Gender-Share-in-Casting-Actors_DL-WS2122_public/exp/exp-003_T-Test-Hypothesis-T...

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Literacy - Project\n",
    "## Gender Share in Movies\n",
    "#### Tobias Stumpp, Sophia Herrmann"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## t-Test Hypothesis Testing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Starting year of the period of years covered by the test\n",
    "start_year = 1980\n",
    "# Ending year of the period of years covered by the test\n",
    "end_year = start_year + 40\n",
    "\n",
    "# Split year of the period of years covered by the test that separates\n",
    "# indicative data (>= start_year and < split_year)\n",
    "# from\n",
    "# data to be verified (>= split_year and < end_year).\n",
    "split_year = start_year + 20\n",
    "\n",
    "# Option to ignore movies where the average rating or the number of votes is below the respective 5% quantile.\n",
    "ignore_irrelevant_movies = False"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Meta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import os\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "path = '../dat/'\n",
    "os.chdir(path)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Read Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['tconst', 'startYear', 'runtimeMinutes', 'genres', 'averageRating', 'numVotes', 'category']\n"
     ]
    }
   ],
   "source": [
    "columns = list(pd.read_csv('data_movie.csv', nrows =1))\n",
    "print(columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 854511 entries, 0 to 854510\n",
      "Data columns (total 6 columns):\n",
      " #   Column          Non-Null Count   Dtype  \n",
      "---  ------          --------------   -----  \n",
      " 0   tconst          854511 non-null  object \n",
      " 1   startYear       854511 non-null  int64  \n",
      " 2   runtimeMinutes  854511 non-null  int64  \n",
      " 3   averageRating   854511 non-null  float64\n",
      " 4   numVotes        854511 non-null  int64  \n",
      " 5   category        854511 non-null  object \n",
      "dtypes: float64(1), int64(3), object(2)\n",
      "memory usage: 39.1+ MB\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "None"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
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     "data": {
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tconst</th>\n",
       "      <th>startYear</th>\n",
       "      <th>runtimeMinutes</th>\n",
       "      <th>averageRating</th>\n",
       "      <th>numVotes</th>\n",
       "      <th>category</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>tt0000502</td>\n",
       "      <td>1905</td>\n",
       "      <td>100</td>\n",
       "      <td>4.5</td>\n",
       "      <td>14</td>\n",
       "      <td>actor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>tt0000502</td>\n",
       "      <td>1905</td>\n",
       "      <td>100</td>\n",
       "      <td>4.5</td>\n",
       "      <td>14</td>\n",
       "      <td>actor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>tt0000574</td>\n",
       "      <td>1906</td>\n",
       "      <td>70</td>\n",
       "      <td>6.1</td>\n",
       "      <td>747</td>\n",
       "      <td>actress</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>tt0000574</td>\n",
       "      <td>1906</td>\n",
       "      <td>70</td>\n",
       "      <td>6.1</td>\n",
       "      <td>747</td>\n",
       "      <td>actor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>tt0000574</td>\n",
       "      <td>1906</td>\n",
       "      <td>70</td>\n",
       "      <td>6.1</td>\n",
       "      <td>747</td>\n",
       "      <td>actor</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      tconst  startYear  runtimeMinutes  averageRating  numVotes category\n",
       "0  tt0000502       1905             100            4.5        14    actor\n",
       "1  tt0000502       1905             100            4.5        14    actor\n",
       "2  tt0000574       1906              70            6.1       747  actress\n",
       "3  tt0000574       1906              70            6.1       747    actor\n",
       "4  tt0000574       1906              70            6.1       747    actor"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "columns_to_read = [c for c in columns if c != 'genres']\n",
    "\n",
    "data_movie = pd.read_csv('data_movie.csv', usecols = columns_to_read)\n",
    "\n",
    "display(data_movie.info())\n",
    "display(data_movie.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Provide the option to only include movies that are relevant based on the average rating and number of votes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>numVotes</th>\n",
       "      <th>averageRating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>8.545110e+05</td>\n",
       "      <td>854511.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.579196e+03</td>\n",
       "      <td>5.928503</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.920979e+04</td>\n",
       "      <td>1.263788</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>5.000000e+00</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.300000e+01</td>\n",
       "      <td>5.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>7.800000e+01</td>\n",
       "      <td>6.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3.820000e+02</td>\n",
       "      <td>6.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.555039e+06</td>\n",
       "      <td>10.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           numVotes  averageRating\n",
       "count  8.545110e+05  854511.000000\n",
       "mean   3.579196e+03       5.928503\n",
       "std    2.920979e+04       1.263788\n",
       "min    5.000000e+00       1.000000\n",
       "25%    2.300000e+01       5.200000\n",
       "50%    7.800000e+01       6.100000\n",
       "75%    3.820000e+02       6.800000\n",
       "max    1.555039e+06      10.000000"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_movie[['numVotes','averageRating']].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9.0"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numVotes_split = data_movie['numVotes'].quantile(0.05)\n",
    "numVotes_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.6"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "averageRating_split = data_movie['averageRating'].quantile(0.05)\n",
    "averageRating_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(854511, 6)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(data_movie.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "if ignore_irrelevant_movies:\n",
    "    data_movie = data_movie[(data_movie['numVotes'] > numVotes_split) & (data_movie['averageRating'] > averageRating_split)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(854511, 6)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(data_movie.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Only include the data to movies of the selected range of years."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(854511, 6)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(data_movie.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_movie = data_movie[(data_movie['startYear'] >= start_year) & (data_movie['startYear'] < end_year)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(545043, 6)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(data_movie.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Prepare Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Add year span as a column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tconst</th>\n",
       "      <th>year_span</th>\n",
       "      <th>startYear</th>\n",
       "      <th>runtimeMinutes</th>\n",
       "      <th>averageRating</th>\n",
       "      <th>numVotes</th>\n",
       "      <th>category</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2495</th>\n",
       "      <td>tt0011216</td>\n",
       "      <td>2000-2020</td>\n",
       "      <td>2019</td>\n",
       "      <td>67</td>\n",
       "      <td>6.9</td>\n",
       "      <td>30</td>\n",
       "      <td>actress</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2496</th>\n",
       "      <td>tt0011216</td>\n",
       "      <td>2000-2020</td>\n",
       "      <td>2019</td>\n",
       "      <td>67</td>\n",
       "      <td>6.9</td>\n",
       "      <td>30</td>\n",
       "      <td>actor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2497</th>\n",
       "      <td>tt0011216</td>\n",
       "      <td>2000-2020</td>\n",
       "      <td>2019</td>\n",
       "      <td>67</td>\n",
       "      <td>6.9</td>\n",
       "      <td>30</td>\n",
       "      <td>actor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2498</th>\n",
       "      <td>tt0011216</td>\n",
       "      <td>2000-2020</td>\n",
       "      <td>2019</td>\n",
       "      <td>67</td>\n",
       "      <td>6.9</td>\n",
       "      <td>30</td>\n",
       "      <td>actor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6056</th>\n",
       "      <td>tt0015724</td>\n",
       "      <td>1980-2000</td>\n",
       "      <td>1993</td>\n",
       "      <td>102</td>\n",
       "      <td>6.2</td>\n",
       "      <td>25</td>\n",
       "      <td>actor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>854494</th>\n",
       "      <td>tt9915872</td>\n",
       "      <td>2000-2020</td>\n",
       "      <td>2019</td>\n",
       "      <td>97</td>\n",
       "      <td>6.9</td>\n",
       "      <td>8</td>\n",
       "      <td>actress</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>854507</th>\n",
       "      <td>tt9916538</td>\n",
       "      <td>2000-2020</td>\n",
       "      <td>2019</td>\n",
       "      <td>123</td>\n",
       "      <td>8.3</td>\n",
       "      <td>6</td>\n",
       "      <td>actress</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>854508</th>\n",
       "      <td>tt9916538</td>\n",
       "      <td>2000-2020</td>\n",
       "      <td>2019</td>\n",
       "      <td>123</td>\n",
       "      <td>8.3</td>\n",
       "      <td>6</td>\n",
       "      <td>actress</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>854509</th>\n",
       "      <td>tt9916538</td>\n",
       "      <td>2000-2020</td>\n",
       "      <td>2019</td>\n",
       "      <td>123</td>\n",
       "      <td>8.3</td>\n",
       "      <td>6</td>\n",
       "      <td>actor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>854510</th>\n",
       "      <td>tt9916538</td>\n",
       "      <td>2000-2020</td>\n",
       "      <td>2019</td>\n",
       "      <td>123</td>\n",
       "      <td>8.3</td>\n",
       "      <td>6</td>\n",
       "      <td>actress</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>545043 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           tconst  year_span  startYear  runtimeMinutes  averageRating  \\\n",
       "2495    tt0011216  2000-2020       2019              67            6.9   \n",
       "2496    tt0011216  2000-2020       2019              67            6.9   \n",
       "2497    tt0011216  2000-2020       2019              67            6.9   \n",
       "2498    tt0011216  2000-2020       2019              67            6.9   \n",
       "6056    tt0015724  1980-2000       1993             102            6.2   \n",
       "...           ...        ...        ...             ...            ...   \n",
       "854494  tt9915872  2000-2020       2019              97            6.9   \n",
       "854507  tt9916538  2000-2020       2019             123            8.3   \n",
       "854508  tt9916538  2000-2020       2019             123            8.3   \n",
       "854509  tt9916538  2000-2020       2019             123            8.3   \n",
       "854510  tt9916538  2000-2020       2019             123            8.3   \n",
       "\n",
       "        numVotes category  \n",
       "2495          30  actress  \n",
       "2496          30    actor  \n",
       "2497          30    actor  \n",
       "2498          30    actor  \n",
       "6056          25    actor  \n",
       "...          ...      ...  \n",
       "854494         8  actress  \n",
       "854507         6  actress  \n",
       "854508         6  actress  \n",
       "854509         6    actor  \n",
       "854510         6  actress  \n",
       "\n",
       "[545043 rows x 7 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "year_span_presplit = f\"{start_year}-{split_year}\"\n",
    "year_span_postsplit = f\"{split_year}-{end_year}\"\n",
    "year_span = np.where(data_movie['startYear'] < split_year, year_span_presplit, year_span_postsplit)\n",
    "data_movie.insert(1, 'year_span' , year_span)\n",
    "\n",
    "display(data_movie)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Add counts and proportions on crew members"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>category</th>\n",
       "      <th>tconst</th>\n",
       "      <th>num_actors</th>\n",
       "      <th>num_actresses</th>\n",
       "      <th>prop_actors</th>\n",
       "      <th>prop_actresses</th>\n",
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       "      <td>tt0011216</td>\n",
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       "      <td>0.75</td>\n",
       "      <td>0.25</td>\n",
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       "      <td>tt0015724</td>\n",
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       "      <td>0.75</td>\n",
       "      <td>0.25</td>\n",
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       "      <td>tt0036606</td>\n",
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       "      <td>0.00</td>\n",
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       "    <tr>\n",
       "      <th>129989</th>\n",
       "      <td>tt9914286</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129990</th>\n",
       "      <td>tt9914942</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.25</td>\n",
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       "    <tr>\n",
       "      <th>129991</th>\n",
       "      <td>tt9915872</td>\n",
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       "      <td>2</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129992</th>\n",
       "      <td>tt9916538</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.75</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>129993 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "category     tconst  num_actors  num_actresses  prop_actors  prop_actresses\n",
       "0         tt0011216           3              1         0.75            0.25\n",
       "1         tt0015724           2              2         0.50            0.50\n",
       "2         tt0035423           3              1         0.75            0.25\n",
       "3         tt0036177           3              1         0.75            0.25\n",
       "4         tt0036606           3              1         0.75            0.25\n",
       "...             ...         ...            ...          ...             ...\n",
       "129988    tt9913936           4              0         1.00            0.00\n",
       "129989    tt9914286           3              1         0.75            0.25\n",
       "129990    tt9914942           3              1         0.75            0.25\n",
       "129991    tt9915872           0              2         0.00            1.00\n",
       "129992    tt9916538           1              3         0.25            0.75\n",
       "\n",
       "[129993 rows x 5 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_cast_numbers = pd.crosstab(data_movie['tconst'], data_movie['category']).reset_index().rename(columns = {\n",
    "    'actor':'num_actors',\n",
    "    'actress':'num_actresses',\n",
    "})\n",
    "\n",
    "data_cast_proportion = data_movie.groupby(['tconst'])['category'].value_counts(normalize=True).unstack().reset_index().fillna(0).rename(columns = {\n",
    "    'actor':'prop_actors',\n",
    "    'actress':'prop_actresses',\n",
    "})\n",
    "\n",
    "data_cast_gender_stat = pd.merge(data_cast_numbers, data_cast_proportion)\n",
    "data_cast_gender_stat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
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       "      <td>97</td>\n",
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       "      <td>8.3</td>\n",
       "      <td>6</td>\n",
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       "  </tbody>\n",
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       "<p>129993 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           tconst  year_span  startYear  runtimeMinutes  averageRating  \\\n",
       "0       tt0011216  2000-2020       2019              67            6.9   \n",
       "1       tt0015724  1980-2000       1993             102            6.2   \n",
       "2       tt0035423  2000-2020       2001             118            6.4   \n",
       "3       tt0036177  2000-2020       2008             100            7.3   \n",
       "4       tt0036606  1980-2000       1983             118            6.5   \n",
       "...           ...        ...        ...             ...            ...   \n",
       "129988  tt9913936  2000-2020       2019             135            7.2   \n",
       "129989  tt9914286  2000-2020       2019              98            7.6   \n",
       "129990  tt9914942  2000-2020       2019              74            6.9   \n",
       "129991  tt9915872  2000-2020       2019              97            6.9   \n",
       "129992  tt9916538  2000-2020       2019             123            8.3   \n",
       "\n",
       "        numVotes  \n",
       "0             30  \n",
       "1             25  \n",
       "2          82687  \n",
       "3            118  \n",
       "4            312  \n",
       "...          ...  \n",
       "129988        58  \n",
       "129989       218  \n",
       "129990       138  \n",
       "129991         8  \n",
       "129992         6  \n",
       "\n",
       "[129993 rows x 6 columns]"
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       "      <td>65</td>\n",
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       "      <td>41</td>\n",
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       "      <td>9</td>\n",
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       "           tconst  year_span  startYear  runtimeMinutes  averageRating  \\\n",
       "1       tt0015724  1980-2000       1993             102            6.2   \n",
       "4       tt0036606  1980-2000       1983             118            6.5   \n",
       "5       tt0038687  1980-2000       1980              58            7.5   \n",
       "6       tt0057461  1980-2000       1983              84            4.9   \n",
       "7       tt0059325  1980-2000       1990             100            6.5   \n",
       "...           ...        ...        ...             ...            ...   \n",
       "129718  tt9799878  1980-2000       1996              65            1.8   \n",
       "129797  tt9828802  1980-2000       1980              93            6.0   \n",
       "129804  tt9832396  1980-2000       1988              71            6.0   \n",
       "129857  tt9855210  1980-2000       1985              91            7.8   \n",
       "129892  tt9870502  1980-2000       1994             108            5.0   \n",
       "\n",
       "        numVotes  num_actors  num_actresses  prop_actors  prop_actresses  \n",
       "1             25           2              2         0.50            0.50  \n",
       "4            312           3              1         0.75            0.25  \n",
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       "129804        15           3              1         0.75            0.25  \n",
       "129857         5           3              1         0.75            0.25  \n",
       "129892         9           3              1         0.75            0.25  \n",
       "\n",
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       "      <td>0.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>tt0035423</td>\n",
       "      <td>2000-2020</td>\n",
       "      <td>2001</td>\n",
       "      <td>118</td>\n",
       "      <td>6.4</td>\n",
       "      <td>82687</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>tt0036177</td>\n",
       "      <td>2000-2020</td>\n",
       "      <td>2008</td>\n",
       "      <td>100</td>\n",
       "      <td>7.3</td>\n",
       "      <td>118</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>tt0069049</td>\n",
       "      <td>2000-2020</td>\n",
       "      <td>2018</td>\n",
       "      <td>122</td>\n",
       "      <td>6.7</td>\n",
       "      <td>7065</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023</th>\n",
       "      <td>tt0083721</td>\n",
       "      <td>2000-2020</td>\n",
       "      <td>2009</td>\n",
       "      <td>102</td>\n",
       "      <td>6.2</td>\n",
       "      <td>55</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.25</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129988</th>\n",
       "      <td>tt9913936</td>\n",
       "      <td>2000-2020</td>\n",
       "      <td>2019</td>\n",
       "      <td>135</td>\n",
       "      <td>7.2</td>\n",
       "      <td>58</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129989</th>\n",
       "      <td>tt9914286</td>\n",
       "      <td>2000-2020</td>\n",
       "      <td>2019</td>\n",
       "      <td>98</td>\n",
       "      <td>7.6</td>\n",
       "      <td>218</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129990</th>\n",
       "      <td>tt9914942</td>\n",
       "      <td>2000-2020</td>\n",
       "      <td>2019</td>\n",
       "      <td>74</td>\n",
       "      <td>6.9</td>\n",
       "      <td>138</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129991</th>\n",
       "      <td>tt9915872</td>\n",
       "      <td>2000-2020</td>\n",
       "      <td>2019</td>\n",
       "      <td>97</td>\n",
       "      <td>6.9</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129992</th>\n",
       "      <td>tt9916538</td>\n",
       "      <td>2000-2020</td>\n",
       "      <td>2019</td>\n",
       "      <td>123</td>\n",
       "      <td>8.3</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.75</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>92654 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           tconst  year_span  startYear  runtimeMinutes  averageRating  \\\n",
       "0       tt0011216  2000-2020       2019              67            6.9   \n",
       "2       tt0035423  2000-2020       2001             118            6.4   \n",
       "3       tt0036177  2000-2020       2008             100            7.3   \n",
       "23      tt0069049  2000-2020       2018             122            6.7   \n",
       "2023    tt0083721  2000-2020       2009             102            6.2   \n",
       "...           ...        ...        ...             ...            ...   \n",
       "129988  tt9913936  2000-2020       2019             135            7.2   \n",
       "129989  tt9914286  2000-2020       2019              98            7.6   \n",
       "129990  tt9914942  2000-2020       2019              74            6.9   \n",
       "129991  tt9915872  2000-2020       2019              97            6.9   \n",
       "129992  tt9916538  2000-2020       2019             123            8.3   \n",
       "\n",
       "        numVotes  num_actors  num_actresses  prop_actors  prop_actresses  \n",
       "0             30           3              1         0.75            0.25  \n",
       "2          82687           3              1         0.75            0.25  \n",
       "3            118           3              1         0.75            0.25  \n",
       "23          7065           2              2         0.50            0.50  \n",
       "2023          55           3              1         0.75            0.25  \n",
       "...          ...         ...            ...          ...             ...  \n",
       "129988        58           4              0         1.00            0.00  \n",
       "129989       218           3              1         0.75            0.25  \n",
       "129990       138           3              1         0.75            0.25  \n",
       "129991         8           0              2         0.00            1.00  \n",
       "129992         6           1              3         0.25            0.75  \n",
       "\n",
       "[92654 rows x 10 columns]"
      ]
     },
     "metadata": {},
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     "data": {
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       "Empty DataFrame\n",
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     },
     "execution_count": 17,
     "metadata": {},
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   ],
   "source": [
    "data_movie_distinct = data_movie.drop(columns=['category']).drop_duplicates(['tconst']).reset_index(drop = True)\n",
    "display(data_movie_distinct)\n",
    "\n",
    "data_movie_gender_stat = pd.merge(data_movie_distinct, data_cast_gender_stat)\n",
    "data_movie_gender_stat.groupby('year_span').apply(display)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Split data into their year spans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_movie_gender_stat_timespan_presplit, data_movie_gender_stat_timespan_postsplit = [\n",
    "    g for _, g in data_movie_gender_stat.groupby(['year_span'])\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
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       "      <th>5</th>\n",
       "      <td>tt0038687</td>\n",
       "      <td>1980-2000</td>\n",
       "      <td>1980</td>\n",
       "      <td>58</td>\n",
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       "      <td>1828</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>tt0057461</td>\n",
       "      <td>1980-2000</td>\n",
       "      <td>1983</td>\n",
       "      <td>84</td>\n",
       "      <td>4.9</td>\n",
       "      <td>21</td>\n",
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       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>tt0059325</td>\n",
       "      <td>1980-2000</td>\n",
       "      <td>1990</td>\n",
       "      <td>100</td>\n",
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       "      <td>240</td>\n",
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       "      <th>129718</th>\n",
       "      <td>tt9799878</td>\n",
       "      <td>1980-2000</td>\n",
       "      <td>1996</td>\n",
       "      <td>65</td>\n",
       "      <td>1.8</td>\n",
       "      <td>41</td>\n",
       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>129797</th>\n",
       "      <td>tt9828802</td>\n",
       "      <td>1980-2000</td>\n",
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       "      <th>129804</th>\n",
       "      <td>tt9832396</td>\n",
       "      <td>1980-2000</td>\n",
       "      <td>1988</td>\n",
       "      <td>71</td>\n",
       "      <td>6.0</td>\n",
       "      <td>15</td>\n",
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       "      <th>129857</th>\n",
       "      <td>tt9855210</td>\n",
       "      <td>1980-2000</td>\n",
       "      <td>1985</td>\n",
       "      <td>91</td>\n",
       "      <td>7.8</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
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       "      <th>129892</th>\n",
       "      <td>tt9870502</td>\n",
       "      <td>1980-2000</td>\n",
       "      <td>1994</td>\n",
       "      <td>108</td>\n",
       "      <td>5.0</td>\n",
       "      <td>9</td>\n",
       "      <td>3</td>\n",
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       "      <td>0.25</td>\n",
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       "<p>37339 rows × 10 columns</p>\n",
       "</div>"
      ],
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       "           tconst  year_span  startYear  runtimeMinutes  averageRating  \\\n",
       "1       tt0015724  1980-2000       1993             102            6.2   \n",
       "4       tt0036606  1980-2000       1983             118            6.5   \n",
       "5       tt0038687  1980-2000       1980              58            7.5   \n",
       "6       tt0057461  1980-2000       1983              84            4.9   \n",
       "7       tt0059325  1980-2000       1990             100            6.5   \n",
       "...           ...        ...        ...             ...            ...   \n",
       "129718  tt9799878  1980-2000       1996              65            1.8   \n",
       "129797  tt9828802  1980-2000       1980              93            6.0   \n",
       "129804  tt9832396  1980-2000       1988              71            6.0   \n",
       "129857  tt9855210  1980-2000       1985              91            7.8   \n",
       "129892  tt9870502  1980-2000       1994             108            5.0   \n",
       "\n",
       "        numVotes  num_actors  num_actresses  prop_actors  prop_actresses  \n",
       "1             25           2              2         0.50            0.50  \n",
       "4            312           3              1         0.75            0.25  \n",
       "5           1828           1              0         1.00            0.00  \n",
       "6             21           4              0         1.00            0.00  \n",
       "7            240           3              1         0.75            0.25  \n",
       "...          ...         ...            ...          ...             ...  \n",
       "129718        41           3              1         0.75            0.25  \n",
       "129797        48           3              1         0.75            0.25  \n",
       "129804        15           3              1         0.75            0.25  \n",
       "129857         5           3              1         0.75            0.25  \n",
       "129892         9           3              1         0.75            0.25  \n",
       "\n",
       "[37339 rows x 10 columns]"
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       "      <td>7.2</td>\n",
       "      <td>58</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129989</th>\n",
       "      <td>tt9914286</td>\n",
       "      <td>2000-2020</td>\n",
       "      <td>2019</td>\n",
       "      <td>98</td>\n",
       "      <td>7.6</td>\n",
       "      <td>218</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129990</th>\n",
       "      <td>tt9914942</td>\n",
       "      <td>2000-2020</td>\n",
       "      <td>2019</td>\n",
       "      <td>74</td>\n",
       "      <td>6.9</td>\n",
       "      <td>138</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129991</th>\n",
       "      <td>tt9915872</td>\n",
       "      <td>2000-2020</td>\n",
       "      <td>2019</td>\n",
       "      <td>97</td>\n",
       "      <td>6.9</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129992</th>\n",
       "      <td>tt9916538</td>\n",
       "      <td>2000-2020</td>\n",
       "      <td>2019</td>\n",
       "      <td>123</td>\n",
       "      <td>8.3</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.75</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>92654 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           tconst  year_span  startYear  runtimeMinutes  averageRating  \\\n",
       "0       tt0011216  2000-2020       2019              67            6.9   \n",
       "2       tt0035423  2000-2020       2001             118            6.4   \n",
       "3       tt0036177  2000-2020       2008             100            7.3   \n",
       "23      tt0069049  2000-2020       2018             122            6.7   \n",
       "2023    tt0083721  2000-2020       2009             102            6.2   \n",
       "...           ...        ...        ...             ...            ...   \n",
       "129988  tt9913936  2000-2020       2019             135            7.2   \n",
       "129989  tt9914286  2000-2020       2019              98            7.6   \n",
       "129990  tt9914942  2000-2020       2019              74            6.9   \n",
       "129991  tt9915872  2000-2020       2019              97            6.9   \n",
       "129992  tt9916538  2000-2020       2019             123            8.3   \n",
       "\n",
       "        numVotes  num_actors  num_actresses  prop_actors  prop_actresses  \n",
       "0             30           3              1         0.75            0.25  \n",
       "2          82687           3              1         0.75            0.25  \n",
       "3            118           3              1         0.75            0.25  \n",
       "23          7065           2              2         0.50            0.50  \n",
       "2023          55           3              1         0.75            0.25  \n",
       "...          ...         ...            ...          ...             ...  \n",
       "129988        58           4              0         1.00            0.00  \n",
       "129989       218           3              1         0.75            0.25  \n",
       "129990       138           3              1         0.75            0.25  \n",
       "129991         8           0              2         0.00            1.00  \n",
       "129992         6           1              3         0.25            0.75  \n",
       "\n",
       "[92654 rows x 10 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(data_movie_gender_stat_timespan_presplit)\n",
    "display(data_movie_gender_stat_timespan_postsplit)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>startYear</th>\n",
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       "      <th>averageRating</th>\n",
       "      <th>numVotes</th>\n",
       "      <th>num_actors</th>\n",
       "      <th>num_actresses</th>\n",
       "      <th>prop_actors</th>\n",
       "      <th>prop_actresses</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>37339.000000</td>\n",
       "      <td>37339.000000</td>\n",
       "      <td>37339.000000</td>\n",
       "      <td>3.733900e+04</td>\n",
       "      <td>37339.000000</td>\n",
       "      <td>37339.000000</td>\n",
       "      <td>37339.000000</td>\n",
       "      <td>37339.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1989.652133</td>\n",
       "      <td>94.122258</td>\n",
       "      <td>5.888034</td>\n",
       "      <td>4.388853e+03</td>\n",
       "      <td>2.715847</td>\n",
       "      <td>1.667720</td>\n",
       "      <td>0.623368</td>\n",
       "      <td>0.376632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>5.796338</td>\n",
       "      <td>14.771530</td>\n",
       "      <td>1.203386</td>\n",
       "      <td>3.438555e+04</td>\n",
       "      <td>1.217245</td>\n",
       "      <td>1.127883</td>\n",
       "      <td>0.227290</td>\n",
       "      <td>0.227290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1980.000000</td>\n",
       "      <td>52.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1985.000000</td>\n",
       "      <td>86.000000</td>\n",
       "      <td>5.100000</td>\n",
       "      <td>2.400000e+01</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1989.000000</td>\n",
       "      <td>92.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>8.500000e+01</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1995.000000</td>\n",
       "      <td>102.000000</td>\n",
       "      <td>6.800000</td>\n",
       "      <td>4.130000e+02</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1999.000000</td>\n",
       "      <td>135.000000</td>\n",
       "      <td>9.700000</td>\n",
       "      <td>1.555039e+06</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          startYear  runtimeMinutes  averageRating      numVotes  \\\n",
       "count  37339.000000    37339.000000   37339.000000  3.733900e+04   \n",
       "mean    1989.652133       94.122258       5.888034  4.388853e+03   \n",
       "std        5.796338       14.771530       1.203386  3.438555e+04   \n",
       "min     1980.000000       52.000000       1.000000  5.000000e+00   \n",
       "25%     1985.000000       86.000000       5.100000  2.400000e+01   \n",
       "50%     1989.000000       92.000000       6.000000  8.500000e+01   \n",
       "75%     1995.000000      102.000000       6.800000  4.130000e+02   \n",
       "max     1999.000000      135.000000       9.700000  1.555039e+06   \n",
       "\n",
       "         num_actors  num_actresses   prop_actors  prop_actresses  \n",
       "count  37339.000000   37339.000000  37339.000000    37339.000000  \n",
       "mean       2.715847       1.667720      0.623368        0.376632  \n",
       "std        1.217245       1.127883      0.227290        0.227290  \n",
       "min        0.000000       0.000000      0.000000        0.000000  \n",
       "25%        2.000000       1.000000      0.500000        0.250000  \n",
       "50%        3.000000       2.000000      0.666667        0.333333  \n",
       "75%        3.000000       2.000000      0.750000        0.500000  \n",
       "max        9.000000       9.000000      1.000000        1.000000  "
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       "      <th></th>\n",
       "      <th>startYear</th>\n",
       "      <th>runtimeMinutes</th>\n",
       "      <th>averageRating</th>\n",
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       "      <th>num_actors</th>\n",
       "      <th>num_actresses</th>\n",
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       "  <tbody>\n",
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       "      <th>count</th>\n",
       "      <td>92654.000000</td>\n",
       "      <td>92654.000000</td>\n",
       "      <td>92654.000000</td>\n",
       "      <td>9.265400e+04</td>\n",
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       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2011.457552</td>\n",
       "      <td>93.993514</td>\n",
       "      <td>5.951174</td>\n",
       "      <td>5.469363e+03</td>\n",
       "      <td>2.550392</td>\n",
       "      <td>1.565620</td>\n",
       "      <td>0.624090</td>\n",
       "      <td>0.375910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>5.393341</td>\n",
       "      <td>16.013381</td>\n",
       "      <td>1.373883</td>\n",
       "      <td>3.664108e+04</td>\n",
       "      <td>1.248609</td>\n",
       "      <td>1.088531</td>\n",
       "      <td>0.247325</td>\n",
       "      <td>0.247325</td>\n",
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       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2000.000000</td>\n",
       "      <td>52.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2007.000000</td>\n",
       "      <td>85.000000</td>\n",
       "      <td>5.100000</td>\n",
       "      <td>2.700000e+01</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2012.000000</td>\n",
       "      <td>93.000000</td>\n",
       "      <td>6.100000</td>\n",
       "      <td>1.150000e+02</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2016.000000</td>\n",
       "      <td>104.000000</td>\n",
       "      <td>6.900000</td>\n",
       "      <td>5.990000e+02</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2019.000000</td>\n",
       "      <td>135.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>1.272676e+06</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          startYear  runtimeMinutes  averageRating      numVotes  \\\n",
       "count  92654.000000    92654.000000   92654.000000  9.265400e+04   \n",
       "mean    2011.457552       93.993514       5.951174  5.469363e+03   \n",
       "std        5.393341       16.013381       1.373883  3.664108e+04   \n",
       "min     2000.000000       52.000000       1.000000  5.000000e+00   \n",
       "25%     2007.000000       85.000000       5.100000  2.700000e+01   \n",
       "50%     2012.000000       93.000000       6.100000  1.150000e+02   \n",
       "75%     2016.000000      104.000000       6.900000  5.990000e+02   \n",
       "max     2019.000000      135.000000      10.000000  1.272676e+06   \n",
       "\n",
       "         num_actors  num_actresses   prop_actors  prop_actresses  \n",
       "count  92654.000000   92654.000000  92654.000000    92654.000000  \n",
       "mean       2.550392       1.565620      0.624090        0.375910  \n",
       "std        1.248609       1.088531      0.247325        0.247325  \n",
       "min        0.000000       0.000000      0.000000        0.000000  \n",
       "25%        2.000000       1.000000      0.500000        0.250000  \n",
       "50%        2.000000       1.000000      0.666667        0.333333  \n",
       "75%        3.000000       2.000000      0.750000        0.500000  \n",
       "max       10.000000       9.000000      1.000000        1.000000  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(data_movie_gender_stat_timespan_presplit.describe())\n",
    "display(data_movie_gender_stat_timespan_postsplit.describe())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Reduce data to proportion of actresses"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "       prop_actresses\n",
       "0                0.50\n",
       "1                0.25\n",
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       "3                0.00\n",
       "4                0.25\n",
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       "\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92649</th>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92650</th>\n",
       "      <td>0.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92651</th>\n",
       "      <td>0.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92652</th>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92653</th>\n",
       "      <td>0.75</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>92654 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       prop_actresses\n",
       "0                0.25\n",
       "1                0.25\n",
       "2                0.25\n",
       "3                0.50\n",
       "4                0.25\n",
       "...               ...\n",
       "92649            0.00\n",
       "92650            0.25\n",
       "92651            0.25\n",
       "92652            1.00\n",
       "92653            0.75\n",
       "\n",
       "[92654 rows x 1 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_prop_actresses_timespan_presplit  = data_movie_gender_stat_timespan_presplit['prop_actresses'].to_frame().reset_index(drop=True)\n",
    "data_prop_actresses_timespan_postsplit = data_movie_gender_stat_timespan_postsplit['prop_actresses'].to_frame().reset_index(drop=True)\n",
    "\n",
    "display(data_prop_actresses_timespan_presplit)\n",
    "display(data_prop_actresses_timespan_postsplit)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Analyze Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Visualize whether the data is Gaussian distributed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX4AAAEJCAYAAACT/UyFAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8QVMy6AAAACXBIWXMAAAsTAAALEwEAmpwYAAAWP0lEQVR4nO3debRlZX3m8e8DBTLPpQtRUkQZ4jJaYGFQURFFZWiHjokY59bQGEWM2hGNS0ibdLA1RmNCbBpE2iAkQRAUJxZCoyhDFRSTBY6oCIayQ0AIyuCv/9i75HJzL/dU1Rnq3vf7Weuse84+++z9e8++9zn7vnvv96SqkCS1Y6NJFyBJGi+DX5IaY/BLUmMMfklqjMEvSY0x+CWpMSML/iSfSHJbkuumTNshyflJvtP/3H5U65ckzWyUe/yfBF44bdoxwAVVtTtwQf9YkjRGGeUFXEmWAJ+vqif2j28EDqiqW5PsDFxUVXvOtZyddtqplixZMrI6JWkhWrFixc+qavH06YvGXMejqupWgD78HznIi5YsWcLy5ctHW5kkLTBJfjjT9A324G6SI5IsT7J89erVky5HkhaMcQf/v/RdPPQ/b5ttxqo6saqWVdWyxYv/w38qkqR1NO7gPxd4bX//tcA5Y16/JDVvlKdzng58E9gzyc1J3gAcDxyU5DvAQf1jSdIYjezgblW9YpannjuqdUqS5rbBHtyVJI2GwS9JjTH4JakxBr8kNWbcV+5qA7LkmPMGmu+m4w8dcSWSxsk9fklqjMEvSY0x+CWpMQa/JDXG4JekxnhWj7QePDNK85F7/JLUGINfkhpj8EtSYwx+SWqMwS9JjTH4JakxBr8kNcbgl6TGGPyS1BiDX5IaY/BLUmMMfklqjMEvSY0x+CWpMQa/JDXG4Jekxhj8ktQYg1+SGmPwS1JjDH5JaozBL0mNMfglqTEGvyQ1ZiLBn+SPk1yf5LokpyfZbBJ1SFKLxh78SXYB3gosq6onAhsDh4+7Dklq1aS6ehYBmydZBGwB3DKhOiSpOWMP/qr6CfAh4EfArcAdVfWVcdchSa2aRFfP9sCLgd2ARwNbJnnVDPMdkWR5kuWrV68ed5mStGBNoqvnecAPqmp1Vd0HnAU8ffpMVXViVS2rqmWLFy8ee5GStFBNIvh/BOyXZIskAZ4LrJpAHZLUpEn08V8GnAlcCVzb13DiuOuQpFYtmsRKq+pY4NhJrFuSWueVu5LUGINfkhpj8EtSYwx+SWqMwS9JjTH4JakxBr8kNcbgl6TGGPyS1BiDX5IaY/BLUmMMfklqjMEvSY0x+CWpMQa/JDXG4Jekxhj8ktQYg1+SGmPwS1JjDH5JaozBL0mNMfglqTEGvyQ1xuCXpMYY/JLUGINfkhpj8EtSYwx+SWqMwS9JjTH4JakxBr8kNcbgl6TGGPyS1BiDX5IaY/BLUmMGCv4khyUZ2odEku2SnJnkhiSrkjxtWMuWJD28QcP8cOA7Sf5nkt8awno/CnypqvYCngysGsIyJUkDGCj4q+pVwN7A94BTknwzyRFJtl7bFSbZBngWcHK/7Hur6t/WdjmSpHUzcPdNVd0JfAY4A9gZeClwZZKj1nKdvwmspvsAuSrJSUm2nD5T/8GyPMny1atXr+UqJEmzGbSP/0VJzga+CmwCPLWqDqbrpnnnWq5zEbAP8PdVtTdwN3DM9Jmq6sSqWlZVyxYvXryWq5AkzWbRgPO9DPjrqrp46sSq+vck/2Ut13kzcHNVXdY/PpMZgl+SNBqDdvXcOj30k3wAoKouWJsVVtVPgR8n2bOf9FzgW2uzDEnSuhs0+A+aYdrB67Heo4DTklwDLAX+x3osS5K0Fh62qyfJm4A/Ah7Xh/QaWwOXrOtKq2olsGxdXy9JWndz9fF/Gvgi8Jc8tB/+51X1ryOrSpI0MnMFf1XVTUnePP2JJDsY/pI0/wyyx38YsAIoIFOeK7pz8iVJ88jDBn9VHdb/3G085UiSRm2ug7v7PNzzVXXlcMuRJI3aXF09f/UwzxVw4BBrkSSNwVxdPc8ZVyGSpPGYq6vnwKr6apL/PNPzVXXWaMqSJI3KXF09z6YbmO0/zfBcAQa/JM0zc3X1HNv/fP14ypEkjdqgwzLvmORvklyZZEWSjybZcdTFSZKGb9BB2s6g+/KU36Ubonk18I+jKkqSNDqDjse/Q1W9f8rjP0/ykhHUI0kasUH3+C9McniSjfrb7wPnjbIwSdJozHU65895cIyetwP/0D+1EXAXcOxIq5MkDd1cZ/VsPa5CJEnjMWgfP0m2B3YHNlszbfrXMUqSNnwDBX+SNwJHA48BVgL7Ad/EsXokad4Z9ODu0cC+wA/78Xv2pjulU5I0zwwa/L+oql8AJHlEVd0A7Dm6siRJozJoH//NSbYDPgucn+R24JZRFSVJGp2Bgr+qXtrfPS7JhcC2wJdGVpUkaWTW5qyefYD96c7rv6Sq7h1ZVZKkkRl0kLb3AacCOwI7Aackee8oC5Mkjcage/yvAPaecoD3eOBK4M9HVZgkaTQGPavnJqZcuAU8Avje0KuRJI3cXGP1fIyuT/+XwPVJzu8fHwR8ffTlSZKGba6unuX9zxXA2VOmXzSSaiRJIzfXIG2nrrmfZFNgj/7hjVV13ygLkySNxqBj9RxAd1bPTXRDND82yWsdpE2S5p9Bz+r5K+D5VXUjQJI9gNOBp4yqMEnSaAx6Vs8ma0IfoKq+DWwympIkSaM06B7/iiQnA5/qH7+S7oCvJGmeGTT4jwTeDLyVro//YuCEURUlSRqdOYM/yUbAiqp6IvDhYa04ycZ0p4v+pKoOG9ZyJUkPb84+/qr6FXB1kl2HvO6jgVVDXqYkaQ6DdvXsTHfl7uXA3WsmVtWL1mWlSR4DHAr8BfD2dVmGJGndDBr8fzbk9X4E+BNg69lmSHIEcATArrsO+58NSWrXXGP1bEZ3YPfxwLXAyVV1//qsMMlhwG1VtaK/MGxGVXUicCLAsmXLan3WKUl60Fx9/KcCy+hC/2C6C7nW1zOAFyW5CTgDODDJPwxhuZKkAczV1fOEqvptgP48/svXd4VV9W7g3f0yDwDeWVWvWt/lSpIGM9ce/68HYlvfLh5J0oZhrj3+Jye5s78fYPP+cYCqqm3WZ+VVdREO8SxJYzXXsMwbj6sQSdJ4DDpImyRpgTD4Jakxg17ANW8tOea8gea76fhDR1yJJG0Y3OOXpMYY/JLUGINfkhpj8EtSYwx+SWqMwS9JjTH4JakxBr8kNcbgl6TGGPyS1BiDX5IaY/BLUmMMfklqjMEvSY0x+CWpMQa/JDXG4Jekxhj8ktQYg1+SGmPwS1JjDH5JaozBL0mNMfglqTEGvyQ1xuCXpMYY/JLUGINfkhpj8EtSYxZNugBJC9OSY84beN6bjj90hJVoOvf4JakxYw/+JI9NcmGSVUmuT3L0uGuQpJZNoqvnfuAdVXVlkq2BFUnOr6pvTaAWSWrO2Pf4q+rWqrqyv/9zYBWwy7jrkKRWTbSPP8kSYG/gsknWIUktmVjwJ9kK+Azwtqq6c4bnj0iyPMny1atXj79ASVqgJhL8STahC/3TquqsmeapqhOrallVLVu8ePF4C5SkBWwSZ/UEOBlYVVUfHvf6Jal1k9jjfwbwauDAJCv72yETqEOSmjT20zmr6utAxr1eSVLHK3clqTEGvyQ1xuCXpMYY/JLUGINfkhpj8EtSYwx+SWqMwS9JjTH4JakxBr8kNcbgl6TGGPyS1BiDX5IaY/BLUmMMfklqjMEvSY0x+CWpMQa/JDXG4Jekxhj8ktQYg1+SGrNo0gWoTUuOOW+g+W46/tARVyK1x+DXguAHiTQ4g1+SxmzQHRUYzc6KffyS1BiDX5IaY/BLUmMMfklqjMEvSY3xrJ4FaG3OGJDW8JTYdhj80jxlUGtdGfzzyKT25A0YaWEx+KUNjF11GjUP7kpSYyayx5/khcBHgY2Bk6rq+EnUsaFwD0/SOI09+JNsDPwdcBBwM3BFknOr6lvjrmXUDPT1N+z3cFLHK/xd0IZkEnv8TwW+W1XfB0hyBvBiYMEFv7QQTfJDbNjrbvWEhEkE/y7Aj6c8vhn4nQnUIWkD4X9E45WqGu8Kk98DXlBVb+wfvxp4alUdNW2+I4Aj+od7Ajeu4yp3An62jq+dr2xzG2xzG9anzb9RVYunT5zEHv/NwGOnPH4McMv0marqRODE9V1ZkuVVtWx9lzOf2OY22OY2jKLNkzid8wpg9yS7JdkUOBw4dwJ1SFKTxr7HX1X3J3kL8GW60zk/UVXXj7sOSWrVRM7jr6ovAF8Y0+rWu7toHrLNbbDNbRh6m8d+cFeSNFkO2SBJjVkwwZ/khUluTPLdJMfM8HyS/E3//DVJ9plEncM0QJtf2bf1miTfSPLkSdQ5THO1ecp8+yZ5IMnLxlnfsA3S3iQHJFmZ5Pok/3fcNQ7bAL/X2yb5XJKr+za/fhJ1DlOSTyS5Lcl1szw/3Pyqqnl/oztI/D3gN4FNgauBJ0yb5xDgi0CA/YDLJl33GNr8dGD7/v7BLbR5ynxfpTuO9LJJ1z3ibbwd3VXvu/aPHznpusfQ5vcAH+jvLwb+Fdh00rWvZ7ufBewDXDfL80PNr4Wyx//rYSCq6l5gzTAQU70Y+D/VuRTYLsnO4y50iOZsc1V9o6pu7x9eSnfNxHw2yHYGOAr4DHDbOIsbgUHa+wfAWVX1I4CqaqHNBWydJMBWdMF//3jLHK6qupiuHbMZan4tlOCfaRiIXdZhnvlkbdvzBro9hvlszjYn2QV4KfDxMdY1KoNs4z2A7ZNclGRFkteMrbrRGKTNfwv8Ft2Fn9cCR1fVr8ZT3sQMNb8WyhexZIZp009XGmSe+WTg9iR5Dl3w7z/SikZvkDZ/BHhXVT3Q7RDOa4O0dxHwFOC5wObAN5NcWlXfHnVxIzJIm18ArAQOBB4HnJ/ka1V154hrm6Sh5tdCCf5BhoEYaKiIeWSg9iR5EnAScHBV/b8x1TYqg7R5GXBGH/o7AYckub+qPjuWCodr0N/rn1XV3cDdSS4GngzM1+AfpM2vB46vrvP7u0l+AOwFXD6eEidiqPm1ULp6BhkG4lzgNf3R8f2AO6rq1nEXOkRztjnJrsBZwKvn8R7gVHO2uap2q6olVbUEOBP4o3ka+jDY7/U5wDOTLEqyBd1It6vGXOcwDdLmH9H9h0OSR9EN4vj9sVY5fkPNrwWxx1+zDAOR5Mj++Y/TneFxCPBd4N/p9hrmrQHb/D5gR+CEfg/4/prHA1wN2OYFY5D2VtWqJF8CrgF+RfeNdjOeEjgfDLiN3w98Msm1dF0g76qqeT1iZ5LTgQOAnZLcDBwLbAKjyS+v3JWkxiyUrh5J0oAMfklqjMEvSY0x+CWpMQa/JDXG4F+A+lEpVya5Lsk/9+d3j3P975n2+BsjXt9efXuvSvK49VjO0iSHDLO2hSLJXf3PRyc5s7/v+zVPGfwL0z1VtbSqngjcCxw59ckkG49ipf3FJRvRjZ74a1X19FGsb4qXAOdU1d5V9b31WM5SunOl/4MkG/w1L6ParlNV1S1VtWao66XM8n6Nyjja2IRJD0fqbfg34K4p948ETqC7OORC4NN0w/huBpxCN8jVVcBz+vlfR3c16JeAG4Fjpyzr7cB1/e1t/bQldFeKntAv5xTgAbqxVE6bWg/dxTYf7F9/LfDyfvoBwEV0V9reAJxGf43JtHYtpRtl9BrgbGB7uuD5KfAT4MIZXvP3wHLgeuDPpkzfF/gG3bC/lwPb0l0Rurqv/eXAcXRfe/eV/n1bTDfq5xX97Rn9sp7dv2Zl/x5sDewMXNxPuw54Zj/v84FvAlcC/wxs1U8/vt8u1wAfmqEdxwGfohtu+jvAH05576Zu14379/iKfln/dZbfkd/r67oauHiAbX/XlO19Hd2QyQ95v6Yt/w3AX095/IfAh/v7r+rf85XA/wI2nmNb3UR3MeLXgcMn/fe1EG4TL8DbCDbqg3+ki/o/5Df1AXE3sFv/3DuAU/r7e/V/xJv1f/y30l3xu3n/R76MbiCwa4Et6YbCvR7Yuw+CXwH7TV//DPX8LnB+H06P6te5c1/bHXTjj2zUB+P+M7TrGuDZ/f3/Dnykv38c8M5Z3osd+p8b0324PKkPre8D+/bPbdO/V68D/nbKa48DVgCb948/vaYuYFdgVX//czz4IbBVv6x3AH86Zd1b040ddDGwZT/9XX2g7UAXtGsuqNxuhnYcRxfSm/fL+THw6Bm26xHAe/v7j6AL0t1mWN61wC5T1zfbtp+2DZfQjxk//f2atvwt6cbV36R//A3gt+lG1fzclOknAK+ZbVv1j28C/mTSf1cL6bbB//uqdbJ5kpX9/a8BJ9N9KcvlVfWDfvr+wMcAquqGJD+kG+IX4PzqB3RLclY/bwFnVzcY2Jrpz6QbQ+SH1Y0RPpf9gdOr6gHgX/pvi9oXuLOv7eZ+2SvpAubra16YZFu6gFrzDVOn0u0xz+X3kxxBF8Y7A0/o23JrVV3Rt//Ofh0zvf7cqrqnv/884AlT5tsmydbAJcCHk5xGNzb+zUmuAD6RZBPgs1W1Msmz+/Vf0i9jU7oPuTuBXwAnJTkP+PwsbTmnr+WeJBfSjV3/bzx0uz4feFIe/OaxbYHdgR9MW9YldMMe/BPdeE5rzLTtl89Sz6yq6u4kXwUOS7KKLuiv7YdjeApwRf8ebM6D35sw07a6pn/uH9e2Bs3O4F+Y7qmqpVMn9H9kd0+d9DCvnz6OR80x/90P89xDyniY53455f4DDOF3M8luwDvp9uxvT/JJuv9qwuBD2k5t20bA06Z8EKxxfB/YhwCXJnleVV2c5FnAocCnknwQuJ0uWF8xQ61PpRt47HDgLXRDDk8303aZXmOAo6rqy9OW/xd9LVR3/OfIJL/TT1uZZOkc65hTki/T/Se3vKreSDcq7Hvouu9OmVLfqVX17mmvnW1brTHo75gG4MHddl0MvBIgyR50XRc39s8dlGSHJJvTHTi9pJ//JUm2SLIl3ZedfG2WZd/X7+nOtM6XJ9k4yWK6r5sbaCjdqroDuD3JM/tJrwbm+n7ZbegC445+FMeD++k3AI9Osi9Akq37g7c/p+uSmc1X6EKZ/nVL+5+Pq6prq+oDdHvHeyX5DeC2qvrfdP9x7UN3fOIZSR7fv26LJHsk2QrYtqq+ALyN7ljGTF6cZLMkO9J18VwxwzxfBt605v3vl79lVf1pH/hTa76sqt4H/IwHh/ydadvP5iHvV1W9oF/HG/vHl/XL/QPg9H62C4CXJXlkX8cO/Xs127bSCLjH364TgI/3IxzeD7yuqn7Z/2fwdboDiY8HPl1VywH6vbA1QX1SVV2VZMkMyz4RuCbJlVX1yinTzwaeRtdXXXT9tj9NsteANb+2r3kLuj76hx2hsKquTnIV3fGI79OHWFXdm+TlwMf6gLuHrhvnQuCYvqvpL2dY5FuBv0tyDd3fzsV0B8/flu7Lbh6gO8D6Rbo99/+W5D7gLrp+7NVJXgecnuQR/TLfSxeg5yRZ89/IH8/SpMuB8+g+pN9fVbf0H9pTnUTXTXZluo25mi7Ap/tgkt379V1At02WMsu2n8VD3q+qmqk75p+ApdV/BWhVfSvJe4Gv9GeA3Qe8uaounWlbaTQcnVMP0QfTsqp6y1zzanySHEd3gPVDI1zH6xjytk/yebqzey4Y1jK1/uzqkTR0SbZL8m26402G/gbGPX5Jaox7/JLUGINfkhpj8EtSYwx+SWqMwS9JjTH4Jakx/x9ssxyDoZuItQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(data_prop_actresses_timespan_presplit['prop_actresses'], density=True, bins=30)\n",
    "plt.ylabel('Probability')\n",
    "plt.xlabel('Proportion of actresses pre-split-year');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(data_prop_actresses_timespan_postsplit['prop_actresses'], density=True, bins=30)\n",
    "plt.ylabel('Probability')\n",
    "plt.xlabel('Proportion of actresses pre-split-year');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Compute t-statistic\n",
    "\n",
    "Our goal is to find out if the mean $\\mu_1$ on the proportion of actresses in principal roles differs significantly  compared to the mean $\\mu_0$ on the proportion of actesses in principal roles before the split year."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We perform a t-test unter the null hypothesis $H_0: \\mu_1 = \\mu_0$.\n",
    "\n",
    "This tells us the probability to observe $m_1$, given $m_0$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.stats import ttest_ind"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "def print_result_two_sided(p_val):\n",
    "    alpha = 0.05\n",
    "    # Significant results?\n",
    "    significant = p_val <= alpha\n",
    "    print(f\"{'Yes' if significant else 'No'}, the result is {'significant' if significant else 'insignificant'} because given the pre-split-year data, observing the post-split-year data has a {p_val*100:.2f}% probability.\")\n",
    "    \n",
    "def print_result_one_sided(t_val, p_val):\n",
    "    alpha = 0.05\n",
    "    # Significant results?\n",
    "    significant = p_val/2 <= alpha\n",
    "    direction = ('of being' + 'greater' if (t_val < 0) else 'smaller') if significant else ''\n",
    "    print(f\"{'Yes' if significant else 'No'}, the result is {'significant' if significant else 'insignificant'} because given the pre-split-year data, observing the post-split-year data has a {p_val*100:.2f}% probability.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "37339"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_samples = np.min([\n",
    "    data_prop_actresses_timespan_presplit['prop_actresses'].shape[0],\n",
    "    data_prop_actresses_timespan_postsplit['prop_actresses'].shape[0]\n",
    "])\n",
    "num_samples"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Are the means $\\mu_1 = \\mu_0$ under the given unequal sample size?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ttest_indResult(statistic=0.4871844269145407, pvalue=0.6261284618829022)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No, the result is insignificant because given the pre-split-year data, observing the post-split-year data has a 62.61% probability.\n"
     ]
    }
   ],
   "source": [
    "res = ttest_ind(\n",
    "    a=data_prop_actresses_timespan_presplit['prop_actresses'],\n",
    "    b=data_prop_actresses_timespan_postsplit['prop_actresses'],\n",
    "    equal_var=True\n",
    ")\n",
    "display(res)\n",
    "\n",
    "print_result_two_sided(res.pvalue)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Are the means $\\mu_1 = \\mu_0$ under an uniformly sampled but equal sample size?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ttest_indResult(statistic=1.1797681843227215, pvalue=0.2380961849995745)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No, the result is insignificant because given the pre-split-year data, observing the post-split-year data has a 23.81% probability.\n"
     ]
    }
   ],
   "source": [
    "res = ttest_ind(\n",
    "    a=data_prop_actresses_timespan_presplit['prop_actresses'].sample(num_samples, replace=True, random_state=42),\n",
    "    b=data_prop_actresses_timespan_postsplit['prop_actresses'].sample(num_samples, replace=True, random_state=42),\n",
    "    equal_var=True\n",
    ")\n",
    "display(res)\n",
    "\n",
    "print_result_two_sided(res.pvalue)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Are the means $\\mu_1 \\neq \\mu_0$ under the given unequal sample size?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ttest_indResult(statistic=0.4871844269145407, pvalue=0.6261284618829022)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No, the result is insignificant because given the pre-split-year data, observing the post-split-year data has a 62.61% probability.\n"
     ]
    }
   ],
   "source": [
    "res = ttest_ind(\n",
    "    a=data_prop_actresses_timespan_presplit['prop_actresses'],\n",
    "    b=data_prop_actresses_timespan_postsplit['prop_actresses'],\n",
    "    equal_var=True\n",
    ")\n",
    "display(res)\n",
    "\n",
    "print_result_one_sided(res.statistic, res.pvalue)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Are the means $\\mu_1 \\neq \\mu_0$ under an uniformly sampled but equal sample size?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ttest_indResult(statistic=1.1797681843227215, pvalue=0.2380961849995745)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No, the result is insignificant because given the pre-split-year data, observing the post-split-year data has a 23.81% probability.\n"
     ]
    }
   ],
   "source": [
    "res = ttest_ind(\n",
    "    a=data_prop_actresses_timespan_presplit['prop_actresses'].sample(num_samples, replace=True, random_state=42),\n",
    "    b=data_prop_actresses_timespan_postsplit['prop_actresses'].sample(num_samples, replace=True, random_state=42),\n",
    "    equal_var=True\n",
    ")\n",
    "display(res)\n",
    "\n",
    "print_result_one_sided(res.statistic, res.pvalue)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Visualize data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.set_style('whitegrid')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "box_plot = sns.boxplot(x='year_span', y='prop_actresses', data=data_movie_gender_stat)\n",
    "\n",
    "medians = data_movie_gender_stat.groupby(['year_span'])['prop_actresses'].median()\n",
    "vertical_offset = data_movie_gender_stat['prop_actresses'].median() * 0.05 # offset from median for display\n",
    "\n",
    "for xtick in box_plot.get_xticks():\n",
    "    box_plot.text(xtick,medians[xtick] + vertical_offset,medians[xtick], \n",
    "            horizontalalignment='center',size='x-small',color='w',weight='semibold')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "viol_plot = sns.violinplot(x='year_span', y='prop_actresses', data=data_movie_gender_stat)\n",
    "\n",
    "medians = data_movie_gender_stat.groupby(['year_span'])['prop_actresses'].median()\n",
    "vertical_offset = data_movie_gender_stat['prop_actresses'].median() * 0.05 # offset from median for display\n",
    "\n",
    "for xtick in box_plot.get_xticks():\n",
    "    box_plot.text(xtick,medians[xtick] + vertical_offset,medians[xtick], \n",
    "            horizontalalignment='center',size='x-small',color='w',weight='semibold')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Result"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The t-tests as well as the plots suggest that the equality $\\mu_1 = \\mu_0$ but also the inequality $\\mu_1 = \\mu_0$ cannot be rejected. One reason for this could be a too discrete distribution of proportions or proportions between actors and actresses, i.e., a too strongly violated requirement of the t-test.\n",
    "The number of main actors in movies is typically in the single digits."
   ]
  }
 ],
 "metadata": {
  "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": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}