://www.the-numbers.com/movie/budgets/all \n",
    "\n",
    "We included the webscraping code in out GitHub Repo called \"Webscraping Budget.ipynb\". We were able to find 6080 movie titles with 4 attributes (original_title, Production_Budget, Domestic_Gross, and Worldwide Gross). \\\n",
    "\\\n",
    "We decided to use this site for the movie gross incomes because we wanted to have another variable to determine success and have a more thorough understanding of our analysis. Because the IMDb site’s data did not provide the incomes for many of the movies, we went on this site to fill in those gaps, even though it did cut down our movie data by a lot. \\\n",
    "\\\n",
    "The website we scraped from is one of the most extensive database of financial information for movies. It was launched in 1997 and has now tracked over 20,000 movies."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "HgY-X2Wrgq41"
   },
   "source": [
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "gL9OStTBgq42"
   },
   "source": [
    "## SETUP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "vELmeC5IwJsu"
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import bs4\n",
    "import requests\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "from bs4 import BeautifulSoup\n",
    "import numpy as np\n",
    "from time import sleep\n",
    "from random import randint\n",
    "from ast import literal_eval"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "dxV6yleggq43"
   },
   "source": [
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "xYT2vapjwJsv"
   },
   "source": [
    "## DATA CLEANING\n",
    "\n",
    "\n",
    "The datasets we collected have many critical information but also many information that we do not need for the purposes of our project. We had to merge df_movies, df_ratings and df_budget into one main DataFrame **df_main**, to be able to analyze the revenue, income. We merged df_tp and df_names into **df_cast** to have the cast members of each film. \n",
    "\n",
    "The following steps are loading in the datasets we will use:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "aYuE3Rp-wJsv"
   },
   "source": [
    "## 1) Movies dataset:\n",
    "What does this dataset have?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 716
    },
    "id": "6Ad4TSpKwJsv",
    "outputId": "9a88dbd1-d6a8-4000-cc1d-aa28353fd538"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "df_movies total (rows, columns):  (85855, 22)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.7/site-packages/IPython/core/interactiveshell.py:3063: DtypeWarning: Columns (3) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  interactivity=interactivity, compiler=compiler, result=result)\n"
     ]
    },
    {
     "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>imdb_title_id</th>\n",
       "      <th>title</th>\n",
       "      <th>original_title</th>\n",
       "      <th>year</th>\n",
       "      <th>date_published</th>\n",
       "      <th>genre</th>\n",
       "      <th>duration</th>\n",
       "      <th>country</th>\n",
       "      <th>language</th>\n",
       "      <th>director</th>\n",
       "      <th>...</th>\n",
       "      <th>actors</th>\n",
       "      <th>description</th>\n",
       "      <th>avg_vote</th>\n",
       "      <th>votes</th>\n",
       "      <th>budget</th>\n",
       "      <th>usa_gross_income</th>\n",
       "      <th>worlwide_gross_income</th>\n",
       "      <th>metascore</th>\n",
       "      <th>reviews_from_users</th>\n",
       "      <th>reviews_from_critics</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>tt0000009</td>\n",
       "      <td>Miss Jerry</td>\n",
       "      <td>Miss Jerry</td>\n",
       "      <td>1894</td>\n",
       "      <td>1894-10-09</td>\n",
       "      <td>Romance</td>\n",
       "      <td>45</td>\n",
       "      <td>USA</td>\n",
       "      <td>None</td>\n",
       "      <td>Alexander Black</td>\n",
       "      <td>...</td>\n",
       "      <td>Blanche Bayliss, William Courtenay, Chauncey D...</td>\n",
       "      <td>The adventures of a female reporter in the 1890s.</td>\n",
       "      <td>5.9</td>\n",
       "      <td>154</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>tt0000574</td>\n",
       "      <td>The Story of the Kelly Gang</td>\n",
       "      <td>The Story of the Kelly Gang</td>\n",
       "      <td>1906</td>\n",
       "      <td>1906-12-26</td>\n",
       "      <td>Biography, Crime, Drama</td>\n",
       "      <td>70</td>\n",
       "      <td>Australia</td>\n",
       "      <td>None</td>\n",
       "      <td>Charles Tait</td>\n",
       "      <td>...</td>\n",
       "      <td>Elizabeth Tait, John Tait, Norman Campbell, Be...</td>\n",
       "      <td>True story of notorious Australian outlaw Ned ...</td>\n",
       "      <td>6.1</td>\n",
       "      <td>589</td>\n",
       "      <td>$ 2250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>tt0001892</td>\n",
       "      <td>Den sorte drøm</td>\n",
       "      <td>Den sorte drøm</td>\n",
       "      <td>1911</td>\n",
       "      <td>1911-08-19</td>\n",
       "      <td>Drama</td>\n",
       "      <td>53</td>\n",
       "      <td>Germany, Denmark</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Urban Gad</td>\n",
       "      <td>...</td>\n",
       "      <td>Asta Nielsen, Valdemar Psilander, Gunnar Helse...</td>\n",
       "      <td>Two men of high rank are both wooing the beaut...</td>\n",
       "      <td>5.8</td>\n",
       "      <td>188</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>tt0002101</td>\n",
       "      <td>Cleopatra</td>\n",
       "      <td>Cleopatra</td>\n",
       "      <td>1912</td>\n",
       "      <td>1912-11-13</td>\n",
       "      <td>Drama, History</td>\n",
       "      <td>100</td>\n",
       "      <td>USA</td>\n",
       "      <td>English</td>\n",
       "      <td>Charles L. Gaskill</td>\n",
       "      <td>...</td>\n",
       "      <td>Helen Gardner, Pearl Sindelar, Miss Fielding, ...</td>\n",
       "      <td>The fabled queen of Egypt's affair with Roman ...</td>\n",
       "      <td>5.2</td>\n",
       "      <td>446</td>\n",
       "      <td>$ 45000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>tt0002130</td>\n",
       "      <td>L'Inferno</td>\n",
       "      <td>L'Inferno</td>\n",
       "      <td>1911</td>\n",
       "      <td>1911-03-06</td>\n",
       "      <td>Adventure, Drama, Fantasy</td>\n",
       "      <td>68</td>\n",
       "      <td>Italy</td>\n",
       "      <td>Italian</td>\n",
       "      <td>Francesco Bertolini, Adolfo Padovan</td>\n",
       "      <td>...</td>\n",
       "      <td>Salvatore Papa, Arturo Pirovano, Giuseppe de L...</td>\n",
       "      <td>Loosely adapted from Dante's Divine Comedy and...</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2237</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>31.0</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  imdb_title_id                        title               original_title  \\\n",
       "0     tt0000009                   Miss Jerry                   Miss Jerry   \n",
       "1     tt0000574  The Story of the Kelly Gang  The Story of the Kelly Gang   \n",
       "2     tt0001892               Den sorte drøm               Den sorte drøm   \n",
       "3     tt0002101                    Cleopatra                    Cleopatra   \n",
       "4     tt0002130                    L'Inferno                    L'Inferno   \n",
       "\n",
       "   year date_published                      genre  duration           country  \\\n",
       "0  1894     1894-10-09                    Romance        45               USA   \n",
       "1  1906     1906-12-26    Biography, Crime, Drama        70         Australia   \n",
       "2  1911     1911-08-19                      Drama        53  Germany, Denmark   \n",
       "3  1912     1912-11-13             Drama, History       100               USA   \n",
       "4  1911     1911-03-06  Adventure, Drama, Fantasy        68             Italy   \n",
       "\n",
       "  language                             director  ...  \\\n",
       "0     None                      Alexander Black  ...   \n",
       "1     None                         Charles Tait  ...   \n",
       "2      NaN                            Urban Gad  ...   \n",
       "3  English                   Charles L. Gaskill  ...   \n",
       "4  Italian  Francesco Bertolini, Adolfo Padovan  ...   \n",
       "\n",
       "                                              actors  \\\n",
       "0  Blanche Bayliss, William Courtenay, Chauncey D...   \n",
       "1  Elizabeth Tait, John Tait, Norman Campbell, Be...   \n",
       "2  Asta Nielsen, Valdemar Psilander, Gunnar Helse...   \n",
       "3  Helen Gardner, Pearl Sindelar, Miss Fielding, ...   \n",
       "4  Salvatore Papa, Arturo Pirovano, Giuseppe de L...   \n",
       "\n",
       "                                         description avg_vote votes   budget  \\\n",
       "0  The adventures of a female reporter in the 1890s.      5.9   154      NaN   \n",
       "1  True story of notorious Australian outlaw Ned ...      6.1   589   $ 2250   \n",
       "2  Two men of high rank are both wooing the beaut...      5.8   188      NaN   \n",
       "3  The fabled queen of Egypt's affair with Roman ...      5.2   446  $ 45000   \n",
       "4  Loosely adapted from Dante's Divine Comedy and...      7.0  2237      NaN   \n",
       "\n",
       "   usa_gross_income worlwide_gross_income metascore reviews_from_users  \\\n",
       "0               NaN                   NaN       NaN                1.0   \n",
       "1               NaN                   NaN       NaN                7.0   \n",
       "2               NaN                   NaN       NaN                5.0   \n",
       "3               NaN                   NaN       NaN               25.0   \n",
       "4               NaN                   NaN       NaN               31.0   \n",
       "\n",
       "   reviews_from_critics  \n",
       "0                   2.0  \n",
       "1                   7.0  \n",
       "2                   2.0  \n",
       "3                   3.0  \n",
       "4                  14.0  \n",
       "\n",
       "[5 rows x 22 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# load in the title_basics dataset\n",
    "df_movies = pd.read_csv('IMDb movies.csv')\n",
    "print(\"df_movies total (rows, columns): \", df_movies.shape)\n",
    "df_movies.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "5XMz6OMkwJsw"
   },
   "source": [
    "## 2) Ratings Dataset:\n",
    "This dataset contains over 85000 IMDb titles and their respective IMDb weighted average vote, along with the number of votes cast."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 238
    },
    "id": "K9wIOVk6wJsw",
    "outputId": "a8cb4df7-e8ca-4f0c-d126-43bcf38c8eb9"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "df_ratings total (rows, columns):  (85855, 49)\n"
     ]
    },
    {
     "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>imdb_title_id</th>\n",
       "      <th>weighted_average_vote</th>\n",
       "      <th>total_votes</th>\n",
       "      <th>mean_vote</th>\n",
       "      <th>median_vote</th>\n",
       "      <th>votes_10</th>\n",
       "      <th>votes_9</th>\n",
       "      <th>votes_8</th>\n",
       "      <th>votes_7</th>\n",
       "      <th>votes_6</th>\n",
       "      <th>...</th>\n",
       "      <th>females_30age_avg_vote</th>\n",
       "      <th>females_30age_votes</th>\n",
       "      <th>females_45age_avg_vote</th>\n",
       "      <th>females_45age_votes</th>\n",
       "      <th>top1000_voters_rating</th>\n",
       "      <th>top1000_voters_votes</th>\n",
       "      <th>us_voters_rating</th>\n",
       "      <th>us_voters_votes</th>\n",
       "      <th>non_us_voters_rating</th>\n",
       "      <th>non_us_voters_votes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>tt0000009</td>\n",
       "      <td>5.9</td>\n",
       "      <td>154</td>\n",
       "      <td>5.9</td>\n",
       "      <td>6.0</td>\n",
       "      <td>12</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>43</td>\n",
       "      <td>28</td>\n",
       "      <td>...</td>\n",
       "      <td>5.7</td>\n",
       "      <td>13.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.7</td>\n",
       "      <td>34.0</td>\n",
       "      <td>6.4</td>\n",
       "      <td>51.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>70.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>tt0000574</td>\n",
       "      <td>6.1</td>\n",
       "      <td>589</td>\n",
       "      <td>6.3</td>\n",
       "      <td>6.0</td>\n",
       "      <td>57</td>\n",
       "      <td>18</td>\n",
       "      <td>58</td>\n",
       "      <td>137</td>\n",
       "      <td>139</td>\n",
       "      <td>...</td>\n",
       "      <td>6.2</td>\n",
       "      <td>23.0</td>\n",
       "      <td>6.6</td>\n",
       "      <td>14.0</td>\n",
       "      <td>6.4</td>\n",
       "      <td>66.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>96.0</td>\n",
       "      <td>6.2</td>\n",
       "      <td>331.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>tt0001892</td>\n",
       "      <td>5.8</td>\n",
       "      <td>188</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>17</td>\n",
       "      <td>44</td>\n",
       "      <td>52</td>\n",
       "      <td>...</td>\n",
       "      <td>5.8</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.8</td>\n",
       "      <td>7.0</td>\n",
       "      <td>5.4</td>\n",
       "      <td>32.0</td>\n",
       "      <td>6.2</td>\n",
       "      <td>31.0</td>\n",
       "      <td>5.9</td>\n",
       "      <td>123.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>tt0002101</td>\n",
       "      <td>5.2</td>\n",
       "      <td>446</td>\n",
       "      <td>5.3</td>\n",
       "      <td>5.0</td>\n",
       "      <td>15</td>\n",
       "      <td>8</td>\n",
       "      <td>16</td>\n",
       "      <td>62</td>\n",
       "      <td>98</td>\n",
       "      <td>...</td>\n",
       "      <td>5.5</td>\n",
       "      <td>14.0</td>\n",
       "      <td>6.1</td>\n",
       "      <td>21.0</td>\n",
       "      <td>4.9</td>\n",
       "      <td>57.0</td>\n",
       "      <td>5.5</td>\n",
       "      <td>207.0</td>\n",
       "      <td>4.7</td>\n",
       "      <td>105.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>tt0002130</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2237</td>\n",
       "      <td>6.9</td>\n",
       "      <td>7.0</td>\n",
       "      <td>210</td>\n",
       "      <td>225</td>\n",
       "      <td>436</td>\n",
       "      <td>641</td>\n",
       "      <td>344</td>\n",
       "      <td>...</td>\n",
       "      <td>7.3</td>\n",
       "      <td>82.0</td>\n",
       "      <td>7.4</td>\n",
       "      <td>77.0</td>\n",
       "      <td>6.9</td>\n",
       "      <td>139.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>488.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1166.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 49 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  imdb_title_id  weighted_average_vote  total_votes  mean_vote  median_vote  \\\n",
       "0     tt0000009                    5.9          154        5.9          6.0   \n",
       "1     tt0000574                    6.1          589        6.3          6.0   \n",
       "2     tt0001892                    5.8          188        6.0          6.0   \n",
       "3     tt0002101                    5.2          446        5.3          5.0   \n",
       "4     tt0002130                    7.0         2237        6.9          7.0   \n",
       "\n",
       "   votes_10  votes_9  votes_8  votes_7  votes_6  ...  females_30age_avg_vote  \\\n",
       "0        12        4       10       43       28  ...                     5.7   \n",
       "1        57       18       58      137      139  ...                     6.2   \n",
       "2         6        6       17       44       52  ...                     5.8   \n",
       "3        15        8       16       62       98  ...                     5.5   \n",
       "4       210      225      436      641      344  ...                     7.3   \n",
       "\n",
       "   females_30age_votes  females_45age_avg_vote  females_45age_votes  \\\n",
       "0                 13.0                     4.5                  4.0   \n",
       "1                 23.0                     6.6                 14.0   \n",
       "2                  4.0                     6.8                  7.0   \n",
       "3                 14.0                     6.1                 21.0   \n",
       "4                 82.0                     7.4                 77.0   \n",
       "\n",
       "   top1000_voters_rating  top1000_voters_votes  us_voters_rating  \\\n",
       "0                    5.7                  34.0               6.4   \n",
       "1                    6.4                  66.0               6.0   \n",
       "2                    5.4                  32.0               6.2   \n",
       "3                    4.9                  57.0               5.5   \n",
       "4                    6.9                 139.0               7.0   \n",
       "\n",
       "   us_voters_votes  non_us_voters_rating  non_us_voters_votes  \n",
       "0             51.0                   6.0                 70.0  \n",
       "1             96.0                   6.2                331.0  \n",
       "2             31.0                   5.9                123.0  \n",
       "3            207.0                   4.7                105.0  \n",
       "4            488.0                   7.0               1166.0  \n",
       "\n",
       "[5 rows x 49 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ratings = pd.read_csv('IMDb ratings.csv')\n",
    "print(\"df_ratings total (rows, columns): \", df_ratings.shape)\n",
    "df_ratings.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "jhxeB5bwwJsw"
   },
   "source": [
    "## 3) Cast Dataset:\n",
    "The dataset **df_TP** contains approximately 83000 IMDb actors' name IDs, along with their order of importance, name of the character they played and the IMDB title for the movies they were in.\n",
    "\n",
    "The second dataset **df_Names** contains the IMDb name Id, the name associated with the ID and other details about this person such as their height, birthday, etc."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 218
    },
    "id": "jjX4LGsHwJsw",
    "outputId": "e2e0b31e-b52a-41ad-f316-5c52d5ee8735"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "df_TP total (rows, columns):  (835513, 6)\n"
     ]
    },
    {
     "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>imdb_title_id</th>\n",
       "      <th>ordering</th>\n",
       "      <th>imdb_name_id</th>\n",
       "      <th>category</th>\n",
       "      <th>job</th>\n",
       "      <th>characters</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>tt0000009</td>\n",
       "      <td>1</td>\n",
       "      <td>nm0063086</td>\n",
       "      <td>actress</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[\"Miss Geraldine Holbrook (Miss Jerry)\"]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>tt0000009</td>\n",
       "      <td>2</td>\n",
       "      <td>nm0183823</td>\n",
       "      <td>actor</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[\"Mr. Hamilton\"]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>tt0000009</td>\n",
       "      <td>3</td>\n",
       "      <td>nm1309758</td>\n",
       "      <td>actor</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[\"Chauncey Depew - the Director of the New Yor...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>tt0000009</td>\n",
       "      <td>4</td>\n",
       "      <td>nm0085156</td>\n",
       "      <td>director</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>tt0000574</td>\n",
       "      <td>1</td>\n",
       "      <td>nm0846887</td>\n",
       "      <td>actress</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[\"Kate Kelly\"]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  imdb_title_id  ordering imdb_name_id  category  job  \\\n",
       "0     tt0000009         1    nm0063086   actress  NaN   \n",
       "1     tt0000009         2    nm0183823     actor  NaN   \n",
       "2     tt0000009         3    nm1309758     actor  NaN   \n",
       "3     tt0000009         4    nm0085156  director  NaN   \n",
       "4     tt0000574         1    nm0846887   actress  NaN   \n",
       "\n",
       "                                          characters  \n",
       "0           [\"Miss Geraldine Holbrook (Miss Jerry)\"]  \n",
       "1                                   [\"Mr. Hamilton\"]  \n",
       "2  [\"Chauncey Depew - the Director of the New Yor...  \n",
       "3                                                NaN  \n",
       "4                                     [\"Kate Kelly\"]  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_TP = pd.read_csv('IMDb title_principals.csv')\n",
    "print(\"df_TP total (rows, columns): \", df_TP.shape)\n",
    "df_TP.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 681
    },
    "id": "qmrAJjQ7wJsx",
    "outputId": "9d4fe8ee-6c77-4fc5-9e02-f1ce35a97410"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "df_Names total (rows, columns):  (297705, 17)\n"
     ]
    },
    {
     "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>imdb_name_id</th>\n",
       "      <th>name</th>\n",
       "      <th>birth_name</th>\n",
       "      <th>height</th>\n",
       "      <th>bio</th>\n",
       "      <th>birth_details</th>\n",
       "      <th>date_of_birth</th>\n",
       "      <th>place_of_birth</th>\n",
       "      <th>death_details</th>\n",
       "      <th>date_of_death</th>\n",
       "      <th>place_of_death</th>\n",
       "      <th>reason_of_death</th>\n",
       "      <th>spouses_string</th>\n",
       "      <th>spouses</th>\n",
       "      <th>divorces</th>\n",
       "      <th>spouses_with_children</th>\n",
       "      <th>children</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>nm0000001</td>\n",
       "      <td>Fred Astaire</td>\n",
       "      <td>Frederic Austerlitz Jr.</td>\n",
       "      <td>177.0</td>\n",
       "      <td>Fred Astaire was born in Omaha, Nebraska, to J...</td>\n",
       "      <td>May 10, 1899 in Omaha, Nebraska, USA</td>\n",
       "      <td>1899-05-10</td>\n",
       "      <td>Omaha, Nebraska, USA</td>\n",
       "      <td>June 22, 1987 in Los Angeles, California, USA ...</td>\n",
       "      <td>1987-06-22</td>\n",
       "      <td>Los Angeles, California, USA</td>\n",
       "      <td>pneumonia</td>\n",
       "      <td>Robyn Smith  (27 June 1980 - 22 June 1987) (hi...</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>nm0000002</td>\n",
       "      <td>Lauren Bacall</td>\n",
       "      <td>Betty Joan Perske</td>\n",
       "      <td>174.0</td>\n",
       "      <td>Lauren Bacall was born Betty Joan Perske on Se...</td>\n",
       "      <td>September 16, 1924 in The Bronx, New York City...</td>\n",
       "      <td>1924-09-16</td>\n",
       "      <td>The Bronx, New York City, New York, USA</td>\n",
       "      <td>August 12, 2014 in New York City, New York, US...</td>\n",
       "      <td>2014-08-12</td>\n",
       "      <td>New York City, New York, USA</td>\n",
       "      <td>stroke</td>\n",
       "      <td>Jason Robards  (4 July 1961 - 10 September 196...</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>nm0000003</td>\n",
       "      <td>Brigitte Bardot</td>\n",
       "      <td>Brigitte Bardot</td>\n",
       "      <td>166.0</td>\n",
       "      <td>Brigitte Bardot was born on September 28, 1934...</td>\n",
       "      <td>September 28, 1934 in Paris, France</td>\n",
       "      <td>1934-09-28</td>\n",
       "      <td>Paris, France</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Bernard d'Ormale  (16 August 1992 - present)\\n...</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>nm0000004</td>\n",
       "      <td>John Belushi</td>\n",
       "      <td>John Adam Belushi</td>\n",
       "      <td>170.0</td>\n",
       "      <td>John Belushi was born in Chicago, Illinois, US...</td>\n",
       "      <td>January 24, 1949 in Chicago, Illinois, USA</td>\n",
       "      <td>1949-01-24</td>\n",
       "      <td>Chicago, Illinois, USA</td>\n",
       "      <td>March 5, 1982 in Hollywood, Los Angeles, Calif...</td>\n",
       "      <td>1982-03-05</td>\n",
       "      <td>Hollywood, Los Angeles, California, USA</td>\n",
       "      <td>acute cocaine and heroin intoxication</td>\n",
       "      <td>Judith Belushi-Pisano  (31 December 1976 - 5 M...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>nm0000005</td>\n",
       "      <td>Ingmar Bergman</td>\n",
       "      <td>Ernst Ingmar Bergman</td>\n",
       "      <td>179.0</td>\n",
       "      <td>Ernst Ingmar Bergman was born July 14, 1918, t...</td>\n",
       "      <td>July 14, 1918 in Uppsala, Uppsala län, Sweden</td>\n",
       "      <td>1918-07-14</td>\n",
       "      <td>Uppsala, Uppsala län, Sweden</td>\n",
       "      <td>July 30, 2007 in Fårö, Gotlands län, Sweden  (...</td>\n",
       "      <td>2007-07-30</td>\n",
       "      <td>Fårö, Gotlands län, Sweden</td>\n",
       "      <td>natural causes</td>\n",
       "      <td>Ingrid Bergman  (11 November 1971 - 20 May 199...</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  imdb_name_id             name               birth_name  height  \\\n",
       "0    nm0000001     Fred Astaire  Frederic Austerlitz Jr.   177.0   \n",
       "1    nm0000002    Lauren Bacall        Betty Joan Perske   174.0   \n",
       "2    nm0000003  Brigitte Bardot          Brigitte Bardot   166.0   \n",
       "3    nm0000004     John Belushi        John Adam Belushi   170.0   \n",
       "4    nm0000005   Ingmar Bergman     Ernst Ingmar Bergman   179.0   \n",
       "\n",
       "                                                 bio  \\\n",
       "0  Fred Astaire was born in Omaha, Nebraska, to J...   \n",
       "1  Lauren Bacall was born Betty Joan Perske on Se...   \n",
       "2  Brigitte Bardot was born on September 28, 1934...   \n",
       "3  John Belushi was born in Chicago, Illinois, US...   \n",
       "4  Ernst Ingmar Bergman was born July 14, 1918, t...   \n",
       "\n",
       "                                       birth_details date_of_birth  \\\n",
       "0               May 10, 1899 in Omaha, Nebraska, USA    1899-05-10   \n",
       "1  September 16, 1924 in The Bronx, New York City...    1924-09-16   \n",
       "2                September 28, 1934 in Paris, France    1934-09-28   \n",
       "3         January 24, 1949 in Chicago, Illinois, USA    1949-01-24   \n",
       "4      July 14, 1918 in Uppsala, Uppsala län, Sweden    1918-07-14   \n",
       "\n",
       "                            place_of_birth  \\\n",
       "0                     Omaha, Nebraska, USA   \n",
       "1  The Bronx, New York City, New York, USA   \n",
       "2                            Paris, France   \n",
       "3                   Chicago, Illinois, USA   \n",
       "4             Uppsala, Uppsala län, Sweden   \n",
       "\n",
       "                                       death_details date_of_death  \\\n",
       "0  June 22, 1987 in Los Angeles, California, USA ...    1987-06-22   \n",
       "1  August 12, 2014 in New York City, New York, US...    2014-08-12   \n",
       "2                                                NaN           NaN   \n",
       "3  March 5, 1982 in Hollywood, Los Angeles, Calif...    1982-03-05   \n",
       "4  July 30, 2007 in Fårö, Gotlands län, Sweden  (...    2007-07-30   \n",
       "\n",
       "                              place_of_death  \\\n",
       "0             Los Angeles, California, USA     \n",
       "1             New York City, New York, USA     \n",
       "2                                        NaN   \n",
       "3  Hollywood, Los Angeles, California, USA     \n",
       "4               Fårö, Gotlands län, Sweden     \n",
       "\n",
       "                         reason_of_death  \\\n",
       "0                              pneumonia   \n",
       "1                                 stroke   \n",
       "2                                    NaN   \n",
       "3  acute cocaine and heroin intoxication   \n",
       "4                         natural causes   \n",
       "\n",
       "                                      spouses_string  spouses  divorces  \\\n",
       "0  Robyn Smith  (27 June 1980 - 22 June 1987) (hi...        2         0   \n",
       "1  Jason Robards  (4 July 1961 - 10 September 196...        2         1   \n",
       "2  Bernard d'Ormale  (16 August 1992 - present)\\n...        4         3   \n",
       "3  Judith Belushi-Pisano  (31 December 1976 - 5 M...        1         0   \n",
       "4  Ingrid Bergman  (11 November 1971 - 20 May 199...        5         4   \n",
       "\n",
       "   spouses_with_children  children  \n",
       "0                      1         2  \n",
       "1                      2         3  \n",
       "2                      1         1  \n",
       "3                      0         0  \n",
       "4                      5         8  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_Names = pd.read_csv('IMDb names.csv')\n",
    "print(\"df_Names total (rows, columns): \", df_Names.shape)\n",
    "df_Names.head()\n",
    "#dfNames = dfNames.drop(labels=['height', 'bio', 'birth_details', 'date_of_birth', 'place_of_birth', 'death_details', 'place_of_death', 'reason_of_death', 'spouses_string', 'spouses', 'divorces', 'spouses_with_children', 'children', 'date_of_death'], axis=1)\n",
    "\n",
    "#dfTP = pd.merge(dfTP, dfNames, on = 'imdb_name_id')\n",
    "#dfTP"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "rzBnHI_i7w-w"
   },
   "source": [
    "## 4) Budgets and Incomes Datasets:\n",
    "\n",
    "These datasets are for movies from Budget.csv and contain the name of the movie/film, the domestic gross and production budget."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 218
    },
    "id": "X3zpZc4O7w-w",
    "outputId": "42808450-f6ab-44c0-9ee3-bf799e0a7284"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "df_budget total (rows, columns):  (6080, 4)\n"
     ]
    },
    {
     "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>original_title</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>Avengers: Endgame</td>\n",
       "      <td>$400,000,000</td>\n",
       "      <td>$858,373,000</td>\n",
       "      <td>$2,797,800,564</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Pirates of the Caribbean: On Stranger Tides</td>\n",
       "      <td>$379,000,000</td>\n",
       "      <td>$241,063,875</td>\n",
       "      <td>$1,045,663,875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Avengers: Age of Ultron</td>\n",
       "      <td>$365,000,000</td>\n",
       "      <td>$459,005,868</td>\n",
       "      <td>$1,396,099,202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Star Wars Ep. VII: The Force Awakens</td>\n",
       "      <td>$306,000,000</td>\n",
       "      <td>$936,662,225</td>\n",
       "      <td>$2,065,478,084</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Avengers: Infinity War</td>\n",
       "      <td>$300,000,000</td>\n",
       "      <td>$678,815,482</td>\n",
       "      <td>$2,044,540,523</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                original_title Production_Budget  \\\n",
       "0                            Avengers: Endgame      $400,000,000   \n",
       "1  Pirates of the Caribbean: On Stranger Tides      $379,000,000   \n",
       "2                      Avengers: Age of Ultron      $365,000,000   \n",
       "3         Star Wars Ep. VII: The Force Awakens      $306,000,000   \n",
       "4                       Avengers: Infinity War      $300,000,000   \n",
       "\n",
       "  Domestic_Gross  Worldwide Gross  \n",
       "0   $858,373,000   $2,797,800,564  \n",
       "1   $241,063,875   $1,045,663,875  \n",
       "2   $459,005,868   $1,396,099,202  \n",
       "3   $936,662,225   $2,065,478,084  \n",
       "4   $678,815,482   $2,044,540,523  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_budget = pd.read_csv('Budget.csv')\n",
    "df_budget = df_budget.rename(columns={'Name': 'original_title'})\n",
    "df_budget = df_budget.rename(columns={'Domestic Gross': 'Domestic_Gross'})\n",
    "df_budget = df_budget.rename(columns={'Production Budget': 'Production_Budget'})\n",
    "df_budget = df_budget.drop(columns={'Unnamed: 0'})\n",
    "print(\"df_budget total (rows, columns): \", df_budget.shape)\n",
    "df_budget.head()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "l1Vt62ofgq4_"
   },
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "BOZx664qwJsx"
   },
   "source": [
    "The following steps are how we cleaned the data to the required format needed:\n",
    " \n",
    " ## A) *df_movies* + *df_ratings* + *df_budget* = *df_main*\n",
    " #### We want to make df_main a dataframe of all possible movies, along with its key attributes including its title, year, genre, income, and rating"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "hgjwamOpwJsx"
   },
   "source": [
    "### 1) Dropping Columns not needed in df_movies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 218
    },
    "id": "E87CyWTKwJsx",
    "outputId": "eb1e1fc2-a291-4e94-fca3-a725f82d53a2"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "df_movies total (rows, columns):  (85855, 8)\n"
     ]
    },
    {
     "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>imdb_title_id</th>\n",
       "      <th>original_title</th>\n",
       "      <th>year</th>\n",
       "      <th>genre</th>\n",
       "      <th>country</th>\n",
       "      <th>language</th>\n",
       "      <th>budget</th>\n",
       "      <th>usa_gross_income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>tt0000009</td>\n",
       "      <td>Miss Jerry</td>\n",
       "      <td>1894</td>\n",
       "      <td>Romance</td>\n",
       "      <td>USA</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>tt0000574</td>\n",
       "      <td>The Story of the Kelly Gang</td>\n",
       "      <td>1906</td>\n",
       "      <td>Biography, Crime, Drama</td>\n",
       "      <td>Australia</td>\n",
       "      <td>None</td>\n",
       "      <td>$ 2250</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>tt0001892</td>\n",
       "      <td>Den sorte drøm</td>\n",
       "      <td>1911</td>\n",
       "      <td>Drama</td>\n",
       "      <td>Germany, Denmark</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>tt0002101</td>\n",
       "      <td>Cleopatra</td>\n",
       "      <td>1912</td>\n",
       "      <td>Drama, History</td>\n",
       "      <td>USA</td>\n",
       "      <td>English</td>\n",
       "      <td>$ 45000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>tt0002130</td>\n",
       "      <td>L'Inferno</td>\n",
       "      <td>1911</td>\n",
       "      <td>Adventure, Drama, Fantasy</td>\n",
       "      <td>Italy</td>\n",
       "      <td>Italian</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  imdb_title_id               original_title  year                      genre  \\\n",
       "0     tt0000009                   Miss Jerry  1894                    Romance   \n",
       "1     tt0000574  The Story of the Kelly Gang  1906    Biography, Crime, Drama   \n",
       "2     tt0001892               Den sorte drøm  1911                      Drama   \n",
       "3     tt0002101                    Cleopatra  1912             Drama, History   \n",
       "4     tt0002130                    L'Inferno  1911  Adventure, Drama, Fantasy   \n",
       "\n",
       "            country language   budget usa_gross_income  \n",
       "0               USA     None      NaN              NaN  \n",
       "1         Australia     None   $ 2250              NaN  \n",
       "2  Germany, Denmark      NaN      NaN              NaN  \n",
       "3               USA  English  $ 45000              NaN  \n",
       "4             Italy  Italian      NaN              NaN  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_movies = df_movies.drop(labels=['title','date_published','duration', 'director', 'writer','production_company', 'actors', 'description', 'avg_vote', 'votes', 'worlwide_gross_income', 'metascore', 'reviews_from_users', 'reviews_from_critics'],\n",
    "                          axis=1)\n",
    "print(\"df_movies total (rows, columns): \", df_movies.shape)\n",
    "df_movies.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "AElY8Cy5wJsx"
   },
   "source": [
    "### 2) Filtering out movies not from the USA and not in English from df_movies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 0
    },
    "id": "HK6KI4NUwJsx",
    "outputId": "b86ccfda-6dea-4520-ad80-f3bc23e6abfd"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "df_movies total (rows, columns):  (24831, 8)\n"
     ]
    },
    {
     "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>imdb_title_id</th>\n",
       "      <th>original_title</th>\n",
       "      <th>year</th>\n",
       "      <th>genre</th>\n",
       "      <th>country</th>\n",
       "      <th>language</th>\n",
       "      <th>budget</th>\n",
       "      <th>usa_gross_income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>tt0002101</td>\n",
       "      <td>Cleopatra</td>\n",
       "      <td>1912</td>\n",
       "      <td>Drama, History</td>\n",
       "      <td>USA</td>\n",
       "      <td>English</td>\n",
       "      <td>$ 45000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>tt0002199</td>\n",
       "      <td>From the Manger to the Cross; or, Jesus of Naz...</td>\n",
       "      <td>1912</td>\n",
       "      <td>Biography, Drama</td>\n",
       "      <td>USA</td>\n",
       "      <td>English</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>tt0003167</td>\n",
       "      <td>Home, Sweet Home</td>\n",
       "      <td>1914</td>\n",
       "      <td>Drama</td>\n",
       "      <td>USA</td>\n",
       "      <td>English</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>tt0003471</td>\n",
       "      <td>Traffic in Souls</td>\n",
       "      <td>1913</td>\n",
       "      <td>Crime, Drama</td>\n",
       "      <td>USA</td>\n",
       "      <td>English</td>\n",
       "      <td>$ 5700</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>tt0003643</td>\n",
       "      <td>The Avenging Conscience: or 'Thou Shalt Not Kill'</td>\n",
       "      <td>1914</td>\n",
       "      <td>Crime, Drama, Horror</td>\n",
       "      <td>USA</td>\n",
       "      <td>English</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   imdb_title_id                                     original_title  year  \\\n",
       "3      tt0002101                                          Cleopatra  1912   \n",
       "5      tt0002199  From the Manger to the Cross; or, Jesus of Naz...  1912   \n",
       "17     tt0003167                                   Home, Sweet Home  1914   \n",
       "19     tt0003471                                   Traffic in Souls  1913   \n",
       "22     tt0003643  The Avenging Conscience: or 'Thou Shalt Not Kill'  1914   \n",
       "\n",
       "                   genre country language   budget usa_gross_income  \n",
       "3         Drama, History     USA  English  $ 45000              NaN  \n",
       "5       Biography, Drama     USA  English      NaN              NaN  \n",
       "17                 Drama     USA  English      NaN              NaN  \n",
       "19          Crime, Drama     USA  English   $ 5700              NaN  \n",
       "22  Crime, Drama, Horror     USA  English      NaN              NaN  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_movies = df_movies[(df_movies['country']=='USA') & (df_movies['language']=='English')]\n",
    "print(\"df_movies total (rows, columns): \", df_movies.shape)\n",
    "df_movies.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "oyBvcTYawJsx"
   },
   "source": [
    "### 3) Filtering out movies before 1990 from df_movies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 0
    },
    "id": "hX5CubnwwJsy",
    "outputId": "787f1d65-11db-4bc7-982f-6cbc616a1123"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "df_movies total (rows, columns):  (14170, 8)\n"
     ]
    },
    {
     "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>imdb_title_id</th>\n",
       "      <th>original_title</th>\n",
       "      <th>year</th>\n",
       "      <th>genre</th>\n",
       "      <th>country</th>\n",
       "      <th>language</th>\n",
       "      <th>budget</th>\n",
       "      <th>usa_gross_income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>19545</th>\n",
       "      <td>tt0081145</td>\n",
       "      <td>Me and the Kid</td>\n",
       "      <td>1993</td>\n",
       "      <td>Comedy, Crime, Drama</td>\n",
       "      <td>USA</td>\n",
       "      <td>English</td>\n",
       "      <td>NaN</td>\n",
       "      <td>$ 60473</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21806</th>\n",
       "      <td>tt0088751</td>\n",
       "      <td>The Naked Monster</td>\n",
       "      <td>2005</td>\n",
       "      <td>Comedy, Horror, Sci-Fi</td>\n",
       "      <td>USA</td>\n",
       "      <td>English</td>\n",
       "      <td>PYG 350000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23805</th>\n",
       "      <td>tt0094900</td>\n",
       "      <td>Committed</td>\n",
       "      <td>1991</td>\n",
       "      <td>Drama, Thriller</td>\n",
       "      <td>USA</td>\n",
       "      <td>English</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24113</th>\n",
       "      <td>tt0095745</td>\n",
       "      <td>Ninja Vengeance</td>\n",
       "      <td>1993</td>\n",
       "      <td>Action, Drama</td>\n",
       "      <td>USA</td>\n",
       "      <td>English</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24157</th>\n",
       "      <td>tt0095878</td>\n",
       "      <td>Pledge Night</td>\n",
       "      <td>1990</td>\n",
       "      <td>Comedy, Horror</td>\n",
       "      <td>USA</td>\n",
       "      <td>English</td>\n",
       "      <td>$ 200000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      imdb_title_id     original_title  year                   genre country  \\\n",
       "19545     tt0081145     Me and the Kid  1993    Comedy, Crime, Drama     USA   \n",
       "21806     tt0088751  The Naked Monster  2005  Comedy, Horror, Sci-Fi     USA   \n",
       "23805     tt0094900          Committed  1991         Drama, Thriller     USA   \n",
       "24113     tt0095745    Ninja Vengeance  1993           Action, Drama     USA   \n",
       "24157     tt0095878       Pledge Night  1990          Comedy, Horror     USA   \n",
       "\n",
       "      language         budget usa_gross_income  \n",
       "19545  English            NaN          $ 60473  \n",
       "21806  English  PYG 350000000              NaN  \n",
       "23805  English            NaN              NaN  \n",
       "24113  English            NaN              NaN  \n",
       "24157  English       $ 200000              NaN  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_movies['year'] = df_movies['year'].replace('TV Movie 2019','2019')\n",
    "df_movies['year'] = df_movies['year'].astype(int)\n",
    "df_movies = df_movies[df_movies['year'] >= 1990]\n",
    "print(\"df_movies total (rows, columns): \", df_movies.shape)\n",
    "df_movies.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "2bJ9jqWewJsy"
   },
   "source": [
    "### 4) Filtering columns not needed in df_ratings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 218
    },
    "id": "q-lxnLSIwJsy",
    "outputId": "188d12de-285a-4fb1-9e50-5154676b8079"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "df_ratings total (rows, columns):  (85855, 2)\n"
     ]
    },
    {
     "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>imdb_title_id</th>\n",
       "      <th>weighted_average_vote</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>tt0000009</td>\n",
       "      <td>5.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>tt0000574</td>\n",
       "      <td>6.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>tt0001892</td>\n",
       "      <td>5.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>tt0002101</td>\n",
       "      <td>5.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>tt0002130</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  imdb_title_id  weighted_average_vote\n",
       "0     tt0000009                    5.9\n",
       "1     tt0000574                    6.1\n",
       "2     tt0001892                    5.8\n",
       "3     tt0002101                    5.2\n",
       "4     tt0002130                    7.0"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ratings = df_ratings.loc[:,['imdb_title_id','weighted_average_vote']]\n",
    "print(\"df_ratings total (rows, columns): \", df_ratings.shape)\n",
    "df_ratings.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "FCWBSbJmwJsy"
   },
   "source": [
    "### 5) Merge ratings with valid titles into df_main"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 0
    },
    "id": "BBfjJFqrwJsy",
    "outputId": "2c329942-b725-47b0-dcbd-52bef47a250d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "df_main total (rows, columns):  (14170, 9)\n"
     ]
    },
    {
     "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>imdb_title_id</th>\n",
       "      <th>original_title</th>\n",
       "      <th>year</th>\n",
       "      <th>genre</th>\n",
       "      <th>country</th>\n",
       "      <th>language</th>\n",
       "      <th>budget</th>\n",
       "      <th>usa_gross_income</th>\n",
       "      <th>weighted_average_vote</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>tt0081145</td>\n",
       "      <td>Me and the Kid</td>\n",
       "      <td>1993</td>\n",
       "      <td>Comedy, Crime, Drama</td>\n",
       "      <td>USA</td>\n",
       "      <td>English</td>\n",
       "      <td>NaN</td>\n",
       "      <td>$ 60473</td>\n",
       "      <td>5.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>tt0088751</td>\n",
       "      <td>The Naked Monster</td>\n",
       "      <td>2005</td>\n",
       "      <td>Comedy, Horror, Sci-Fi</td>\n",
       "      <td>USA</td>\n",
       "      <td>English</td>\n",
       "      <td>PYG 350000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>tt0094900</td>\n",
       "      <td>Committed</td>\n",
       "      <td>1991</td>\n",
       "      <td>Drama, Thriller</td>\n",
       "      <td>USA</td>\n",
       "      <td>English</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>tt0095745</td>\n",
       "      <td>Ninja Vengeance</td>\n",
       "      <td>1993</td>\n",
       "      <td>Action, Drama</td>\n",
       "      <td>USA</td>\n",
       "      <td>English</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>tt0095878</td>\n",
       "      <td>Pledge Night</td>\n",
       "      <td>1990</td>\n",
       "      <td>Comedy, Horror</td>\n",
       "      <td>USA</td>\n",
       "      <td>English</td>\n",
       "      <td>$ 200000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  imdb_title_id     original_title  year                   genre country  \\\n",
       "0     tt0081145     Me and the Kid  1993    Comedy, Crime, Drama     USA   \n",
       "1     tt0088751  The Naked Monster  2005  Comedy, Horror, Sci-Fi     USA   \n",
       "2     tt0094900          Committed  1991         Drama, Thriller     USA   \n",
       "3     tt0095745    Ninja Vengeance  1993           Action, Drama     USA   \n",
       "4     tt0095878       Pledge Night  1990          Comedy, Horror     USA   \n",
       "\n",
       "  language         budget usa_gross_income  weighted_average_vote  \n",
       "0  English            NaN          $ 60473                    5.4  \n",
       "1  English  PYG 350000000              NaN                    5.4  \n",
       "2  English            NaN              NaN                    5.1  \n",
       "3  English            NaN              NaN                    2.2  \n",
       "4  English       $ 200000              NaN                    4.8  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_main = pd.merge(df_movies,df_ratings, on='imdb_title_id')\n",
    "print(\"df_main total (rows, columns): \", df_main.shape)\n",
    "df_main.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "u9w6bZ-KwJsz"
   },
   "source": [
    "### 6) Order the titles by year in df_main"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 0
    },
    "id": "wNCvq9a_wJsz",
    "outputId": "9fff4531-6155-4e6b-f41c-1c48a89ca92a",
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "df_main total (rows, columns):  (14170, 9)\n"
     ]
    },
    {
     "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>imdb_title_id</th>\n",
       "      <th>original_title</th>\n",
       "      <th>year</th>\n",
       "      <th>genre</th>\n",
       "      <th>country</th>\n",
       "      <th>language</th>\n",
       "      <th>budget</th>\n",
       "      <th>usa_gross_income</th>\n",
       "      <th>weighted_average_vote</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>tt0183419</td>\n",
       "      <td>The Lost Platoon</td>\n",
       "      <td>1990</td>\n",
       "      <td>Action, Horror, War</td>\n",
       "      <td>USA</td>\n",
       "      <td>English</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>tt0100201</td>\n",
       "      <td>Mr. Destiny</td>\n",
       "      <td>1990</td>\n",
       "      <td>Comedy, Fantasy, Romance</td>\n",
       "      <td>USA</td>\n",
       "      <td>English</td>\n",
       "      <td>$ 20000000</td>\n",
       "      <td>$ 15379253</td>\n",
       "      <td>6.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>tt0100212</td>\n",
       "      <td>My Blue Heaven</td>\n",
       "      <td>1990</td>\n",
       "      <td>Comedy, Crime</td>\n",
       "      <td>USA</td>\n",
       "      <td>English</td>\n",
       "      <td>NaN</td>\n",
       "      <td>$ 23591472</td>\n",
       "      <td>6.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>tt0100224</td>\n",
       "      <td>Narrow Margin</td>\n",
       "      <td>1990</td>\n",
       "      <td>Action, Crime, Thriller</td>\n",
       "      <td>USA</td>\n",
       "      <td>English</td>\n",
       "      <td>NaN</td>\n",
       "      <td>$ 10873237</td>\n",
       "      <td>6.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>tt0100247</td>\n",
       "      <td>Night Angel</td>\n",
       "      <td>1990</td>\n",
       "      <td>Horror</td>\n",
       "      <td>USA</td>\n",
       "      <td>English</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  imdb_title_id    original_title  year                     genre country  \\\n",
       "0     tt0183419  The Lost Platoon  1990       Action, Horror, War     USA   \n",
       "1     tt0100201       Mr. Destiny  1990  Comedy, Fantasy, Romance     USA   \n",
       "2     tt0100212    My Blue Heaven  1990             Comedy, Crime     USA   \n",
       "3     tt0100224     Narrow Margin  1990   Action, Crime, Thriller     USA   \n",
       "4     tt0100247       Night Angel  1990                    Horror     USA   \n",
       "\n",
       "  language      budget usa_gross_income  weighted_average_vote  \n",
       "0  English         NaN              NaN                    4.1  \n",
       "1  English  $ 20000000       $ 15379253                    6.4  \n",
       "2  English         NaN       $ 23591472                    6.3  \n",
       "3  English         NaN       $ 10873237                    6.6  \n",
       "4  English         NaN              NaN                    4.5  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_main = df_main.sort_values(by='year',ascending=True)\n",
    "df_main = df_main.reset_index(drop=True)\n",
    "print(\"df_main total (rows, columns): \", df_main.shape)\n",
    "df_main.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "jQVo4IsH7w-0"
   },
   "source": [
    "### 7) Merge movies in df_budgets to df_main"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 0
    },
    "id": "x9IdEOh77w-1",
    "outputId": "a60e5647-8328-4668-95df-a8198e125a1a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "df_main total (rows, columns):  (3086, 12)\n"
     ]
    },
    {
     "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>imdb_title_id</th>\n",
       "      <th>original_title</th>\n",
       "      <th>year</th>\n",
       "      <th>genre</th>\n",
       "      <th>country</th>\n",
       "      <th>language</th>\n",
       "      <th>budget</th>\n",
       "      <th>usa_gross_income</th>\n",
       "      <th>weighted_average_vote</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>1</th>\n",
       "      <td>tt0100201</td>\n",
       "      <td>Mr. Destiny</td>\n",
       "      <td>1990</td>\n",
       "      <td>Comedy, Fantasy, Romance</td>\n",
       "      <td>USA</td>\n",
       "      <td>English</td>\n",
       "      <td>$ 20000000</td>\n",
       "      <td>$ 15379253</td>\n",
       "      <td>6.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>tt0100258</td>\n",
       "      <td>Night of the Living Dead</td>\n",
       "      <td>1990</td>\n",
       "      <td>Horror</td>\n",
       "      <td>USA</td>\n",
       "      <td>English</td>\n",
       "      <td>$ 4200000</td>\n",
       "      <td>$ 5835247</td>\n",
       "      <td>6.9</td>\n",
       "      <td>$4,200,000</td>\n",
       "      <td>$5,835,247</td>\n",
       "      <td>$5,835,247</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>tt0100258</td>\n",
       "      <td>Night of the Living Dead</td>\n",
       "      <td>1990</td>\n",
       "      <td>Horror</td>\n",
       "      <td>USA</td>\n",
       "      <td>English</td>\n",
       "      <td>$ 4200000</td>\n",
       "      <td>$ 5835247</td>\n",
       "      <td>6.9</td>\n",
       "      <td>$114,000</td>\n",
       "      <td>$12,087,064</td>\n",
       "      <td>$30,087,064</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>tt0100260</td>\n",
       "      <td>Nightbreed</td>\n",
       "      <td>1990</td>\n",
       "      <td>Action, Fantasy, Horror</td>\n",
       "      <td>USA</td>\n",
       "      <td>English</td>\n",
       "      <td>$ 11000000</td>\n",
       "      <td>$ 8862354</td>\n",
       "      <td>6.6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>tt0100318</td>\n",
       "      <td>Pacific Heights</td>\n",
       "      <td>1990</td>\n",
       "      <td>Thriller</td>\n",
       "      <td>USA</td>\n",
       "      <td>English</td>\n",
       "      <td>$ 18000000</td>\n",
       "      <td>$ 29381956</td>\n",
       "      <td>6.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   imdb_title_id            original_title  year                     genre  \\\n",
       "1      tt0100201               Mr. Destiny  1990  Comedy, Fantasy, Romance   \n",
       "6      tt0100258  Night of the Living Dead  1990                    Horror   \n",
       "7      tt0100258  Night of the Living Dead  1990                    Horror   \n",
       "8      tt0100260                Nightbreed  1990   Action, Fantasy, Horror   \n",
       "11     tt0100318           Pacific Heights  1990                  Thriller   \n",
       "\n",
       "   country language      budget usa_gross_income  weighted_average_vote  \\\n",
       "1      USA  English  $ 20000000       $ 15379253                    6.4   \n",
       "6      USA  English   $ 4200000        $ 5835247                    6.9   \n",
       "7      USA  English   $ 4200000        $ 5835247                    6.9   \n",
       "8      USA  English  $ 11000000        $ 8862354                    6.6   \n",
       "11     USA  English  $ 18000000       $ 29381956                    6.4   \n",
       "\n",
       "   Production_Budget Domestic_Gross Worldwide Gross  \n",
       "1                NaN            NaN             NaN  \n",
       "6         $4,200,000     $5,835,247      $5,835,247  \n",
       "7           $114,000    $12,087,064     $30,087,064  \n",
       "8                NaN            NaN             NaN  \n",
       "11               NaN            NaN             NaN  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_main = pd.merge(df_main, df_budget, how='left',on='original_title')\n",
    "\n",
    "mask = df_main.isna()\n",
    "df_main = df_main[~((mask.usa_gross_income == True)&(mask.Domestic_Gross == True))]\n",
    "df_main\n",
    "\n",
    "mask2 =df_main.isna()\n",
    "df_main= df_main[~((mask2.budget == True)&(mask2.Production_Budget ==True))]\n",
    "print(\"df_main total (rows, columns): \", df_main.shape)\n",
    "df_main.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "lWYar92Ngq5H"
   },
   "source": [
    "### 8) Drop columns not needed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 218
    },
    "id": "Njalc6KC7w-1",
    "outputId": "a2c55e45-5cde-4a1c-a258-f2f16fd6ddea"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "df_main total (rows, columns):  (3086, 6)\n"
     ]
    },
    {
     "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>imdb_title_id</th>\n",
       "      <th>original_title</th>\n",
       "      <th>year</th>\n",
       "      <th>genre</th>\n",
       "      <th>weighted_average_vote</th>\n",
       "      <th>usa_gross_income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>tt0100201</td>\n",
       "      <td>Mr. Destiny</td>\n",
       "      <td>1990</td>\n",
       "      <td>Comedy, Fantasy, Romance</td>\n",
       "      <td>6.4</td>\n",
       "      <td>$ 15379253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>tt0100258</td>\n",
       "      <td>Night of the Living Dead</td>\n",
       "      <td>1990</td>\n",
       "      <td>Horror</td>\n",
       "      <td>6.9</td>\n",
       "      <td>$ 5835247</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>tt0100258</td>\n",
       "      <td>Night of the Living Dead</td>\n",
       "      <td>1990</td>\n",
       "      <td>Horror</td>\n",
       "      <td>6.9</td>\n",
       "      <td>$ 5835247</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>tt0100260</td>\n",
       "      <td>Nightbreed</td>\n",
       "      <td>1990</td>\n",
       "      <td>Action, Fantasy, Horror</td>\n",
       "      <td>6.6</td>\n",
       "      <td>$ 8862354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>tt0100318</td>\n",
       "      <td>Pacific Heights</td>\n",
       "      <td>1990</td>\n",
       "      <td>Thriller</td>\n",
       "      <td>6.4</td>\n",
       "      <td>$ 29381956</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   imdb_title_id            original_title  year                     genre  \\\n",
       "1      tt0100201               Mr. Destiny  1990  Comedy, Fantasy, Romance   \n",
       "6      tt0100258  Night of the Living Dead  1990                    Horror   \n",
       "7      tt0100258  Night of the Living Dead  1990                    Horror   \n",
       "8      tt0100260                Nightbreed  1990   Action, Fantasy, Horror   \n",
       "11     tt0100318           Pacific Heights  1990                  Thriller   \n",
       "\n",
       "    weighted_average_vote usa_gross_income  \n",
       "1                     6.4       $ 15379253  \n",
       "6                     6.9        $ 5835247  \n",
       "7                     6.9        $ 5835247  \n",
       "8                     6.6        $ 8862354  \n",
       "11                    6.4       $ 29381956  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_main['budget'] = df_main['budget'].fillna(df_main['Production_Budget'])\n",
    "df_main['usa_gross_income'] = df_main['usa_gross_income'].fillna(df_main['Domestic_Gross'])\n",
    "df_main = df_main.loc[:,['imdb_title_id','original_title','year','genre','weighted_average_vote','usa_gross_income']]\n",
    "print(\"df_main total (rows, columns): \", df_main.shape)\n",
    "df_main.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "wH0yHIHGgq5I"
   },
   "source": [
    "### 9) Standardizing income column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 218
    },
    "id": "VmWnFffg7w-1",
    "outputId": "39a3a132-756a-4a0b-ac3b-9f583a808968"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "df_main total (rows, columns):  (3086, 6)\n"
     ]
    },
    {
     "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>imdb_title_id</th>\n",
       "      <th>original_title</th>\n",
       "      <th>year</th>\n",
       "      <th>genre</th>\n",
       "      <th>weighted_average_vote</th>\n",
       "      <th>usa_gross_income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>tt0100201</td>\n",
       "      <td>Mr. Destiny</td>\n",
       "      <td>1990</td>\n",
       "      <td>Comedy, Fantasy, Romance</td>\n",
       "      <td>6.4</td>\n",
       "      <td>15379253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>tt0100258</td>\n",
       "      <td>Night of the Living Dead</td>\n",
       "      <td>1990</td>\n",
       "      <td>Horror</td>\n",
       "      <td>6.9</td>\n",
       "      <td>5835247</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>tt0100258</td>\n",
       "      <td>Night of the Living Dead</td>\n",
       "      <td>1990</td>\n",
       "      <td>Horror</td>\n",
       "      <td>6.9</td>\n",
       "      <td>5835247</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>tt0100260</td>\n",
       "      <td>Nightbreed</td>\n",
       "      <td>1990</td>\n",
       "      <td>Action, Fantasy, Horror</td>\n",
       "      <td>6.6</td>\n",
       "      <td>8862354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>tt0100318</td>\n",
       "      <td>Pacific Heights</td>\n",
       "      <td>1990</td>\n",
       "      <td>Thriller</td>\n",
       "      <td>6.4</td>\n",
       "      <td>29381956</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   imdb_title_id            original_title  year                     genre  \\\n",
       "1      tt0100201               Mr. Destiny  1990  Comedy, Fantasy, Romance   \n",
       "6      tt0100258  Night of the Living Dead  1990                    Horror   \n",
       "7      tt0100258  Night of the Living Dead  1990                    Horror   \n",
       "8      tt0100260                Nightbreed  1990   Action, Fantasy, Horror   \n",
       "11     tt0100318           Pacific Heights  1990                  Thriller   \n",
       "\n",
       "    weighted_average_vote  usa_gross_income  \n",
       "1                     6.4          15379253  \n",
       "6                     6.9           5835247  \n",
       "7                     6.9           5835247  \n",
       "8                     6.6           8862354  \n",
       "11                    6.4          29381956  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_main['usa_gross_income'] = df_main['usa_gross_income'].str.replace('$', '')\n",
    "df_main['usa_gross_income'] = df_main['usa_gross_income'].str.replace(',', '')\n",
    "\n",
    "df_main['usa_gross_income'] = df_main['usa_gross_income'].astype(int)\n",
    "print(\"df_main total (rows, columns): \", df_main.shape)\n",
    "df_main.head()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "kBDac_mPgq5I"
   },
   "source": [
    "### 10) Delete duplicate rows and order by tidy data format"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 218
    },
    "id": "OeP74JlXgq5J",
    "outputId": "17aad348-ae2c-4872-9a5a-2f606ee00524"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "df_main total (rows, columns):  (3030, 6)\n"
     ]
    },
    {
     "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>imdb_title_id</th>\n",
       "      <th>year</th>\n",
       "      <th>original_title</th>\n",
       "      <th>weighted_average_vote</th>\n",
       "      <th>usa_gross_income</th>\n",
       "      <th>genre</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>tt0100201</td>\n",
       "      <td>1990</td>\n",
       "      <td>Mr. Destiny</td>\n",
       "      <td>6.4</td>\n",
       "      <td>15379253</td>\n",
       "      <td>Comedy, Fantasy, Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>tt0100258</td>\n",
       "      <td>1990</td>\n",
       "      <td>Night of the Living Dead</td>\n",
       "      <td>6.9</td>\n",
       "      <td>5835247</td>\n",
       "      <td>Horror</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>tt0100260</td>\n",
       "      <td>1990</td>\n",
       "      <td>Nightbreed</td>\n",
       "      <td>6.6</td>\n",
       "      <td>8862354</td>\n",
       "      <td>Action, Fantasy, Horror</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>tt0100318</td>\n",
       "      <td>1990</td>\n",
       "      <td>Pacific Heights</td>\n",
       "      <td>6.4</td>\n",
       "      <td>29381956</td>\n",
       "      <td>Thriller</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>tt0100403</td>\n",
       "      <td>1990</td>\n",
       "      <td>Predator 2</td>\n",
       "      <td>6.3</td>\n",
       "      <td>30669413</td>\n",
       "      <td>Action, Horror, Sci-Fi</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  imdb_title_id  year            original_title  weighted_average_vote  \\\n",
       "0     tt0100201  1990               Mr. Destiny                    6.4   \n",
       "1     tt0100258  1990  Night of the Living Dead                    6.9   \n",
       "2     tt0100260  1990                Nightbreed                    6.6   \n",
       "3     tt0100318  1990           Pacific Heights                    6.4   \n",
       "4     tt0100403  1990                Predator 2                    6.3   \n",
       "\n",
       "   usa_gross_income                     genre  \n",
       "0          15379253  Comedy, Fantasy, Romance  \n",
       "1           5835247                    Horror  \n",
       "2           8862354   Action, Fantasy, Horror  \n",
       "3          29381956                  Thriller  \n",
       "4          30669413    Action, Horror, Sci-Fi  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_main.drop_duplicates(subset='imdb_title_id', keep='first', inplace=True) #only keep eldest occurance\n",
    "df_main = df_main.reset_index(drop=True) #reset the index of the table\n",
    "\n",
    "df_main = df_main[['imdb_title_id', 'year', 'original_title', 'weighted_average_vote', 'usa_gross_income', 'genre']]\n",
    "print(\"df_main total (rows, columns): \", df_main.shape)\n",
    "df_main.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "trsJIHVugq5K"
   },
   "source": [
    "   "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "vbRmbmYrgq5K"
   },
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "aUJJFbCnwJsz"
   },
   "source": [
    "## B) *df_TP* + *df_Names*  = *df_cast*\n",
    "### 1) Drop columns not needed in *df_TP*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 218
    },
    "id": "W1qSvhpMwJsz",
    "outputId": "c3f75435-777c-4bce-ed2b-eadcc0eb673e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "df_TP total (rows, columns):  (835513, 3)\n"
     ]
    },
    {
     "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>imdb_title_id</th>\n",
       "      <th>imdb_name_id</th>\n",
       "      <th>category</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>tt0000009</td>\n",
       "      <td>nm0063086</td>\n",
       "      <td>actress</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>tt0000009</td>\n",
       "      <td>nm0183823</td>\n",
       "      <td>actor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>tt0000009</td>\n",
       "      <td>nm1309758</td>\n",
       "      <td>actor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>tt0000009</td>\n",
       "      <td>nm0085156</td>\n",
       "      <td>director</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>tt0000574</td>\n",
       "      <td>nm0846887</td>\n",
       "      <td>actress</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  imdb_title_id imdb_name_id  category\n",
       "0     tt0000009    nm0063086   actress\n",
       "1     tt0000009    nm0183823     actor\n",
       "2     tt0000009    nm1309758     actor\n",
       "3     tt0000009    nm0085156  director\n",
       "4     tt0000574    nm0846887   actress"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# drop columns \"job\" and \"characters\"\n",
    "df_TP = df_TP.drop(labels=['ordering', 'job', 'characters'], axis=1)\n",
    "print(\"df_TP total (rows, columns): \", df_TP.shape)\n",
    "df_TP.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "fEip7zvWwJsz"
   },
   "source": [
    "### 2) Keep only actors and actresses"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "UKe8aVW2wJsz",
    "outputId": "c2768799-676e-4eb9-8767-53b49c74648e"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['actress', 'actor', 'director', 'producer', 'composer',\n",
       "       'cinematographer', 'writer', 'production_designer', 'editor',\n",
       "       'archive_footage', 'self', 'archive_sound'], dtype=object)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check category types\n",
    "df_TP['category'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 0
    },
    "id": "Qagi6mLHwJsz",
    "outputId": "b7530c33-0911-41e7-9d7d-1dc131ac1b0f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "df_TP total (rows, columns):  (355751, 3)\n"
     ]
    },
    {
     "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>imdb_title_id</th>\n",
       "      <th>imdb_name_id</th>\n",
       "      <th>category</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>tt0000009</td>\n",
       "      <td>nm0063086</td>\n",
       "      <td>actress</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>tt0000009</td>\n",
       "      <td>nm0183823</td>\n",
       "      <td>actor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>tt0000009</td>\n",
       "      <td>nm1309758</td>\n",
       "      <td>actor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>tt0000574</td>\n",
       "      <td>nm0846887</td>\n",
       "      <td>actress</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>tt0000574</td>\n",
       "      <td>nm0846894</td>\n",
       "      <td>actor</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  imdb_title_id imdb_name_id category\n",
       "0     tt0000009    nm0063086  actress\n",
       "1     tt0000009    nm0183823    actor\n",
       "2     tt0000009    nm1309758    actor\n",
       "4     tt0000574    nm0846887  actress\n",
       "5     tt0000574    nm0846894    actor"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# only keep categories \"actor\" and \"actress\"\n",
    "dfTP1 = df_TP[df_TP['category'] == 'actor']\n",
    "dfTP2 = df_TP[df_TP['category'] == 'actress']\n",
    "df_TP = dfTP1.combine_first(dfTP2)\n",
    "print(\"df_TP total (rows, columns): \", df_TP.shape)\n",
    "df_TP.head()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "sIPw6uoZwJsz"
   },
   "source": [
    "### 3) Drop columns not needed in *df_Names*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 0
    },
    "id": "KvCZqrwMwJs0",
    "outputId": "81b41e51-dea0-444b-efaa-b8b03fa47f51"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "df_Names total (rows, columns):  (297705, 3)\n"
     ]
    },
    {
     "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>imdb_name_id</th>\n",
       "      <th>name</th>\n",
       "      <th>birth_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>nm0000001</td>\n",
       "      <td>Fred Astaire</td>\n",
       "      <td>Frederic Austerlitz Jr.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>nm0000002</td>\n",
       "      <td>Lauren Bacall</td>\n",
       "      <td>Betty Joan Perske</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>nm0000003</td>\n",
       "      <td>Brigitte Bardot</td>\n",
       "      <td>Brigitte Bardot</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>nm0000004</td>\n",
       "      <td>John Belushi</td>\n",
       "      <td>John Adam Belushi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>nm0000005</td>\n",
       "      <td>Ingmar Bergman</td>\n",
       "      <td>Ernst Ingmar Bergman</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  imdb_name_id             name               birth_name\n",
       "0    nm0000001     Fred Astaire  Frederic Austerlitz Jr.\n",
       "1    nm0000002    Lauren Bacall        Betty Joan Perske\n",
       "2    nm0000003  Brigitte Bardot          Brigitte Bardot\n",
       "3    nm0000004     John Belushi        John Adam Belushi\n",
       "4    nm0000005   Ingmar Bergman     Ernst Ingmar Bergman"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_Names = df_Names.drop(labels=['height', 'bio', 'birth_details', 'date_of_birth', 'place_of_birth', 'death_details', 'place_of_death', 'reason_of_death', 'spouses_string', 'spouses', 'divorces', 'spouses_with_children', 'children', 'date_of_death'], axis=1)\n",
    "print(\"df_Names total (rows, columns): \", df_Names.shape)\n",
    "df_Names.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "l0x9BNR_wJs0"
   },
   "source": [
    "### 4) Merge names by *imdb_name_id* into *df_cast*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 0
    },
    "id": "BFvRsAh5wJs0",
    "outputId": "887540b8-e3e5-4231-c0cc-db4f55e73425"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "df_cast total (rows, columns):  (355750, 5)\n"
     ]
    },
    {
     "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>imdb_title_id</th>\n",
       "      <th>imdb_name_id</th>\n",
       "      <th>category</th>\n",
       "      <th>name</th>\n",
       "      <th>birth_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>tt0000009</td>\n",
       "      <td>nm0063086</td>\n",
       "      <td>actress</td>\n",
       "      <td>Blanche Bayliss</td>\n",
       "      <td>Blanche Bayliss</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>tt0000009</td>\n",
       "      <td>nm0183823</td>\n",
       "      <td>actor</td>\n",
       "      <td>William Courtenay</td>\n",
       "      <td>William Hancock Kelly</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>tt0020403</td>\n",
       "      <td>nm0183823</td>\n",
       "      <td>actor</td>\n",
       "      <td>William Courtenay</td>\n",
       "      <td>William Hancock Kelly</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>tt0000009</td>\n",
       "      <td>nm1309758</td>\n",
       "      <td>actor</td>\n",
       "      <td>Chauncey Depew</td>\n",
       "      <td>Chauncey Mitchell Depew</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>tt0000574</td>\n",
       "      <td>nm0846887</td>\n",
       "      <td>actress</td>\n",
       "      <td>Elizabeth Tait</td>\n",
       "      <td>Elizabeth Tait</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  imdb_title_id imdb_name_id category               name  \\\n",
       "0     tt0000009    nm0063086  actress    Blanche Bayliss   \n",
       "1     tt0000009    nm0183823    actor  William Courtenay   \n",
       "2     tt0020403    nm0183823    actor  William Courtenay   \n",
       "3     tt0000009    nm1309758    actor     Chauncey Depew   \n",
       "4     tt0000574    nm0846887  actress     Elizabeth Tait   \n",
       "\n",
       "                birth_name  \n",
       "0          Blanche Bayliss  \n",
       "1    William Hancock Kelly  \n",
       "2    William Hancock Kelly  \n",
       "3  Chauncey Mitchell Depew  \n",
       "4           Elizabeth Tait  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cast = pd.merge(df_TP, df_Names, on = 'imdb_name_id')\n",
    "print(\"df_cast total (rows, columns): \", df_cast.shape)\n",
    "df_cast.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "HQBN_ULMwJs0"
   },
   "source": [
    "### 5) Keep casts members from movies in *df_main*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 0
    },
    "id": "yk_P6I28wJs0",
    "outputId": "9c27c2ae-ea55-44a4-9574-1736a55df94e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "df_cast total (rows, columns):  (12147, 5)\n"
     ]
    },
    {
     "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>imdb_title_id</th>\n",
       "      <th>imdb_name_id</th>\n",
       "      <th>category</th>\n",
       "      <th>name</th>\n",
       "      <th>birth_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>tt0100201</td>\n",
       "      <td>nm0000323</td>\n",
       "      <td>actor</td>\n",
       "      <td>Michael Caine</td>\n",
       "      <td>Maurice Joseph Micklewhite</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>tt0100201</td>\n",
       "      <td>nm0000902</td>\n",
       "      <td>actor</td>\n",
       "      <td>Jim Belushi</td>\n",
       "      <td>James Adam Belushi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>tt0100201</td>\n",
       "      <td>nm0000157</td>\n",
       "      <td>actress</td>\n",
       "      <td>Linda Hamilton</td>\n",
       "      <td>Linda Carroll Hamilton</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>tt0100201</td>\n",
       "      <td>nm0001484</td>\n",
       "      <td>actor</td>\n",
       "      <td>Jon Lovitz</td>\n",
       "      <td>Jonathan Michael Lovitz</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>tt0100258</td>\n",
       "      <td>nm0869981</td>\n",
       "      <td>actor</td>\n",
       "      <td>Tom Towles</td>\n",
       "      <td>Thomas Towles</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  imdb_title_id imdb_name_id category            name  \\\n",
       "0     tt0100201    nm0000323    actor   Michael Caine   \n",
       "1     tt0100201    nm0000902    actor     Jim Belushi   \n",
       "2     tt0100201    nm0000157  actress  Linda Hamilton   \n",
       "3     tt0100201    nm0001484    actor      Jon Lovitz   \n",
       "4     tt0100258    nm0869981    actor      Tom Towles   \n",
       "\n",
       "                   birth_name  \n",
       "0  Maurice Joseph Micklewhite  \n",
       "1          James Adam Belushi  \n",
       "2      Linda Carroll Hamilton  \n",
       "3     Jonathan Michael Lovitz  \n",
       "4               Thomas Towles  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cast = pd.merge(df_main, df_cast, on='imdb_title_id', how='left')\n",
    "df_cast = df_cast.loc[:, ['imdb_title_id', 'imdb_name_id', 'category', 'name', 'birth_name']]\n",
    "df_cast = df_cast.reset_index(drop=True)\n",
    "\n",
    "print(\"df_cast total (rows, columns): \", df_cast.shape)\n",
    "df_cast.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "pDyC3F3Rgq5O"
   },
   "source": [
    "### 6) Standardize gender"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "id": "XSpyb0yp7w-6"
   },
   "outputs": [],
   "source": [
    "def standardize_gender(category):\n",
    "    if category == 'actor' :\n",
    "        return 0\n",
    "\n",
    "    elif category == 'actress':\n",
    "        return 1\n",
    "\n",
    "    else:\n",
    "        return None\n",
    "\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 218
    },
    "id": "vz813F587w-8",
    "outputId": "c3ea4e0b-2e31-417f-a607-5bfe95bbe9f6"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "df_cast total (rows, columns):  (12146, 6)\n"
     ]
    },
    {
     "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>imdb_title_id</th>\n",
       "      <th>imdb_name_id</th>\n",
       "      <th>category</th>\n",
       "      <th>name</th>\n",
       "      <th>birth_name</th>\n",
       "      <th>gender</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>tt0100201</td>\n",
       "      <td>nm0000323</td>\n",
       "      <td>actor</td>\n",
       "      <td>Michael Caine</td>\n",
       "      <td>Maurice Joseph Micklewhite</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>tt0100201</td>\n",
       "      <td>nm0000902</td>\n",
       "      <td>actor</td>\n",
       "      <td>Jim Belushi</td>\n",
       "      <td>James Adam Belushi</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>tt0100201</td>\n",
       "      <td>nm0000157</td>\n",
       "      <td>actress</td>\n",
       "      <td>Linda Hamilton</td>\n",
       "      <td>Linda Carroll Hamilton</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>tt0100201</td>\n",
       "      <td>nm0001484</td>\n",
       "      <td>actor</td>\n",
       "      <td>Jon Lovitz</td>\n",
       "      <td>Jonathan Michael Lovitz</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>tt0100258</td>\n",
       "      <td>nm0869981</td>\n",
       "      <td>actor</td>\n",
       "      <td>Tom Towles</td>\n",
       "      <td>Thomas Towles</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  imdb_title_id imdb_name_id category            name  \\\n",
       "0     tt0100201    nm0000323    actor   Michael Caine   \n",
       "1     tt0100201    nm0000902    actor     Jim Belushi   \n",
       "2     tt0100201    nm0000157  actress  Linda Hamilton   \n",
       "3     tt0100201    nm0001484    actor      Jon Lovitz   \n",
       "4     tt0100258    nm0869981    actor      Tom Towles   \n",
       "\n",
       "                   birth_name  gender  \n",
       "0  Maurice Joseph Micklewhite     0.0  \n",
       "1          James Adam Belushi     0.0  \n",
       "2      Linda Carroll Hamilton     1.0  \n",
       "3     Jonathan Michael Lovitz     0.0  \n",
       "4               Thomas Towles     0.0  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cast['gender'] = df_cast['category'].apply(standardize_gender)\n",
    "df_cast = df_cast.dropna()\n",
    "print(\"df_cast total (rows, columns): \", df_cast.shape)\n",
    "df_cast.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "   "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ZS5YV6XWwJs0"
   },
   "source": [
    "## DATA ANALYSIS & RESULTS"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## EDA\n",
    "\n",
    "We checked the distributions of movies per year and movies per genre to check if we have a balanced distribution of movies to work with. The limitations arised from this analysis is explained more in the conclusion. We also checked if we have the same amount of movies in df_main and df_cast to make sure we can complete our analysis across both datasets. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ulD0dlybwJs0"
   },
   "source": [
    "### 1) Check that there is no repeating movies in df_main"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "6xn_hFv3wJs0",
    "outputId": "210ecb29-7e7f-4634-f3ba-d887bb5a4ca5"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of unique titles in df_main:  3030\n",
      "Number of rows in df_main:  3030\n",
      "Number of unique titles match number of rows in df_main:  True\n"
     ]
    }
   ],
   "source": [
    "print(\"Number of unique titles in df_main: \", len(df_main['imdb_title_id'].unique()))\n",
    "print(\"Number of rows in df_main: \", len(df_main.index))\n",
    "print(\"Number of unique titles match number of rows in df_main: \", len(df_main.index) == len(df_main['imdb_title_id'].unique()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Xp9hotcUgq5R"
   },
   "source": [
    "Looks like in total we have 3,030 movies to work it for our analysis after data cleaning."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "MCGH-hhiwJs0"
   },
   "source": [
    "### 2) Check that we have cast members for all movies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "F22-LPyr7w-9",
    "outputId": "7b3d216f-5009-41a5-a73f-afd3266055ab"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of titles in df_main:  3030\n",
      "Number of titles in df_cast:  3029\n",
      "Number of unique titles in df_main match number of titles in df_cast:  False\n"
     ]
    }
   ],
   "source": [
    "print(\"Number of titles in df_main: \", len(df_main['imdb_title_id'].unique()))\n",
    "print(\"Number of titles in df_cast: \", len(df_cast['imdb_title_id'].unique()))\n",
    "print(\"Number of unique titles in df_main match number of titles in df_cast: \", len(df_main['imdb_title_id'].unique()) == len(df_cast['imdb_title_id'].unique()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "WHq0Cmt-gq5S"
   },
   "source": [
    "Looks like we have are missing one movie for whom we do not have any cast member. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "3IgZlZIBwJs0"
   },
   "source": [
    "### 3) Checking how many movies per year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "fdfKrF-GwJs1",
    "outputId": "f53815ee-3109-47ea-e7f5-2bb5ea6fe0ec"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "number of pre 2010 movies:  1949 \n",
      "number of post 2010 movies:  1081\n",
      "total number of movies:  3030\n"
     ]
    }
   ],
   "source": [
    "counts = pd.DataFrame(df_main[['imdb_title_id','year']].groupby('year').count())\n",
    "\n",
    "counts.astype('int32').dtypes\n",
    "counts['imdb_title_id'] = counts['imdb_title_id'].astype(int)\n",
    "\n",
    "ind = 1990 \n",
    "pre2010_sum = 0\n",
    "while ind != 2010:\n",
    "    pre2010_sum += counts.loc[ind, 'imdb_title_id']\n",
    "    ind +=1\n",
    "\n",
    "post2010_sum = 0\n",
    "while ind != 2021:\n",
    "    post2010_sum += counts.loc[ind, 'imdb_title_id']\n",
    "    ind += 1\n",
    "    \n",
    "print('number of pre 2010 movies: ', pre2010_sum, '\\nnumber of post 2010 movies: ', post2010_sum)\n",
    "print('total number of movies: ', pre2010_sum+post2010_sum)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "oP6jzjlVz1pV"
   },
   "source": [
    "### 4) Checking how many movies per genre pre 2010 and post 2010\n",
    "### 4a) Identify different genres"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "def unique_genre(genres): \n",
    "    ans = [] \n",
    "    \n",
    "    for one_row in genres: \n",
    "        each_genre = one_row.split(',')\n",
    "        \n",
    "        \n",
    "        for x in each_genre: \n",
    "            x = x.strip()\n",
    "            \n",
    "            if x not in ans: \n",
    "                ans.append(x) \n",
    "            \n",
    "    return ans "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Comedy',\n",
       " 'Fantasy',\n",
       " 'Romance',\n",
       " 'Horror',\n",
       " 'Action',\n",
       " 'Thriller',\n",
       " 'Sci-Fi',\n",
       " 'Mystery',\n",
       " 'Family',\n",
       " 'Animation',\n",
       " 'Adventure',\n",
       " 'Crime',\n",
       " 'Drama',\n",
       " 'History',\n",
       " 'Music',\n",
       " 'Western',\n",
       " 'Musical',\n",
       " 'Biography',\n",
       " 'Sport',\n",
       " 'War']"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unique_genres = unique_genre(df_main['genre'])\n",
    "unique_genres"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4b) Split genres into two dataframes of *analysispre2010* and *analysispost2010* "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pre2010 total (rows, columns):  (1949, 6)\n"
     ]
    },
    {
     "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>imdb_title_id</th>\n",
       "      <th>year</th>\n",
       "      <th>original_title</th>\n",
       "      <th>weighted_average_vote</th>\n",
       "      <th>usa_gross_income</th>\n",
       "      <th>genre</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>tt0100201</td>\n",
       "      <td>1990</td>\n",
       "      <td>Mr. Destiny</td>\n",
       "      <td>6.4</td>\n",
       "      <td>15379253</td>\n",
       "      <td>Comedy, Fantasy, Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>tt0100258</td>\n",
       "      <td>1990</td>\n",
       "      <td>Night of the Living Dead</td>\n",
       "      <td>6.9</td>\n",
       "      <td>5835247</td>\n",
       "      <td>Horror</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>tt0100260</td>\n",
       "      <td>1990</td>\n",
       "      <td>Nightbreed</td>\n",
       "      <td>6.6</td>\n",
       "      <td>8862354</td>\n",
       "      <td>Action, Fantasy, Horror</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>tt0100318</td>\n",
       "      <td>1990</td>\n",
       "      <td>Pacific Heights</td>\n",
       "      <td>6.4</td>\n",
       "      <td>29381956</td>\n",
       "      <td>Thriller</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>tt0100403</td>\n",
       "      <td>1990</td>\n",
       "      <td>Predator 2</td>\n",
       "      <td>6.3</td>\n",
       "      <td>30669413</td>\n",
       "      <td>Action, Horror, Sci-Fi</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  imdb_title_id  year            original_title  weighted_average_vote  \\\n",
       "0     tt0100201  1990               Mr. Destiny                    6.4   \n",
       "1     tt0100258  1990  Night of the Living Dead                    6.9   \n",
       "2     tt0100260  1990                Nightbreed                    6.6   \n",
       "3     tt0100318  1990           Pacific Heights                    6.4   \n",
       "4     tt0100403  1990                Predator 2                    6.3   \n",
       "\n",
       "   usa_gross_income                     genre  \n",
       "0          15379253  Comedy, Fantasy, Romance  \n",
       "1           5835247                    Horror  \n",
       "2           8862354   Action, Fantasy, Horror  \n",
       "3          29381956                  Thriller  \n",
       "4          30669413    Action, Horror, Sci-Fi  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pre2010 = pd.DataFrame()\n",
    "pre2010 = df_main[df_main['year'] < 2010]\n",
    "print(\"pre2010 total (rows, columns): \", pre2010.shape)\n",
    "pre2010.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "post2010 total (rows, columns):  (1081, 6)\n"
     ]
    },
    {
     "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>imdb_title_id</th>\n",
       "      <th>year</th>\n",
       "      <th>original_title</th>\n",
       "      <th>weighted_average_vote</th>\n",
       "      <th>usa_gross_income</th>\n",
       "      <th>genre</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1949</th>\n",
       "      <td>tt1354687</td>\n",
       "      <td>2010</td>\n",
       "      <td>Feed the Fish</td>\n",
       "      <td>6.1</td>\n",
       "      <td>101170</td>\n",
       "      <td>Comedy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1950</th>\n",
       "      <td>tt1104001</td>\n",
       "      <td>2010</td>\n",
       "      <td>Tron</td>\n",
       "      <td>6.8</td>\n",
       "      <td>172062763</td>\n",
       "      <td>Action, Adventure, Sci-Fi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1951</th>\n",
       "      <td>tt1336617</td>\n",
       "      <td>2010</td>\n",
       "      <td>Cyrus</td>\n",
       "      <td>6.3</td>\n",
       "      <td>7468936</td>\n",
       "      <td>Comedy, Drama, Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1952</th>\n",
       "      <td>tt1341188</td>\n",
       "      <td>2010</td>\n",
       "      <td>How Do You Know</td>\n",
       "      <td>5.4</td>\n",
       "      <td>30212620</td>\n",
       "      <td>Comedy, Drama, Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1953</th>\n",
       "      <td>tt1107319</td>\n",
       "      <td>2010</td>\n",
       "      <td>Dirty Girl</td>\n",
       "      <td>6.4</td>\n",
       "      <td>55125</td>\n",
       "      <td>Comedy, Drama</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     imdb_title_id  year   original_title  weighted_average_vote  \\\n",
       "1949     tt1354687  2010    Feed the Fish                    6.1   \n",
       "1950     tt1104001  2010             Tron                    6.8   \n",
       "1951     tt1336617  2010            Cyrus                    6.3   \n",
       "1952     tt1341188  2010  How Do You Know                    5.4   \n",
       "1953     tt1107319  2010       Dirty Girl                    6.4   \n",
       "\n",
       "      usa_gross_income                      genre  \n",
       "1949            101170                     Comedy  \n",
       "1950         172062763  Action, Adventure, Sci-Fi  \n",
       "1951           7468936     Comedy, Drama, Romance  \n",
       "1952          30212620     Comedy, Drama, Romance  \n",
       "1953             55125              Comedy, Drama  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "post2010 = pd.DataFrame()\n",
    "post2010 = df_main[df_main['year'] >= 2010]\n",
    "print(\"post2010 total (rows, columns): \", post2010.shape)\n",
    "post2010.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4c) Count number of movies of genre in these two dataframes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "def num_movies(unique_genres, df):\n",
    "    \n",
    "    ans = []\n",
    "    num_of_movies = 0\n",
    "\n",
    "    for each_genre in unique_genres:\n",
    "        \n",
    "        for i in df.index:\n",
    "            \n",
    "            if each_genre in df.loc[i, 'genre']:\n",
    "                num_of_movies += 1\n",
    "      \n",
    "        ans.append(num_of_movies)\n",
    "        \n",
    "        num_of_movies = 0\n",
    "    \n",
    "    return ans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "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>genres</th>\n",
       "      <th>preNumMovies</th>\n",
       "      <th>NumMovies</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Comedy</td>\n",
       "      <td>954</td>\n",
       "      <td>408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Fantasy</td>\n",
       "      <td>135</td>\n",
       "      <td>53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Romance</td>\n",
       "      <td>404</td>\n",
       "      <td>171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Horror</td>\n",
       "      <td>170</td>\n",
       "      <td>176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Action</td>\n",
       "      <td>290</td>\n",
       "      <td>190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Thriller</td>\n",
       "      <td>296</td>\n",
       "      <td>188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Sci-Fi</td>\n",
       "      <td>101</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Mystery</td>\n",
       "      <td>145</td>\n",
       "      <td>101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Family</td>\n",
       "      <td>148</td>\n",
       "      <td>62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Animation</td>\n",
       "      <td>80</td>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Adventure</td>\n",
       "      <td>206</td>\n",
       "      <td>131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Crime</td>\n",
       "      <td>365</td>\n",
       "      <td>144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Drama</td>\n",
       "      <td>1051</td>\n",
       "      <td>608</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>History</td>\n",
       "      <td>20</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Music</td>\n",
       "      <td>90</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Western</td>\n",
       "      <td>11</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Musical</td>\n",
       "      <td>16</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Biography</td>\n",
       "      <td>59</td>\n",
       "      <td>69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Sport</td>\n",
       "      <td>65</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>War</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       genres  preNumMovies  NumMovies\n",
       "0      Comedy           954        408\n",
       "1     Fantasy           135         53\n",
       "2     Romance           404        171\n",
       "3      Horror           170        176\n",
       "4      Action           290        190\n",
       "5    Thriller           296        188\n",
       "6      Sci-Fi           101         68\n",
       "7     Mystery           145        101\n",
       "8      Family           148         62\n",
       "9   Animation            80         48\n",
       "10  Adventure           206        131\n",
       "11      Crime           365        144\n",
       "12      Drama          1051        608\n",
       "13    History            20         18\n",
       "14      Music            90         61\n",
       "15    Western            11         10\n",
       "16    Musical            16         15\n",
       "17  Biography            59         69\n",
       "18      Sport            65         34\n",
       "19        War             7          8"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total = pd.DataFrame()\n",
    "total['genres'] = unique_genres\n",
    "total['preNumMovies'] = num_movies(unique_genres, pre2010)\n",
    "total['NumMovies'] = num_movies(unique_genres, post2010)\n",
    "total"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can plot these numbers in a bar graph to see the difference of how many movies of each genre we have pre and post 2010."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 391
    },
    "id": "XpXdeYDIzyG9",
    "outputId": "1db393bb-ed63-4702-9e83-d9d849d83c0b"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "genre_ax = total.plot.bar(x='genres',y=['preNumMovies','NumMovies'],figsize=(12,5), title=\"Movies per Genre (Pre2010 vs Post2010)\")\n",
    "genre_ax.set_ylabel('Number of Movies')\n",
    "l= genre_ax.legend()\n",
    "l.get_texts()[0].set_text('Pre-2010s')\n",
    "l.get_texts()[1].set_text( 'Post-2010s')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "BuoTcn9F7w--"
   },
   "source": [
    "## ANALYSIS \n",
    "\n",
    "From these datasets, we were able to match actors and actresses to their gender and the movies they are in (if they were in the top 10 most important contributors of the movie). Then, we were able to figure out which movies where the main cast was majority female and which ones were majority male. We were also able to group each movie into their genres to more accurately analyze the success of movies released pre and post 2010. To measure the success of each genre before and after 2010 based on their increase in gender diversity, we also matched the movies with their weighted average ratings from IMDb, as well as their budget and gross income. We took these values (pre and post 2010) and averaged them across all genres. Within each genre, if the genre values surpassed the average calculated, we labeled the value as having changed/improved since before and after 2010. Depending on whether gender-diverse movies’ revenue and/or ratings increased over the years, we would consider each genre as being not successful, moderately successful, and successful in relation with gender diversity.\n",
    " \n",
    "The following are steps we took to form the analysis:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "BM8GKNCL7w--"
   },
   "source": [
    "### 1) Find gender diversity of each movie"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 441
    },
    "id": "mL0iW65i7w--",
    "outputId": "48f937af-170e-40b2-dcab-27a69e47e9c8"
   },
   "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>num_of_cast</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>imdb_title_id</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>tt0098375</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tt0098987</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tt0099018</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tt0099077</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tt0099088</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               num_of_cast\n",
       "imdb_title_id             \n",
       "tt0098375                4\n",
       "tt0098987                4\n",
       "tt0099018                4\n",
       "tt0099077                4\n",
       "tt0099088                4"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cast_count = pd.DataFrame(df_cast[['imdb_title_id', 'imdb_name_id']].groupby('imdb_title_id').count())\n",
    "cast_count = cast_count.rename(columns={\"imdb_name_id\": \"num_of_cast\"})\n",
    "cast_count.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 441
    },
    "id": "zTUVvtso7w--",
    "outputId": "52429b62-576c-4dbd-8f9d-a0e6d59dac7e"
   },
   "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>num_of_woman</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>imdb_title_id</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>tt0098375</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tt0098987</th>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tt0099018</th>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tt0099077</th>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tt0099088</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               num_of_woman\n",
       "imdb_title_id              \n",
       "tt0098375               1.0\n",
       "tt0098987               2.0\n",
       "tt0099018               2.0\n",
       "tt0099077               2.0\n",
       "tt0099088               1.0"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "woman_count = pd.DataFrame(df_cast[['imdb_title_id','gender']].groupby('imdb_title_id').sum())\n",
    "woman_count = woman_count.rename(columns={\"gender\": \"num_of_woman\"})\n",
    "woman_count.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 441
    },
    "id": "h3pZk7NH7w--",
    "outputId": "37500fe6-ba26-4435-fa06-041deb4fa218"
   },
   "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>num_of_cast</th>\n",
       "      <th>num_of_woman</th>\n",
       "      <th>diversity</th>\n",
       "      <th>isDiverse</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>imdb_title_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>tt0098375</th>\n",
       "      <td>4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tt0098987</th>\n",
       "      <td>4</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.50</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tt0099018</th>\n",
       "      <td>4</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.50</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tt0099077</th>\n",
       "      <td>4</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.50</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tt0099088</th>\n",
       "      <td>4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               num_of_cast  num_of_woman  diversity  isDiverse\n",
       "imdb_title_id                                                 \n",
       "tt0098375                4           1.0       0.25        0.0\n",
       "tt0098987                4           2.0       0.50        1.0\n",
       "tt0099018                4           2.0       0.50        1.0\n",
       "tt0099077                4           2.0       0.50        1.0\n",
       "tt0099088                4           1.0       0.25        0.0"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movie_diversity = pd.merge(cast_count, woman_count, on='imdb_title_id')\n",
    "movie_diversity['diversity'] = movie_diversity['num_of_woman'] / movie_diversity['num_of_cast']\n",
    "#Creating a catergory that tells up if its diverse or not\n",
    "for i in movie_diversity.index:\n",
    "    if movie_diversity.loc[i,'diversity'] >= .5:\n",
    "        movie_diversity.loc[i,'isDiverse'] = 1\n",
    "    else:\n",
    "        movie_diversity.loc[i,'isDiverse'] = 0\n",
    "        \n",
    "movie_diversity.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "iuTDJX_igq5X"
   },
   "source": [
    "### 2) Add diversity boolean to each movie"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 357
    },
    "id": "YUPIGNRHgq5X",
    "outputId": "efd46521-9ebd-492b-88d3-399792c11933"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "df_main total (rows, columns):  (3029, 10)\n"
     ]
    },
    {
     "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>imdb_title_id</th>\n",
       "      <th>year</th>\n",
       "      <th>original_title</th>\n",
       "      <th>weighted_average_vote</th>\n",
       "      <th>usa_gross_income</th>\n",
       "      <th>genre</th>\n",
       "      <th>num_of_cast</th>\n",
       "      <th>num_of_woman</th>\n",
       "      <th>diversity</th>\n",
       "      <th>isDiverse</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>tt0100201</td>\n",
       "      <td>1990</td>\n",
       "      <td>Mr. Destiny</td>\n",
       "      <td>6.4</td>\n",
       "      <td>15379253</td>\n",
       "      <td>Comedy, Fantasy, Romance</td>\n",
       "      <td>4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>tt0100258</td>\n",
       "      <td>1990</td>\n",
       "      <td>Night of the Living Dead</td>\n",
       "      <td>6.9</td>\n",
       "      <td>5835247</td>\n",
       "      <td>Horror</td>\n",
       "      <td>4</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.50</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>tt0100260</td>\n",
       "      <td>1990</td>\n",
       "      <td>Nightbreed</td>\n",
       "      <td>6.6</td>\n",
       "      <td>8862354</td>\n",
       "      <td>Action, Fantasy, Horror</td>\n",
       "      <td>4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>tt0100318</td>\n",
       "      <td>1990</td>\n",
       "      <td>Pacific Heights</td>\n",
       "      <td>6.4</td>\n",
       "      <td>29381956</td>\n",
       "      <td>Thriller</td>\n",
       "      <td>4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>tt0100403</td>\n",
       "      <td>1990</td>\n",
       "      <td>Predator 2</td>\n",
       "      <td>6.3</td>\n",
       "      <td>30669413</td>\n",
       "      <td>Action, Horror, Sci-Fi</td>\n",
       "      <td>4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  imdb_title_id  year            original_title  weighted_average_vote  \\\n",
       "0     tt0100201  1990               Mr. Destiny                    6.4   \n",
       "1     tt0100258  1990  Night of the Living Dead                    6.9   \n",
       "2     tt0100260  1990                Nightbreed                    6.6   \n",
       "3     tt0100318  1990           Pacific Heights                    6.4   \n",
       "4     tt0100403  1990                Predator 2                    6.3   \n",
       "\n",
       "   usa_gross_income                     genre  num_of_cast  num_of_woman  \\\n",
       "0          15379253  Comedy, Fantasy, Romance            4           1.0   \n",
       "1           5835247                    Horror            4           2.0   \n",
       "2           8862354   Action, Fantasy, Horror            4           1.0   \n",
       "3          29381956                  Thriller            4           1.0   \n",
       "4          30669413    Action, Horror, Sci-Fi            4           0.0   \n",
       "\n",
       "   diversity  isDiverse  \n",
       "0       0.25        0.0  \n",
       "1       0.50        1.0  \n",
       "2       0.25        0.0  \n",
       "3       0.25        0.0  \n",
       "4       0.00        0.0  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_main = pd.merge(df_main, movie_diversity, on = 'imdb_title_id')\n",
    "print(\"df_main total (rows, columns): \", df_main.shape)\n",
    "df_main.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ZBP2RuM47w-_"
   },
   "source": [
    "### 3) Update *pre2010* and *post2010* from *df_main*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 357
    },
    "id": "Fld6J_kDgq5Y",
    "outputId": "2aa3591f-bc71-415d-b5f3-c8a09e4a09cd"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pre2010 total (rows, columns):  (1948, 10)\n"
     ]
    },
    {
     "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>imdb_title_id</th>\n",
       "      <th>year</th>\n",
       "      <th>original_title</th>\n",
       "      <th>weighted_average_vote</th>\n",
       "      <th>usa_gross_income</th>\n",
       "      <th>genre</th>\n",
       "      <th>num_of_cast</th>\n",
       "      <th>num_of_woman</th>\n",
       "      <th>diversity</th>\n",
       "      <th>isDiverse</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>tt0100201</td>\n",
       "      <td>1990</td>\n",
       "      <td>Mr. Destiny</td>\n",
       "      <td>6.4</td>\n",
       "      <td>15379253</td>\n",
       "      <td>Comedy, Fantasy, Romance</td>\n",
       "      <td>4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>tt0100258</td>\n",
       "      <td>1990</td>\n",
       "      <td>Night of the Living Dead</td>\n",
       "      <td>6.9</td>\n",
       "      <td>5835247</td>\n",
       "      <td>Horror</td>\n",
       "      <td>4</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.50</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>tt0100260</td>\n",
       "      <td>1990</td>\n",
       "      <td>Nightbreed</td>\n",
       "      <td>6.6</td>\n",
       "      <td>8862354</td>\n",
       "      <td>Action, Fantasy, Horror</td>\n",
       "      <td>4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>tt0100318</td>\n",
       "      <td>1990</td>\n",
       "      <td>Pacific Heights</td>\n",
       "      <td>6.4</td>\n",
       "      <td>29381956</td>\n",
       "      <td>Thriller</td>\n",
       "      <td>4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>tt0100403</td>\n",
       "      <td>1990</td>\n",
       "      <td>Predator 2</td>\n",
       "      <td>6.3</td>\n",
       "      <td>30669413</td>\n",
       "      <td>Action, Horror, Sci-Fi</td>\n",
       "      <td>4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  imdb_title_id  year            original_title  weighted_average_vote  \\\n",
       "0     tt0100201  1990               Mr. Destiny                    6.4   \n",
       "1     tt0100258  1990  Night of the Living Dead                    6.9   \n",
       "2     tt0100260  1990                Nightbreed                    6.6   \n",
       "3     tt0100318  1990           Pacific Heights                    6.4   \n",
       "4     tt0100403  1990                Predator 2                    6.3   \n",
       "\n",
       "   usa_gross_income                     genre  num_of_cast  num_of_woman  \\\n",
       "0          15379253  Comedy, Fantasy, Romance            4           1.0   \n",
       "1           5835247                    Horror            4           2.0   \n",
       "2           8862354   Action, Fantasy, Horror            4           1.0   \n",
       "3          29381956                  Thriller            4           1.0   \n",
       "4          30669413    Action, Horror, Sci-Fi            4           0.0   \n",
       "\n",
       "   diversity  isDiverse  \n",
       "0       0.25        0.0  \n",
       "1       0.50        1.0  \n",
       "2       0.25        0.0  \n",
       "3       0.25        0.0  \n",
       "4       0.00        0.0  "
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pre2010 = df_main[df_main['year'] < 2010]\n",
    "print(\"pre2010 total (rows, columns): \", pre2010.shape)\n",
    "pre2010.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 357
    },
    "id": "g6lQDXktgq5Y",
    "outputId": "9afcda50-45c4-48e8-be4d-ed25a89dc8be"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "post2010 total (rows, columns):  (1081, 10)\n"
     ]
    },
    {
     "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>imdb_title_id</th>\n",
       "      <th>year</th>\n",
       "      <th>original_title</th>\n",
       "      <th>weighted_average_vote</th>\n",
       "      <th>usa_gross_income</th>\n",
       "      <th>genre</th>\n",
       "      <th>num_of_cast</th>\n",
       "      <th>num_of_woman</th>\n",
       "      <th>diversity</th>\n",
       "      <th>isDiverse</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1948</th>\n",
       "      <td>tt1354687</td>\n",
       "      <td>2010</td>\n",
       "      <td>Feed the Fish</td>\n",
       "      <td>6.1</td>\n",
       "      <td>101170</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>4</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.50</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1949</th>\n",
       "      <td>tt1104001</td>\n",
       "      <td>2010</td>\n",
       "      <td>Tron</td>\n",
       "      <td>6.8</td>\n",
       "      <td>172062763</td>\n",
       "      <td>Action, Adventure, Sci-Fi</td>\n",
       "      <td>4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1950</th>\n",
       "      <td>tt1336617</td>\n",
       "      <td>2010</td>\n",
       "      <td>Cyrus</td>\n",
       "      <td>6.3</td>\n",
       "      <td>7468936</td>\n",
       "      <td>Comedy, Drama, Romance</td>\n",
       "      <td>4</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.50</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1951</th>\n",
       "      <td>tt1341188</td>\n",
       "      <td>2010</td>\n",
       "      <td>How Do You Know</td>\n",
       "      <td>5.4</td>\n",
       "      <td>30212620</td>\n",
       "      <td>Comedy, Drama, Romance</td>\n",
       "      <td>4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1952</th>\n",
       "      <td>tt1107319</td>\n",
       "      <td>2010</td>\n",
       "      <td>Dirty Girl</td>\n",
       "      <td>6.4</td>\n",
       "      <td>55125</td>\n",
       "      <td>Comedy, Drama</td>\n",
       "      <td>4</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.50</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     imdb_title_id  year   original_title  weighted_average_vote  \\\n",
       "1948     tt1354687  2010    Feed the Fish                    6.1   \n",
       "1949     tt1104001  2010             Tron                    6.8   \n",
       "1950     tt1336617  2010            Cyrus                    6.3   \n",
       "1951     tt1341188  2010  How Do You Know                    5.4   \n",
       "1952     tt1107319  2010       Dirty Girl                    6.4   \n",
       "\n",
       "      usa_gross_income                      genre  num_of_cast  num_of_woman  \\\n",
       "1948            101170                     Comedy            4           2.0   \n",
       "1949         172062763  Action, Adventure, Sci-Fi            4           1.0   \n",
       "1950           7468936     Comedy, Drama, Romance            4           2.0   \n",
       "1951          30212620     Comedy, Drama, Romance            4           1.0   \n",
       "1952             55125              Comedy, Drama            4           2.0   \n",
       "\n",
       "      diversity  isDiverse  \n",
       "1948       0.50        1.0  \n",
       "1949       0.25        0.0  \n",
       "1950       0.50        1.0  \n",
       "1951       0.25        0.0  \n",
       "1952       0.50        1.0  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# post2010 = pd.DataFrame()\n",
    "post2010 = df_main[df_main['year'] >= 2010]\n",
    "print(\"post2010 total (rows, columns): \", post2010.shape)\n",
    "post2010.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "FaYApCa9gq5Z"
   },
   "source": [
    "### 4) Create analysis dataframes (*analysispre2010* and *analysispost2010*) by each genre"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "id": "Ava6RLrqgq5a"
   },
   "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>genres</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Comedy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Fantasy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Horror</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Action</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Thriller</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Sci-Fi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Mystery</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Family</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Animation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Adventure</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Crime</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>History</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Music</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Western</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Musical</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Biography</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Sport</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>War</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       genres\n",
       "0      Comedy\n",
       "1     Fantasy\n",
       "2     Romance\n",
       "3      Horror\n",
       "4      Action\n",
       "5    Thriller\n",
       "6      Sci-Fi\n",
       "7     Mystery\n",
       "8      Family\n",
       "9   Animation\n",
       "10  Adventure\n",
       "11      Crime\n",
       "12      Drama\n",
       "13    History\n",
       "14      Music\n",
       "15    Western\n",
       "16    Musical\n",
       "17  Biography\n",
       "18      Sport\n",
       "19        War"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "analysispre2010 = pd.DataFrame()\n",
    "analysispre2010['genres'] = unique_genres \n",
    "analysispre2010"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "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>genres</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Comedy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Fantasy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Horror</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Action</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Thriller</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Sci-Fi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Mystery</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Family</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Animation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Adventure</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Crime</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>History</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Music</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Western</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Musical</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Biography</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Sport</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>War</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       genres\n",
       "0      Comedy\n",
       "1     Fantasy\n",
       "2     Romance\n",
       "3      Horror\n",
       "4      Action\n",
       "5    Thriller\n",
       "6      Sci-Fi\n",
       "7     Mystery\n",
       "8      Family\n",
       "9   Animation\n",
       "10  Adventure\n",
       "11      Crime\n",
       "12      Drama\n",
       "13    History\n",
       "14      Music\n",
       "15    Western\n",
       "16    Musical\n",
       "17  Biography\n",
       "18      Sport\n",
       "19        War"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "analysispost2010 = pd.DataFrame()\n",
    "analysispost2010['genres'] = unique_genres \n",
    "analysispost2010"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "EhnV6WLJ7w_B"
   },
   "source": [
    "### 5) Find average diversity of each genre of movie in pre2010 and post2010"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "id": "wmLtPSss7w_B"
   },
   "outputs": [],
   "source": [
    "def avg_diversity(unique_genres, df):\n",
    "    \n",
    "    ans = []\n",
    "    sum_diversity = 0\n",
    "    num_of_movies = 0\n",
    "\n",
    "    for each_genre in unique_genres:\n",
    "        \n",
    "        for i in df.index:\n",
    "            \n",
    "            if each_genre in df.loc[i, 'genre']:\n",
    "                sum_diversity += df_main.loc[i, 'diversity']\n",
    "                num_of_movies += 1\n",
    "\n",
    "        if num_of_movies == 0:\n",
    "            ans.append(0)\n",
    "        else:\n",
    "            ans.append(sum_diversity/num_of_movies)\n",
    "        \n",
    "\n",
    "        sum_diversity = 0\n",
    "        num_of_movies = 0\n",
    "    \n",
    "    return ans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 655
    },
    "id": "kuVF-ekv7w_C",
    "outputId": "97bfbd49-a067-4312-a3e3-76f592867c91"
   },
   "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>genres</th>\n",
       "      <th>Pavg_diversity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Comedy</td>\n",
       "      <td>0.371171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Fantasy</td>\n",
       "      <td>0.348148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Romance</td>\n",
       "      <td>0.433492</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Horror</td>\n",
       "      <td>0.383403</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Action</td>\n",
       "      <td>0.254310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Thriller</td>\n",
       "      <td>0.357264</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Sci-Fi</td>\n",
       "      <td>0.303748</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Mystery</td>\n",
       "      <td>0.379310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Family</td>\n",
       "      <td>0.365889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Animation</td>\n",
       "      <td>0.309675</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Adventure</td>\n",
       "      <td>0.268204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Crime</td>\n",
       "      <td>0.285616</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Drama</td>\n",
       "      <td>0.377396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>History</td>\n",
       "      <td>0.312500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Music</td>\n",
       "      <td>0.365212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Western</td>\n",
       "      <td>0.136364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Musical</td>\n",
       "      <td>0.408482</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Biography</td>\n",
       "      <td>0.338983</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Sport</td>\n",
       "      <td>0.271429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>War</td>\n",
       "      <td>0.250000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       genres  Pavg_diversity\n",
       "0      Comedy        0.371171\n",
       "1     Fantasy        0.348148\n",
       "2     Romance        0.433492\n",
       "3      Horror        0.383403\n",
       "4      Action        0.254310\n",
       "5    Thriller        0.357264\n",
       "6      Sci-Fi        0.303748\n",
       "7     Mystery        0.379310\n",
       "8      Family        0.365889\n",
       "9   Animation        0.309675\n",
       "10  Adventure        0.268204\n",
       "11      Crime        0.285616\n",
       "12      Drama        0.377396\n",
       "13    History        0.312500\n",
       "14      Music        0.365212\n",
       "15    Western        0.136364\n",
       "16    Musical        0.408482\n",
       "17  Biography        0.338983\n",
       "18      Sport        0.271429\n",
       "19        War        0.250000"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "analysispre2010['Pavg_diversity'] = avg_diversity(unique_genres, pre2010)\n",
    "analysispre2010"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 655
    },
    "id": "nmNPMh967w_C",
    "outputId": "aa8a61ac-e9ca-4ce1-d78f-a3ed08465357"
   },
   "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>genres</th>\n",
       "      <th>avg_diversity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Comedy</td>\n",
       "      <td>0.429768</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Fantasy</td>\n",
       "      <td>0.410377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Romance</td>\n",
       "      <td>0.497076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Horror</td>\n",
       "      <td>0.446699</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Action</td>\n",
       "      <td>0.292105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Thriller</td>\n",
       "      <td>0.391844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Sci-Fi</td>\n",
       "      <td>0.324055</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Mystery</td>\n",
       "      <td>0.462871</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Family</td>\n",
       "      <td>0.413978</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Animation</td>\n",
       "      <td>0.326389</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Adventure</td>\n",
       "      <td>0.335878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Crime</td>\n",
       "      <td>0.302331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Drama</td>\n",
       "      <td>0.416060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>History</td>\n",
       "      <td>0.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Music</td>\n",
       "      <td>0.487705</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Western</td>\n",
       "      <td>0.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Musical</td>\n",
       "      <td>0.516667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Biography</td>\n",
       "      <td>0.365942</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Sport</td>\n",
       "      <td>0.323529</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>War</td>\n",
       "      <td>0.437500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       genres  avg_diversity\n",
       "0      Comedy       0.429768\n",
       "1     Fantasy       0.410377\n",
       "2     Romance       0.497076\n",
       "3      Horror       0.446699\n",
       "4      Action       0.292105\n",
       "5    Thriller       0.391844\n",
       "6      Sci-Fi       0.324055\n",
       "7     Mystery       0.462871\n",
       "8      Family       0.413978\n",
       "9   Animation       0.326389\n",
       "10  Adventure       0.335878\n",
       "11      Crime       0.302331\n",
       "12      Drama       0.416060\n",
       "13    History       0.250000\n",
       "14      Music       0.487705\n",
       "15    Western       0.300000\n",
       "16    Musical       0.516667\n",
       "17  Biography       0.365942\n",
       "18      Sport       0.323529\n",
       "19        War       0.437500"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "analysispost2010['avg_diversity'] = avg_diversity(unique_genres, post2010)\n",
    "analysispost2010"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "7QrQt_Gr7w_C"
   },
   "source": [
    " ### 6) Find average rating of each genre of movie in pre2010 and post2010"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "id": "ZgWYDdJ27w_C"
   },
   "outputs": [],
   "source": [
    "def avg_rating(unique_genres, df):\n",
    "    \n",
    "    ans = []\n",
    "    sum_ratings = 0\n",
    "    num_of_movies = 0\n",
    "    \n",
    "\n",
    "    for each_genre in unique_genres:\n",
    "        \n",
    "        for i in df.index:\n",
    "            \n",
    "            if each_genre in df.loc[i, 'genre']:\n",
    "                sum_ratings += df_main.loc[i, 'weighted_average_vote']\n",
    "                num_of_movies += 1\n",
    "\n",
    "        if num_of_movies == 0:\n",
    "            ans.append(0)\n",
    "        else:\n",
    "            ans.append(sum_ratings/num_of_movies)\n",
    "            \n",
    "        sum_ratings = 0\n",
    "        num_of_movies = 0\n",
    "    \n",
    "    return ans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 0
    },
    "id": "xO0ZbdIU7w_D",
    "outputId": "6d24bb21-dfb1-4a06-f8b2-9ef13044e953"
   },
   "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>genres</th>\n",
       "      <th>Pavg_diversity</th>\n",
       "      <th>Pavg_rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Comedy</td>\n",
       "      <td>0.371171</td>\n",
       "      <td>5.849003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Fantasy</td>\n",
       "      <td>0.348148</td>\n",
       "      <td>5.834074</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Romance</td>\n",
       "      <td>0.433492</td>\n",
       "      <td>6.011139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Horror</td>\n",
       "      <td>0.383403</td>\n",
       "      <td>5.425882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Action</td>\n",
       "      <td>0.254310</td>\n",
       "      <td>5.792759</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Thriller</td>\n",
       "      <td>0.357264</td>\n",
       "      <td>5.701689</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Sci-Fi</td>\n",
       "      <td>0.303748</td>\n",
       "      <td>5.728713</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Mystery</td>\n",
       "      <td>0.379310</td>\n",
       "      <td>6.115862</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Family</td>\n",
       "      <td>0.365889</td>\n",
       "      <td>5.653741</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Animation</td>\n",
       "      <td>0.309675</td>\n",
       "      <td>6.326582</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Adventure</td>\n",
       "      <td>0.268204</td>\n",
       "      <td>5.978641</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Crime</td>\n",
       "      <td>0.285616</td>\n",
       "      <td>6.079178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Drama</td>\n",
       "      <td>0.377396</td>\n",
       "      <td>6.254329</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>History</td>\n",
       "      <td>0.312500</td>\n",
       "      <td>6.805000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Music</td>\n",
       "      <td>0.365212</td>\n",
       "      <td>5.848889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Western</td>\n",
       "      <td>0.136364</td>\n",
       "      <td>5.845455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Musical</td>\n",
       "      <td>0.408482</td>\n",
       "      <td>5.425000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Biography</td>\n",
       "      <td>0.338983</td>\n",
       "      <td>6.861017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Sport</td>\n",
       "      <td>0.271429</td>\n",
       "      <td>6.129231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>War</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>6.400000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       genres  Pavg_diversity  Pavg_rating\n",
       "0      Comedy        0.371171     5.849003\n",
       "1     Fantasy        0.348148     5.834074\n",
       "2     Romance        0.433492     6.011139\n",
       "3      Horror        0.383403     5.425882\n",
       "4      Action        0.254310     5.792759\n",
       "5    Thriller        0.357264     5.701689\n",
       "6      Sci-Fi        0.303748     5.728713\n",
       "7     Mystery        0.379310     6.115862\n",
       "8      Family        0.365889     5.653741\n",
       "9   Animation        0.309675     6.326582\n",
       "10  Adventure        0.268204     5.978641\n",
       "11      Crime        0.285616     6.079178\n",
       "12      Drama        0.377396     6.254329\n",
       "13    History        0.312500     6.805000\n",
       "14      Music        0.365212     5.848889\n",
       "15    Western        0.136364     5.845455\n",
       "16    Musical        0.408482     5.425000\n",
       "17  Biography        0.338983     6.861017\n",
       "18      Sport        0.271429     6.129231\n",
       "19        War        0.250000     6.400000"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "analysispre2010['Pavg_rating'] = avg_rating(unique_genres, pre2010)\n",
    "analysispre2010"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 0
    },
    "id": "GblL5elz7w_D",
    "outputId": "e3c02224-3e7f-4fa9-cdd1-0ef44af029b7"
   },
   "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>genres</th>\n",
       "      <th>avg_diversity</th>\n",
       "      <th>avg_rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Comedy</td>\n",
       "      <td>0.429768</td>\n",
       "      <td>5.938480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Fantasy</td>\n",
       "      <td>0.410377</td>\n",
       "      <td>5.764151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Romance</td>\n",
       "      <td>0.497076</td>\n",
       "      <td>5.980702</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Horror</td>\n",
       "      <td>0.446699</td>\n",
       "      <td>4.784091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Action</td>\n",
       "      <td>0.292105</td>\n",
       "      <td>5.777895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Thriller</td>\n",
       "      <td>0.391844</td>\n",
       "      <td>5.248936</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Sci-Fi</td>\n",
       "      <td>0.324055</td>\n",
       "      <td>5.697059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Mystery</td>\n",
       "      <td>0.462871</td>\n",
       "      <td>5.570297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Family</td>\n",
       "      <td>0.413978</td>\n",
       "      <td>5.922581</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Animation</td>\n",
       "      <td>0.326389</td>\n",
       "      <td>6.579167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Adventure</td>\n",
       "      <td>0.335878</td>\n",
       "      <td>6.261832</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Crime</td>\n",
       "      <td>0.302331</td>\n",
       "      <td>5.929167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Drama</td>\n",
       "      <td>0.416060</td>\n",
       "      <td>6.074013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>History</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>6.422222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Music</td>\n",
       "      <td>0.487705</td>\n",
       "      <td>6.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Western</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>5.640000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Musical</td>\n",
       "      <td>0.516667</td>\n",
       "      <td>5.873333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Biography</td>\n",
       "      <td>0.365942</td>\n",
       "      <td>6.818841</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Sport</td>\n",
       "      <td>0.323529</td>\n",
       "      <td>6.226471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>War</td>\n",
       "      <td>0.437500</td>\n",
       "      <td>6.025000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       genres  avg_diversity  avg_rating\n",
       "0      Comedy       0.429768    5.938480\n",
       "1     Fantasy       0.410377    5.764151\n",
       "2     Romance       0.497076    5.980702\n",
       "3      Horror       0.446699    4.784091\n",
       "4      Action       0.292105    5.777895\n",
       "5    Thriller       0.391844    5.248936\n",
       "6      Sci-Fi       0.324055    5.697059\n",
       "7     Mystery       0.462871    5.570297\n",
       "8      Family       0.413978    5.922581\n",
       "9   Animation       0.326389    6.579167\n",
       "10  Adventure       0.335878    6.261832\n",
       "11      Crime       0.302331    5.929167\n",
       "12      Drama       0.416060    6.074013\n",
       "13    History       0.250000    6.422222\n",
       "14      Music       0.487705    6.300000\n",
       "15    Western       0.300000    5.640000\n",
       "16    Musical       0.516667    5.873333\n",
       "17  Biography       0.365942    6.818841\n",
       "18      Sport       0.323529    6.226471\n",
       "19        War       0.437500    6.025000"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "analysispost2010['avg_rating'] = avg_rating(unique_genres, post2010)\n",
    "analysispost2010"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "zUHCP2Gq7w_D"
   },
   "source": [
    "### 7) Find woman-diverse movie's average ratings pre2010 and post2010"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "id": "Jcu9Rnsv7w_F"
   },
   "outputs": [],
   "source": [
    "def woman_diverse_ratings(unique_genres, df):\n",
    "    \n",
    "    ans = []\n",
    "    sum_average = 0\n",
    "    num_of_movies = 0\n",
    "\n",
    "    for each_genre in unique_genres:\n",
    "        \n",
    "        for i in df.index:\n",
    "            \n",
    "            if each_genre in df.loc[i, 'genre']:\n",
    "                \n",
    "                if df.loc[i, 'diversity'] >= 0.5:\n",
    "                    sum_average += df_main.loc[i, 'weighted_average_vote']\n",
    "                    num_of_movies += 1\n",
    "\n",
    "        if num_of_movies == 0:\n",
    "            ans.append(0)\n",
    "        else:\n",
    "            ans.append(sum_average/num_of_movies)\n",
    "        \n",
    "        sum_average = 0\n",
    "        num_of_movies = 0\n",
    "    \n",
    "\n",
    "    return ans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 0
    },
    "id": "l8LivECc7w_G",
    "outputId": "279f8244-c867-48ee-c5c9-10343749a0c8"
   },
   "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>genres</th>\n",
       "      <th>Pavg_diversity</th>\n",
       "      <th>Pavg_rating</th>\n",
       "      <th>Pwoman_avg_rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Comedy</td>\n",
       "      <td>0.371171</td>\n",
       "      <td>5.849003</td>\n",
       "      <td>5.889145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Fantasy</td>\n",
       "      <td>0.348148</td>\n",
       "      <td>5.834074</td>\n",
       "      <td>5.807692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Romance</td>\n",
       "      <td>0.433492</td>\n",
       "      <td>6.011139</td>\n",
       "      <td>5.999180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Horror</td>\n",
       "      <td>0.383403</td>\n",
       "      <td>5.425882</td>\n",
       "      <td>5.363855</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Action</td>\n",
       "      <td>0.254310</td>\n",
       "      <td>5.792759</td>\n",
       "      <td>5.696429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Thriller</td>\n",
       "      <td>0.357264</td>\n",
       "      <td>5.701689</td>\n",
       "      <td>5.533594</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Sci-Fi</td>\n",
       "      <td>0.303748</td>\n",
       "      <td>5.728713</td>\n",
       "      <td>5.760000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Mystery</td>\n",
       "      <td>0.379310</td>\n",
       "      <td>6.115862</td>\n",
       "      <td>5.882609</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Family</td>\n",
       "      <td>0.365889</td>\n",
       "      <td>5.653741</td>\n",
       "      <td>5.663492</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Animation</td>\n",
       "      <td>0.309675</td>\n",
       "      <td>6.326582</td>\n",
       "      <td>6.254545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Adventure</td>\n",
       "      <td>0.268204</td>\n",
       "      <td>5.978641</td>\n",
       "      <td>5.941667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Crime</td>\n",
       "      <td>0.285616</td>\n",
       "      <td>6.079178</td>\n",
       "      <td>5.956436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Drama</td>\n",
       "      <td>0.377396</td>\n",
       "      <td>6.254329</td>\n",
       "      <td>6.187377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>History</td>\n",
       "      <td>0.312500</td>\n",
       "      <td>6.805000</td>\n",
       "      <td>6.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Music</td>\n",
       "      <td>0.365212</td>\n",
       "      <td>5.848889</td>\n",
       "      <td>5.697500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Western</td>\n",
       "      <td>0.136364</td>\n",
       "      <td>5.845455</td>\n",
       "      <td>6.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Musical</td>\n",
       "      <td>0.408482</td>\n",
       "      <td>5.425000</td>\n",
       "      <td>5.214286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Biography</td>\n",
       "      <td>0.338983</td>\n",
       "      <td>6.861017</td>\n",
       "      <td>6.768000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Sport</td>\n",
       "      <td>0.271429</td>\n",
       "      <td>6.129231</td>\n",
       "      <td>6.235294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>War</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>6.400000</td>\n",
       "      <td>6.800000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       genres  Pavg_diversity  Pavg_rating  Pwoman_avg_rating\n",
       "0      Comedy        0.371171     5.849003           5.889145\n",
       "1     Fantasy        0.348148     5.834074           5.807692\n",
       "2     Romance        0.433492     6.011139           5.999180\n",
       "3      Horror        0.383403     5.425882           5.363855\n",
       "4      Action        0.254310     5.792759           5.696429\n",
       "5    Thriller        0.357264     5.701689           5.533594\n",
       "6      Sci-Fi        0.303748     5.728713           5.760000\n",
       "7     Mystery        0.379310     6.115862           5.882609\n",
       "8      Family        0.365889     5.653741           5.663492\n",
       "9   Animation        0.309675     6.326582           6.254545\n",
       "10  Adventure        0.268204     5.978641           5.941667\n",
       "11      Crime        0.285616     6.079178           5.956436\n",
       "12      Drama        0.377396     6.254329           6.187377\n",
       "13    History        0.312500     6.805000           6.500000\n",
       "14      Music        0.365212     5.848889           5.697500\n",
       "15    Western        0.136364     5.845455           6.600000\n",
       "16    Musical        0.408482     5.425000           5.214286\n",
       "17  Biography        0.338983     6.861017           6.768000\n",
       "18      Sport        0.271429     6.129231           6.235294\n",
       "19        War        0.250000     6.400000           6.800000"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "analysispre2010['Pwoman_avg_rating'] = woman_diverse_ratings(unique_genres, pre2010)\n",
    "analysispre2010"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 0
    },
    "id": "BwAq5ZBk7w_H",
    "outputId": "602831ba-f90b-4cdb-8a2b-d61785c685dc"
   },
   "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>genres</th>\n",
       "      <th>avg_diversity</th>\n",
       "      <th>avg_rating</th>\n",
       "      <th>woman_avg_rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Comedy</td>\n",
       "      <td>0.429768</td>\n",
       "      <td>5.938480</td>\n",
       "      <td>5.902789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Fantasy</td>\n",
       "      <td>0.410377</td>\n",
       "      <td>5.764151</td>\n",
       "      <td>5.450000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Romance</td>\n",
       "      <td>0.497076</td>\n",
       "      <td>5.980702</td>\n",
       "      <td>5.937879</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Horror</td>\n",
       "      <td>0.446699</td>\n",
       "      <td>4.784091</td>\n",
       "      <td>4.840367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Action</td>\n",
       "      <td>0.292105</td>\n",
       "      <td>5.777895</td>\n",
       "      <td>5.445283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Thriller</td>\n",
       "      <td>0.391844</td>\n",
       "      <td>5.248936</td>\n",
       "      <td>5.097030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Sci-Fi</td>\n",
       "      <td>0.324055</td>\n",
       "      <td>5.697059</td>\n",
       "      <td>5.940000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Mystery</td>\n",
       "      <td>0.462871</td>\n",
       "      <td>5.570297</td>\n",
       "      <td>5.595588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Family</td>\n",
       "      <td>0.413978</td>\n",
       "      <td>5.922581</td>\n",
       "      <td>5.964516</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Animation</td>\n",
       "      <td>0.326389</td>\n",
       "      <td>6.579167</td>\n",
       "      <td>6.760000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Adventure</td>\n",
       "      <td>0.335878</td>\n",
       "      <td>6.261832</td>\n",
       "      <td>6.018367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Crime</td>\n",
       "      <td>0.302331</td>\n",
       "      <td>5.929167</td>\n",
       "      <td>5.604167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Drama</td>\n",
       "      <td>0.416060</td>\n",
       "      <td>6.074013</td>\n",
       "      <td>6.023295</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>History</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>6.422222</td>\n",
       "      <td>6.480000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Music</td>\n",
       "      <td>0.487705</td>\n",
       "      <td>6.300000</td>\n",
       "      <td>6.179070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Western</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>5.640000</td>\n",
       "      <td>5.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Musical</td>\n",
       "      <td>0.516667</td>\n",
       "      <td>5.873333</td>\n",
       "      <td>5.430000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Biography</td>\n",
       "      <td>0.365942</td>\n",
       "      <td>6.818841</td>\n",
       "      <td>6.875000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Sport</td>\n",
       "      <td>0.323529</td>\n",
       "      <td>6.226471</td>\n",
       "      <td>5.987500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>War</td>\n",
       "      <td>0.437500</td>\n",
       "      <td>6.025000</td>\n",
       "      <td>6.366667</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       genres  avg_diversity  avg_rating  woman_avg_rating\n",
       "0      Comedy       0.429768    5.938480          5.902789\n",
       "1     Fantasy       0.410377    5.764151          5.450000\n",
       "2     Romance       0.497076    5.980702          5.937879\n",
       "3      Horror       0.446699    4.784091          4.840367\n",
       "4      Action       0.292105    5.777895          5.445283\n",
       "5    Thriller       0.391844    5.248936          5.097030\n",
       "6      Sci-Fi       0.324055    5.697059          5.940000\n",
       "7     Mystery       0.462871    5.570297          5.595588\n",
       "8      Family       0.413978    5.922581          5.964516\n",
       "9   Animation       0.326389    6.579167          6.760000\n",
       "10  Adventure       0.335878    6.261832          6.018367\n",
       "11      Crime       0.302331    5.929167          5.604167\n",
       "12      Drama       0.416060    6.074013          6.023295\n",
       "13    History       0.250000    6.422222          6.480000\n",
       "14      Music       0.487705    6.300000          6.179070\n",
       "15    Western       0.300000    5.640000          5.400000\n",
       "16    Musical       0.516667    5.873333          5.430000\n",
       "17  Biography       0.365942    6.818841          6.875000\n",
       "18      Sport       0.323529    6.226471          5.987500\n",
       "19        War       0.437500    6.025000          6.366667"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "analysispost2010['woman_avg_rating'] = woman_diverse_ratings(unique_genres, post2010)\n",
    "analysispost2010"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "4KAbBpu5gq5e"
   },
   "source": [
    "### 8) Find avg gross income per genre pre2010 and post2010"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "id": "MQvwFxDqgq5e"
   },
   "outputs": [],
   "source": [
    "def avg_income(unique_genres, df):\n",
    "    \n",
    "    ans = []\n",
    "    sum_income = 0\n",
    "    num_of_movies = 0\n",
    "    \n",
    "\n",
    "    for each_genre in unique_genres:\n",
    "        \n",
    "        for i in df.index:\n",
    "            \n",
    "            if each_genre in df.loc[i, 'genre']:\n",
    "                sum_income += df_main.loc[i, 'usa_gross_income']\n",
    "                num_of_movies += 1\n",
    "\n",
    "        if num_of_movies == 0:\n",
    "            ans.append(0)\n",
    "        else:\n",
    "            ans.append(sum_income/num_of_movies)\n",
    "            \n",
    "        sum_income = 0\n",
    "        num_of_movies = 0\n",
    "    \n",
    "    return ans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 655
    },
    "id": "dDYyR5HSgq5e",
    "outputId": "5ef6a431-25a8-4d27-8d19-b87ccdf92df3"
   },
   "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>genres</th>\n",
       "      <th>Pavg_diversity</th>\n",
       "      <th>Pavg_rating</th>\n",
       "      <th>Pwoman_avg_rating</th>\n",
       "      <th>Pavg_income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Comedy</td>\n",
       "      <td>0.371171</td>\n",
       "      <td>5.849003</td>\n",
       "      <td>5.889145</td>\n",
       "      <td>3.023432e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Fantasy</td>\n",
       "      <td>0.348148</td>\n",
       "      <td>5.834074</td>\n",
       "      <td>5.807692</td>\n",
       "      <td>5.005759e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Romance</td>\n",
       "      <td>0.433492</td>\n",
       "      <td>6.011139</td>\n",
       "      <td>5.999180</td>\n",
       "      <td>2.667396e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Horror</td>\n",
       "      <td>0.383403</td>\n",
       "      <td>5.425882</td>\n",
       "      <td>5.363855</td>\n",
       "      <td>2.229678e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Action</td>\n",
       "      <td>0.254310</td>\n",
       "      <td>5.792759</td>\n",
       "      <td>5.696429</td>\n",
       "      <td>4.589862e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Thriller</td>\n",
       "      <td>0.357264</td>\n",
       "      <td>5.701689</td>\n",
       "      <td>5.533594</td>\n",
       "      <td>2.732175e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Sci-Fi</td>\n",
       "      <td>0.303748</td>\n",
       "      <td>5.728713</td>\n",
       "      <td>5.760000</td>\n",
       "      <td>4.310803e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Mystery</td>\n",
       "      <td>0.379310</td>\n",
       "      <td>6.115862</td>\n",
       "      <td>5.882609</td>\n",
       "      <td>2.986919e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Family</td>\n",
       "      <td>0.365889</td>\n",
       "      <td>5.653741</td>\n",
       "      <td>5.663492</td>\n",
       "      <td>4.527907e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Animation</td>\n",
       "      <td>0.309675</td>\n",
       "      <td>6.326582</td>\n",
       "      <td>6.254545</td>\n",
       "      <td>8.773103e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Adventure</td>\n",
       "      <td>0.268204</td>\n",
       "      <td>5.978641</td>\n",
       "      <td>5.941667</td>\n",
       "      <td>7.261728e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Crime</td>\n",
       "      <td>0.285616</td>\n",
       "      <td>6.079178</td>\n",
       "      <td>5.956436</td>\n",
       "      <td>2.093925e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Drama</td>\n",
       "      <td>0.377396</td>\n",
       "      <td>6.254329</td>\n",
       "      <td>6.187377</td>\n",
       "      <td>2.213472e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>History</td>\n",
       "      <td>0.312500</td>\n",
       "      <td>6.805000</td>\n",
       "      <td>6.500000</td>\n",
       "      <td>3.572388e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Music</td>\n",
       "      <td>0.365212</td>\n",
       "      <td>5.848889</td>\n",
       "      <td>5.697500</td>\n",
       "      <td>1.843432e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Western</td>\n",
       "      <td>0.136364</td>\n",
       "      <td>5.845455</td>\n",
       "      <td>6.600000</td>\n",
       "      <td>3.604225e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Musical</td>\n",
       "      <td>0.408482</td>\n",
       "      <td>5.425000</td>\n",
       "      <td>5.214286</td>\n",
       "      <td>1.641704e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Biography</td>\n",
       "      <td>0.338983</td>\n",
       "      <td>6.861017</td>\n",
       "      <td>6.768000</td>\n",
       "      <td>3.330100e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Sport</td>\n",
       "      <td>0.271429</td>\n",
       "      <td>6.129231</td>\n",
       "      <td>6.235294</td>\n",
       "      <td>3.102405e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>War</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>6.400000</td>\n",
       "      <td>6.800000</td>\n",
       "      <td>4.242087e+06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       genres  Pavg_diversity  Pavg_rating  Pwoman_avg_rating   Pavg_income\n",
       "0      Comedy        0.371171     5.849003           5.889145  3.023432e+07\n",
       "1     Fantasy        0.348148     5.834074           5.807692  5.005759e+07\n",
       "2     Romance        0.433492     6.011139           5.999180  2.667396e+07\n",
       "3      Horror        0.383403     5.425882           5.363855  2.229678e+07\n",
       "4      Action        0.254310     5.792759           5.696429  4.589862e+07\n",
       "5    Thriller        0.357264     5.701689           5.533594  2.732175e+07\n",
       "6      Sci-Fi        0.303748     5.728713           5.760000  4.310803e+07\n",
       "7     Mystery        0.379310     6.115862           5.882609  2.986919e+07\n",
       "8      Family        0.365889     5.653741           5.663492  4.527907e+07\n",
       "9   Animation        0.309675     6.326582           6.254545  8.773103e+07\n",
       "10  Adventure        0.268204     5.978641           5.941667  7.261728e+07\n",
       "11      Crime        0.285616     6.079178           5.956436  2.093925e+07\n",
       "12      Drama        0.377396     6.254329           6.187377  2.213472e+07\n",
       "13    History        0.312500     6.805000           6.500000  3.572388e+07\n",
       "14      Music        0.365212     5.848889           5.697500  1.843432e+07\n",
       "15    Western        0.136364     5.845455           6.600000  3.604225e+07\n",
       "16    Musical        0.408482     5.425000           5.214286  1.641704e+07\n",
       "17  Biography        0.338983     6.861017           6.768000  3.330100e+07\n",
       "18      Sport        0.271429     6.129231           6.235294  3.102405e+07\n",
       "19        War        0.250000     6.400000           6.800000  4.242087e+06"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "analysispre2010['Pavg_income'] = avg_income(unique_genres, pre2010)\n",
    "analysispre2010"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 655
    },
    "id": "aRxXEUOZgq5f",
    "outputId": "adca4b50-93ba-42bb-a459-a76e082ec274"
   },
   "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>genres</th>\n",
       "      <th>avg_diversity</th>\n",
       "      <th>avg_rating</th>\n",
       "      <th>woman_avg_rating</th>\n",
       "      <th>avg_income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Comedy</td>\n",
       "      <td>0.429768</td>\n",
       "      <td>5.938480</td>\n",
       "      <td>5.902789</td>\n",
       "      <td>3.836266e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Fantasy</td>\n",
       "      <td>0.410377</td>\n",
       "      <td>5.764151</td>\n",
       "      <td>5.450000</td>\n",
       "      <td>8.049504e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Romance</td>\n",
       "      <td>0.497076</td>\n",
       "      <td>5.980702</td>\n",
       "      <td>5.937879</td>\n",
       "      <td>2.252266e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Horror</td>\n",
       "      <td>0.446699</td>\n",
       "      <td>4.784091</td>\n",
       "      <td>4.840367</td>\n",
       "      <td>1.457188e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Action</td>\n",
       "      <td>0.292105</td>\n",
       "      <td>5.777895</td>\n",
       "      <td>5.445283</td>\n",
       "      <td>7.984553e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Thriller</td>\n",
       "      <td>0.391844</td>\n",
       "      <td>5.248936</td>\n",
       "      <td>5.097030</td>\n",
       "      <td>1.689424e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Sci-Fi</td>\n",
       "      <td>0.324055</td>\n",
       "      <td>5.697059</td>\n",
       "      <td>5.940000</td>\n",
       "      <td>9.522093e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Mystery</td>\n",
       "      <td>0.462871</td>\n",
       "      <td>5.570297</td>\n",
       "      <td>5.595588</td>\n",
       "      <td>1.943471e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Family</td>\n",
       "      <td>0.413978</td>\n",
       "      <td>5.922581</td>\n",
       "      <td>5.964516</td>\n",
       "      <td>5.433023e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Animation</td>\n",
       "      <td>0.326389</td>\n",
       "      <td>6.579167</td>\n",
       "      <td>6.760000</td>\n",
       "      <td>1.498386e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Adventure</td>\n",
       "      <td>0.335878</td>\n",
       "      <td>6.261832</td>\n",
       "      <td>6.018367</td>\n",
       "      <td>1.487639e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Crime</td>\n",
       "      <td>0.302331</td>\n",
       "      <td>5.929167</td>\n",
       "      <td>5.604167</td>\n",
       "      <td>1.937037e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Drama</td>\n",
       "      <td>0.416060</td>\n",
       "      <td>6.074013</td>\n",
       "      <td>6.023295</td>\n",
       "      <td>1.884961e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>History</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>6.422222</td>\n",
       "      <td>6.480000</td>\n",
       "      <td>2.454257e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Music</td>\n",
       "      <td>0.487705</td>\n",
       "      <td>6.300000</td>\n",
       "      <td>6.179070</td>\n",
       "      <td>2.753182e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Western</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>5.640000</td>\n",
       "      <td>5.400000</td>\n",
       "      <td>1.795343e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Musical</td>\n",
       "      <td>0.516667</td>\n",
       "      <td>5.873333</td>\n",
       "      <td>5.430000</td>\n",
       "      <td>5.079586e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Biography</td>\n",
       "      <td>0.365942</td>\n",
       "      <td>6.818841</td>\n",
       "      <td>6.875000</td>\n",
       "      <td>2.939120e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Sport</td>\n",
       "      <td>0.323529</td>\n",
       "      <td>6.226471</td>\n",
       "      <td>5.987500</td>\n",
       "      <td>2.076697e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>War</td>\n",
       "      <td>0.437500</td>\n",
       "      <td>6.025000</td>\n",
       "      <td>6.366667</td>\n",
       "      <td>1.161476e+07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       genres  avg_diversity  avg_rating  woman_avg_rating    avg_income\n",
       "0      Comedy       0.429768    5.938480          5.902789  3.836266e+07\n",
       "1     Fantasy       0.410377    5.764151          5.450000  8.049504e+07\n",
       "2     Romance       0.497076    5.980702          5.937879  2.252266e+07\n",
       "3      Horror       0.446699    4.784091          4.840367  1.457188e+07\n",
       "4      Action       0.292105    5.777895          5.445283  7.984553e+07\n",
       "5    Thriller       0.391844    5.248936          5.097030  1.689424e+07\n",
       "6      Sci-Fi       0.324055    5.697059          5.940000  9.522093e+07\n",
       "7     Mystery       0.462871    5.570297          5.595588  1.943471e+07\n",
       "8      Family       0.413978    5.922581          5.964516  5.433023e+07\n",
       "9   Animation       0.326389    6.579167          6.760000  1.498386e+08\n",
       "10  Adventure       0.335878    6.261832          6.018367  1.487639e+08\n",
       "11      Crime       0.302331    5.929167          5.604167  1.937037e+07\n",
       "12      Drama       0.416060    6.074013          6.023295  1.884961e+07\n",
       "13    History       0.250000    6.422222          6.480000  2.454257e+07\n",
       "14      Music       0.487705    6.300000          6.179070  2.753182e+07\n",
       "15    Western       0.300000    5.640000          5.400000  1.795343e+07\n",
       "16    Musical       0.516667    5.873333          5.430000  5.079586e+07\n",
       "17  Biography       0.365942    6.818841          6.875000  2.939120e+07\n",
       "18      Sport       0.323529    6.226471          5.987500  2.076697e+07\n",
       "19        War       0.437500    6.025000          6.366667  1.161476e+07"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "analysispost2010['avg_income'] = avg_income(unique_genres, post2010)\n",
    "analysispost2010"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "YxEsey-6gq5f"
   },
   "source": [
    "### 9) Find woman-diverse movie's average income pre2010 and post2010"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "id": "DL02XJPsgq5g"
   },
   "outputs": [],
   "source": [
    "def woman_diverse_income(unique_genres, df):\n",
    "    \n",
    "    ans = []\n",
    "    sum_income = 0\n",
    "    num_of_movies = 0\n",
    "\n",
    "    for each_genre in unique_genres:\n",
    "        \n",
    "        for i in df.index:\n",
    "            \n",
    "            if each_genre in df.loc[i, 'genre']:\n",
    "                \n",
    "                if df.loc[i, 'diversity'] >= 0.5:\n",
    "                    sum_income += df_main.loc[i, 'usa_gross_income']\n",
    "                    num_of_movies += 1\n",
    "\n",
    "        if num_of_movies == 0:\n",
    "            ans.append(0)\n",
    "        else:\n",
    "            ans.append(sum_income/num_of_movies)\n",
    "        \n",
    "        sum_income = 0\n",
    "        num_of_movies = 0\n",
    "    \n",
    "\n",
    "    return ans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 655
    },
    "id": "4Q8Adtn-gq5g",
    "outputId": "b7b35c97-1295-469b-917b-580c2739156b"
   },
   "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>genres</th>\n",
       "      <th>Pavg_diversity</th>\n",
       "      <th>Pavg_rating</th>\n",
       "      <th>Pwoman_avg_rating</th>\n",
       "      <th>Pavg_income</th>\n",
       "      <th>Pwoman_avg_income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Comedy</td>\n",
       "      <td>0.371171</td>\n",
       "      <td>5.849003</td>\n",
       "      <td>5.889145</td>\n",
       "      <td>3.023432e+07</td>\n",
       "      <td>3.056833e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Fantasy</td>\n",
       "      <td>0.348148</td>\n",
       "      <td>5.834074</td>\n",
       "      <td>5.807692</td>\n",
       "      <td>5.005759e+07</td>\n",
       "      <td>5.172043e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Romance</td>\n",
       "      <td>0.433492</td>\n",
       "      <td>6.011139</td>\n",
       "      <td>5.999180</td>\n",
       "      <td>2.667396e+07</td>\n",
       "      <td>2.772020e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Horror</td>\n",
       "      <td>0.383403</td>\n",
       "      <td>5.425882</td>\n",
       "      <td>5.363855</td>\n",
       "      <td>2.229678e+07</td>\n",
       "      <td>2.565785e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Action</td>\n",
       "      <td>0.254310</td>\n",
       "      <td>5.792759</td>\n",
       "      <td>5.696429</td>\n",
       "      <td>4.589862e+07</td>\n",
       "      <td>4.157883e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Thriller</td>\n",
       "      <td>0.357264</td>\n",
       "      <td>5.701689</td>\n",
       "      <td>5.533594</td>\n",
       "      <td>2.732175e+07</td>\n",
       "      <td>2.447919e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Sci-Fi</td>\n",
       "      <td>0.303748</td>\n",
       "      <td>5.728713</td>\n",
       "      <td>5.760000</td>\n",
       "      <td>4.310803e+07</td>\n",
       "      <td>3.386927e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Mystery</td>\n",
       "      <td>0.379310</td>\n",
       "      <td>6.115862</td>\n",
       "      <td>5.882609</td>\n",
       "      <td>2.986919e+07</td>\n",
       "      <td>2.440670e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Family</td>\n",
       "      <td>0.365889</td>\n",
       "      <td>5.653741</td>\n",
       "      <td>5.663492</td>\n",
       "      <td>4.527907e+07</td>\n",
       "      <td>4.941232e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Animation</td>\n",
       "      <td>0.309675</td>\n",
       "      <td>6.326582</td>\n",
       "      <td>6.254545</td>\n",
       "      <td>8.773103e+07</td>\n",
       "      <td>8.773331e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Adventure</td>\n",
       "      <td>0.268204</td>\n",
       "      <td>5.978641</td>\n",
       "      <td>5.941667</td>\n",
       "      <td>7.261728e+07</td>\n",
       "      <td>7.655572e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Crime</td>\n",
       "      <td>0.285616</td>\n",
       "      <td>6.079178</td>\n",
       "      <td>5.956436</td>\n",
       "      <td>2.093925e+07</td>\n",
       "      <td>1.897903e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Drama</td>\n",
       "      <td>0.377396</td>\n",
       "      <td>6.254329</td>\n",
       "      <td>6.187377</td>\n",
       "      <td>2.213472e+07</td>\n",
       "      <td>2.117588e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>History</td>\n",
       "      <td>0.312500</td>\n",
       "      <td>6.805000</td>\n",
       "      <td>6.500000</td>\n",
       "      <td>3.572388e+07</td>\n",
       "      <td>2.179673e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Music</td>\n",
       "      <td>0.365212</td>\n",
       "      <td>5.848889</td>\n",
       "      <td>5.697500</td>\n",
       "      <td>1.843432e+07</td>\n",
       "      <td>1.774103e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Western</td>\n",
       "      <td>0.136364</td>\n",
       "      <td>5.845455</td>\n",
       "      <td>6.600000</td>\n",
       "      <td>3.604225e+07</td>\n",
       "      <td>7.538356e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Musical</td>\n",
       "      <td>0.408482</td>\n",
       "      <td>5.425000</td>\n",
       "      <td>5.214286</td>\n",
       "      <td>1.641704e+07</td>\n",
       "      <td>1.054608e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Biography</td>\n",
       "      <td>0.338983</td>\n",
       "      <td>6.861017</td>\n",
       "      <td>6.768000</td>\n",
       "      <td>3.330100e+07</td>\n",
       "      <td>2.548233e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Sport</td>\n",
       "      <td>0.271429</td>\n",
       "      <td>6.129231</td>\n",
       "      <td>6.235294</td>\n",
       "      <td>3.102405e+07</td>\n",
       "      <td>3.451321e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>War</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>6.400000</td>\n",
       "      <td>6.800000</td>\n",
       "      <td>4.242087e+06</td>\n",
       "      <td>1.589330e+05</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       genres  Pavg_diversity  Pavg_rating  Pwoman_avg_rating   Pavg_income  \\\n",
       "0      Comedy        0.371171     5.849003           5.889145  3.023432e+07   \n",
       "1     Fantasy        0.348148     5.834074           5.807692  5.005759e+07   \n",
       "2     Romance        0.433492     6.011139           5.999180  2.667396e+07   \n",
       "3      Horror        0.383403     5.425882           5.363855  2.229678e+07   \n",
       "4      Action        0.254310     5.792759           5.696429  4.589862e+07   \n",
       "5    Thriller        0.357264     5.701689           5.533594  2.732175e+07   \n",
       "6      Sci-Fi        0.303748     5.728713           5.760000  4.310803e+07   \n",
       "7     Mystery        0.379310     6.115862           5.882609  2.986919e+07   \n",
       "8      Family        0.365889     5.653741           5.663492  4.527907e+07   \n",
       "9   Animation        0.309675     6.326582           6.254545  8.773103e+07   \n",
       "10  Adventure        0.268204     5.978641           5.941667  7.261728e+07   \n",
       "11      Crime        0.285616     6.079178           5.956436  2.093925e+07   \n",
       "12      Drama        0.377396     6.254329           6.187377  2.213472e+07   \n",
       "13    History        0.312500     6.805000           6.500000  3.572388e+07   \n",
       "14      Music        0.365212     5.848889           5.697500  1.843432e+07   \n",
       "15    Western        0.136364     5.845455           6.600000  3.604225e+07   \n",
       "16    Musical        0.408482     5.425000           5.214286  1.641704e+07   \n",
       "17  Biography        0.338983     6.861017           6.768000  3.330100e+07   \n",
       "18      Sport        0.271429     6.129231           6.235294  3.102405e+07   \n",
       "19        War        0.250000     6.400000           6.800000  4.242087e+06   \n",
       "\n",
       "    Pwoman_avg_income  \n",
       "0        3.056833e+07  \n",
       "1        5.172043e+07  \n",
       "2        2.772020e+07  \n",
       "3        2.565785e+07  \n",
       "4        4.157883e+07  \n",
       "5        2.447919e+07  \n",
       "6        3.386927e+07  \n",
       "7        2.440670e+07  \n",
       "8        4.941232e+07  \n",
       "9        8.773331e+07  \n",
       "10       7.655572e+07  \n",
       "11       1.897903e+07  \n",
       "12       2.117588e+07  \n",
       "13       2.179673e+07  \n",
       "14       1.774103e+07  \n",
       "15       7.538356e+07  \n",
       "16       1.054608e+07  \n",
       "17       2.548233e+07  \n",
       "18       3.451321e+07  \n",
       "19       1.589330e+05  "
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "analysispre2010['Pwoman_avg_income'] = woman_diverse_income(unique_genres, pre2010)\n",
    "analysispre2010"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 655
    },
    "id": "R9GPk1iTgq5h",
    "outputId": "7b7206cb-f168-4c9b-ebba-76bd208185da"
   },
   "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>genres</th>\n",
       "      <th>avg_diversity</th>\n",
       "      <th>avg_rating</th>\n",
       "      <th>woman_avg_rating</th>\n",
       "      <th>avg_income</th>\n",
       "      <th>woman_avg_income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Comedy</td>\n",
       "      <td>0.429768</td>\n",
       "      <td>5.938480</td>\n",
       "      <td>5.902789</td>\n",
       "      <td>3.836266e+07</td>\n",
       "      <td>3.178994e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Fantasy</td>\n",
       "      <td>0.410377</td>\n",
       "      <td>5.764151</td>\n",
       "      <td>5.450000</td>\n",
       "      <td>8.049504e+07</td>\n",
       "      <td>6.218591e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Romance</td>\n",
       "      <td>0.497076</td>\n",
       "      <td>5.980702</td>\n",
       "      <td>5.937879</td>\n",
       "      <td>2.252266e+07</td>\n",
       "      <td>2.188548e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Horror</td>\n",
       "      <td>0.446699</td>\n",
       "      <td>4.784091</td>\n",
       "      <td>4.840367</td>\n",
       "      <td>1.457188e+07</td>\n",
       "      <td>1.485507e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Action</td>\n",
       "      <td>0.292105</td>\n",
       "      <td>5.777895</td>\n",
       "      <td>5.445283</td>\n",
       "      <td>7.984553e+07</td>\n",
       "      <td>7.076072e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Thriller</td>\n",
       "      <td>0.391844</td>\n",
       "      <td>5.248936</td>\n",
       "      <td>5.097030</td>\n",
       "      <td>1.689424e+07</td>\n",
       "      <td>1.864165e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Sci-Fi</td>\n",
       "      <td>0.324055</td>\n",
       "      <td>5.697059</td>\n",
       "      <td>5.940000</td>\n",
       "      <td>9.522093e+07</td>\n",
       "      <td>5.981949e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Mystery</td>\n",
       "      <td>0.462871</td>\n",
       "      <td>5.570297</td>\n",
       "      <td>5.595588</td>\n",
       "      <td>1.943471e+07</td>\n",
       "      <td>1.621510e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Family</td>\n",
       "      <td>0.413978</td>\n",
       "      <td>5.922581</td>\n",
       "      <td>5.964516</td>\n",
       "      <td>5.433023e+07</td>\n",
       "      <td>5.613483e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Animation</td>\n",
       "      <td>0.326389</td>\n",
       "      <td>6.579167</td>\n",
       "      <td>6.760000</td>\n",
       "      <td>1.498386e+08</td>\n",
       "      <td>1.942130e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Adventure</td>\n",
       "      <td>0.335878</td>\n",
       "      <td>6.261832</td>\n",
       "      <td>6.018367</td>\n",
       "      <td>1.487639e+08</td>\n",
       "      <td>1.378007e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Crime</td>\n",
       "      <td>0.302331</td>\n",
       "      <td>5.929167</td>\n",
       "      <td>5.604167</td>\n",
       "      <td>1.937037e+07</td>\n",
       "      <td>1.596530e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Drama</td>\n",
       "      <td>0.416060</td>\n",
       "      <td>6.074013</td>\n",
       "      <td>6.023295</td>\n",
       "      <td>1.884961e+07</td>\n",
       "      <td>1.673250e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>History</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>6.422222</td>\n",
       "      <td>6.480000</td>\n",
       "      <td>2.454257e+07</td>\n",
       "      <td>3.666431e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Music</td>\n",
       "      <td>0.487705</td>\n",
       "      <td>6.300000</td>\n",
       "      <td>6.179070</td>\n",
       "      <td>2.753182e+07</td>\n",
       "      <td>1.716135e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Western</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>5.640000</td>\n",
       "      <td>5.400000</td>\n",
       "      <td>1.795343e+07</td>\n",
       "      <td>2.054182e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Musical</td>\n",
       "      <td>0.516667</td>\n",
       "      <td>5.873333</td>\n",
       "      <td>5.430000</td>\n",
       "      <td>5.079586e+07</td>\n",
       "      <td>2.161083e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Biography</td>\n",
       "      <td>0.365942</td>\n",
       "      <td>6.818841</td>\n",
       "      <td>6.875000</td>\n",
       "      <td>2.939120e+07</td>\n",
       "      <td>3.464249e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Sport</td>\n",
       "      <td>0.323529</td>\n",
       "      <td>6.226471</td>\n",
       "      <td>5.987500</td>\n",
       "      <td>2.076697e+07</td>\n",
       "      <td>1.980798e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>War</td>\n",
       "      <td>0.437500</td>\n",
       "      <td>6.025000</td>\n",
       "      <td>6.366667</td>\n",
       "      <td>1.161476e+07</td>\n",
       "      <td>1.595590e+06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       genres  avg_diversity  avg_rating  woman_avg_rating    avg_income  \\\n",
       "0      Comedy       0.429768    5.938480          5.902789  3.836266e+07   \n",
       "1     Fantasy       0.410377    5.764151          5.450000  8.049504e+07   \n",
       "2     Romance       0.497076    5.980702          5.937879  2.252266e+07   \n",
       "3      Horror       0.446699    4.784091          4.840367  1.457188e+07   \n",
       "4      Action       0.292105    5.777895          5.445283  7.984553e+07   \n",
       "5    Thriller       0.391844    5.248936          5.097030  1.689424e+07   \n",
       "6      Sci-Fi       0.324055    5.697059          5.940000  9.522093e+07   \n",
       "7     Mystery       0.462871    5.570297          5.595588  1.943471e+07   \n",
       "8      Family       0.413978    5.922581          5.964516  5.433023e+07   \n",
       "9   Animation       0.326389    6.579167          6.760000  1.498386e+08   \n",
       "10  Adventure       0.335878    6.261832          6.018367  1.487639e+08   \n",
       "11      Crime       0.302331    5.929167          5.604167  1.937037e+07   \n",
       "12      Drama       0.416060    6.074013          6.023295  1.884961e+07   \n",
       "13    History       0.250000    6.422222          6.480000  2.454257e+07   \n",
       "14      Music       0.487705    6.300000          6.179070  2.753182e+07   \n",
       "15    Western       0.300000    5.640000          5.400000  1.795343e+07   \n",
       "16    Musical       0.516667    5.873333          5.430000  5.079586e+07   \n",
       "17  Biography       0.365942    6.818841          6.875000  2.939120e+07   \n",
       "18      Sport       0.323529    6.226471          5.987500  2.076697e+07   \n",
       "19        War       0.437500    6.025000          6.366667  1.161476e+07   \n",
       "\n",
       "    woman_avg_income  \n",
       "0       3.178994e+07  \n",
       "1       6.218591e+07  \n",
       "2       2.188548e+07  \n",
       "3       1.485507e+07  \n",
       "4       7.076072e+07  \n",
       "5       1.864165e+07  \n",
       "6       5.981949e+07  \n",
       "7       1.621510e+07  \n",
       "8       5.613483e+07  \n",
       "9       1.942130e+08  \n",
       "10      1.378007e+08  \n",
       "11      1.596530e+07  \n",
       "12      1.673250e+07  \n",
       "13      3.666431e+07  \n",
       "14      1.716135e+07  \n",
       "15      2.054182e+06  \n",
       "16      2.161083e+07  \n",
       "17      3.464249e+07  \n",
       "18      1.980798e+07  \n",
       "19      1.595590e+06  "
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "analysispost2010['woman_avg_income'] = woman_diverse_income(unique_genres, post2010)\n",
    "analysispost2010"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "KOoPlyfegq5h"
   },
   "source": [
    "### 10) Find averages of each different metrics (diversity, rating, gross income)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "_A-wwEPC7w_I",
    "outputId": "5d555c90-002d-4013-ff4c-125fc0da5e8b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pre2010_avg_diversity:  0.32602981981984835  post2010_avg_diversity: 0.3865387350746339\n",
      "change in diversity:  0.06050891525478552\n"
     ]
    }
   ],
   "source": [
    "pre2010_avg_diversity = analysispre2010['Pavg_diversity'].mean()\n",
    "post2010_avg_diversity = analysispost2010['avg_diversity'].mean()\n",
    "diversity_change = post2010_avg_diversity - pre2010_avg_diversity\n",
    "\n",
    "print('pre2010_avg_diversity: ', pre2010_avg_diversity, ' post2010_avg_diversity:', post2010_avg_diversity)\n",
    "print('change in diversity: ', diversity_change)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Fl3Em7j-7w_I",
    "outputId": "62f8f5a9-a9c9-4dc7-aaff-892fb3c290e8"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pre2010_avg_rating:  6.003309196796755  post2010_avg_rating: 5.941711834013813\n",
      "change in ratings:  -0.06159736278294137\n"
     ]
    }
   ],
   "source": [
    "pre2010_avg_rating = analysispre2010['Pavg_rating'].mean()\n",
    "post2010_avg_rating = analysispost2010['avg_rating'].mean()\n",
    "ratings_change = post2010_avg_rating - pre2010_avg_rating\n",
    "\n",
    "print('pre2010_avg_rating: ', pre2010_avg_rating, ' post2010_avg_rating:', post2010_avg_rating)\n",
    "print('change in ratings: ', ratings_change)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "wlcbBihw7w_I",
    "outputId": "6dc78fcb-9aff-44b7-97c1-57771c4d694a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pre2010_avg_woman_rating:  5.987555047845201  post2010_avg_woman_rating: 5.864875879670069\n",
      "change in woman ratings:  -0.12267916817513225\n"
     ]
    }
   ],
   "source": [
    "pre2010_avg_woman_rating = analysispre2010['Pwoman_avg_rating'].mean()\n",
    "post2010_avg_woman_rating = analysispost2010['woman_avg_rating'].mean()\n",
    "woman_ratings_change = post2010_avg_woman_rating - pre2010_avg_woman_rating\n",
    "\n",
    "print('pre2010_avg_woman_rating: ', pre2010_avg_woman_rating, ' post2010_avg_woman_rating:', post2010_avg_woman_rating)\n",
    "print('change in woman ratings: ', woman_ratings_change)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "6yUrG3J07w_J",
    "outputId": "9cf2f939-03ac-4f69-d55b-4977003f6c56"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pre2010_avg_income:  34967310.586881205  post2010_avg_income: 47054852.80758206\n",
      "change in income:  12087542.220700853\n"
     ]
    }
   ],
   "source": [
    "pre2010_avg_income = analysispre2010['Pavg_income'].mean()\n",
    "post2010_avg_income = analysispost2010['avg_income'].mean()\n",
    "income_change = post2010_avg_income - pre2010_avg_income\n",
    "\n",
    "print('pre2010_avg_income: ', pre2010_avg_income, ' post2010_avg_income:', post2010_avg_income)\n",
    "print('change in income: ', income_change)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "pKjCrciW7w_J",
    "outputId": "d9afcca0-1e59-4d89-bb2a-1bdb6508d9b2"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pre2010_avg_woman_income:  34973946.96479179  post2010_avg_woman_income: 42526819.298336014\n",
      "change in woman income:  7552872.3335442245\n"
     ]
    }
   ],
   "source": [
    "pre2010_avg_woman_income = analysispre2010['Pwoman_avg_income'].mean()\n",
    "post2010_avg_woman_income = analysispost2010['woman_avg_income'].mean()\n",
    "woman_income_change = post2010_avg_woman_income - pre2010_avg_woman_income\n",
    "\n",
    "print('pre2010_avg_woman_income: ', pre2010_avg_woman_income, ' post2010_avg_woman_income:', post2010_avg_woman_income)\n",
    "print('change in woman income: ', woman_income_change)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "v2260C7Bgq5j"
   },
   "source": [
    "### 11) Conduct final analysis on all 3 metrics defined above into *analysis*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "id": "0Pq6Yr1j7w_J"
   },
   "outputs": [],
   "source": [
    "analysis = pd.DataFrame()\n",
    "analysis['genres'] = unique_genres\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "id": "gQSoiFSZ7w_J"
   },
   "outputs": [],
   "source": [
    "def analyze(pre_df, post_df):\n",
    "\n",
    "    ans = []\n",
    "\n",
    "    for i in pre_df.index:\n",
    "\n",
    "        diversity_incr = ((post_df.loc[i,'avg_diversity'] - pre_df.loc[i, 'Pavg_diversity']) > diversity_change)\n",
    "        avg_rating_incr = ((post_df.loc[i,'avg_rating'] - pre_df.loc[i, 'Pavg_rating']) > ratings_change)\n",
    "        woman_rating_incr = ((post_df.loc[i,'woman_avg_rating'] - pre_df.loc[i, 'Pwoman_avg_rating']) > woman_ratings_change)\n",
    "        avg_income_incr = ((post_df.loc[i,'avg_income'] - pre_df.loc[i, 'Pavg_income']) > income_change)\n",
    "        womman_income_incr = ((post_df.loc[i,'woman_avg_income'] - pre_df.loc[i, 'Pwoman_avg_income']) > woman_income_change)\n",
    "        \n",
    "        if (diversity_incr) & (avg_rating_incr) & (woman_rating_incr) & (avg_income_incr) & (womman_income_incr):\n",
    "            ans.append('Successful')\n",
    "                             \n",
    "        elif (diversity_incr) & (avg_rating_incr) & (not woman_rating_incr) & (avg_income_incr) & (womman_income_incr):\n",
    "            ans.append('Moderately successful, woman rating not keeping up with rating incr')\n",
    "        \n",
    "        elif (diversity_incr) & (avg_rating_incr) & (not woman_rating_incr) & (avg_income_incr) & (not womman_income_incr):\n",
    "            ans.append('Not successful')\n",
    "            \n",
    "        elif (diversity_incr) & (avg_rating_incr) & (woman_rating_incr) & (avg_income_incr) & (not womman_income_incr):\n",
    "            ans.append('Moderately successful, woman rev not keeping up with rev incr')\n",
    "        \n",
    "        elif (diversity_incr) & (avg_rating_incr) & (woman_rating_incr) & (not avg_income_incr) & (womman_income_incr):\n",
    "            ans.append('Successful')\n",
    "            \n",
    "        elif (diversity_incr) & (avg_rating_incr) & (not woman_rating_incr) & (not avg_income_incr) & (womman_income_incr):\n",
    "            ans.append('Moderately successful, woman rating not keeping up with rating incr')\n",
    "        \n",
    "        elif (diversity_incr) & (avg_rating_incr) & (not woman_rating_incr) & (not avg_income_incr) & (not womman_income_incr):\n",
    "            ans.append('Not successful')\n",
    "            \n",
    "        elif (diversity_incr) & (avg_rating_incr) & (woman_rating_incr) & (not avg_income_incr) & (not womman_income_incr):\n",
    "            ans.append('Moderately successful, woman rev not incr')\n",
    "        \n",
    "        elif (diversity_incr) & (not avg_rating_incr) & (woman_rating_incr) & (avg_income_incr) & (womman_income_incr):\n",
    "            ans.append('Successful')\n",
    "            \n",
    "        elif (diversity_incr) & (not avg_rating_incr) & (not woman_rating_incr) & (avg_income_incr) & (womman_income_incr):\n",
    "            ans.append('Moderately successful, woman rating not incr')\n",
    "            \n",
    "        elif (diversity_incr) & (not avg_rating_incr) & (not woman_rating_incr) & (avg_income_incr) & (not womman_income_incr):\n",
    "            ans.append('Not successful')\n",
    "            \n",
    "        elif (diversity_incr) & (not avg_rating_incr) & (woman_rating_incr) & (avg_income_incr) & (not womman_income_incr):\n",
    "            ans.append('Moderately successful, woman rev not incr')\n",
    "\n",
    "        elif (diversity_incr) & (not avg_rating_incr) & (woman_rating_incr) & (not avg_income_incr) & (womman_income_incr):\n",
    "            ans.append('Successful')\n",
    "            \n",
    "        elif (diversity_incr) & (not avg_rating_incr) & (not woman_rating_incr) & (not avg_income_incr) & (womman_income_incr):\n",
    "            ans.append('Moderately successful, woman rating not incr')\n",
    "            \n",
    "        elif (diversity_incr) & (not avg_rating_incr) & (not woman_rating_incr) & (not avg_income_incr) & (not womman_income_incr):\n",
    "            ans.append('Not Successful')\n",
    "            \n",
    "        elif (diversity_incr) & (not avg_rating_incr) & (woman_rating_incr) & (not avg_income_incr) & (not womman_income_incr):\n",
    "            ans.append('Moderately successful, woman rev not incr')\n",
    "            \n",
    "        elif (not diversity_incr) & (avg_rating_incr) & (woman_rating_incr) & (avg_income_incr) & (womman_income_incr):\n",
    "            ans.append('Successful')\n",
    "            \n",
    "        elif (not diversity_incr) & (avg_rating_incr) & (not woman_rating_incr) & (avg_income_incr) & (womman_income_incr):\n",
    "            ans.append('Moderately successful, woman rev incr despite no incr in diversity')\n",
    "            \n",
    "        elif (not diversity_incr) & (avg_rating_incr) & (not woman_rating_incr) & (avg_income_incr) & (not womman_income_incr):\n",
    "            ans.append('Inconclusive')\n",
    "            \n",
    "        elif (not diversity_incr) & (avg_rating_incr) & (woman_rating_incr) & (avg_income_incr) & (not womman_income_incr):\n",
    "            ans.append('Moderately successful, woman rating incr despite no incr in diversity')\n",
    "            \n",
    "        elif (not diversity_incr) & (avg_rating_incr) & (woman_rating_incr) & (not avg_income_incr) & (womman_income_incr):\n",
    "            ans.append('Successful')\n",
    "            \n",
    "        elif (not diversity_incr) & (avg_rating_incr) & (not woman_rating_incr) & (not avg_income_incr) & (womman_income_incr):\n",
    "            ans.append('Moderately successful, woman revenue incr despite no incr in diversity')\n",
    "            \n",
    "        elif (not diversity_incr) & (avg_rating_incr) & (not woman_rating_incr) & (not avg_income_incr) & (not womman_income_incr):\n",
    "            ans.append('Inconclusive')\n",
    "            \n",
    "        elif (not diversity_incr) & (avg_rating_incr) & (woman_rating_incr) & (not avg_income_incr) & (not womman_income_incr):\n",
    "            ans.append('Moderately successful, woman rating incr despite no incr in diversity')\n",
    "            \n",
    "        elif (not diversity_incr) & (not avg_rating_incr) & (woman_rating_incr) & (avg_income_incr) & (womman_income_incr):\n",
    "            ans.append('Successful')\n",
    "            \n",
    "        elif (not diversity_incr) & (not avg_rating_incr) & (not woman_rating_incr) & (avg_income_incr) & (womman_income_incr):\n",
    "            ans.append('Moderately successful, woman revenue incr despite no incr in diversity')\n",
    "            \n",
    "        elif (not diversity_incr) & (not avg_rating_incr) & (not woman_rating_incr) & (avg_income_incr) & (not womman_income_incr):\n",
    "            ans.append('Inconclusive')\n",
    "            \n",
    "        elif (not diversity_incr) & (not avg_rating_incr) & (woman_rating_incr) & (avg_income_incr) & (not womman_income_incr):\n",
    "            ans.append('Moderately successful, woman rating incr despite no incr in diversity')\n",
    "            \n",
    "        elif (not diversity_incr) & (not avg_rating_incr) & (woman_rating_incr) & (not avg_income_incr) & (womman_income_incr):\n",
    "            ans.append('Successful')\n",
    "            \n",
    "        elif (not diversity_incr) & (not avg_rating_incr) & (not woman_rating_incr) & (not avg_income_incr) & (womman_income_incr):\n",
    "            ans.append('Moderately successful, woman rev incr despite no incr in diversity')\n",
    "            \n",
    "        elif (not diversity_incr) & (not avg_rating_incr) & (not woman_rating_incr) & (not avg_income_incr) & (not womman_income_incr):\n",
    "            ans.append('Inconclusive')\n",
    "            \n",
    "        elif (not diversity_incr) & (not avg_rating_incr) & (woman_rating_incr) & (not avg_income_incr) & (not womman_income_incr):\n",
    "            ans.append('Moderately successful, woman rating incr despite no incr in diversity')\n",
    "            \n",
    "\n",
    "            \n",
    "        else:\n",
    "            print('diversity: ', diversity_incr, ' avg rating: ', avg_rating_incr, ' woman: ', woman_rating_incr )\n",
    "            ans.append('nothing to say')\n",
    "            \n",
    "    return ans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 655
    },
    "id": "wS0-hYM77w_K",
    "outputId": "00aadf30-887b-4d5e-d68c-1d4d7a791c17"
   },
   "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>genres</th>\n",
       "      <th>result</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Comedy</td>\n",
       "      <td>Moderately successful, woman rating incr despi...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Fantasy</td>\n",
       "      <td>Moderately successful, woman rating not incr</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Romance</td>\n",
       "      <td>Moderately successful, woman rev not incr</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Horror</td>\n",
       "      <td>Not Successful</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Action</td>\n",
       "      <td>Moderately successful, woman rev incr despite ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Thriller</td>\n",
       "      <td>Inconclusive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Sci-Fi</td>\n",
       "      <td>Successful</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Mystery</td>\n",
       "      <td>Not Successful</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Family</td>\n",
       "      <td>Moderately successful, woman rating incr despi...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Animation</td>\n",
       "      <td>Successful</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Adventure</td>\n",
       "      <td>Successful</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Crime</td>\n",
       "      <td>Inconclusive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Drama</td>\n",
       "      <td>Inconclusive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>History</td>\n",
       "      <td>Successful</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Music</td>\n",
       "      <td>Moderately successful, woman rev not incr</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Western</td>\n",
       "      <td>Not Successful</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Musical</td>\n",
       "      <td>Successful</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Biography</td>\n",
       "      <td>Successful</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Sport</td>\n",
       "      <td>Inconclusive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>War</td>\n",
       "      <td>Not Successful</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       genres                                             result\n",
       "0      Comedy  Moderately successful, woman rating incr despi...\n",
       "1     Fantasy       Moderately successful, woman rating not incr\n",
       "2     Romance          Moderately successful, woman rev not incr\n",
       "3      Horror                                     Not Successful\n",
       "4      Action  Moderately successful, woman rev incr despite ...\n",
       "5    Thriller                                       Inconclusive\n",
       "6      Sci-Fi                                         Successful\n",
       "7     Mystery                                     Not Successful\n",
       "8      Family  Moderately successful, woman rating incr despi...\n",
       "9   Animation                                         Successful\n",
       "10  Adventure                                         Successful\n",
       "11      Crime                                       Inconclusive\n",
       "12      Drama                                       Inconclusive\n",
       "13    History                                         Successful\n",
       "14      Music          Moderately successful, woman rev not incr\n",
       "15    Western                                     Not Successful\n",
       "16    Musical                                         Successful\n",
       "17  Biography                                         Successful\n",
       "18      Sport                                       Inconclusive\n",
       "19        War                                     Not Successful"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "analysis['result'] = analyze(analysispre2010, analysispost2010)\n",
    "analysis\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This chart can be used to determine whether or not gender diversity has an impact on the success of a movie. Discussed more in the conclusion section. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "NVC0WZlJA1u0"
   },
   "source": [
    "# DATA VIZ"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "7Zdo3hRcgq5l"
   },
   "source": [
    "### 1) Create Barplots of different metrics of success analysis (ratings, income, diversity) of each genre comapred by *pre2010* and *post2010*\n",
    "\n",
    "We are creating barplots of each metric of success and comparing them by each genre's pre 2010 and post 2010 status. This helps us to look at the trends of each genre before and after 2010 given that metric. Barplots allow to do this comparison easily side by side. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "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>genres</th>\n",
       "      <th>Pavg_diversity</th>\n",
       "      <th>Pavg_rating</th>\n",
       "      <th>Pwoman_avg_rating</th>\n",
       "      <th>Pavg_income</th>\n",
       "      <th>Pwoman_avg_income</th>\n",
       "      <th>avg_diversity</th>\n",
       "      <th>avg_rating</th>\n",
       "      <th>woman_avg_rating</th>\n",
       "      <th>avg_income</th>\n",
       "      <th>woman_avg_income</th>\n",
       "      <th>pNumMovies</th>\n",
       "      <th>NumMovies</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Comedy</td>\n",
       "      <td>0.371171</td>\n",
       "      <td>5.849003</td>\n",
       "      <td>5.889145</td>\n",
       "      <td>3.023432e+07</td>\n",
       "      <td>3.056833e+07</td>\n",
       "      <td>0.429768</td>\n",
       "      <td>5.938480</td>\n",
       "      <td>5.902789</td>\n",
       "      <td>3.836266e+07</td>\n",
       "      <td>3.178994e+07</td>\n",
       "      <td>953</td>\n",
       "      <td>408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Fantasy</td>\n",
       "      <td>0.348148</td>\n",
       "      <td>5.834074</td>\n",
       "      <td>5.807692</td>\n",
       "      <td>5.005759e+07</td>\n",
       "      <td>5.172043e+07</td>\n",
       "      <td>0.410377</td>\n",
       "      <td>5.764151</td>\n",
       "      <td>5.450000</td>\n",
       "      <td>8.049504e+07</td>\n",
       "      <td>6.218591e+07</td>\n",
       "      <td>135</td>\n",
       "      <td>53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Romance</td>\n",
       "      <td>0.433492</td>\n",
       "      <td>6.011139</td>\n",
       "      <td>5.999180</td>\n",
       "      <td>2.667396e+07</td>\n",
       "      <td>2.772020e+07</td>\n",
       "      <td>0.497076</td>\n",
       "      <td>5.980702</td>\n",
       "      <td>5.937879</td>\n",
       "      <td>2.252266e+07</td>\n",
       "      <td>2.188548e+07</td>\n",
       "      <td>404</td>\n",
       "      <td>171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Horror</td>\n",
       "      <td>0.383403</td>\n",
       "      <td>5.425882</td>\n",
       "      <td>5.363855</td>\n",
       "      <td>2.229678e+07</td>\n",
       "      <td>2.565785e+07</td>\n",
       "      <td>0.446699</td>\n",
       "      <td>4.784091</td>\n",
       "      <td>4.840367</td>\n",
       "      <td>1.457188e+07</td>\n",
       "      <td>1.485507e+07</td>\n",
       "      <td>170</td>\n",
       "      <td>176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Action</td>\n",
       "      <td>0.254310</td>\n",
       "      <td>5.792759</td>\n",
       "      <td>5.696429</td>\n",
       "      <td>4.589862e+07</td>\n",
       "      <td>4.157883e+07</td>\n",
       "      <td>0.292105</td>\n",
       "      <td>5.777895</td>\n",
       "      <td>5.445283</td>\n",
       "      <td>7.984553e+07</td>\n",
       "      <td>7.076072e+07</td>\n",
       "      <td>290</td>\n",
       "      <td>190</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    genres  Pavg_diversity  Pavg_rating  Pwoman_avg_rating   Pavg_income  \\\n",
       "0   Comedy        0.371171     5.849003           5.889145  3.023432e+07   \n",
       "1  Fantasy        0.348148     5.834074           5.807692  5.005759e+07   \n",
       "2  Romance        0.433492     6.011139           5.999180  2.667396e+07   \n",
       "3   Horror        0.383403     5.425882           5.363855  2.229678e+07   \n",
       "4   Action        0.254310     5.792759           5.696429  4.589862e+07   \n",
       "\n",
       "   Pwoman_avg_income  avg_diversity  avg_rating  woman_avg_rating  \\\n",
       "0       3.056833e+07       0.429768    5.938480          5.902789   \n",
       "1       5.172043e+07       0.410377    5.764151          5.450000   \n",
       "2       2.772020e+07       0.497076    5.980702          5.937879   \n",
       "3       2.565785e+07       0.446699    4.784091          4.840367   \n",
       "4       4.157883e+07       0.292105    5.777895          5.445283   \n",
       "\n",
       "     avg_income  woman_avg_income  pNumMovies  NumMovies  \n",
       "0  3.836266e+07      3.178994e+07         953        408  \n",
       "1  8.049504e+07      6.218591e+07         135         53  \n",
       "2  2.252266e+07      2.188548e+07         404        171  \n",
       "3  1.457188e+07      1.485507e+07         170        176  \n",
       "4  7.984553e+07      7.076072e+07         290        190  "
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total = pd.merge(analysispre2010, analysispost2010, on='genres')\n",
    "total['pNumMovies'] = num_movies(unique_genres, pre2010)\n",
    "total['NumMovies'] = num_movies(unique_genres, post2010)\n",
    "total.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "6KVaVTbP2a-X"
   },
   "source": [
    "### 1a) Genres & Average Ratings\n",
    "In this visualization, we were interested in seeing if any genres were performing differently Pre-2010s and Post-2010s. We can interpret from this barplot that average ratings per genre post 2010 are more or less close to its pre 2010 status by ~0.5. We can learn from this visualization that ratings should not be the only metric of success for our movies."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 409
    },
    "id": "_oY0W1EOejg8",
    "outputId": "025432bb-de29-415c-aa3f-6573d3d9db0d"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Average Ratings')"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# genre rating 1990s vs 2010s\n",
    "fig2 = total.plot.bar(x='genres',y=['Pavg_rating','avg_rating'],figsize=(12,5), title=\"Ratings by Genre Pre2010 vs Post2010\")\n",
    "fig2.set_ylabel('Average Ratings')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "KrWIZ8e9gq5m"
   },
   "source": [
    "### 1b) genres & avg income\n",
    "In this visualization, we were interested in seeing if any genres were performing differently Pre-2010s and Post-2010s. We can interpret from this barplot that average income is overall drastically increasing in many genres. Many other genres are not making as much as it used to before 2010. We can learn from this barplot that income may play a significant role in our metrics of success. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 409
    },
    "id": "-7-b8JLngq5n",
    "outputId": "e50dcfb8-9650-4eb8-9fee-e7349bbe33fd"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Average Income')"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# genre budget 200s vs 2010s\n",
    "compareBudgeFig = total.plot.bar(x='genres',y=['Pavg_income','avg_income'],figsize=(12,5), title=\"Gross Income by Genre Pre2010 vs Post2010\")\n",
    "l= compareBudgeFig.legend()\n",
    "l.get_texts()[0].set_text('Pre-2010s')\n",
    "l.get_texts()[1].set_text('Post-2010s')\n",
    "compareBudgeFig.set_ylabel('Average Income')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "NZ5m1PsTgq5n"
   },
   "source": [
    "### 1c) genres & avg diversity\n",
    "In this visualization, we were interested in seeing which genres Pre2010s and Post-2010s were casting more female actors. We can interpret from this barplot that all genres of movies except history have some level of increase in female representation post 2010. We can learn from this graph that there is a general overall increase in female representations in film. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 409
    },
    "id": "bvo29OMSgq5n",
    "outputId": "9284aa72-76b8-45ad-c710-3cd89860935b"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Diversity Ratio')"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAmEAAAF2CAYAAAAmzk/1AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+j8jraAAAgAElEQVR4nOzdd7hcZbn+8e+dRqiBkKBAKBFQ6YFfgCBFQOmC2OhFiogeVPCcCAoKqBxQULrESDEICCoIEUMRMBRBIYHQBCRCOARQQwhIaCbh+f3xrklmT2bP3knmnZU9uT/Xta+9Z62Z9bwze2bWs96qiMDMzMzMWqtX2QUwMzMzWxI5CTMzMzMrgZMwMzMzsxI4CTMzMzMrgZMwMzMzsxI4CTMzMzMrgZMwsx5C0naSni4h7kxJH2h1XDOzduckzHosSeMlzZC0VNllWVSSTpU0S9Ibxc/fJF0oadXKfSLinoj4UKvLFhHLRcSzRTl/Lun7rS5Ddyk5VtKjkt6S9I/ifbJ/2WWDue/Zd4rE9hVJ11f/jxfwWGdLeqZ4vzwl6dCa/cMkTSxeh4mShlXt20jSrUUZ5pssUtJASb+V9Kak5yUduDBlXMDnE0W8mZJelPRjSb0X4Xgd3quSlpJ0afF83pD0sKTdax7zseK1fEvSHyWtVbVvx2Lb65Km1Im3drH/reIYH1/YstuSw0mY9UiS1ga2AwLYO8Px+zT7mN1wbUQsDwwEPgW8H5i4sCfp7irpuS6SBmU+HzgO+G9gZWB14GRgtwxlkKSF+Q49NiKWAz4IrAicU+fY3fmfvAnsBQwADgPOk/SR4vH9gBuBK4GVgDHAjcV2gFnAr4AjOzn2RcB/gPcBBwEXS9qwW89u0WxavDYfAw4EvtDEY/cBXgA+SnrNvg38qvguQdIg4Ppi+0BgAnBt1ePfBC4DRnZy/F8CD5PedycBv5E0uInlt3YUEf7xT4/7Ab4D/An4MXBTsW0p4DVgo6r7DQbeBlYpbn8CmFTc7z5gk6r7TgFOAB4F3iV9aZ8I/B14A/gr8Kmq+/cGfgS8AjwHHEtKCvsU+wcAlwIvAy8C3wd6d/J8TgWurNnWG3gEOLu4vQMwtfj7ROA3Nfc/Dzi/q9jA54vX7hzg1WLfusBdwOvF87m26rhR7D+adPL+DzAT+B3phHRdTTkuAM7t5HlOAb5ZvJYzgMuB/lX7F+j/U3PsDwJzgOFdvHe6em3uBc4uyvccsHvVY8cDpxev39vF6/Jh4A/Fa/k0sG+D2OOBo6pu/xfweIP334jidXiteC/s0ODYY4H/Lv7epXhuqtr/f8BuNY9ZF4iabcsW/+MPVm37BXBmnZgNP3PAIOCm4j6vAvcAvTopfwDrVt3+NXBh8fcXgMnFMcYCqxXbRXof/4v03n0U2Ig679VOYj4KfKb4+2jgvprX4W3gwzWP+Tgwpc57711g+apt9wDHNPN7zz/t9+OaMOupDgWuKn52lfS+iHiXdCV7QNX99gXuioh/SdqcdCX7RdLV6k+BsTXNmQcAewIrRsRsUgK2HenEfRpwZVXN1BeA3YFhwObAPjVlHAPMJp3oNiOdGI/q7hOMiDmk2ozt6uz+JbCHpBUAimabfYGruxl7K+BZ0onydOB7wG2kWpMhpESqtjyjSa/3DyM1Ue5FqmnZTdKKRTn6APuRTtqdOQjYFViHdPI6uXjswvx/qu0EvBARExrEhu69Nk+TEogfApdKUtX+Q0gn7OWBaaQE7GrSa3kA8JPu1BoVNS+fIdWezPf8SLVQvycliQOB/wGuq1e7ImlpYAvgiWLThsCjEVHd1Phosb0rHwTmRMTfqrY9Uu+xXX3mSDWSU0mJ2fuAb5GSrYYkbUB63z8saSfgjOK4qwLPA9cUd90F2J55tYr7AdM7ea/Wxnhf8bjq1+yRquf2Junz353XbEPg2Yh4o2pb3dfMrJqTMOtxJG0LrAX8KiImkr4oK31WrqbjCeFA5iUmXwB+GhF/iYg5ETGGdPU6our+50fECxHxNkBE/DoiXoqI9yLiWuAZYMvivvsC50XE1IiYAZxZVcb3kRK04yLizeKEdA6woH2TXiKdgDuIiOeBh5iX+O0EvBURf+5m7Jci4oKImF0811mk13S1iHgnIu7tTuEi4mXgbuBzxabdgFeK/0tnLixe41dJCWDl/7XA/58ag4B/VG+QNFXSa0U/rLW6+do8HxE/K5LgMaQT//uq9v88Ip4oksDdSLUilxev5UPAdcBnGzz/8yVVarZeBr7eyfM7GBgXEeOK998fSE1ke9Q55qjieLcWt5cj1QxVe52UOHZlQR/b6DM3i/T6rRURsyL1a2yUhD0kaQaplvUSUk3pQcBlEfFQkfR9E9i6aEacVZTrw6RavyeL92RDkvqSkrQxEfFUsbmVr5kZ4CTMeqbDgNsi4pXi9tXFNoA7gaUlbVV0qh0G/LbYtxbw38VJ+bXiRLgGsFrVsV+oDiTpUEmTqu6/EelkT/G4Fzp57FpAX+Dlqsf+lFRbsiBWJzXB1FN98qs+8XUndofnCXyD1LTzgKQnJB2xAGUcQ0oYKH43qgWrjf08817/Bf7/1JhOOuHPFRFDSP+vpUjPrzuvzT+qHv9W8edynZRhLWCrmjIfROrP15mvRsSKEbF6RBwUEdMaHPtzNcfetvY5SjqL9L7ctyrBmQmsUBN3BVKzelcW9LGNPnNnkZoRb5P0rKQTu4i9eUSsFBHrRMTJEfEe6f//fOUOETGT9L9ePSLuBC4k9WH7p6TRldrhzhT9+H5Baqo8dhGed7VFeawtwXpch1xbshXNLvsCvSVVTpZLAStK2jQiHpH0K1Jy8k9Sf7HKF+ELwOkRcXqDEHOv0osTys9InYTvj4g5kiaRTuaQajGGVD12jaq/XyDV4gyq02zW3efai9Tx+vZO7vJr4EeShpA68m+9ALE71EZExD8oOkEXNY23S7o7IiY3elzhBlLH7Y1Ifbq+0fCJdXyd1iTV9lXK3e3/Tx13AhdKGt6gSXKR/y81ZXiB1PS280Ieq6tj/yIiOu2cLuk0Us3eRyPi31W7niAltKpKzDYhJStd+RvQR9J6EfFMsW1T5jXbdSxwxHudfeaK3/9dlGVD4I+SHoyIO7pRjoqXSAkpAJKWJTVXv1jEOJ9Uu7gKabDBSFLn+nqjPkXqD/g+YI+ImFW1+wnmXcxV4qzT2fOu8QTwAUnLV33fbMq8CyOzulwTZj3NPqTO1xuQrriHAeuTOsFWhuhfTeobchAdvwR/BhxTXLFL0rKS9pTUWZPBsqQv8mkAkg4n1ThU/Ar4mqTViz5RJ1R2FE0it5GSpBUk9ZK0jqSPdvUEJfWVtD6p39f7SYMP5lPUoIwnNdk8FxFPLmxsSZ8rkjlIHdKD9DrX+ifQYc6wiHgH+A3ptX4gIv6vi6f4X5KGSBpI6iNUGYG2oP+fDiLiaVKt1jWSdpa0dNFX7iNV91no/0snbgI+KOmQ4v/WV9IWxf9vUV0J7CVpV0m9JfWXtEPl/yTpm6Qa0J0jYnrNY8eT/n9fVZqaoVLjc2fxWEnqD/QrbvdX0feu6At1PfDd4n+wDfBJGtdw1v3MSfqEpHWL5OffRZnqva8auRo4XGnKjaWA/wX+EhFTitd6K6XmxTeBd6qOP997FbiY9H2xV50m7d8CG0n6TPHafIfUr+6p4rn0Krb3TTfVX8Vo06L/3CTglGL7p0hJ73UL+FxtSROLwegA//inuz/ALcCP6mzfl9SMVBmZWBlJ1a/mfrsBD5JGa71Mqk1avtg3Bfh4zf1PL47zCikZuotidBupJvkcUtPIc8DxpD4qKvYPIH3pTyX1D3kY2L+T53Vq8diZpJPJM8BPSE0ulfvsQDE6smrbIaSEaWTN9k5jU4wArLn/D0k1CzNJfeyOrto3d9QasB7zRi/eUHWfbYv7Hd7F/28K80ZHvkZqylxmYf8/dY4v4KvAY6SRbS8X/7N9KUblLcRrU/38x1M1urHY9iFSB/ppxXvhTmBYJ+Wb7/E1r03t+2+rovyvFsf/PbBmVbneLf5nlZ9vVT12M2Bi8To8BGxWtW/t4vHVP1Oq9g8k1XC+SRpVeWA3PpvzfeZIn4kpxXGmAt9u8PgOoyNr9h1TvC9fJSW+Q4rtHyMNOJhJ+oxeBSxX771Kqk0LUqJW/ZodVBXn48BTxWs2Hli75vNX+5qNr3lNxxePfbr2f+kf/9T7qZwszGwRKU38OCoi1uryzm1G0pqkk9f7o2OzWO39ppCSkM6aWM3MlhhujjRbSEVz1x6S+khaHTiFeR2SlxhF37WvA9c0SsDMzKwjd8w3W3gizR12LakJ4vekfiRLjKLz8j9Jo9eaPiu9mVk7c3OkmZmZWQncHGlmZmZWgh7XHDlo0KBYe+21yy6GmZmZWZcmTpz4SkTUXcy9xyVha6+9NhMmdLU0nJmZmVn5JD3f2T43R5qZmZmVwEmYmZmZWQmchJmZmZmVoMf1CTOzxd+sWbOYOnUq77zzTtlFaWv9+/dnyJAh9O3bt+yimNlCcBJmZk03depUll9+edZee23S2s3WbBHB9OnTmTp1KkOHDi27OGa2ENwcaWZN984777Dyyis7ActIEiuvvLJrG816MCdhZpaFE7D8/Bqb9WxOwszMzMxK4D5hZpbd2if+vqnHm3Lmnl3ep3fv3my88cbMnj2b9ddfnzFjxrDMMsssVLwf//jHXHLJJfTp04fBgwdz2WWXsdZaawEwZswYvv/97wNw8sknc9hhhwFw4YUXcu655/L3v/+dadOmMWjQICD15fra177GuHHjWGaZZfj5z3/O5ptvvlDlMrOezTVhZtaWll56aSZNmsTjjz9Ov379GDVqVIf9c+bM6faxNttsMyZMmMCjjz7KZz/7Wb7xjW8A8Oqrr3Laaafxl7/8hQceeIDTTjuNGTNmALDNNttw++23z03WKm6++WaeeeYZnnnmGUaPHs2XvvSlRXymZtZTOQkzs7a33XbbMXnyZMaPH8+OO+7IgQceyMYbb8ycOXMYOXIkW2yxBZtssgk//elP6z5+xx13nFuLNmLECKZOnQrArbfeys4778zAgQNZaaWV2HnnnbnllluAlLjVW+f2xhtv5NBDD0USI0aM4LXXXuPll1/mzTffZM8992TTTTdlo4024tprr83zYpjZYsPNkWbW1mbPns3NN9/MbrvtBsADDzzA448/ztChQxk9ejQDBgzgwQcf5N1332WbbbZhl112aTjlw6WXXsruu+8OwIsvvsgaa6wxd9+QIUN48cUXG5ans8fcd999rLbaavz+96np9vXXX1/o52w216kDGuzze6xsrgkzs7b09ttvM2zYMIYPH86aa67JkUceCcCWW245N8m67bbbuOKKKxg2bBhbbbUV06dP55lnnun0mFdeeSUTJkxg5MiRQOrfVaurEYudPWbjjTfm9ttv54QTTuCee+5hwIAGJ08zawuuCTOztlTpE1Zr2WWXnft3RHDBBRew6667drjPSSedNLdGqnKM22+/ndNPP5277rqLpZZaCki1WOPHj5/7uKlTp7LDDjs0LNeQIUN44YUXOjxmtdVWY9VVV2XixImMGzeOb37zm+yyyy585zvfWaDnbGY9i2vCzGyJteuuu3LxxRcza9YsAP72t7/x5ptvcvrppzNp0qS5CdjDDz/MF7/4RcaOHcsqq6zS4fG33XYbM2bMYMaMGdx2223zJXS19t57b6644goigj//+c8MGDCAVVddlZdeeollllmGgw8+mP/5n//hoYceyvfEzWyx4JowM8uuO1NKlOGoo45iypQpbL755kQEgwcP5oYbbpjvfiNHjmTmzJl87nOfA2DNNddk7NixDBw4kG9/+9tsscUWAHznO99h4MCBAJx//vn88Ic/5B//+AebbLIJe+yxB5dccgl77LEH48aNY91112WZZZbh8ssvB+Cxxx5j5MiR9OrVi759+3LxxRe36FUws7KoXv+Eph1c2g04D+gNXBIRZ9bs3wG4EXiu2HR9RHy30TGHDx8eEyZMyFBaM2uWJ598kvXXX7/sYiwR/FpbQ+6YXzpJEyNieL192WrCJPUGLgJ2BqYCD0oaGxF/rbnrPRHxiVzlMDMzM1sc5ewTtiUwOSKejYj/ANcAn8wYz8zMzKzHyJmErQ68UHV7arGt1taSHpF0s6QNM5bHzMzMbLGRs2N+vclyajugPQSsFREzJe0B3ACsN9+BpKOBoyF1iDUzMzPr6XImYVOBNapuDwFeqr5DRPy76u9xkn4iaVBEvFJzv9HAaEgd8/MVuQdz50szM7MeJWdz5IPAepKGSuoH7A+Mrb6DpPermF5a0pZFeaZnLJOZmZnZYiFbTVhEzJZ0LHAraYqKyyLiCUnHFPtHAZ8FviRpNvA2sH/knDPDzMrRqKZ2oY7Xde1u79692XjjjZk9ezbrr78+Y8aMmbsId3dMmTKF++67jwMPPLDu/pEjR/K73/2Ofv36sc4663D55Zez4oorAnDGGWdw6aWX0rt3b84///y5E7iedNJJXHHFFcyYMYOZM2fOPda7777LoYceysSJE1l55ZW59tpr6y7+bWbtJeuM+RExLiI+GBHrRMTpxbZRRQJGRFwYERtGxKYRMSIi7stZHjNbclSWLXr88cfp168fo0aNWqDHT5kyhauvvrrT/TvvvDOPP/44jz76KB/84Ac544wzAPjrX//KNddcwxNPPMEtt9zCl7/8ZebMmQPAXnvtxQMPPDDfsS699FJWWmklJk+ezPHHH88JJ5ywQGU1s57JyxaZWdvbbrvtmDx5Mq+++ir77LMPm2yyCSNGjODRRx8F4K677mLYsGEMGzaMzTbbjDfeeIMTTzyRe+65h2HDhnHOOefMd8xddtmFPn1SY8KIESOYOnUqADfeeCP7778/Sy21FEOHDmXdddedm3iNGDGCVVdddb5j3XjjjRx22GEAfPazn+WOO+4gInj55ZfZfvvtGTZsGBtttBH33HNPltfHzMrhZYvMrK3Nnj2bm2++md12241TTjmFzTbbjBtuuIE777yTQw89lEmTJnH22Wdz0UUXsc022zBz5kz69+/PmWeeydlnn81NN93UZYzLLruM/fbbD4AXX3yRESNGzN03ZMgQXnzxxYaPf/HFF1ljjTSOqU+fPgwYMIDp06dz9dVXs+uuu3LSSScxZ84c3nrrrUV4JawuD2qyEjkJM7O29PbbbzNs2DAg1YQdeeSRbLXVVlx33XUA7LTTTkyfPp3XX3+dbbbZhq9//escdNBBfPrTn2bIkCHdjnP66afTp08fDjroIADqdWstxh91qrPHbLHFFhxxxBHMmjWLffbZZ+7zMbP24OZIM2tLlT5hkyZN4oILLqBfv36dJjsnnngil1xyCW+//TYjRozgqaeemu9+hx9+OMOGDWOPPfaYu23MmDHcdNNNXHXVVXMTrSFDhvDCC/PmqZ46dSqrrbZaw7JWP2b27Nm8/vrrDBw4kO233567776b1VdfnUMOOYQrrrhioV4LM1s8OQkzsyXG9ttvz1VXXQXA+PHjGTRoECussAJ///vf2XjjjTnhhBMYPnw4Tz31FMsvvzxvvPHG3MdefvnlTJo0iXHjxgFwyy238IMf/ICxY8d2GHW59957c8011/Duu+/y3HPP8cwzz7Dllls2LNfee+/NmDFjAPjNb37DTjvthCSef/55VlllFb7whS9w5JFH8tBDDzX7JTGzErk50szyW0z61px66qkcfvjhbLLJJiyzzDJzE59zzz2XP/7xj/Tu3ZsNNtiA3XffnV69etGnTx823XRTPv/5z3P88cd3ONaxxx7Lu+++y8477wykTvejRo1iww03ZN9992WDDTagT58+XHTRRfTu3RuAb3zjG1x99dW89dZbDBkyhKOOOopTTz2VI488kkMOOYR1112XgQMHcs011wApUTzrrLPo27cvyy23nGvCzNqMetq0XMOHD48JEyaUXYzFjzuX2mLkySefZP311y+7GEsEv9aLqN2/O9v9+fUAkiZGxPB6+9wcaWZmZlYCJ2FmZmZmJXASZmZZ9LSuDj2RX2Ozns1JmJk1Xf/+/Zk+fbqThIwigunTp9O/f/+yi2JmC8mjI82s6YYMGcLUqVOZNm1a2UVpa/3791+giWXNbPHiJMzMmq5v374MHTq07GKYmS3WnISZLWk8ZN3MbLHgPmFmZmZmJXASZmZmZlYCJ2FmZmZmJXASZmZmZlYCJ2FmZmZmJXASZmZmZlYCJ2FmZmZmJfA8YbZwPNeUmZnZInFNmJmZmVkJnISZmZmZlcBJmJmZmVkJnISZmZmZlcAd883MzMy6kmFAmmvCzMzMzErgmrAeZO0Tf9/pvin9W1gQMzMzW2SuCTMzMzMrwZJTE+bJRc3MzGwx4powMzMzsxI4CTMzMzMrgZMwMzMzsxI4CTMzMzMrgZMwMzMzsxI4CTMzMzMrwZIzRYWZmS04T+9jlo2TMDPLyydxM7O6nITl4hOPmZmZNeAkzMzMrAfzusI9lzvmm5mZmZUgaxImaTdJT0uaLOnEBvfbQtIcSZ/NWR4zMzOzxUW2JExSb+AiYHdgA+AASRt0cr8fALfmKouZmZnZ4iZnTdiWwOSIeDYi/gNcA3yyzv2+AlwH/CtjWczMzMwWKzmTsNWBF6puTy22zSVpdeBTwKhGB5J0tKQJkiZMmzat6QU1MzMza7WcSZjqbIua2+cCJ0TEnEYHiojRETE8IoYPHjy4aQU0MzMzK0vOKSqmAmtU3R4CvFRzn+HANZIABgF7SJodETdkLJeZmZlZ6XImYQ8C60kaCrwI7A8cWH2HiBha+VvSz4GbnICZmZnZkiBbEhYRsyUdSxr12Bu4LCKekHRMsb9hPzCzUnnFAzMzyyzrjPkRMQ4YV7OtbvIVEZ/PWRYzMzOzxYlnzDczMzMrgZMwMzMzsxJ4AW/rlBeFNTMzy8c1YWZmZmYlcE2YWRtyLaaZ2eLPSZiZ2RLOSbtZOdwcaWZmZlYCJ2FmZmZmJWir5khXqZuZmVlP4ZowMzMzsxI4CTMzMzMrQVs1R5pZOdwVwBZnfn+2yKkDGux7vXXl6EFcE2ZmZmZWAidhZmZmZiVwEmZmZmZWAvcJsyWW+4mYmVmZXBNmZmZmVgInYWZmZmYlcBJmZmZmVgInYWZmZmYlcMf8ReCO3WZmZrawXBNmZmZmVgInYWZmZmYlcBJmZmZmVoIu+4RJ6gt8Cdi+2HQXMCoiZuUsmJmZmS1e3Be6ubrTMf9ioC/wk+L2IcW2o3IVyszMzKzddScJ2yIiNq26faekR3IVyMysRzl1QIN9r7euHGbW43SnT9gcSetUbkj6ADAnX5HMzMzM2l93asJGAn+U9CwgYC3g8KylMjMzM2tzXSZhEXGHpPWAD5GSsKci4t3sJTMzMzNrY50mYZJ2iog7JX26Ztc6koiI6zOXzczMzKxtNaoJ+yhwJ7BXnX0BOAkzs8WPO8qbWQ/RaRIWEacUf343Ip6r3idpaNZSmS1pnDiYmS2YNvje7M7oyOvqbPtNswtiZmZmtiRp1Cfsw8CGwICafmErAJ4X18zMzGwRNOoT9iHgE8CKdOwX9gbwhZyFMjMzM2t3jfqE3QjcKGnriLi/hWUyMzMza3vdmaz1YUn/RWqanNsMGRFHZCuVmZm1rYaLQJ+5ZwtLYlau7nTM/wXwfmBX4C5gCKlJ0szMzMwWUndqwtaNiM9J+mREjJF0NXBr7oKZmXWmYU2Khw2ZWQ/RnZqwWcXv1yRtBAwA1s5WIjMzM7MlQHeSsNGSVgJOBsYCfwV+0J2DS9pN0tOSJks6sc7+T0p6VNIkSRMkbbtApTczMzProbqzgPclxZ93Ax8AkLRWV4+T1Bu4CNgZmAo8KGlsRPy16m53AGMjIiRtAvwK+PCCPQWznsFNaGZmVq1hEiZpa2B14O6I+FeRKJ0IbAes0cWxtwQmR8SzxbGuAT5JqkkDICJmVt1/WdKalGZm1pk2WKrFzJJGM+afRZqsdRJwgqSbgC8D/wt0Z3qK1YEXqm5PBbaqE+dTwBnAKkDdscmSjgaOBlhzzTW7EdrMzGwx5CTaqjSqCdsT2Cwi3in6hL0EbBIRz3Tz2Kqzbb6aroj4LfBbSdsD3wM+Xuc+o4HRAMOHD3dtmZmZmfV4jTrmvx0R7wBExAzg6QVIwCDVfFU3WQ4hJXJ1RcTdwDqSBi1ADDMzM7MeqVFN2DqSxlbdXrv6dkTs3cWxHwTWkzQUeBHYHziw+g6S1gX+XnTM3xzoB0xfkCdgZmZm1hM1SsI+WXP7Rwty4IiYLelY0sSuvYHLIuIJSccU+0cBnwEOlTQLeBvYLyLc3Gjzcz8KMzNrM40W8L5rUQ8eEeOAcTXbRlX9/QO6OeeYmZmZWTvpzmStZmZmZtZk3Vk70szMWsgT+5otGbqsCSvWizQzMzOzJupOc+QoSQ9I+rKkFbOXyMzMzGwJ0GUSFhHbAgeR5vyaIOlqSTtnL5mZmZlZG+tWx/xiktaTgROAjwLnS3pK0qdzFs7MzMysXXWnT9gmks4BngR2AvaKiPWLv8/JXD4zMzOzttSd0ZEXAj8DvhURb1c2RsRLkk7OVjIzMzOzNtadJOz6iPhF9QZJX4uI82q3m5mZmTVLu0/X0p0+YYfW2fb5JpfDzMzMbInSaU2YpANIC24PrVnIe3m8yLaZLUHa/WrczMrRqDnyPuBlYBAdF+9+A3g0Z6HMzMzM2l2jBbyfB54Htm5dcczMzMyWDI2aI++NiG0lvQFE9S4gImKF7KUzMzMza1ONasK2LX4v37rimJmZmS0ZujNZ6zqSlir+3kHSV72GpJmZmdmi6c4UFdcBcyStC1wKDAWuzloqMzMzszbXncla34uI2ZI+BZwbERdIejh3wWzJ42kAzMxsSdKdmrBZxZxhhwE3Fdv65iuSmZmZWfvrTk3Y4cAxwOkR8ZykocCVeYtlZmZLpFMHNNj3euvKYUukVrfINEzCJPUmLdx9cGVbRDwHnNn8opiZmZktORo2R0bEHGCwpH4tKo+ZmZnZEqE7zZFTgD8V60e+WdkYET/OVSgzMzOzdtedJOyl4qcXafFuMzMzM1tEXWpTO9kAACAASURBVCZhEXEagKRlI+LNru5vZmZmZl3rzoz5W0v6K/BkcXtTST/JXjIzMzOzNtadecLOBXYFpgNExCPA9jkLZWZmZtbuupOEEREv1Gyak6EsZmZmZkuM7nTMf0HSR4Aopqr4KkXTpJmZmZktnO7UhB0D/BewOjAVGFbcNjMzM7OF1J2aMEXEQdlLYmZmZrYE6U5N2H2SbpN0pKQVs5fIzMzMbAnQZRIWEesBJwMbAg9JuknSwV08zMzMzMwa6O7oyAci4uvAlsCrwJispTIzMzNrc92ZrHUFSYdJuhm4D3iZlIyZmZmZ2ULqTsf8R4AbgO9GxP2Zy2NmZma2ROhOEvaBiIjsJTEzMzNbgnSahEk6NyKOA8ZKmi8Ji4i9s5bMzMzMrI01qgn7RfH77FYUxMzMzGxJ0mkSFhETi993SRpc/D2tVQUzMzMza2edjo5UcqqkV4CngL9JmibpO60rnpmZmVl7ajRFxXHANsAWEbFyRKwEbAVsI+n47hxc0m6SnpY0WdKJdfYfJOnR4uc+SZsu1LMwMzMz62EaJWGHAgdExHOVDRHxLHBwsa8hSb2Bi4DdgQ2AAyRtUHO354CPRsQmwPeA0QtWfDMzM7OeqVES1jciXqndWPQL69uNY28JTI6IZyPiP8A1wCdrjnVfRMwobv4ZGNK9YpuZmZn1bI2SsP8s5L6K1YEXqm5PLbZ15kjg5no7JB0taYKkCdOmeWyAmZmZ9XyNpqjYVNK/62wX0L8bx1adbXUnfZW0IykJ27be/ogYTdFUOXz4cE8ca2ZmZj1eoykqei/isacCa1TdHgK8VHsnSZsAlwC7R8T0RYxpZmZm1iN0uYD3IngQWE/SUEn9gP2BsdV3kLQmcD1wSET8LWNZzMzMzBYr3Vk7cqFExGxJxwK3Ar2ByyLiCUnHFPtHAd8BVgZ+IglgdkQMz1UmMzMzs8VFtiQMICLGAeNqto2q+vso4KicZTAzMzNbHOVsjjQzMzOzTjgJMzMzMyuBkzAzMzOzEjgJMzMzMyuBkzAzMzOzEjgJMzMzMyuBkzAzMzOzEjgJMzMzMyuBkzAzMzOzEjgJMzMzMyuBkzAzMzOzEjgJMzMzMyuBkzAzMzOzEjgJMzMzMyuBkzAzMzOzEjgJMzMzMyuBkzAzMzOzEjgJMzMzMyuBkzAzMzOzEjgJMzMzMyuBkzAzMzOzEjgJMzMzMyuBkzAzMzOzEjgJMzMzMyuBkzAzMzOzEjgJMzMzMytBn7ILYGZm1k7WPvH3ne6b0r+FBbHFnmvCzMzMzErgJMzMzMysBE7CzMzMzErgJMzMzMysBE7CzMzMzErgJMzMzMysBE7CzMzMzErgJMzMzMysBE7CzMzMzErgJMzMzMysBE7CzMzMzErgJMzMzMysBE7CzMzMzErgJMzMzMysBFmTMEm7SXpa0mRJJ9bZ/2FJ90t6V9L/5CyLmZmZ2eKkT64DS+oNXATsDEwFHpQ0NiL+WnW3V4GvAvvkKoeZmZnZ4ihnTdiWwOSIeDYi/gNcA3yy+g4R8a+IeBCYlbEcZmZmZoudnEnY6sALVbenFtsWmKSjJU2QNGHatGlNKZyZmZlZmXImYaqzLRbmQBExOiKGR8TwwYMHL2KxzMzMzMqXMwmbCqxRdXsI8FLGeGZmZmY9Rs4k7EFgPUlDJfUD9gfGZoxnZmZm1mNkGx0ZEbMlHQvcCvQGLouIJyQdU+wfJen9wARgBeA9SccBG0TEv3OVy8zMzGxxkC0JA4iIccC4mm2jqv7+B6mZ0szMzGyJ4hnzzczMzErgJMzMzMysBE7CzMzMzErgJMzMzMysBE7CzMzMzErgJMzMzMysBE7CzMzMzErgJMzMzMysBE7CzMzMzErgJMzMzMysBE7CzMzMzErgJMzMzMysBE7CzMzMzErgJMzMzMysBE7CzMzMzErgJMzMzMysBE7CzMzMzErgJMzMzMysBE7CzMzMzErgJMzMzMysBE7CzMzMzErgJMzMzMysBE7CzMzMzErgJMzMzMysBE7CzMzMzErgJMzMzMysBE7CzMzMzErgJMzMzMysBE7CzMzMzErgJMzMzMysBE7CzMzMzErgJMzMzMysBE7CzMzMzErgJMzMzMysBE7CzMzMzErgJMzMzMysBE7CzMzMzErgJMzMzMysBE7CzMzMzErgJMzMzMysBE7CzMzMzEqQNQmTtJukpyVNlnRinf2SdH6x/1FJm+csj5mZmdniIlsSJqk3cBGwO7ABcICkDWrutjuwXvFzNHBxrvKYmZmZLU5y1oRtCUyOiGcj4j/ANcAna+7zSeCKSP4MrChp1YxlMjMzM1ssKCLyHFj6LLBbRBxV3D4E2Coijq26z03AmRFxb3H7DuCEiJhQc6yjSTVlAB8Cnl6IIg0CXlmIxy0sx3O8xTVeOz83x3M8xysvXjs/t0WJt1ZEDK63o8+ilach1dlWm/F15z5ExGhg9CIVRpoQEcMX5RiO53jtEK+dn5vjOZ7jlRevnZ9brng5myOnAmtU3R4CvLQQ9zEzMzNrOzmTsAeB9SQNldQP2B8YW3OfscChxSjJEcDrEfFyxjKZmZmZLRayNUdGxGxJxwK3Ar2ByyLiCUnHFPtHAeOAPYDJwFvA4bnKwyI2Zzqe47VRvHZ+bo7neI5XXrx2fm5Z4mXrmG9mZmZmnfOM+WZmZmYlcBJmZmZmVgInYbbEk9RL0r5ll8PMzJYsTsKaSNLAssuQi6Teko4vuxw5RMR7wLFd3tHMsmjn705rLknbdGdbk2JlP++1dcf8YnTmVRExo0XxngEmAZcDN0fmF1eSgIOAD0TEdyWtCbw/Ih7IFG98ROyQ49h1Yi0FfAZYm6pRvBHx3Uzxvg28DVwLvFkV79Umx/lwRDzV2WL1EfFQM+NVxf0EMK5IOLOTNIH0Obi6FZ+/Yq3a99HxvfJ/GeMtDawZEQuzeseixF02It7s+p49S6u/O1ut1e/PVirhPPtQRGze1bYmxst63ss5Y/7i4P3Ag5IeAi4Dbs384f4g8HHgCOACSdcCP4+Iv2WK9xPgPWAn4LvAG8B1wBaZ4v1J0oXMn6jkSBxuBF4HJgLvZjh+rSOK3/9VtS2ADzQ5ztdJS3D9qM6+IP0vc9gfOE/SdcDlEfFkpjjV8Q4nff4qCdltOT5/kr4CnAL8k/R5gPRabtLsWEW8vYCzgX7AUEnDgO9GxN454hUxPwJcAiwHrClpU+CLEfHlTPHWA84ANgD6V7ZHRLM/DxUt/e6U9AfgcxHxWnF7JeCaiNg1Q6yWvT8l/Y46q85UZHqPtuQ8K2lr4CPAYElfr9q1AmkarFyynvfauiYM5tYW7UI6IQwHfgVcGhF/zxx3R+BKYFngEeDEiLi/yTEeiojNJT0cEZsV2x6JiE2bGacq3h/rbI6IaHriIOnxiNio2cctm6RPR8T1xd8Dm13T1kXsFYADSJ+FICVGv4yINzLG7AV8AriYdAK6DDivmc9b0mTSurTTm3XMLuJNJCXL46s+d49GRJakrzj+X4DPAmOrYmb7jEi6l5Q4nAPsRXrPKCJOyRGvJnYrvjvnfmc22takWC17f0r6aKP9EXFXprjZz7PFc9sBOAYYVbXrDeB3EfFMs2LVxM163mv3mjAiIiT9A/gHMBtYCfiNpD9ExDeaGUvSysDBwCGkq56vkFYFGAb8GhjazHjArKKaO4r4g5l3pdV0EbFjrmPXcZ+kjSPisVYEk7QMqZZqzYg4uqgJ+FBE3NTkUCcD1xd/3w5kqUKvJyL+XdSELQ0cB3wKGCnp/Ii4oNnxJG1C+lLeg1RDexWwLXAn6TPRLC+Qak1bZXZEvJ7OO60TES/UxJyTMdzSEXGHJEXE88Cpku4hJWZNV8J353uS1qw0CUpaiwY1SIuoZe/PXElWN+JmP89GxF3FxcHGEXFaM47ZzbhZz3ttnYRJ+ipwGGnV80uAkRExq7g6fwZoahIG3A/8AtgnIqZWbZ8gaVQnj1kU5wO/BVaRdDrpSvnkDHEAkDSA9CW8fbHpLlIzTI4vmG2Bz0t6jtQcKdJnPVdtw+Wkps+PFLenkr78m52EqZO/s5K0NykhWof0Ht0yIv5VJJ9PAk1NworaoteAS0k1GZUm5b9k6ET7LDBe0u+parqOiB83OU7F45IOBHoXyfpXgfsyxap4oWiSjGIZuK+S/m+5vFP5niz6/LwIrJIxXqu/O08C7pVUSVq2J3UTyKHV78+WNie38jwbEXPKGMQhaU9gQzq+lk3pn9zWSRgwCPh0cSU3V0S8V3RUbrYPVdrCizfgchHx7yLmD5odLCKuKk52HyOd0PfJ3NfnMuBxoDKdwyGk5OXTGWLtnuGYjawTEftJOgAgIt5WnqqOpSVtRhqZ3L/4e26cXB3zSYMczomIu6s3RsRbko7o5DGL4nMR8Wy9HRHR7PfL/xU//Yqf3L5COom/C1xNWprt+5ljHgOcB6xOukC4jY79F5vtOGAZUrL3PVLz62E5AhW1+TdFxPfq7c/03XlLMThmBOnzd3xEvNLsOIVWvz8hfS9XmpN3pGhOzhSr1efZhyWNJV0kV/fRur7zhyy84iJgGdLreAmpsqNpg9/ask9YV5lyrn44kq4mfVnOIdWqDAB+HBFnZYo3Anii0qdH0vLABhHxl0zxJkXEsK62NTHepsB2xc17IuKRHHGKWPeRktk/Ff3s1iH1l9qyyXHq9S+oyNW/rjeps+zHm33sOrG+3mh/5qv/5VOImJkxRsteyyWJpDsi4mMtiNPS0cnF+2VMRBzczON2I+7EiPh/kh6LiI2LbfdExHZdPXYh47Vs9Keky+tsjojIcTE5t79n1e/lgOsjYpdmHL9da8Imktr3BawJzCj+XpF0RdLs/gUVGxT9bg4iLU5+QlGWLEkYqbNz9ZfJm3W2NdPbkraNiHth7twsb+cIJOlrwBeY13/qSkmjc/RdKpwC3AKsIekqYBvg880O0uJ+dZWYcyS9JWlApqbjastnPv58JG1EasoaWNx+BTg0Ip5odqwWv5ZzSRpKqoFbm44nuiwjMiUNJ9X2rVUTL1d3gEktqt1o6ejk4v0yWFK/iPhPM4/dhZY1JxfHP5UWjU6OiMNzHLeBd4rfb0laDZhOE3OItkzCImIozK1GHBsR44rbu5OGQefSV1JfYB/gwqJdPGdVoyrNnzC3+jfn//QY4Iqibxik5DZLEwVwJGlE0ZsAkn5A6jeSJQmLiD8oDbGuNE98LWPzRAdFcpmrP0rFO8BjSkPzq09yX21mkFZ2mK0yGvh6RPwRQNIOwM+Y17+v2VryWta4gdS/7ndkHHxT5SpgJPBYi+INJJ3cqpOgYN5FWFNUPmctvhiaQprmYCwd3y/ZaoWZvzl5R/J9Vx9H6orTqtHJQ0jngW1I75F7Sd/XUxs+cMHjHAf8CRgraUXgh8BDRcxLmhWnLZOwKltExDGVGxFxs6S6/Q6a5KekD9wjwN3FiJt/Z4z3bNEp8uLi9pdJnUCbrqhuPjgiNlWa6oBKf7dMRMfRX3PI0KehTrPEy8XvNYvRU7n6aFUb3oIYvy9+spL0jYj4oaQLqDPaLFOismwlAStijJe0bIY4FS15LWu8ExHntzDetIgY26pgra7dkPQ54JaIeEPSyaTWg+9FxMMZwr1U/PSiRTXFEfFg8edMUn+wnFo9OvlyUl/MzxW3Dy627dzkOENI/TDXJ1Xe3AccBdzfzISzLfuEVUi6FbiHNOdMkP5Z20eGCfkalKFPRMzOdOxVSCMkdyI9vzuA4yLiX5ni3Zmjz1Insb5OunL7bbFpH9Lkjec2OU7L+2jVKcMtEbFbC+Jkn+Vd0l4R8TtJda+6I2JMhpi/JV2h/qLYdDAwPCL2aXasshSjMdcjdcivHmGXa4WFj5HmlLujJl6uzs8tqd2oilfp37MtaRTh2cC3ImKrHPGKmC1b7UAtmIy2qv/nhsCHSBcm2Ud/ltA3uR/pQvkjwNbFz2sRsUEzjt/uNWEHkPr6/Jb0wb672JZNvaGspNnsm65ItvbPcexOtGxUSkT8WNJ40lQVAg7PcZVaRh+tOmVoRQLWklneI+J3xe+mJ1sNHAGcRmq6Eulznu3qX2nalHq1fLlmkwfYmDQaeSc69rvJdZFwOPBhoG9NvCxJGK2r3aio1LLvCVwcETdKOjVHIKWZ3i+lRasdFAZVEjCAiJhRXLQ3U6VWr9WjP1+RdDDwy+L2AaSm7FyWJs3KP6D4eYnUTN8UbV0TViFpuZwjpqri1B3KGhFHZoo3mNR5fW06dp7NNUok+6gUSSsUgxvqjnBt9shWSQ2nS2h2ginp3Ig4Tp0sL5Kxo3W9Wd7njpzKEK/VHbtbRmli0Yr+pMRhYER8J2PMp4BNWtW5O+d7o5N4ra7duInUWf3jwP8jDTB6IDKsNqIWr3ZQHH8i8KnoOBntbyPT+opFjBVI54NsK3AUcdYELiTVSEHqt/W1qJkiowlxRpMqVN4A/gL8GfhzNHmNzLauCVOL11sDPlI1lPU0ST8i35UjpPUV7yHNvJ5z9uxKn7BXImJkzjikq+FPMG+E69wikGctx70a7Mtx5V9pMju7ycftSr1Z3nNegWXv2F1WQlunP8i5SjN5Z0vCSP1MVwSydDWo48+SNoiIv7YoXqtrN/YFdgPOjojXJK1Ker9mEa1d7QBaOBltccF1OUXNmKTXgSMiYmKOeEVimW2d1iprAkuRJpx9kTQ/32sNH7EQ2joJI01Utytp+Qsi4hFJ2zd+yCKpTNeQZShrHctExAkZjz9XMdQ6+xI7EfGJ4nfO1606Xks7BFd9MU0A3o6I92BukrtUxtCtnuW9FR27S0loaz4HvUj9RXJ3uH4f8JSkB+nY7ybXyWhb4DC1bsWKI0i1G+eQEur7yNuhfBDpM1ipWQF4KlOsVq920OrJaC8DvhwR9wAU/ewuJ9MUFZI+QOowP4L0Xrmf9PyaOigtInZTypw3JPUH+29gI0mvkjrnN2UJr3ZPwlp9BXJTMZT1LDIMZe0k3h5RTMHRAq2ay6fu5I31tjUhzsERcaU6mWQ04zDyO0hNIZVm8qVJna5zTatQb5b3nCOFT5F0CRk7dlcltMMi4rzqfUrzzOVaR696jqnZwHPMW0Uil+wLZ9fI3k+xxhq1CaXSPIRZJvwkdSKvzCXZn3Sx/DTphNts9VY7yNIao/kno32p+J1ztPcblQQMICLulZSzSfJq4CLS2reQ+kX/Emj6oIpI/bUel/QaaQTo66SWmi1p0mey3ZOwVl+B/DDSGnnXFX0O+jNvorccvgZ8S9K7wCzmXa2ukCle9rl8JPUn9asbVIzoqWTQKwCrNStOlcpUBq2eZLR/dT/FiJiptI5jLntGxEmkRAyYO0z/15nitbJj92Gkk1y1z9fZ1ixH1l51K02mmk20eGHmSv+aojN3/y7u3gwXMP8k0/W2NUVtf7ciaflijlikObQOqom3DakvU7O1dDLawgOSfkpKhALYj7RW5uaQZQSvIuIXVbevVJowtrlB0vRPHyGN2J1F+n/dT6r5c8f87pA0iPRF/HHSyfw2Uge+LH0NJD1U2/Gx3jbrXFGDcRwp4XqReUnYv4GfRcSFGWL2Br4aEec0+9gNYv4J+ErlC6roV3FBRGzd+JELHa+l781WdOxWWufzQFLT2T1Vu5YH5kSmpYU6eS0nRsT/yxDr3ojYtqhZmK+PZK4LLqUF339E+hz+izTA4smIaGpNUTFy8COkz3z1528FUsfypneUb1CWLJ+Hdj8vqMXT/Eg6k9Q36xrmJX1LkWrHmjZ4S9KPSc3if4qIl7u6/8Jq65qwog38oC7vuIgkvZ9U1VxZnLm69iZn7UZl/pf16Li6+92dP2KRYmWfy6doVjpP0lci3xJFtTHnFCedliVhpJPOryW9RHotVyN9mTSV0ioRewCrS6qe7HMFUlNaLq3o2H0faXLdQXS88n8DeLTZwSR9mNRcNaBmVO0KZKotiohti9+trqn9HqnPze0RsZmkHckzvU8/0sCpPnSsjf43aURhFjXdD3qRatymNTlGJcEcXBNvBaB3M2PVid2yyWij9dP8VL4nKwMNKufbI2ji4K2IaLgObrO0dRKm1q23tiup+WMIUN2H6A3gW02ONZeko0hNkkOASaQvzfvJN3dQK+fyeU/SitFxssEDIuInGWIB3CfpQuBaOvZ3a/aCvlsAL0TEg8VJ/YvAp0nrVj7XzFiFl0gdkPcmjTiteAM4PkO8iuwdu4sms+eZN1Q9tw+R+oOsSMdRtW+QporJQmkNwEdzTmlQx6yImC6pl6ReEfFHpaXDmqpoZr1L0s+rmkB7ActF3hU5qhO+2aQ+Ytc1OUYpCWbh2xHx66KT/K6kwSujyNBvCurPjxkRTZ0fs+q7s7Is4WHAZ0ir1JzarBqwVmv35shHSJPkdRgmn6t/haTPRESzP8iN4j0GbEGau2RYcVI/LSKaXqNSxGvZXD6dxHo4inl2MsSrV6Weoyr9IeDjEfFqMVL3GtKFwjBg/YjI8uUsqW9EzCr+XonUEbrptUVV8daqtz2aPJdPEWsEqYZ2fdKJrzfwZsamuq0j4v4cx24Q8yrgm8Xw/FbEu520SsUZpJrGf5GWgcsycETS1aQO7HNIFwsDgB9HxFk54rWSpLVanGDO/a6UdAbwWERcnev7Uy2aH7Os787c2romjNavt3aT0jQAa9Ox5i3LjPmk5/eOJCQtVYyK+VCmWNDauXx6SZq7QHnRbyvbbMwtrFLvXXXFth8wukjcr5M0KWPcPxRNrn1ItabTJN2Vq8q9xR27LySNkPo1abqIQ4F1M8abLOlbtGiS5MKqwBOSHqBjTW2uKSo+SZpy53hSl44BZFr5o7BBpEmaDwLGASeQkrGmJmFKo7s7len1PENShwRTUu4E88Wis/zHgR9IWorU7JpDq+bHLOu7M6t2T8LOk3QKLVpvjTR56uukD9q7Xdy3GaYqTYlxA+kkO4N5Q5JzqDeXT64Tz63Ar4qrrCBdJd+cKRbFl9RnyJ9A99a89UQ/RscJFHN+HgcUJ7mjgMsj4hRJOWvC6nbsJs8UAETEZEm9I2IOcLmknHOgtXKS5HVJc4SdVrPro6SBKzli9gZuLAY2vAe0YgmqvpL6kmrfLoyIWZJyNNNsTVpw+pekWdDV+O5N0ZIEs0YrJ6OtzACQe37Msr47s+qxBe+mVq+3NiRasA5gRURU5kk5tWhOG0DqW5QrXqtmKob0RXU08CXSF+XDpNqAXFqVQP+S1AfmFVJNQ2WCw3WL+Ln0Kb6I96VqmoqMWtWxG9KXfz/SPHY/JHXWX7aLxyyKlk2SDJxLWli6Q8Is6U3SPEWXNjtgMVDlLUkDIiLne7LaT0l9ex4B7i6as3M02b2f1Ie1MrL298AvI+KJDLEqWpVgVmvlZLS/0/zzY/4sQ5yyvjuzavc+Ya1eb200aZqBps0h0o2YKwFr0LH2ptmdyS+gwRI3EfHVZsarijuM9EW5H/AscF1kmKKiiJV1LbeaWCNICeVtEfFmse2DpL4iWWppi9FS3wbujYgvK806fVZEfCZTvAkRMbzol7lZRLwn6YGI2DJDrLVItW19Sc1nA4CfRMTkZscq4n0fuC9aMElyo/el8q79+StSEv0HOjZ/Zvmsd1KGSq1HruMvRUrGziItZp9lNLbSfFMnkBLMPUnL4VwZEdvliFfEfIw6k9FG86cY6QWMiIj7ittLkeZAzJIUlfHdmVu7J2HXkuZiasl6a5L+SuqL0pKlPiR9jzQq81mqavoydCY/rOrmadTMFBwRTWuuKD5Q+zOvv9m1wP9ERN2O3k2M2/IEupUkDWzl6KFWd+xuJaU5u5YF/lP8ZJuzS9LkiKjbv63RvibEPaze9mZ+1os4LV+xokgU9iR9x6xNWtbusojI0rzbSRmyJph14m1OWje56RPSSro/Ms1vuCRo9yRsPGn9qpast9bKEWFFvKeBjVtV01fEzDZCsTj+e6Rq5iMrNRmSno2IZi/cXYn3OCmB7UOab+1ZWrNWXktJeobUIf9y4ObI9MFXWhrl/yQtS2oy6MW8jt1XRYaJkiV9gtT8uRbp/5h75YiWkfRL4M6I+FnN9iOBXSLTSOgixmCAiGjq/Fk1Mb4YET8t+u7OJyJq+8ItarwxwEak/qXXRMTjzTx+JzHfB/wvsFpE7C5pA2DriGh6U3IX5cg1Ge1ppHn5rs/1vdLO2j0J+2i97ZF5CZDaEWGRaVi5pOuAL7Wqpq+ImXWmZ0mfItWEfYTUv+0a4JLItKB3MZih0yk2ciXQrSZJpJFSR5DWPbsW+HlE/K3Jcea+PyRdl6u5sybmZNJca4+14iRQvJYHAUMj4nuS1gBWjYgHMsR6H/BbUo1bZZ634aSRwp+KiH80OZ5INd3HkpLZXqR5tC7IMEil5YqLvErzaktWIJB0M+ni56SI2FRSH+DhXE3JRcx6k9GuHBG7ZohVqRmeTeqk3zYXQa3Q1kkYzP0S26K4+UDOhKWzEWHNboevijec1KH8cVpQ01fEbMlyG0VNyj6kJoOdSCO0fhsRtzU5TtssH9JdRSf5K0lfnI8AJ0aT5r2qrinNXWtaFfOPwMci4r0u79yceBeTak93ioj1i36Zt0XEFl08dFFi7kiqwQF4IiLuzBTneNIKC0dHxHPFtg8AF5NmYG/qqhLquIrDfFrZBy0XSQ9GxBY1n40s8ytWxayuWZxNGvRwXUTkXMvYFkJbj46UtC+p0+V4UnZ+gaSREfGbTCFbOSIMUmLyA2omo202dVy3bhlJlVFL2a54ik6XVwFXSRpImqX/RNJ0I820Smf9UYpyNL1PShkkrUxa4eAQ4J+kSQ7HkmoBf03zhpRHJ3/n9A1gnKS76Hgxkut/t1VEbC7p4SLODKXRmdlExB+BRmv0NcuhwM6RlnyrxH5WaX7A22j+0l7VqzjM19+0TbxZfP4qcx6OHDQt+QAACP9JREFUIPNovmY34zZS9Der9TrwfCv7vfVUbZ2EkYbib1Gp/Sr6ONwO5ErCWrLUR5VXogWT0Ubr162rjf8qaQj7TzMcvjdpaZFWzBdUpvuBXwD7RMe1PicozcXWLJsWSbpIa6lmT9iB04GZpC4AWZOhwiylubQqJ9XBZLwIarG+1QlYRURMU5pmoamqO/pLOq7ZHf/LJOk44E+ki4QbgQ9I+hMwmHlLvzU7ZhmT0f6E1NxZGdS0MamGfWVJxzS79aLdtHsS1qum+XE6+WYNBnhN0nLA3aQanH+Rd5HkiUrLUoylNZPRtqOX26GvSzd8qLP+UhHRtAuFiMi6MHEnBkbELi2Mdz6pn9Yqkk4nLdNycgvj59RokE/uAUDt1jdmCHAe8GHSHF1/ILXKXFsv0W2SMiajnUIaSPUEQDHwYCSpZeh6mt960Vbauk+YpLNIoyMry+zsR1oIt6kTLZYxIqyI25L1DttZq/otlaWkK+OWknQmaQRhy77sldZp/RjpJHdHRDzZqtg5SZpD1bxg1btI8z81vTasKnZb9s8smqqHkwYbbV38vBYRG2SI1Zt5k9FuQgsmo63Xv62yLXfft3bQlkmYiqU+IuJPkj4NbEv6EplBSor+3uR4LR8RZs2hFs+f1WqSptHgyjj3SOFWqBqd9S4wi8yjsySdR6rNyLk00hKhtr8p8FZlF20ywk7SAFLitU3xe0XSSN7DM8dt1WS01wKvkkayQ6rsGETqf3pvzgEr7aBdk7CbqL/Ux3DglIjYq8nxWj4irIg1gNSRdfti012kD1uPXcLBmquMK+N2pzSR6X7AB0nNktdGxIRyS2WLG6UJoDcE3iBdAP0Z+HNEzMgct6WT0UpaGvgy8yo77iX1E3uHtMTXzBxx20W7JmEtXeqjpiasZVXqxTxhjzNvgd1DgE0j4tOtiG89S6uujFtF0ocj4qlORmdl7xtZjNr9DGleuzUjYr2c8axnkXQLqUboceA+0uCYx3POZacSJqMt4vYDPkSq1Xw6Ima1Im47aNckrKVLfVT1oxCwNC2qUm/UFp8jnvVMrb4ybhVJoyPi/7d3b6GejWEcx7+/GZPdUA6pSYiIhBhnjaFBKSFyPjWUGiQjNW5EKSlFLlxQTmGcRzndmKFoGIM5NAfNjBw2V+QwkXMaPxfv2jP/xh6G9vq/+7/273Oz91p7r/2+62Lv/axnve/zzKm1NlLScZSM2LnAurHOsMfga4rfHkpZDzaDEiBtBJbaHvNyHJWK0c6iJAI+b8bZB7jS9uKxHquLuhqEVWv10U+SlgI3236nOT4RuMfp4xWNWk/GXdaUnTkP+BR4ntKu5fu6s4rxTNLelDVhM4CzKNXrd607q7EhaQVwme2PmuODKEsejq47s8HQ1SCsr60+apE0nfIEsgvlCWQj5QlkzT9eGBNGjSfjGiTNoGT5Npfdsf1ES2NdC7zQYpmB6ABJcylB14mUDSNLKK8kl1AW5neitpykNd6qx+5o52J0nQzCRvSr1Udtkkb+kf4CXGz7qZrziegnSfOBAygNyjc1p+0xbnmzrbVnI1KfL3pJupeyFmyJ7S9rz6ctkh6lPODNb05dDuzQ9u7Pruh0ENZVTdB1PbAXpRLzG83xPGC17XMqTi+iryStBw5pc8FzM87I2rMhSmZ9NSWjeDjwvu2ZbY4fMR41a06vZ8vuyMXA/bZ//8cLA0gQNpAkvUypebaUUjByN8qr1httr6o5t4h+k7QAmNuvbIOkZ4E7ba9tjg8D5tm+qh/jR0R3JAgbQL1lNpo6UN9Stsj/WHdmEf3XZKimAx+wpX2X28oIZ1dyBEh63vZFktYySsuprAnbPl3vHdlVm2uw2N4kaTgBWExgt/d8LsprkUtbHG+9pIeBJyn/fK4A1rU4XsR4dGPz8ayqsxhwyYQNoK36u/XWJuvUjreI7dXsFL4MuAgYppSNaKtNyxBwHXAS5XduJbCf7avbGC9iUEjaA/iu7fWZXZJM2ACyPbn2HCJqa+oRXULJen0HPEd5sDylzXFt/9a8At2TUqx1N+CFNseMGG8knQDcRSmNdAdld+QewCRJs22/VnN+gyKZsIgYSE0NtLeBq21/0pz7zPb+LY03WtA3z/a+bYwXMZ5JWg7cQqlT+SBwhu33JB1MKdbalx7Kg25S7QlERPxP5wNfAW9KekjSaZTXg23ZQNmNfLbtmc3rzk3/ck1EV+1ge5HtBcBXtt8DsL2h8rwGSoKwiBhItl9sWpAdDLwF3ARMk/SApNNbGLLfQV/EeNZb8f/Xrb6WV2zbKa8jI6IzJO0OXEjpHNFKA29JO1Gadl8KnEppHfai7UVtjBcxHvVsEOvdHEZzPGR7Sq25DZIEYRER/1M/gr6I6K4EYREREREVZE1YRERERAUJwiIiIiIqSBAWERERUUGCsIiIbZCU7hQR0ZoEYRHRGZJuk7RB0uuSnpE0T9IBkl6TtELS201FbyQ9Juk+Se9K+kzSBc35WZLelPQ0sFbSZEl3S1omaY2ka5rv21PSYkmrJH0o6aSKtx4RAyi9IyOiEyQdQymoeiTlb9tKYAWlpcq1tj+WdDxwP6W+F5T+jzMpBV9fYUsPyOOAw2wPS5oD/GD7WEk7AkskLQLOAxbavrPJmE3ty41GRGckCIuIrpgJvGz7VwBJrwJDwAxggbS5uP2OPde8ZPtPYJ2kaT3nP7A93Hx+OnD4SKaM0ivvQGAZ8KikKc3PWdXGTUVEdyUIi4iuGK2F0CTge9vTt3HN79u4/uetzt9ge+HfBpROBs4E5ku62/YT/3HOETGBZU1YRHTFO8DZkoYk7UwJjn4BhiVdCKDiiP/4cxcC1zUZLyQdJGknSfsCX9t+CHgEOGrM7iQiJoRkwiKiE2wvk/QKsBr4AlgO/ABcDjwg6VZgCvBs8z3b62FgP2ClyjvNbyi9I2cBN0v6A/gJmD02dxIRE0XaFkVEZ0ja2fZPkqYCi4E5tlfWnldExGiSCYuILnlQ0iGUBfmPJwCLiPEsmbCIiIiICrIwPyIiIqKCBGERERERFSQIi4iIiKggQVhEREREBQnCIiIiIir4C6/zwJK/TIh5AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# diversity  with the diverse(>= 50)\n",
    "fig2 = total.plot.bar(x='genres', y=['Pavg_diversity','avg_diversity'], figsize=(10,5), title=\"Average Diversity per Genre Pre2010 vs Post2010\")\n",
    "l = fig2.legend()\n",
    "l.get_texts()[0].set_text('Pre-2010s')\n",
    "l.get_texts()[1].set_text('Post-2010s')\n",
    "fig2.set_ylabel('Diversity Ratio')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "wnSsQPHJgq5o"
   },
   "source": [
    "## 2) Line graphs of overall movies vs. woman diverse movies\n",
    "\n",
    "This dataframe calculates the following attributes for all movies in general despite female representation for each year between 1990-2020: weighted_average_vote, usa_gross_income, num_of_cast, num_of_woman, diversity, isDiverse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 230
    },
    "id": "htjRtgrNgq5o",
    "outputId": "5a2feabc-05c4-4c27-b0e3-f20507658384"
   },
   "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>weighted_average_vote</th>\n",
       "      <th>usa_gross_income</th>\n",
       "      <th>num_of_cast</th>\n",
       "      <th>num_of_woman</th>\n",
       "      <th>diversity</th>\n",
       "      <th>isDiverse</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1990</th>\n",
       "      <td>6.207463</td>\n",
       "      <td>3.307797e+07</td>\n",
       "      <td>4.059701</td>\n",
       "      <td>1.253731</td>\n",
       "      <td>0.309701</td>\n",
       "      <td>0.328358</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1991</th>\n",
       "      <td>6.119718</td>\n",
       "      <td>2.591368e+07</td>\n",
       "      <td>3.971831</td>\n",
       "      <td>1.338028</td>\n",
       "      <td>0.338028</td>\n",
       "      <td>0.408451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992</th>\n",
       "      <td>6.317284</td>\n",
       "      <td>3.072228e+07</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.333333</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.358025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1993</th>\n",
       "      <td>6.047674</td>\n",
       "      <td>2.501155e+07</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.372093</td>\n",
       "      <td>0.343023</td>\n",
       "      <td>0.418605</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1994</th>\n",
       "      <td>6.033750</td>\n",
       "      <td>2.720920e+07</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.200000</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>0.325000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      weighted_average_vote  usa_gross_income  num_of_cast  num_of_woman  \\\n",
       "year                                                                       \n",
       "1990               6.207463      3.307797e+07     4.059701      1.253731   \n",
       "1991               6.119718      2.591368e+07     3.971831      1.338028   \n",
       "1992               6.317284      3.072228e+07     4.000000      1.333333   \n",
       "1993               6.047674      2.501155e+07     4.000000      1.372093   \n",
       "1994               6.033750      2.720920e+07     4.000000      1.200000   \n",
       "\n",
       "      diversity  isDiverse  \n",
       "year                        \n",
       "1990   0.309701   0.328358  \n",
       "1991   0.338028   0.408451  \n",
       "1992   0.333333   0.358025  \n",
       "1993   0.343023   0.418605  \n",
       "1994   0.300000   0.325000  "
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Overall_Average_Yearly = df_main.groupby(['year']).mean()\n",
    "Overall_Average_Yearly.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This dataframe calculates the following attributes for movies with more than 50% female representation for each year between 1990-2020: weighted_average_vote, usa_gross_income, num_of_cast, num_of_woman, diversity, isDiverse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 230
    },
    "id": "Za0cX0qkgq5p",
    "outputId": "9a1c9cff-49f6-468c-8615-107a5d2e0a4a"
   },
   "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>weighted_average_vote</th>\n",
       "      <th>usa_gross_income</th>\n",
       "      <th>num_of_cast</th>\n",
       "      <th>num_of_woman</th>\n",
       "      <th>diversity</th>\n",
       "      <th>isDiverse</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1990</th>\n",
       "      <td>6.331818</td>\n",
       "      <td>3.890861e+07</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>2.136364</td>\n",
       "      <td>0.534091</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1991</th>\n",
       "      <td>6.058621</td>\n",
       "      <td>2.632930e+07</td>\n",
       "      <td>3.931034</td>\n",
       "      <td>2.206897</td>\n",
       "      <td>0.560345</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992</th>\n",
       "      <td>6.086207</td>\n",
       "      <td>3.113431e+07</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>2.344828</td>\n",
       "      <td>0.586207</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1993</th>\n",
       "      <td>5.925000</td>\n",
       "      <td>1.834211e+07</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>2.166667</td>\n",
       "      <td>0.541667</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1994</th>\n",
       "      <td>6.265385</td>\n",
       "      <td>3.902267e+07</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>2.076923</td>\n",
       "      <td>0.519231</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      weighted_average_vote  usa_gross_income  num_of_cast  num_of_woman  \\\n",
       "year                                                                       \n",
       "1990               6.331818      3.890861e+07     4.000000      2.136364   \n",
       "1991               6.058621      2.632930e+07     3.931034      2.206897   \n",
       "1992               6.086207      3.113431e+07     4.000000      2.344828   \n",
       "1993               5.925000      1.834211e+07     4.000000      2.166667   \n",
       "1994               6.265385      3.902267e+07     4.000000      2.076923   \n",
       "\n",
       "      diversity  isDiverse  \n",
       "year                        \n",
       "1990   0.534091        1.0  \n",
       "1991   0.560345        1.0  \n",
       "1992   0.586207        1.0  \n",
       "1993   0.541667        1.0  \n",
       "1994   0.519231        1.0  "
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unique_year = pd.DataFrame()\n",
    "unique_year['year'] = df_main['year'].unique\n",
    "Woman_Averages_Yearly = df_main[df_main.isDiverse == 1]\n",
    "Woman_Averages_Yearly = Woman_Averages_Yearly.groupby(['year']).mean()\n",
    "Woman_Averages_Yearly.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "bnLoX3zxgq5p"
   },
   "source": [
    "### 2a) general average income vs woman average income \n",
    "In this line plot we are graphing from 1990 to 2020, the average gross income per year, for all movies and all gender diverse movies. (gender diverse meaning the ratio for women and general cast is greater than or equal to 50%.)\n",
    "\n",
    "\n",
    "We can interpret that women diverse movie income is all over the place, whereas all movie incomes have a less steeper peaks and falls, the income for women-diverse films are very drastic. However, women-diverse movies performed almost just as well as the other films in 2016. There is a drastic negative slope near 2020 because we do not have a completed list of all movies in this year. \n",
    "\n",
    "We can learn from this line graph that female-diverse movies have been historically under performing in terms of income compared to all of the other movies except for the spikes around 1994 and 2004. Only recently from 2015 have female-diverse movies catching up to the general average income of all other movies. \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 421
    },
    "id": "KCQSxIqcwoaC",
    "outputId": "33ddbc60-bc16-4393-fe69-ba0c0b22b8f6",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Average Movie Incomes Overall(USA)')"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax1 =Overall_Average_Yearly.plot.line(y=['usa_gross_income'],legend=True, color='navy',alpha=.75, figsize=(15,6), linewidth = 3.0)\n",
    "\n",
    "ax2 =Woman_Averages_Yearly.plot.line(y=['usa_gross_income'],color='#B3B0B0', ax=ax1,legend=True,linewidth = 3.0 )\n",
    "\n",
    "l= ax2.legend()\n",
    "l.get_texts()[0].set_text('All Movies')\n",
    "l.get_texts()[1].set_text('Gender Diverse Movies')\n",
    "\n",
    "ax1.set_ylabel('Gross Income (10 Million) ',color='black', fontsize=12)\n",
    "ax1.set_title('Average Movie Incomes Overall(USA)')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "O1wVnYItgq5q"
   },
   "source": [
    "### 2b) General Average Ratings vs woman avg ratings \n",
    "In this line plot we are graphing from 1990 to 2020, the average rating per year, for all movies and all gender diverse movies.\n",
    "\n",
    "We can interpret from this linegraph that women diverse movie ratings follow the same curve from around 2001 to present. There is a great dip for women diverse movie ratings in 2016. For 2020, there was a significantly smaller amount of movies to analyze. There is a great amount of fluctuation for women diverse movies from 1990 to 2000. There is a drastic negative slope near 2020 because we do not have a completed list of all movies in this year.\n",
    "\n",
    "We can learn from this linegraph that female-diverse movies have been keeping up with the trends of all movies's general average rating for a longer time than the average general income.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 404
    },
    "id": "Yf7IBiL5ppH4",
    "outputId": "3a5a3483-d8df-4fe3-e86b-fba12b9d420f"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax3 =Overall_Average_Yearly.plot.line(y=['weighted_average_vote'],legend=True,color=\"navy\", figsize=(15,6))\n",
    "\n",
    "ax4 =Woman_Averages_Yearly.plot.line(y=['weighted_average_vote'], ax=ax3,color='#B3B0B0', legend=True,linewidth = 3.0 )\n",
    "\n",
    "l= ax3.legend()\n",
    "l.get_texts()[0].set_text('All Movies')\n",
    "l.get_texts()[1].set_text('Gender Diverse Movies')\n",
    "ax3.set_ylabel('weighted_average_vote',color='black', fontsize=14)\n",
    "ax3.set_title('Average Movie Ratings Overall(USA)',fontsize=14)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2c) Average Women-Men Ratio Over Time\n",
    "\n",
    "In this line graph, we can looking at the average diversity rates over time across all movies from 1990 to 2020.\n",
    "\n",
    "We can interpret from this line graph that the diversity rate in movies has been generally increasing despite some ups and downs from year to year. \n",
    "\n",
    "We can learn from this line graph that there has been an increase in female representation over time. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Average Women-Men Ratio Over Time')"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax5 =Overall_Average_Yearly.plot.line(y=['diversity'],legend=True, color='navy',alpha=.75, figsize=(15,6), linewidth = 3.0)\n",
    "\n",
    "l= ax5.legend()\n",
    "l.get_texts()[0].set_text('Women-Men Ratio')\n",
    "\n",
    "ax5.set_ylabel('Women-Men Ratio ',color='black', fontsize=12)\n",
    "ax5.set_title('Average Women-Men Ratio Over Time')\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "NakBM9ttdMyq"
   },
   "source": [
    "## ETHICS & PRIVACY\n",
    "Our goal is to collect data from trustworthy, public sites without any inherent biases. Here are ways we ensured the highest quality of data ethics and privacy in our project for the different types of data we worked with:\n",
    " \n",
    "#### Movie Title, Genre, Main cast\n",
    "This information is safe to use for the purpose of our project since we do not have any intentions of defamation for any observations used in our datasets. There are no outward privacy concerns regarding these attributes since the title, genre, and names of the main cast are released publicly when it airs. To avoid any potential biases, it is important to fact-check the site’s sources  to ensure it is a trustworthy, public site that does not hide any titles or provide misleading values to collect a comprehensive and complete list. This information does not have any other potential issues with data ethics and privacy as long as we follow the guidelines explained above.\n",
    " \n",
    "#### Main Cast Gender\n",
    "It is permissible to use this information for those actors who have publicly acknowledged their gender identity. Our purpose is to simply use this attribute as a statistic to count the number of different gender representations in movies with no intentions of defamation. If there is any actor who has not publicly disclosed their gender identity, then due to privacy concerns we will not be able to use their name in our ethnic diversity measurements. That being said, nonbinary and transgender people were not able to be identified. Even though we would have liked to include these populations, we respect their privacy. Because there is a lack of data due to this lack of information, it is highly possible that some nonbinary/transgender people were included in our dataset, but were encoded strictly as male or female. We will not be able to place a fake value based on personal guesses. To take care of any outward biases, we need to fact-check the source we get this information from to make sure it is accurate and not misleading or faulty.  This information does not have any other potential issues with data ethics and privacy as long as we follow the guidelines explained above. \n",
    " \n",
    " \n",
    "#### Movie Ratings and IMBd Data\n",
    "We gathered our movie ratings from the service IMDb for the purpose of our project since we are analyzing it for personal and non commercial use only. It is important that we consult the service’s terms and conditions to double check that we comply with their permissions. The greatest privacy concern regarding this data is that we do not invade any individual viewer information. IMDb publishes the weighted vote averages rather than the raw data averages. Additionally, they accept and consider all votes received by the users, but not all votes have the same impact on the final rating. We are aware that there is no foolproof way for IMBd to verify that a voter has viewed a movie before actually rating it. That being said, some “controversial” films may contain a skewed score from positive or negative “ballot-stuffing” (although IMDb does apply several countermeasures to unskew ratings). The ratings from IMDb are all user submitted and therefore do not come from other external sources. A film may have lesser amounts of votes or a lower score due to several factors. These factors could potentially be that not all people are willing to vote or register to rate a movie they do not necessarily feel passionate about or that a film’s demographic does not use IMDb. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "OdoniylLdOhP"
   },
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "c9l_lBtcgq5t"
   },
   "source": [
    "## CONCLUSION & DISCUSSION\n",
    "\n",
    "Discuss your project. Summarize your data and question. Briefly describe your analysis. Summarize your results and conclusions. Be sure to mention any limitations of your project. Discuss the impact of this work on society. (2-3 paragraphs)\n",
    "#### Describe analysis and summarizing results and conclusions\n",
    "We found 4 categories of success per Genre as influenced by the women-men ratio :\n",
    " * Successful (6) : Animation, Sci-Fi, Adventure, Musical, Biography, History. These genres are doing very well with increased female representation. It can be seen by the increase in income and ratings of movies with half or more women-men ratio casts from pre-2010 and post-2010 eras as those values are keeping up with the general average increases or even doing better than the genre in general. \n",
    " * Moderately Successful (6) : Comedy, Fantasy ,Romance, Action, Family, Music. These genres are doing relatively well with increased female representation. It can be seen by the increase in either income or ratings of movies with half or more women-men ratio casts from pre-2010 and post-2010 eras as it is attempting to keep up with the general average increases. \n",
    " * Not Successful (4) : Horror, Mystery, Western, War. These genres are doing relatively poorly with the increased female representation. It can be seen when the income and rating is constant or not increasing in movies where half or more women-men ratio casts from pre-2010 and post-2010 eras as it is barely keeping up or doing even worse than the general average.\n",
    " * Inconclusive (4) : Sport, Drama, Crime ,Thriller, these genres had a constant or no change in their representation of women, thus no change could be observed.\n",
    "\n",
    "\n",
    "These 4 categories of success can be compared with the overall trends of average income and average ratings in all movies and in movies with higher female representation. As we can see, average ratings in female-diverse movies are keeping up with the trends in average ratings across all movies, whereas average income in female-diverse movies, is not keeping up with the average income trends like all movies. There are significant differences in incomes of female-diverse movies and all movies, sometimes surpassing and sometimes doing poorly. Since we are using these two items as our metrics of success (income and rating) it makes sense that we have an even distribution of genres that are for sure having great success and some with moderate success as diversity is increasing. \n",
    "\n",
    "\n",
    "#### Discussion \n",
    " * We discovered that from 1990 to present year there has been a steady increase in the gender ratio in movie casting, from ~30% to ~39%, certain years having a larger boost than others. For many of the genres, gender-diverse movies end up matching the general curve in ratings when comparing pre and post 2010 results. However, we did not observe this same pattern when it came to comparing the average revenue for women films vs all other films. \n",
    " * Even though our dataset is small and limited, we can still extrapolate our findings into the real world, however we recognize that our data is only a subset of movies in a certain era. \n",
    " * Our findings are important as the movie industry grows because it indicates that gender-diverse movies are faring well ratings wise.\n",
    "\n",
    "\n",
    "#### Limitations\n",
    "Despite being able to come to a concrete conclusion, during the course of our project we stumbled upon a variety of obstacles that limited our data analysis process. These limitations being:\n",
    "\n",
    "##### Reframed Question\n",
    " * We initially had a slightly different research question than what was concluded. Rather than being centered on gender diversity, our original research question regards BIPOC diversity in cinema and how that may or may not influence a film’s success. However, this specific direction was not fitting for our project because we repetitively reached dead ends when searching for datasets. We terminated this angle and reframed our research question and hypothesis relevant to gender diversity. Gender diversity is still something that is important to us and in the range of what we initially were going for. With the project guidelines and timeframe, this angle was more feasible.  \n",
    "\n",
    "##### Termination of TV Shows\n",
    " * We initially wanted to analyze not only movies but also TV shows. TV shows are as relevant as movies and felt as though the two categories combined would give us great insight towards our overarching research question. However, we found that TV shows were not available as a dataset. We learned that film box office revenue is not comparable to television revenue. Also, as a result,  TV shows on IMDb were less likely to have a score. \n",
    "\n",
    "##### Change in Movie Streaming Platforms\n",
    " * It was in our intentions to add more meaningful data such as streaming viewership numbers and rating websites aside from IMBd. However, we had difficulty attaining it because that data was not publicly available. In addition, movie streaming platforms have drastically changed over the years, so our movie income and ratings are not a holistic way of defining success. The datasets we sought either required large fees, were confidential to the company, or rejected our proposal for API/Token access.\n",
    "\n",
    "##### Unfair Distribution \n",
    " * We did have a few limitations within IMBd and therefore did not have a fair distribution of pre and post 2010 movies and we also had an unfair distribution within each genre of movies. Some datasets from our primary site were still missing a variety of values, such as budget and titles from the late 2000s. In addition, there was an inconsistent distribution of votes between all the movies.  \n",
    "\n",
    "##### Subset of Movies\n",
    " * We were not able to get every movie budget, income, and ratings for every movie from 1990 to 2020. Because of this limitation, the dataset that we are working with is only a small subset of movies from 1990 to 2020.\n",
    "\n",
    "##### Unable to Identify Full Cast \n",
    " * We were not able to validate if we have the gender of the full cast of each movie in our resulting dataset. Although we did have access to the list of actors in the movie, we did not know if we had their names in the dataset we used to gather the genders. This dataset provided us only the top 10 people in each movie, which included non-actors like directors and writers, but that was the extent of our knowledge. So it is safe to conclude that we may have lost some actors within the actors lists we attained so our female:men ratio may not be fully accurate.\n",
    "\n",
    "##### Inability to Identify Nonbinary and Transgender People\n",
    " * As mentioned in our Ethics and Privacy section, we were not able to identify nonbinary and transgender peoples. The individuals within our datasets were strictly encoded as male or female therefore it is very likely that there could have been nonbinary and transgender people within our datasets. But due to a lack of information and with respect to their privacy, these orientations were not disclosed. We would have liked to widen our analysis from just male and female to communities like nonbinary and transgender people because this should also be included in gender diversity and is just as important. However, because this information was not readily available to us, we had to limit our analysis to only male and female. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Team Contributions\n",
    "\n",
    "To proceed in the most productive manner, we decided to allocate different jobs to different team members according to efficiency and skill. During the early stages of our project, Jalyn helped with the background and prior works section. She found references that would later act as a basis for our project’s overall goal and foundation. Following that, Winnie, Richard, and Jocelyn found different datasets according to our decided topic and then webscraped to extract that information. Tabassum, Winnie, and Richard primarily worked on the analysis for movies and budget. At the end, Jalyn, Jocelynand Winnie took all that was done and formulated it in the write up portion of the project. Even though each team member had their own role everyone contributed and offered help where and when it was needed. Whenever we had questions we were all attentive and responsive via Slack and we all attended all weekly zoom meetings, except for when very rare personal absences would occur.\n"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [
    "AElY8Cy5wJsx",
    "oyBvcTYawJsx",
    "FCWBSbJmwJsy",
    "u9w6bZ-KwJsz",
    "jQVo4IsH7w-0",
    "fEip7zvWwJsz",
    "sIPw6uoZwJsz",
    "l0x9BNR_wJs0",
    "HQBN_ULMwJs0",
    "ulD0dlybwJs0",
    "MCGH-hhiwJs0",
    "3IgZlZIBwJs0",
    "7QrQt_Gr7w_C",
    "zUHCP2Gq7w_D",
    "NakBM9ttdMyq"
   ],
   "name": "project 1215 1045pm.ipynb",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}