OTOT-A3-Python-0203.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>title</th>\n",
       "      <th>type</th>\n",
       "      <th>year</th>\n",
       "      <th>rating</th>\n",
       "      <th>imdb_score</th>\n",
       "      <th>rotten_score</th>\n",
       "      <th>decade</th>\n",
       "      <th>imdb_bucket</th>\n",
       "      <th>genres_Action_Adventure</th>\n",
       "      <th>genres_Animation</th>\n",
       "      <th>...</th>\n",
       "      <th>platforms_shudder</th>\n",
       "      <th>platforms_starz</th>\n",
       "      <th>platforms_sundancenow</th>\n",
       "      <th>platforms_syfy_tveverywhere</th>\n",
       "      <th>platforms_tbs</th>\n",
       "      <th>platforms_tnt</th>\n",
       "      <th>platforms_trutv_tveverywhere</th>\n",
       "      <th>platforms_urbanmoviechannel</th>\n",
       "      <th>platforms_velocity_go</th>\n",
       "      <th>platforms_watch_tcm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1987</th>\n",
       "      <td>Episode #1.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2010</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2010-2019</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1988</th>\n",
       "      <td>Miniverse</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2010-2019</td>\n",
       "      <td>6.01-7.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1989</th>\n",
       "      <td>Elephant Kingdom</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016</td>\n",
       "      <td>PG/TV-PG</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2010-2019</td>\n",
       "      <td>0-5.00</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990</th>\n",
       "      <td>Alpha and Omega: The Big Fureeze</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016</td>\n",
       "      <td>G/TV-G</td>\n",
       "      <td>4.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2010-2019</td>\n",
       "      <td>0-5.00</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1991</th>\n",
       "      <td>Jason Silva: The Road to the Singularity</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2010-2019</td>\n",
       "      <td>6.01-7.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2082</th>\n",
       "      <td>Mystery Signal From Space</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2018</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2010-2019</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2083</th>\n",
       "      <td>The Norwegian Fjords</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2018</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2010-2019</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2084</th>\n",
       "      <td>Mandrake Telefilm: Part 1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2013</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2010-2019</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2085</th>\n",
       "      <td>What Animals See</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2018</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2010-2019</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2086</th>\n",
       "      <td>Top Science Stories of 2019</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2019</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2010-2019</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 75 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                         title type  year    rating  \\\n",
       "1987                              Episode #1.1  NaN  2010       NaN   \n",
       "1988                                 Miniverse  NaN  2017       NaN   \n",
       "1989                          Elephant Kingdom  NaN  2016  PG/TV-PG   \n",
       "1990          Alpha and Omega: The Big Fureeze  NaN  2016    G/TV-G   \n",
       "1991  Jason Silva: The Road to the Singularity  NaN  2016       NaN   \n",
       "...                                        ...  ...   ...       ...   \n",
       "2082                 Mystery Signal From Space  NaN  2018       NaN   \n",
       "2083                      The Norwegian Fjords  NaN  2018       NaN   \n",
       "2084                 Mandrake Telefilm: Part 1  NaN  2013       NaN   \n",
       "2085                          What Animals See  NaN  2018       NaN   \n",
       "2086               Top Science Stories of 2019  NaN  2019       NaN   \n",
       "\n",
       "      imdb_score  rotten_score     decade imdb_bucket  \\\n",
       "1987         NaN           NaN  2010-2019         NaN   \n",
       "1988         6.6           NaN  2010-2019   6.01-7.00   \n",
       "1989         4.0           NaN  2010-2019      0-5.00   \n",
       "1990         4.8           NaN  2010-2019      0-5.00   \n",
       "1991         7.0           NaN  2010-2019   6.01-7.00   \n",
       "...          ...           ...        ...         ...   \n",
       "2082         NaN           NaN  2010-2019         NaN   \n",
       "2083         NaN           NaN  2010-2019         NaN   \n",
       "2084         NaN           NaN  2010-2019         NaN   \n",
       "2085         NaN           NaN  2010-2019         NaN   \n",
       "2086         NaN           NaN  2010-2019         NaN   \n",
       "\n",
       "      genres_Action_Adventure  genres_Animation  ...  platforms_shudder  \\\n",
       "1987                        0                 0  ...                  0   \n",
       "1988                        0                 0  ...                  0   \n",
       "1989                        1                 1  ...                  0   \n",
       "1990                        1                 1  ...                  0   \n",
       "1991                        0                 0  ...                  0   \n",
       "...                       ...               ...  ...                ...   \n",
       "2082                        0                 0  ...                  0   \n",
       "2083                        0                 0  ...                  0   \n",
       "2084                        0                 0  ...                  0   \n",
       "2085                        0                 0  ...                  0   \n",
       "2086                        0                 0  ...                  0   \n",
       "\n",
       "      platforms_starz  platforms_sundancenow  platforms_syfy_tveverywhere  \\\n",
       "1987                0                      0                            0   \n",
       "1988                0                      0                            0   \n",
       "1989                0                      0                            0   \n",
       "1990                0                      0                            0   \n",
       "1991                0                      0                            0   \n",
       "...               ...                    ...                          ...   \n",
       "2082                0                      0                            0   \n",
       "2083                0                      0                            0   \n",
       "2084                0                      0                            0   \n",
       "2085                0                      0                            0   \n",
       "2086                0                      0                            0   \n",
       "\n",
       "      platforms_tbs  platforms_tnt  platforms_trutv_tveverywhere  \\\n",
       "1987              0              0                             0   \n",
       "1988              0              0                             0   \n",
       "1989              0              0                             0   \n",
       "1990              0              0                             0   \n",
       "1991              0              0                             0   \n",
       "...             ...            ...                           ...   \n",
       "2082              0              0                             0   \n",
       "2083              0              0                             0   \n",
       "2084              0              0                             0   \n",
       "2085              0              0                             0   \n",
       "2086              0              0                             0   \n",
       "\n",
       "      platforms_urbanmoviechannel  platforms_velocity_go  platforms_watch_tcm  \n",
       "1987                            0                      0                    0  \n",
       "1988                            0                      0                    0  \n",
       "1989                            0                      0                    0  \n",
       "1990                            0                      0                    0  \n",
       "1991                            0                      0                    0  \n",
       "...                           ...                    ...                  ...  \n",
       "2082                            0                      0                    0  \n",
       "2083                            0                      0                    0  \n",
       "2084                            0                      0                    0  \n",
       "2085                            0                      0                    0  \n",
       "2086                            0                      0                    0  \n",
       "\n",
       "[100 rows x 75 columns]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "df = pd.read_csv('HBO_dataset/HBO_MAX_Content.csv')\n",
    "\n",
    "# df.head(100)\n",
    "df.tail(100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>imdb_score</th>\n",
       "      <th>rotten_score</th>\n",
       "      <th>genres_Action_Adventure</th>\n",
       "      <th>genres_Animation</th>\n",
       "      <th>genres_Anime</th>\n",
       "      <th>genres_Biography</th>\n",
       "      <th>genres_Children</th>\n",
       "      <th>genres_Comedy</th>\n",
       "      <th>genres_Crime</th>\n",
       "      <th>...</th>\n",
       "      <th>platforms_shudder</th>\n",
       "      <th>platforms_starz</th>\n",
       "      <th>platforms_sundancenow</th>\n",
       "      <th>platforms_syfy_tveverywhere</th>\n",
       "      <th>platforms_tbs</th>\n",
       "      <th>platforms_tnt</th>\n",
       "      <th>platforms_trutv_tveverywhere</th>\n",
       "      <th>platforms_urbanmoviechannel</th>\n",
       "      <th>platforms_velocity_go</th>\n",
       "      <th>platforms_watch_tcm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2087.000000</td>\n",
       "      <td>2025.000000</td>\n",
       "      <td>1365.000000</td>\n",
       "      <td>2087.000000</td>\n",
       "      <td>2087.000000</td>\n",
       "      <td>2087.000000</td>\n",
       "      <td>2087.000000</td>\n",
       "      <td>2087.000000</td>\n",
       "      <td>2087.000000</td>\n",
       "      <td>2087.00000</td>\n",
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       "      <td>2087.000000</td>\n",
       "      <td>2087.000000</td>\n",
       "      <td>2087.000000</td>\n",
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       "      <td>2087.000000</td>\n",
       "      <td>2087.000000</td>\n",
       "      <td>2087.000000</td>\n",
       "      <td>2087.000000</td>\n",
       "      <td>2087.000000</td>\n",
       "      <td>2087.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1997.639195</td>\n",
       "      <td>6.940593</td>\n",
       "      <td>72.652015</td>\n",
       "      <td>0.232870</td>\n",
       "      <td>0.090561</td>\n",
       "      <td>0.017250</td>\n",
       "      <td>0.065165</td>\n",
       "      <td>0.027791</td>\n",
       "      <td>0.334931</td>\n",
       "      <td>0.14183</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000479</td>\n",
       "      <td>0.002396</td>\n",
       "      <td>0.000479</td>\n",
       "      <td>0.000958</td>\n",
       "      <td>0.002875</td>\n",
       "      <td>0.002875</td>\n",
       "      <td>0.003833</td>\n",
       "      <td>0.000479</td>\n",
       "      <td>0.000479</td>\n",
       "      <td>0.008625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>22.898744</td>\n",
       "      <td>1.088414</td>\n",
       "      <td>26.435748</td>\n",
       "      <td>0.422761</td>\n",
       "      <td>0.287052</td>\n",
       "      <td>0.130231</td>\n",
       "      <td>0.246876</td>\n",
       "      <td>0.164413</td>\n",
       "      <td>0.472079</td>\n",
       "      <td>0.34896</td>\n",
       "      <td>...</td>\n",
       "      <td>0.021890</td>\n",
       "      <td>0.048900</td>\n",
       "      <td>0.021890</td>\n",
       "      <td>0.030949</td>\n",
       "      <td>0.053554</td>\n",
       "      <td>0.053554</td>\n",
       "      <td>0.061809</td>\n",
       "      <td>0.021890</td>\n",
       "      <td>0.021890</td>\n",
       "      <td>0.092491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1915.000000</td>\n",
       "      <td>1.700000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</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>1989.000000</td>\n",
       "      <td>6.300000</td>\n",
       "      <td>56.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</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>50%</th>\n",
       "      <td>2006.000000</td>\n",
       "      <td>7.100000</td>\n",
       "      <td>83.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</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>75%</th>\n",
       "      <td>2015.000000</td>\n",
       "      <td>7.700000</td>\n",
       "      <td>94.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</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>max</th>\n",
       "      <td>2020.000000</td>\n",
       "      <td>9.900000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 70 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              year   imdb_score  rotten_score  genres_Action_Adventure  \\\n",
       "count  2087.000000  2025.000000   1365.000000              2087.000000   \n",
       "mean   1997.639195     6.940593     72.652015                 0.232870   \n",
       "std      22.898744     1.088414     26.435748                 0.422761   \n",
       "min    1915.000000     1.700000      3.000000                 0.000000   \n",
       "25%    1989.000000     6.300000     56.000000                 0.000000   \n",
       "50%    2006.000000     7.100000     83.000000                 0.000000   \n",
       "75%    2015.000000     7.700000     94.000000                 0.000000   \n",
       "max    2020.000000     9.900000    100.000000                 1.000000   \n",
       "\n",
       "       genres_Animation  genres_Anime  genres_Biography  genres_Children  \\\n",
       "count       2087.000000   2087.000000       2087.000000      2087.000000   \n",
       "mean           0.090561      0.017250          0.065165         0.027791   \n",
       "std            0.287052      0.130231          0.246876         0.164413   \n",
       "min            0.000000      0.000000          0.000000         0.000000   \n",
       "25%            0.000000      0.000000          0.000000         0.000000   \n",
       "50%            0.000000      0.000000          0.000000         0.000000   \n",
       "75%            0.000000      0.000000          0.000000         0.000000   \n",
       "max            1.000000      1.000000          1.000000         1.000000   \n",
       "\n",
       "       genres_Comedy  genres_Crime  ...  platforms_shudder  platforms_starz  \\\n",
       "count    2087.000000    2087.00000  ...        2087.000000      2087.000000   \n",
       "mean        0.334931       0.14183  ...           0.000479         0.002396   \n",
       "std         0.472079       0.34896  ...           0.021890         0.048900   \n",
       "min         0.000000       0.00000  ...           0.000000         0.000000   \n",
       "25%         0.000000       0.00000  ...           0.000000         0.000000   \n",
       "50%         0.000000       0.00000  ...           0.000000         0.000000   \n",
       "75%         1.000000       0.00000  ...           0.000000         0.000000   \n",
       "max         1.000000       1.00000  ...           1.000000         1.000000   \n",
       "\n",
       "       platforms_sundancenow  platforms_syfy_tveverywhere  platforms_tbs  \\\n",
       "count            2087.000000                  2087.000000    2087.000000   \n",
       "mean                0.000479                     0.000958       0.002875   \n",
       "std                 0.021890                     0.030949       0.053554   \n",
       "min                 0.000000                     0.000000       0.000000   \n",
       "25%                 0.000000                     0.000000       0.000000   \n",
       "50%                 0.000000                     0.000000       0.000000   \n",
       "75%                 0.000000                     0.000000       0.000000   \n",
       "max                 1.000000                     1.000000       1.000000   \n",
       "\n",
       "       platforms_tnt  platforms_trutv_tveverywhere  \\\n",
       "count    2087.000000                   2087.000000   \n",
       "mean        0.002875                      0.003833   \n",
       "std         0.053554                      0.061809   \n",
       "min         0.000000                      0.000000   \n",
       "25%         0.000000                      0.000000   \n",
       "50%         0.000000                      0.000000   \n",
       "75%         0.000000                      0.000000   \n",
       "max         1.000000                      1.000000   \n",
       "\n",
       "       platforms_urbanmoviechannel  platforms_velocity_go  platforms_watch_tcm  \n",
       "count                  2087.000000            2087.000000          2087.000000  \n",
       "mean                      0.000479               0.000479             0.008625  \n",
       "std                       0.021890               0.021890             0.092491  \n",
       "min                       0.000000               0.000000             0.000000  \n",
       "25%                       0.000000               0.000000             0.000000  \n",
       "50%                       0.000000               0.000000             0.000000  \n",
       "75%                       0.000000               0.000000             0.000000  \n",
       "max                       1.000000               1.000000             1.000000  \n",
       "\n",
       "[8 rows x 70 columns]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['1910-1919',\n",
       " '1920-1929',\n",
       " '1930-1939',\n",
       " '1940-1949',\n",
       " '1950-1959',\n",
       " '1960-1969',\n",
       " '1970-1979',\n",
       " '1980-1989',\n",
       " '1990-1999',\n",
       " '2000-2009',\n",
       " '2010-2019',\n",
       " '2020-2029']"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted(df['decade'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['rating'] = df['rating'].fillna('No Rating')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_groupby = df.groupby(['rating', 'decade']).size().reset_index(name='counts')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\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>rating</th>\n",
       "      <th>decade</th>\n",
       "      <th>counts</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>R/TV-MA</td>\n",
       "      <td>1960-1969</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>R/TV-MA</td>\n",
       "      <td>1970-1979</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>R/TV-MA</td>\n",
       "      <td>1980-1989</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>R/TV-MA</td>\n",
       "      <td>1990-1999</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>R/TV-MA</td>\n",
       "      <td>2000-2009</td>\n",
       "      <td>128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>R/TV-MA</td>\n",
       "      <td>2010-2019</td>\n",
       "      <td>240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>R/TV-MA</td>\n",
       "      <td>2020-2029</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     rating     decade  counts\n",
       "46  R/TV-MA  1960-1969       7\n",
       "47  R/TV-MA  1970-1979      35\n",
       "48  R/TV-MA  1980-1989      65\n",
       "49  R/TV-MA  1990-1999     110\n",
       "50  R/TV-MA  2000-2009     128\n",
       "51  R/TV-MA  2010-2019     240\n",
       "52  R/TV-MA  2020-2029      15"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_groupby.loc[df_groupby['rating'] == df['rating'].unique()[0]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "    .dataframe tbody tr th {\n",
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       "\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>rating</th>\n",
       "      <th>decade</th>\n",
       "      <th>counts</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>G/TV-G</td>\n",
       "      <td>1910-1919</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>G/TV-G</td>\n",
       "      <td>1920-1929</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>G/TV-G</td>\n",
       "      <td>1930-1939</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>G/TV-G</td>\n",
       "      <td>1940-1949</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>G/TV-G</td>\n",
       "      <td>1950-1959</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>G/TV-G</td>\n",
       "      <td>1960-1969</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>G/TV-G</td>\n",
       "      <td>1970-1979</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>G/TV-G</td>\n",
       "      <td>1980-1989</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>G/TV-G</td>\n",
       "      <td>1990-1999</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>G/TV-G</td>\n",
       "      <td>2000-2009</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>G/TV-G</td>\n",
       "      <td>2010-2019</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>G/TV-G</td>\n",
       "      <td>2020-2029</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>No Rating</td>\n",
       "      <td>1910-1919</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>No Rating</td>\n",
       "      <td>1920-1929</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>No Rating</td>\n",
       "      <td>1930-1939</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>No Rating</td>\n",
       "      <td>1940-1949</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>No Rating</td>\n",
       "      <td>1950-1959</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>No Rating</td>\n",
       "      <td>1960-1969</td>\n",
       "      <td>59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>No Rating</td>\n",
       "      <td>1970-1979</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>No Rating</td>\n",
       "      <td>1980-1989</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>No Rating</td>\n",
       "      <td>1990-1999</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>No Rating</td>\n",
       "      <td>2000-2009</td>\n",
       "      <td>59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>No Rating</td>\n",
       "      <td>2010-2019</td>\n",
       "      <td>225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>No Rating</td>\n",
       "      <td>2020-2029</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>PG-13/TV-14</td>\n",
       "      <td>1920-1929</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>PG-13/TV-14</td>\n",
       "      <td>1930-1939</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>PG-13/TV-14</td>\n",
       "      <td>1940-1949</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>PG-13/TV-14</td>\n",
       "      <td>1950-1959</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>PG-13/TV-14</td>\n",
       "      <td>1960-1969</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>PG-13/TV-14</td>\n",
       "      <td>1970-1979</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>PG-13/TV-14</td>\n",
       "      <td>1980-1989</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>PG-13/TV-14</td>\n",
       "      <td>1990-1999</td>\n",
       "      <td>62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>PG-13/TV-14</td>\n",
       "      <td>2000-2009</td>\n",
       "      <td>140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>PG-13/TV-14</td>\n",
       "      <td>2010-2019</td>\n",
       "      <td>188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>PG-13/TV-14</td>\n",
       "      <td>2020-2029</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>PG/TV-PG</td>\n",
       "      <td>1920-1929</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>PG/TV-PG</td>\n",
       "      <td>1930-1939</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>PG/TV-PG</td>\n",
       "      <td>1940-1949</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>PG/TV-PG</td>\n",
       "      <td>1950-1959</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>PG/TV-PG</td>\n",
       "      <td>1960-1969</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>PG/TV-PG</td>\n",
       "      <td>1970-1979</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>PG/TV-PG</td>\n",
       "      <td>1980-1989</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>PG/TV-PG</td>\n",
       "      <td>1990-1999</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>PG/TV-PG</td>\n",
       "      <td>2000-2009</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>PG/TV-PG</td>\n",
       "      <td>2010-2019</td>\n",
       "      <td>120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>PG/TV-PG</td>\n",
       "      <td>2020-2029</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>R/TV-MA</td>\n",
       "      <td>1960-1969</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>R/TV-MA</td>\n",
       "      <td>1970-1979</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>R/TV-MA</td>\n",
       "      <td>1980-1989</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>R/TV-MA</td>\n",
       "      <td>1990-1999</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>R/TV-MA</td>\n",
       "      <td>2000-2009</td>\n",
       "      <td>128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>R/TV-MA</td>\n",
       "      <td>2010-2019</td>\n",
       "      <td>240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>R/TV-MA</td>\n",
       "      <td>2020-2029</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         rating     decade  counts\n",
       "0        G/TV-G  1910-1919       2\n",
       "1        G/TV-G  1920-1929       4\n",
       "2        G/TV-G  1930-1939       6\n",
       "3        G/TV-G  1940-1949       4\n",
       "4        G/TV-G  1950-1959       5\n",
       "5        G/TV-G  1960-1969      14\n",
       "6        G/TV-G  1970-1979      10\n",
       "7        G/TV-G  1980-1989       4\n",
       "8        G/TV-G  1990-1999      18\n",
       "9        G/TV-G  2000-2009      20\n",
       "10       G/TV-G  2010-2019      36\n",
       "11       G/TV-G  2020-2029       3\n",
       "12    No Rating  1910-1919       5\n",
       "13    No Rating  1920-1929      16\n",
       "14    No Rating  1930-1939      29\n",
       "15    No Rating  1940-1949      41\n",
       "16    No Rating  1950-1959      49\n",
       "17    No Rating  1960-1969      59\n",
       "18    No Rating  1970-1979      23\n",
       "19    No Rating  1980-1989      13\n",
       "20    No Rating  1990-1999      18\n",
       "21    No Rating  2000-2009      59\n",
       "22    No Rating  2010-2019     225\n",
       "23    No Rating  2020-2029      13\n",
       "24  PG-13/TV-14  1920-1929       1\n",
       "25  PG-13/TV-14  1930-1939       3\n",
       "26  PG-13/TV-14  1940-1949       1\n",
       "27  PG-13/TV-14  1950-1959       2\n",
       "28  PG-13/TV-14  1960-1969       7\n",
       "29  PG-13/TV-14  1970-1979       1\n",
       "30  PG-13/TV-14  1980-1989      28\n",
       "31  PG-13/TV-14  1990-1999      62\n",
       "32  PG-13/TV-14  2000-2009     140\n",
       "33  PG-13/TV-14  2010-2019     188\n",
       "34  PG-13/TV-14  2020-2029       4\n",
       "35     PG/TV-PG  1920-1929       1\n",
       "36     PG/TV-PG  1930-1939       5\n",
       "37     PG/TV-PG  1940-1949       3\n",
       "38     PG/TV-PG  1950-1959      14\n",
       "39     PG/TV-PG  1960-1969      11\n",
       "40     PG/TV-PG  1970-1979      27\n",
       "41     PG/TV-PG  1980-1989      34\n",
       "42     PG/TV-PG  1990-1999      65\n",
       "43     PG/TV-PG  2000-2009      90\n",
       "44     PG/TV-PG  2010-2019     120\n",
       "45     PG/TV-PG  2020-2029       4\n",
       "46      R/TV-MA  1960-1969       7\n",
       "47      R/TV-MA  1970-1979      35\n",
       "48      R/TV-MA  1980-1989      65\n",
       "49      R/TV-MA  1990-1999     110\n",
       "50      R/TV-MA  2000-2009     128\n",
       "51      R/TV-MA  2010-2019     240\n",
       "52      R/TV-MA  2020-2029      15"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['rating', 'decade']).size().reset_index(name='counts').iloc()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {
    "scrolled": true
   },
   "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",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rating</th>\n",
       "      <th>decade</th>\n",
       "      <th>counts</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>G/TV-G</td>\n",
       "      <td>1910-1919</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>G/TV-G</td>\n",
       "      <td>1920-1929</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>G/TV-G</td>\n",
       "      <td>1930-1939</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>G/TV-G</td>\n",
       "      <td>1940-1949</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>G/TV-G</td>\n",
       "      <td>1950-1959</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>G/TV-G</td>\n",
       "      <td>1960-1969</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>G/TV-G</td>\n",
       "      <td>1970-1979</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>G/TV-G</td>\n",
       "      <td>1980-1989</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>G/TV-G</td>\n",
       "      <td>1990-1999</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>G/TV-G</td>\n",
       "      <td>2000-2009</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>G/TV-G</td>\n",
       "      <td>2010-2019</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>G/TV-G</td>\n",
       "      <td>2020-2029</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>No Rating</td>\n",
       "      <td>1910-1919</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>No Rating</td>\n",
       "      <td>1920-1929</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>No Rating</td>\n",
       "      <td>1930-1939</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>No Rating</td>\n",
       "      <td>1940-1949</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>No Rating</td>\n",
       "      <td>1950-1959</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>No Rating</td>\n",
       "      <td>1960-1969</td>\n",
       "      <td>59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>No Rating</td>\n",
       "      <td>1970-1979</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>No Rating</td>\n",
       "      <td>1980-1989</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>No Rating</td>\n",
       "      <td>1990-1999</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>No Rating</td>\n",
       "      <td>2000-2009</td>\n",
       "      <td>59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>No Rating</td>\n",
       "      <td>2010-2019</td>\n",
       "      <td>225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>No Rating</td>\n",
       "      <td>2020-2029</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>PG-13/TV-14</td>\n",
       "      <td>1920-1929</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>PG-13/TV-14</td>\n",
       "      <td>1930-1939</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>PG-13/TV-14</td>\n",
       "      <td>1940-1949</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>PG-13/TV-14</td>\n",
       "      <td>1950-1959</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>PG-13/TV-14</td>\n",
       "      <td>1960-1969</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>PG-13/TV-14</td>\n",
       "      <td>1970-1979</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>PG-13/TV-14</td>\n",
       "      <td>1980-1989</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>PG-13/TV-14</td>\n",
       "      <td>1990-1999</td>\n",
       "      <td>62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>PG-13/TV-14</td>\n",
       "      <td>2000-2009</td>\n",
       "      <td>140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>PG-13/TV-14</td>\n",
       "      <td>2010-2019</td>\n",
       "      <td>188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>PG-13/TV-14</td>\n",
       "      <td>2020-2029</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>PG/TV-PG</td>\n",
       "      <td>1920-1929</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>PG/TV-PG</td>\n",
       "      <td>1930-1939</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>PG/TV-PG</td>\n",
       "      <td>1940-1949</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>PG/TV-PG</td>\n",
       "      <td>1950-1959</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>PG/TV-PG</td>\n",
       "      <td>1960-1969</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>PG/TV-PG</td>\n",
       "      <td>1970-1979</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>PG/TV-PG</td>\n",
       "      <td>1980-1989</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>PG/TV-PG</td>\n",
       "      <td>1990-1999</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>PG/TV-PG</td>\n",
       "      <td>2000-2009</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>PG/TV-PG</td>\n",
       "      <td>2010-2019</td>\n",
       "      <td>120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>PG/TV-PG</td>\n",
       "      <td>2020-2029</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>R/TV-MA</td>\n",
       "      <td>1960-1969</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>R/TV-MA</td>\n",
       "      <td>1970-1979</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>R/TV-MA</td>\n",
       "      <td>1980-1989</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>R/TV-MA</td>\n",
       "      <td>1990-1999</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>R/TV-MA</td>\n",
       "      <td>2000-2009</td>\n",
       "      <td>128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>R/TV-MA</td>\n",
       "      <td>2010-2019</td>\n",
       "      <td>240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>R/TV-MA</td>\n",
       "      <td>2020-2029</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         rating     decade  counts\n",
       "0        G/TV-G  1910-1919       2\n",
       "1        G/TV-G  1920-1929       4\n",
       "2        G/TV-G  1930-1939       6\n",
       "3        G/TV-G  1940-1949       4\n",
       "4        G/TV-G  1950-1959       5\n",
       "5        G/TV-G  1960-1969      14\n",
       "6        G/TV-G  1970-1979      10\n",
       "7        G/TV-G  1980-1989       4\n",
       "8        G/TV-G  1990-1999      18\n",
       "9        G/TV-G  2000-2009      20\n",
       "10       G/TV-G  2010-2019      36\n",
       "11       G/TV-G  2020-2029       3\n",
       "12    No Rating  1910-1919       5\n",
       "13    No Rating  1920-1929      16\n",
       "14    No Rating  1930-1939      29\n",
       "15    No Rating  1940-1949      41\n",
       "16    No Rating  1950-1959      49\n",
       "17    No Rating  1960-1969      59\n",
       "18    No Rating  1970-1979      23\n",
       "19    No Rating  1980-1989      13\n",
       "20    No Rating  1990-1999      18\n",
       "21    No Rating  2000-2009      59\n",
       "22    No Rating  2010-2019     225\n",
       "23    No Rating  2020-2029      13\n",
       "24  PG-13/TV-14  1920-1929       1\n",
       "25  PG-13/TV-14  1930-1939       3\n",
       "26  PG-13/TV-14  1940-1949       1\n",
       "27  PG-13/TV-14  1950-1959       2\n",
       "28  PG-13/TV-14  1960-1969       7\n",
       "29  PG-13/TV-14  1970-1979       1\n",
       "30  PG-13/TV-14  1980-1989      28\n",
       "31  PG-13/TV-14  1990-1999      62\n",
       "32  PG-13/TV-14  2000-2009     140\n",
       "33  PG-13/TV-14  2010-2019     188\n",
       "34  PG-13/TV-14  2020-2029       4\n",
       "35     PG/TV-PG  1920-1929       1\n",
       "36     PG/TV-PG  1930-1939       5\n",
       "37     PG/TV-PG  1940-1949       3\n",
       "38     PG/TV-PG  1950-1959      14\n",
       "39     PG/TV-PG  1960-1969      11\n",
       "40     PG/TV-PG  1970-1979      27\n",
       "41     PG/TV-PG  1980-1989      34\n",
       "42     PG/TV-PG  1990-1999      65\n",
       "43     PG/TV-PG  2000-2009      90\n",
       "44     PG/TV-PG  2010-2019     120\n",
       "45     PG/TV-PG  2020-2029       4\n",
       "46      R/TV-MA  1960-1969       7\n",
       "47      R/TV-MA  1970-1979      35\n",
       "48      R/TV-MA  1980-1989      65\n",
       "49      R/TV-MA  1990-1999     110\n",
       "50      R/TV-MA  2000-2009     128\n",
       "51      R/TV-MA  2010-2019     240\n",
       "52      R/TV-MA  2020-2029      15"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_groupby"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "df_groupby = df_groupby.set_index('decade')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "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>decade</th>\n",
       "      <th>R/TV-MA</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1910-1919</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1920-1929</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1930-1939</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1940-1949</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1950-1959</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1960-1969</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1970-1979</td>\n",
       "      <td>35.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1980-1989</td>\n",
       "      <td>65.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1990-1999</td>\n",
       "      <td>110.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2000-2009</td>\n",
       "      <td>128.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2010-2019</td>\n",
       "      <td>240.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2020-2029</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       decade  R/TV-MA\n",
       "0   1910-1919      0.0\n",
       "1   1920-1929      0.0\n",
       "2   1930-1939      0.0\n",
       "3   1940-1949      0.0\n",
       "4   1950-1959      0.0\n",
       "5   1960-1969      7.0\n",
       "6   1970-1979     35.0\n",
       "7   1980-1989     65.0\n",
       "8   1990-1999    110.0\n",
       "9   2000-2009    128.0\n",
       "10  2010-2019    240.0\n",
       "11  2020-2029     15.0"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_merged = df_decade.merge(df_groupby.loc[df_groupby['rating'] == df['rating'].unique()[0]].iloc[:, 1:3], \n",
    "                left_on='decade', right_on='decade', how='left')\n",
    "df_merged['counts'] = df_merged['counts'].fillna(0)\n",
    "df_merged.rename(columns={\"counts\": df['rating'].unique()[0]})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "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>decade</th>\n",
       "      <th>R/TV-MA</th>\n",
       "      <th>PG-13/TV-14</th>\n",
       "      <th>PG/TV-PG</th>\n",
       "      <th>No Rating</th>\n",
       "      <th>G/TV-G</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1910-1919</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1920-1929</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>16</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1930-1939</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>29</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1940-1949</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>41</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1950-1959</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>49</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1960-1969</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>59</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1970-1979</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>23</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1980-1989</td>\n",
       "      <td>65.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>13</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1990-1999</td>\n",
       "      <td>110.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2000-2009</td>\n",
       "      <td>128.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>59</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2010-2019</td>\n",
       "      <td>240.0</td>\n",
       "      <td>188.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>225</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2020-2029</td>\n",
       "      <td>15.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>13</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       decade  R/TV-MA  PG-13/TV-14  PG/TV-PG  No Rating  G/TV-G\n",
       "0   1910-1919      0.0          0.0       0.0          5       2\n",
       "1   1920-1929      0.0          1.0       1.0         16       4\n",
       "2   1930-1939      0.0          3.0       5.0         29       6\n",
       "3   1940-1949      0.0          1.0       3.0         41       4\n",
       "4   1950-1959      0.0          2.0      14.0         49       5\n",
       "5   1960-1969      7.0          7.0      11.0         59      14\n",
       "6   1970-1979     35.0          1.0      27.0         23      10\n",
       "7   1980-1989     65.0         28.0      34.0         13       4\n",
       "8   1990-1999    110.0         62.0      65.0         18      18\n",
       "9   2000-2009    128.0        140.0      90.0         59      20\n",
       "10  2010-2019    240.0        188.0     120.0        225      36\n",
       "11  2020-2029     15.0          4.0       4.0         13       3"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_decade = pd.DataFrame(sorted(df['decade'].unique()), columns = ['decade'])\n",
    "df_merged = df_decade\n",
    "# variables = locals()\n",
    "for i in range(len(df['rating'].unique())):\n",
    "    df_merged = df_merged.merge(df_groupby.loc[df_groupby['rating'] == df['rating'].unique()[i]].iloc[:, 1:3], \n",
    "                left_on='decade', right_on='decade', how='left')\n",
    "    df_merged['counts'] = df_merged['counts'].fillna(0)\n",
    "    df_merged = df_merged.rename(columns={\"counts\": df['rating'].unique()[i]})\n",
    "\n",
    "df_merged"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['R/TV-MA', 'PG-13/TV-14', 'PG/TV-PG', 'No Rating', 'G/TV-G'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['rating'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-152-34e0f720f222>:19: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
      "  ax.set_xticklabels(sorted(df['decade'].unique()), rotation = 90)\n"
     ]
    },
    {
     "data": {
      "image/png": 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/RkTfct0Vy8cPl8uH1DObJElSo6hbQRYR6wFfBkZk5mZAL2B/4BTgR5n5PuBF4JDyKYcAL5btPyrXkyRJWu7VewxZb2CliHgTWBmYCYwEPlsunwCcCJwJ7FneB7gA+FlERC7rvBwnrrFMT198ey8vcZVevXoxbNgw5s+fz9ChQ5kwYQIrr7wys2bN4phjjmHSpEkMGDCAvn37cuyxx7LXXnstto0//elPnHjiiUybNo3bb7+dESOKs2Rvv/12DjvsMAAykxNPPHGR548bN46HHnqIF198kTfeeIPHHnuM97///QAcc8wxXH311YvM1v/cc88xdOhQZsyYwYorrriw/Wc/+xk//vGPeeSRR5g9ezZrr732IvnuuOMOPvKRjzBx4kT22WefTnyAkiSppbr1kGXmU8APgCcpCrGXgTuBlzJzfrnaDGC98v56wPTyufPL9ddqud2IOCwiJkfE5NmzZ9cr/jJZcOmk+++/n759+3LWWWeRmYwaNYodd9yRRx99lDvvvJOJEycyY8aMVrex2WabcdFFF7Hjjjsu1j558mSmTJnC1VdfzeGHH878+fMXLp80aRLXXXcdU6ZM4corr+S9730vU6ZMYcqUKey1115ce+21zJ07d+H6F1xwAZ/+9KcXKcYAtttuO6677jo22GCDxbK99dZbHHfccXziE59Ylo9JkiSV6nnIcgBFr9eGwLrAKkDHrhvUjsw8OzNHZOaIgQMHLuvm6m6HHXbg4Ycf5oYbbqBv376MGzdu4bINNtiAI488stXnDR06dGHPVq2VV16Z3r2Ljs158+YREQuXTZs2jU022YRevXq1us3VV1+dj33sY1x++eUL29q6ePmWW27JkCFDWt3OT3/6U/bee2/e9a53tbpckiR1Tj0H9f8b8Fhmzs7MN4GLgO2A/hGx4FDpIOCp8v5TwGCAcvkawPN1zFd38+fP56qrrmLYsGFMnTqVrbbaqku2e9ttt7HpppsybNgwzjrrrIUFWkeulTlmzBgmTpwIwNNPP80//vEPRo7s+OnJTz31FBdffDFHHHHE0r8BSZK0iHoWZE8C20bEylF04+wMPADcCCwYdDQWuLS8f1n5mHL5Dcs8fqwir7/+OsOHD2fEiBGsv/76HHLIIYutM378eLbYYgu23nrrTm9/m222YerUqdxxxx18//vfZ968eQBcc801SyzIPvnJT3LLLbfwyiuvcP7557P33nu32aPWmqOPPppTTjmFFVZwxhRJkrpK3Qb1Z+ZtEXEBcBcwH7gbOBv4MzAxIv6rbDu3fMq5wO8i4mHgBYozMpvSgjFktTbddFMuvPDChY/POOMMnnvuuYWD9Q8++GDuvvtu1l13Xa688soOvc7QoUNZddVVuf/++/ngBz/ISy+9xLrrrrvEbLvtthsXX3wxEydO5LTTTgNg1113ZdasWYwYMYJzzjmnzedPnjyZ/fcvvjXPPfccV155Jb1792bUqFEdyixJkhZX17MsM/PbwLdbND8KfLiVdecB+9YzT5VGjhzJ17/+dc4888yFh/tqB9f/+te/7tB2HnvsMQYPHkzv3r154oknePDBBxkyZAg33ngjH//4xzu0jTFjxnD88cfzyiuv8JGPfAQoetc6+voLHHTQQXzqU5+yGJMkaRkt/5dO6sA0Fd0hIrjkkks45phjOPXUUxk4cCCrrLIKp5zS+nRrF198MUceeSSzZ8/mk5/8JMOHD+eaa67h5ptv5uSTT6ZPnz6ssMIK/PznP2fttdfmqquu6vD0E7vssgsHHngghxxyyCInBdQ6/fTTOfXUU3nmmWfYfPPN2WOPPdrtOZMkSUsvmnSYFgAjRozIyZMnL9I2bdo0hg4dWlGi6my11Vbcdttt9OnTp+ooQM/9PkjS8sBrWdZHRNyZmSNaW7b895D1EHfddVfVESRJ0lLyVDlJkqSKWZBJkiRVzIJMkiSpYhZkkiRJFbMgkyRJqthyf5blsAnDunR79429b4nr9OrVi2HDhjF//nyGDh3KhAkTWHnllZk1axbHHHMMkyZNYsCAAfTt25djjz2Wvfbaa+FzP/ShD/H222+Tmbzwwgu8/vrrrLfeegAMGDCA/fffn8MPP3zh+pdccgm/+MUvuOqqq7osgySpZxt50/hl3MK0LsnRk9hDVgcLLp10//3307dvX8466ywyk1GjRrHjjjvy6KOPcueddzJx4kRmzJix8HmPPfYY6623HnfffTdTpkzhpJNOYvTo0UyZMoUpU6ZwwgknLLww+AITJ05kzJgxXZZBkiR1PwuyOtthhx14+OGHueGGG+jbty/jxo1buGyDDTbgyCOPXPj46quvbvfi4DvvvDMPPvggM2fOBGDOnDlcd911S7x0UWcySJKk7mdBVkfz58/nqquuYtiwYUydOpWtttqq3fWXVJD16tWLvffem/PPPx+Ayy+/nJ122onVV1+9yzJIkqTuZ0FWB6+//jrDhw9nxIgRrL/++hxyyCGLrTN+/Hi22GILtt56awDeeOMNZsyYwUYbbdTutseMGbPwsGVbhyuXNoMkSarGcj+ovwoLxm/V2nTTTbnwwgsXPj7jjDN47rnnGDGiuKTV3/72N7bffvslbvujH/0oM2fO5J577uHWW29l4sSJTJ8+nU9/+tMAjBs3jnHjxi1VBkmSVA17yLrJyJEjmTdvHmeeeebCtrlz5y68f/XVV7P77rsvcTsRwejRoxk7diy77747/fr1Y/DgwQsH/teOD+tsBkmSVI3lvoesI9NUdIeI4JJLLuGYY47h1FNPZeDAgayyyiqccsopANx0002cdNJJHdrWmDFjOPXUUzn55JO7NIMkSarGcl+QVeG1115rtf0973nPYtNWAMyYMYO1116blVZaaZH2gw46iIMOOmix9YcPH05mdmkGSZJUHQ9ZNoBBgwYtNrGrJEnqOSzIJEmSKmZBJkmSVDELMkmSpIpZkEmSJFXMgkySJKliy/20F9M+MLRLtzf0wWlLXCci+MpXvsIPf/hDAH7wgx/w2muvceKJJ3boNX7zm9/wta99jfXWW4958+Zx+OGHc8wxxyzxOZ/4xCdYd911AfjCF77AV77yFT74wQ926DUlSVJ17CGrgxVXXJGLLrqI5557bqm3MXr0aKZMmcItt9zC9773PaZPn97u+r/5zW94+umnFz4+55xzLMYkSWoSFmR10Lt3bw477DB+9KMfLbbs8ccfZ+TIkWy++ebsvPPOPPnkk+1ua6211uJ973sfM2fOBOCkk05i6623ZrPNNuOwww4jM7nggguYPHkyn/vc5xg+fDivv/46O+20E5MnTwZg1VVX5Rvf+AZbbLEF2267LbNmzQLgkUceYdttt2XYsGF885vfZNVVV+3iT0KSJHWEBVmdjB8/nt///ve8/PLLi7QfeeSRjB07lnvvvZfPfe5zfPnLX253O08++STz5s1j8803B+BLX/oSd9xxB/fffz+vv/46V1xxBfvssw8jRozg97//PVOmTFlsxv85c+aw7bbbcs8997Djjjvyy1/+EoCjjjqKo446ivvuu49BgwZ14buXJEmdYUFWJ6uvvjoHHnggp59++iLtf//73/nsZz8LwAEHHMDNN9/c6vP/+Mc/svnmm/O+972PL37xi/Tr1w+AG2+8kW222YZhw4Zxww03MHXq1CVm6du3L5/61KcA+NCHPsTjjz++MMu+++4LsDCTJEnqfhZkdXT00Udz7rnnMmfOnE4/d/To0dx7773ceuutHH/88TzzzDPMmzePL37xi1xwwQXcd999HHroocybN2+J2+rTpw8RAUCvXr2YP39+p/NIkqT6sSCrozXXXJP99tuPc889d2HbRz/60YUX9/7973/PDjvs0O42RowYwQEHHMBPfvKThcXX2muvzWuvvcYFF1ywcL3VVluNV199tVP5tt12Wy688EIALzguSVKFlvtpLzoyTUU9ffWrX+VnP/vZwsc//elPOfjgg/mf//kfBg4cyK9//eslbuO4445jq6224utf/zqHHnoom222Ge9+97vZeuutF65z0EEHMW7cOFZaaSX+/ve/dyjbj3/8Yz7/+c/zve99j91224011lij829QkiQts8jMqjMstREjRuSCMwkXmDZtGkOHdu3cY8uruXPnstJKKxERTJw4kfPOO49LL720S7bt90GSmteyzuFZdWdIo4qIOzNzRGvLlvseMrXtzjvv5Etf+hKZSf/+/fnVr35VdSRJknokC7IebIcdduCee+6pOoYkST2eg/olSZIqZkEmSZJUMQsySZKkilmQSZIkVWy5H9R/xrgbunR7488a2aH1Zs2axTHHHMOkSZMYMGAAffv25dhjj2WvvfYCiksYvf3222QmL7zwAq+//jrrrbceAAMGDGD//ffn8MMPX7i9Sy65hF/84hdcddVVi73Waaedxtlnn02fPn1YYYUV2HnnnTnllFPo06dPF7xjSZJUb/aQ1UFmMmrUKHbccUceffRR7rzzTiZOnMiMGTMAeOyxx1hvvfW4++67mTJlCieddBKjR49mypQpTJkyhRNOOGGxmfMnTpzImDFjFnuts846i7/85S9MmjSJ++67jzvuuIN3vetdvP76693yXiVJ0rKzIKuDG264gb59+zJu3LiFbRtssAFHHnkkAFdffTW77bZbm8/feeedefDBB5k5cyYAc+bM4brrrmPUqFGLrfu9732PM888k/79+wPFhcSPP/54Vl999a57Q5Ikqa4syOpg6tSpbLXVVm0uX1JB1qtXL/bee2/OP/98AC6//HJ22mmnxYqsV155hddee40NN9ywa4JLkqRK1K0gi4j3R8SUmtsrEXF0RKwZEddGxD/LrwPK9SMiTo+IhyPi3ohou6JpMuPHj2eLLbZg66235o033mDGjBlstNFG7T5nzJgxCw9btnW4sqVrrrmG4cOHM2TIEG699dYuyS5JkuqvbgVZZj6UmcMzczjwIWAucDFwPHB9Zm4MXF8+Btgd2Li8HQacWa9s9bbpppty1113LXx8xhlncP311zN79mz+9re/sf322y9xGx/96EeZOXMm99xzD7feeiuf/OQnmT59OsOHD2f48OGcddZZrL766qy66qo89thjAOy6665MmTKFzTbbjDfeeKNu70+SJHWt7jpkuTPwSGY+AewJTCjbJwCjyvt7Ar/NwiSgf0S8p5vydamRI0cyb948zjzznZpy7ty5QHG4cvfdd1/iNiKC0aNHM3bsWHbffXf69evH4MGDFw78XzA+7YQTTuCII47gpZdeAooTCubNm9f1b0qSJNVNd017sT9wXnl/ncycWd5/BlinvL8eML3mOTPKtpk1bUTEYRQ9aKy//vpLfOGOTlPRlSKCSy65hGOOOYZTTz2VgQMHssoqq3DKKafwgx/8gJNOOqlD2xkzZgynnnoqJ598cpvrHHHEEcyZM4dtttmGFVdckVVXXZXtttuOLbfcsqvejiRJqrPIzPq+QERf4Glg08ycFREvZWb/muUvZuaAiLgCODkzby7brweOy8zJbW17xIgROXnyoounTZvG0KFD6/FWltmMGTM49NBDW51LbHnTyN8HSVL7pn1g2X5/D31wWhclWb5ExJ2ZOaK1Zd1xyHJ34K7MnFU+nrXgUGT59dmy/SlgcM3zBpVty41Bgwb1iGJMkiR1TncUZGN453AlwGXA2PL+WODSmvYDy7MttwVerjm0KUmStNyq6xiyiFgF2AU4vKb5ZOD8iDgEeALYr2y/EtgDeJjijMyDl/Z1M5OIWNqnaxnV+zC4JEnLm7oWZJk5B1irRdvzFGddtlw3gfHL+pr9+vXj+eefZ6211rIoq0Bm8vzzz9OvX7+qo0iS1DSWu4uLDxo0iBkzZjB79uyqo/RY/fr1Y9CgQVXHkCSpaSx3BVmfPn28lJAkSWoqXstSkiSpYhZkkiRJFbMgkyRJqpgFmSRJUsUsyCRJkipmQSZJklQxCzJJkqSKWZBJkiRVzIJMkiSpYhZkkiRJFbMgkyRJqpgFmSRJUsUsyCRJkipmQSZJklQxCzJJkqSKWZBJkiRVzIJMkiSpYhZkkiRJFbMgkyRJqpgFmSRJUsUsyCRJkipmQSZJklQxCzJJkqSKWZBJkiRVzIJMkiSpYhZkkiRJFbMgkyRJqpgFmSRJUsUsyCRJkipmQSZJklQxCzJJkqSKWZBJkiRVzIJMkiSpYhZkkiRJFbMgkyRJqpgFmSRJUsUsyCRJkipmQSZJklQxCzJJkqSKWZBJkiRVrK4FWUT0j4gLIuLBiJgWER+JiDUj4tqI+Gf5dUC5bkTE6RHxcETcGxFb1TObJElSo6h3D9lPgKsz8wPAFsA04Hjg+szcGLi+fAywO7BxeTsMOLPO2SRJkhpC3QqyiFgD2BE4FyAz38jMl4A9gQnlahOAUeX9PYHfZmES0D8i3lOvfJIkSY2inj1kGwKzgV9HxN0RcU5ErAKsk5kzy3WeAdYp768HTK95/oyybRERcVhETI6IybNnz65jfEmSpO5Rz4KsN7AVcGZmbgnM4Z3DkwBkZgLZmY1m5tmZOSIzRwwcOLDLwkqSJFWlngXZDGBGZt5WPr6AokCbteBQZPn12XL5U8DgmucPKtskSZKWa3UryDLzGWB6RLy/bNoZeAC4DBhbto0FLi3vXwYcWJ5tuS3wcs2hTUmSpOVW7zpv/0jg9xHRF3gUOJiiCDw/Ig4BngD2K9e9EtgDeBiYW64rSZK03KtrQZaZU4ARrSzauZV1ExhfzzySJEmNyJn6JUmSKmZBJkmSVDELMkmSpIpZkEmSJFXMgkySJKliFmSSJEkVsyCTJEmqmAWZJElSxSzIJEmSKmZBJkmSVDELMkmSpIpZkEmSJFXMgkySJKliFmSSJEkVsyCTJEmqmAWZJElSxSzIJEmSKmZBJkmSVDELMkmSpIpZkEmSJFXMgkySJKlivasOIEmSGst+JyxbeXBfF+XoSewhkyRJqpgFmSRJUsUsyCRJkipmQSZJklQxCzJJkqSKWZBJkiRVzIJMkiSpYhZkkiRJFbMgkyRJqpgFmSRJUsUsyCRJkipmQSZJklQxCzJJkqSKWZBJkiRVzIJMkiSpYhZkkiRJFbMgkyRJqpgFmSRJUsUsyCRJkipmQSZJklSxuhZkEfF4RNwXEVMiYnLZtmZEXBsR/yy/DijbIyJOj4iHI+LeiNiqntkkSZIaRXf0kH08M4dn5ojy8fHA9Zm5MXB9+Rhgd2Dj8nYYcGY3ZJMkSapcFYcs9wQmlPcnAKNq2n+bhUlA/4h4TwX5JEmSulW9C7IE/hIRd0bEYWXbOpk5s7z/DLBOeX89YHrNc2eUbYuIiMMiYnJETJ49e3a9ckuSJHWb3nXe/vaZ+VREvAu4NiIerF2YmRkR2ZkNZubZwNkAI0aM6NRzJUmSGlFde8gy86ny67PAxcCHgVkLDkWWX58tV38KGFzz9EFlmyRJ0nKtbgVZRKwSEastuA98ArgfuAwYW642Fri0vH8ZcGB5tuW2wMs1hzYlSZKWW/U8ZLkOcHFELHidP2Tm1RFxB3B+RBwCPAHsV65/JbAH8DAwFzi4jtkkSZIaRt0Kssx8FNiilfbngZ1baU9gfL3ySJIkNSpn6pckSaqYBZkkSVLFLMgkSZIqZkEmSZJUMQsySZKkilmQSZIkVcyCTJIkqWIWZJIkSRVbYkEWEUd1pE2SJElLpyM9ZGNbaTuoi3NIkiT1WG1eOikixgCfBTaMiMtqFq0GvFDvYJIkST1Fe9eyvBWYCawN/LCm/VXg3nqGkiRJ6knaLMgy8wngCeAj3RdHkiSp5+nIoP5/j4h/RsTLEfFKRLwaEa90RzhJkqSeoL1DlgucCnw6M6fVO4wkSVJP1JGzLGdZjEmSJNVPR3rIJkfEH4FLgH8taMzMi+oVSpIkqSfpSEG2OjAX+ERNWwIWZJIkSV1giQVZZh7cHUEkSZJ6qiUWZBHxa4oesUVk5n/UJZEkSVIP05FDllfU3O8H7AU8XZ84kiRJPU9HDlleWPs4Is4Dbq5bIkmSpB6mI9NetLQx8K6uDiJJktRTdWQM2asUY8ii/PoMcFydc0mSJPUYHTlkuVp3BJEkSeqpOjKon4j4DLBj+fCmzLyivfUlSZLUcR25uPjJwFHAA+XtqIj473oHkyRJ6ik60kO2BzA8M98GiIgJwN3A1+sZTJKk5cUZ425Y6ueOP2tkFyZRo+roWZb9a+6vUYcckiRJPVZHesi+D9wdETdSnGm5I3B8XVNJkiT1IB05y/K8iLgJ2LpsOi4zn6lrKkmSpB6kI/OQ7QXckJmXlY/7R8SozLyk3uEkSVL3u++xJ6uO0ON0ZAzZtzPz5QUPMvMl4Nt1SyRJktTDdKQga22dDs1fJkmSpCXrSEE2OSJOi4j3lrfTgDvrHUySJKmn6EhBdiTwBvBHYCIwDxhfz1CSJEk9SUfOspyD01xIkiTVjWPBJEmqs5E3LcuBpWldlkONq6Mz9UuSJKlOLMgkSZIqtsSCLCI2iYjrI+L+8vHmEfHN+keTJEnqGTrSQ/ZL4ATgTYDMvBfYv56hJEmSepKOFGQrZ+btLdrmd/QFIqJXRNwdEVeUjzeMiNsi4uGI+GNE9C3bVywfP1wuH9LhdyFJktTEOlKQPRcR7wUSICL2AWZ24jWOYtFTRE4BfpSZ7wNeBA4p2w8BXizbf1SuJ0mStNzrSEE2HvgF8IGIeAo4GjiiIxuPiEHAJ4FzyscBjAQuKFeZAIwq7+9ZPqZcvnO5viRJ0nKtIxPDPgr8W0SsAqyQma92Yvs/Bo4FVisfrwW8lJkLDnnOANYr768HTC9fc35EvFyu/1wnXk+SJKnptFmQRcRX2mgHIDNPa2/DEfEp4NnMvDMidlr6iItt9zDgMID111+/qzYrSZJUmfZ6yFZrZ1l2YNvbAZ+JiD2AfsDqwE+A/hHRu+wlGwQ8Va7/FDAYmBERvYE1gOcXe+HMs4GzAUaMGNGRHJIkSQ2tzYIsM78DEBHbZeYttcsiYrslbTgzT6CYLoOyh+w/M/NzEfEnYB+KC5WPBS4tn3JZ+fjv5fIbMtOCS5IkLfc6Mqj/px1s66jjgK9ExMMUY8TOLdvPBdYq27+CFzSXJEk9RHtjyD4CfBQY2GI82epAr868SGbeBNxU3n8U+HAr68wD9u3MdiVJkpYH7Y0h6wusWq5TO57sFYpDipIkSeoC7Y0h+yvw14j4TWY+0Y2ZJEmSepT2Dln+LDO/BPwsIhYbXJ+Zn6lrMkmSpB6ivUOWBwJfAn7QTVkkSZJ6pPYKskdg4aFLSZIk1Ul7BVnLsysXsaSZ+iVJktQx7RVkvSjOsvQC35IkSXXUXkE2MzNP6rYkkiRJPVR7M/XbMyZJktQN2ivIdu62FJIkST1YmwVZZr7QnUEkSZJ6qo5cXFySJEl1ZEEmSZJUMQsySZKkilmQSZIkVcyCTJIkqWLtTQwrSZK6wH4nLP2f2/u6MIcalz1kkiRJFbMgkyRJqpgFmSRJUsUsyCRJkipmQSZJklQxCzJJkqSKWZBJkiRVzIJMkiSpYhZkkiRJFbMgkyRJqpgFmSRJUsUsyCRJkipmQSZJklQxCzJJkqSKWZBJkiRVzIJMkiSpYhZkkiRJFbMgkyRJqpgFmSRJUsUsyCRJkipmQSZJklQxCzJJkqSKWZBJkiRVzIJMkiSpYhZkkiRJFatbQRYR/SLi9oi4JyKmRsR3yvYNI+K2iHg4Iv4YEX3L9hXLxw+Xy4fUK5skSVIjqWcP2b+AkZm5BTAc2C0itgVOAX6Ume8DXgQOKdc/BHixbP9RuZ4kSdJyr24FWRZeKx/2KW8JjAQuKNsnAKPK+3uWjymX7xwRUa98kiRJjaKuY8gioldETAGeBa4FHgFeysz55SozgPXK++sB0wHK5S8Da9UznyRJUiOoa0GWmW9l5nBgEPBh4APLus2IOCwiJkfE5NmzZy/r5iRJkirXLWdZZuZLwI3AR4D+EdG7XDQIeKq8/xQwGKBcvgbwfCvbOjszR2TmiIEDB9Y7uiRJUt3V8yzLgRHRv7y/ErALMI2iMNunXG0scGl5/7LyMeXyGzIz65VPkiSpUfRe8ipL7T3AhIjoRVH4nZ+ZV0TEA8DEiPgv4G7g3HL9c4HfRcTDwAvA/nXMJkmS1DDqVpBl5r3Alq20P0oxnqxl+zxg33rlkSRJalTO1C9JklQxCzJJkqSKWZBJkiRVzIJMkiSpYhZkkiRJFbMgkyRJqpgFmSRJUsUsyCRJkipmQSZJklSxel46SZKkLnfGuBuW6fnjzxrZRUmkrmMPmSRJUsXsIZMkqc7ue+zJqiOowdlDJkmSVDELMkmSpIpZkEmSJFXMgkySJKliFmSSJEkVsyCTJEmqmNNeSJKaysibxi/jFqZ1SQ6pK9lDJkmSVDELMkmSpIpZkEmSJFXMgkySJKliFmSSJEkVsyCTJEmqmNNeSJKkRQyZ94dlev7jXROjR7GHTJIkqWL2kEmSmsp+Jyzbn677uiiH1JXsIZMkSaqYBZkkSVLFLMgkSZIqZkEmSZJUMQsySZKkilmQSZIkVcyCTJIkqWIWZJIkSRWzIJMkSaqYBZkkSVLFLMgkSZIqZkEmSZJUMQsySZKkilmQSZIkVcyCTJIkqWJ1K8giYnBE3BgRD0TE1Ig4qmxfMyKujYh/ll8HlO0REadHxMMRcW9EbFWvbJIkSY2knj1k84GvZuYHgW2B8RHxQeB44PrM3Bi4vnwMsDuwcXk7DDizjtkkSZIaRt0KssycmZl3lfdfBaYB6wF7AhPK1SYAo8r7ewK/zcIkoH9EvKde+SRJkhpFt4whi4ghwJbAbcA6mTmzXPQMsE55fz1ges3TZpRtLbd1WERMjojJs2fPrl9oSZKkblL3giwiVgUuBI7OzFdql2VmAtmZ7WXm2Zk5IjNHDBw4sAuTSpIkVaOuBVlE9KEoxn6fmReVzbMWHIosvz5btj8FDK55+qCyTZIkablWz7MsAzgXmJaZp9UsugwYW94fC1xa035gebbltsDLNYc2JUmSllu967jt7YADgPsiYkrZ9nXgZOD8iDgEeALYr1x2JbAH8DAwFzi4jtkkSZIaRt0Kssy8GYg2Fu/cyvoJjK9XHkmSpEblTP2SJEkVsyCTJEmqmAWZJElSxSzIJEmSKmZBJkmSVDELMkmSpIpZkEmSJFXMgkySJKli9ZypX5KkLnffY09WHUHqcvaQSZIkVcyCTJIkqWIWZJIkSRWzIJMkSaqYBZkkSVLFLMgkSZIqZkEmSZJUMQsySZKkilmQSZIkVcyCTJIkqWIWZJIkSRWzIJMkSaqYBZkkSVLFLMgkSZIq1rvqAJKk6pwx7oZlev74s0Z2URKpZ7OHTJIkqWL2kElSDzbypvHLuIVpXZJD6unsIZMkSaqYBZkkSVLFLMgkSZIqZkEmSZJUMQsySZKkinmWpST1YPudsGx/Bu7rohzLuyHz/rDUz32862KogdlDJkmSVDELMkmSpIpZkEmSJFXMgkySJKliFmSSJEkVsyCTJEmqmAWZJElSxSzIJEmSKubEsJKkprIsk6yCE62qMdlDJkmSVLG6FWQR8auIeDYi7q9pWzMiro2If5ZfB5TtERGnR8TDEXFvRGxVr1ySJEmNpp49ZL8BdmvRdjxwfWZuDFxfPgbYHdi4vB0GnFnHXJIkSQ2lbgVZZv4f8EKL5j2BCeX9CcComvbfZmES0D8i3lOvbJIkSY2ku8eQrZOZM8v7zwDrlPfXA6bXrDejbFtMRBwWEZMjYvLs2bPrl1SSJKmbVHaWZWZmRORSPO9s4GyAESNGdPr5kprHGeNuWKbnjz9rZBclkaT66u6CbFZEvCczZ5aHJJ8t258CBtesN6hsk6SmsixFpAWk1HN19yHLy4Cx5f2xwKU17QeWZ1tuC7xcc2hTkiRpuVa3HrKIOA/YCVg7ImYA3wZOBs6PiEOAJ4D9ytWvBPYAHgbmAgfXK5ck6R33PfZk1REkUceCLDPHtLFo51bWTWB8vbJIkiQ1MmfqlyRJqpjXspSkLjTypmXp7J/WZTkkNRcLMkkNa9mKG7DAkdQsPGQpSZJUMQsySZKkilmQSZIkVcyCTJIkqWIWZJIkSRWzIJMkSaqY015IPcSyXPQavPC1JNWTPWSSJEkVsyCTJEmqmIcspR7CWe/VmiHz/rBMz3+8a2JIPZ4FmSR1of1OWPpfq/d1YQ5JzcVDlpIkSRWzIJMkSaqYBZkkSVLFLMgkSZIqZkEmSZJUMQsySZKkijnthbSUluVSRF6GSJJUyx4ySZKkitlDJi2lZZv53lnvJUnvsCBTQ/DwnySpJ/OQpSRJUsXsIZMkSU1vWY60QPVHWyzI1BAcjyVJ6sksyCSpC7067eSqI0g90rL9Yw9V/3PvGDJJkqSKWZBJkiRVzIJMkiSpYhZkkiRJFbMgkyRJqphnWUpqWPudsGy/ou7rohySVG8WZMuhZp8cT5Kkzmr2f+A8ZClJklQxe8iWQ80+OZ4kSZ1132NPVh1hmViQST1Es3fnS9LyzIJMUsNq9v94JXWfIfP+sEzPf7xrYiw1x5BJkiRVzIJMkiSpYh6yVENYlvFNjm2SJDW7hirIImI34CdAL+CczDy54kjO6SVVqNnHhEhSRzVMQRYRvYAzgF2AGcAdEXFZZj5QZS6nkFBb7NWTJHWVhinIgA8DD2fmowARMRHYE6i0IGtGzTi9gWfT1Z+fsSQ1rsjMqjMAEBH7ALtl5hfKxwcA22Tml1qsdxhwWPnw/cBD3Rp0cWsDz1WcobPMXH/NlhfM3B2aLS+Yubs0W+ZmywuNkXmDzBzY2oJG6iHrkMw8Gzi76hwLRMTkzBxRdY7OMHP9NVteMHN3aLa8YObu0myZmy0vNH7mRpr24ilgcM3jQWWbJEnScq2RCrI7gI0jYsOI6AvsD1xWcSZJkqS6a5hDlpk5PyK+BFxDMe3FrzJzasWxOqJhDp92gpnrr9nygpm7Q7PlBTN3l2bL3Gx5ocEzN8ygfkmSpJ6qkQ5ZSpIk9UgWZJIkSRWzIJMkSaqYBZkkSVLFGuYsS0mSeoqIGEgx3+ZbwKOZ+VrFkdoVEasCu1HMF/oW8A/gL5n5dqXB2tFsmT3LUg2p6X6Qmiyv1Bb35fqKiA8CpwNDgPWBu4F3AX8FjsrMl6tL17qI2A/4T+Be4OPArRRH2IYBn8vMKi6B3K6mzGxB1jkR8XFgbxb9ZXVOZj5cabB2NFvmZvtBara8CzTbfgFmrjf35fqLiEnA2Mx8KCI+DIzPzLERcSiwa2buU3HExUTEvcC2mTk3ItYGfp+Zu0bE5sBZmfnRiiMuphkzO4asEyLi+8CBwCTgTeCR8vaniNi3ymxtacbMwDeBncoLzW8DvCszPwd8HvhFpcla12x5m3K/MHO3cF+uv5Uy8yGAzLydotglM38JbFplsHYE8Hp5fw5Fjx6ZeS+welWhlqD5Mmemtw7egPtq7vcGbinvDwDurzrf8pSZd3pvVwLurlnWcJmbLW8z7xdmrn9e9+W6570I+H/AdsAPKa5KA9AHeKjqfG1kPoXiKjrfAP4GfL1sXxOYWnW+5SWzPWSd83ZErFneX5fiEk9k5osU1XgjasbMVwJXR8Q3gL8AfwIo30cjZm62vNCc+4WZ6899uf7+A1gNOAGYBxxVtq8MjK0qVHsy8zjgJ8C/gJMy87/LRS8BW1WVqz3NmNmzLDvnv4G7I+IfwPuBI2Dh2TL3VBmsHU2XOTOPi4g9gA9S/CBdWy56iQb8QWq2vKWm2y8wc925L9dfZr4EHNtK+8sUh10bUmZeSVGw17a9TVHwNKRmy+yg/k4q/xPbCHi4/MFqeM2YuaWIWDMzX6g6R0dFxGcy87Kqc7SnGfcLM9dfRPTOzPnl/VWBD1BMy9CwP3/N9BlHxBoUvWN7AusACTwLXAqc3Oj5W4qI+zJzWNU5WoqIwcD/AOsBVwH/k5lvlssuycxRFcZrlT1knVT+UlrsF1NEfCAzH6wg0hJl5gsR8eqCnXGBiFg7M5+rKldbImI74BzgbYru/f8CNoqIvsB+mfn3KvO1FBH/3rIJOCMiegNk5kXdn2rJavfl8g/vJhR/eF+qMldbyu//i5k5uXz8cYpemwcy86pKw7UhIjbPYhBxwxYztSLiIOCHEfE8xaG0M4DHgE0i4tjMPK/KfG0pf8dtBHw8It4C/tGov4+B84EbgI9n5jMAEfFuisOV5wOfqDBbq1r5HbdwEfDu7szSCb8CLqTodTwE+GtEfDoznwc2qDRZG+wh6yIR8WRmrl91jpbKP1q/A/oBdwGHZebj5bK7MrPhDkNExO0UP0CrApcDozLz5ojYCvhpZm5XacAWIuJNisGjs2ua9wEuADIz/6OSYO2IiJ9n5hfL+9sDf6A4M+19wOFlV39DiYh7KM4AfDEivgbsRXE44mPAnZl5fKUBW1EWB48CE4HzMvOBiiO1KyLuo5juYjWKw31bZuYjEbEOcG1mbl5pwFZExMcoBse/BHwIuIViQP+bwAGZOb26dIuLiIcy8/2dXVal8nfc7yl681raJzNX6+ZISxQRUzJzeM3jz1P0TH4G+FMj/u2zh6wTIuL0thYB/bsxSmecSjG3zdSI2Ae4NiIOyMxJNOaAV4A+Wc53FBGzM/NmgMy8KyJWqjZaqz4KnAzcnplnAkTETpl5cLWx2rVtzf3vUhS9d5W9DOfTYtxFg+hVDtQGGA3skJmvR8TJFP9sNFxBRjGf1wHAGOCyiJgDnAdMXPCPUYN5q+w1fy4iXsvMRwAyc1ZEo/664MfAJzJzdkRsCJyWmdtFxC7AuTRej9MTEXEsMCEzZwGUBe9BQEMVjzXuBX6Qmfe3XBAR/1ZBno7oExH9MnMeQGb+b0Q8Q/HP8yrVRmudZ1l2zsHA/cCdLW6TgTcqzNWevpk5FSAzLwBGARMiYhSt/7fTCGr3yxNaLOvbnUE6IjPvAHYB+kbEjeVkj4362bZm9cy8CyAzH6Vxfy+8EhGblfefo+j1heIfy0bNnJl5f2Z+IzPfBxxKMR/SzRFxa8XZWvNkRHw/In4GPBgRP4yI7SLi28DMqsO1oVdmLuidfpLycFR5QsJ6laVq22hgLYpDaC9GxIvATRTTMexXZbB2HA280sayvboxR2ecQzGX3kKZeR2wL8Xf8YbjIctOiIgbgG9m5mK/SCPisczcsIJY7YqIycCnFoxVKNsGAVcA723QrubPANdl5twW7e8F9s7MU6tJtmQRsR7wI2BEZm5UdZ62RMRc4GGKXtIhwPrlocAVgHszc7P2nl+FKGbY/h3vnDm3HfB/FBNrnpaZf6gqW1si4u7M3LKV9gB2zMy/VhCrTRGxOjCe4h+Kn1FcQukgikLnu5nZcEVZRPyKIu8NFIejnsrMr0TEysBdmfmBSgNKHWRB1gnlmTzzWhYKjazsTp6dmfe0aO9PccmO71USTJWKiJaDWmdm5htRXGJkx0Y9ESEielEcgtqEomdsBnBNA5+I8NlGLBSXJxHRh6Ln8YMUxfqvMvOtcnjDuzLziUoDtiIidqU4WrGgB+8p4NLMvLqyUO0oT1A6hKI3bN2y+SmKM0PPbXnCWCNoyswWZGo0ZS/NQRTXphvEO9emOyszb6ouWeuaLa/UlnJfHsvi14U8s9F685pVRPyY4h+K31L8QwHF740DgX9m5lFtPLUyEXEexUkTE1g081hgzcwcXVG0NjVlZguyjiunBjiWd/7wvkFxZtpZmfmbCqO1qUkz/xp4AriO4mzFVygufXEcxX+RP60w3mKaLS807X5h5jpr8n353ymKyEb/jP+RmZu00h4U03VsXEGsdrWVeUnLqtSUmS3IOi4iLgUupvhltR/FmRoTKS7I+1Rmfr3CeK1q0sz31p5eHxGTMnPbiFgRmJKZQyuMt5hmywtNu1+Yuc7cl+svIu4FDilPBqpt/zDFobRGnGR1EsXUIhdmMdP9gt7UfYGvZOY27T2/Cs2YufKLaTbTDbinxeM7yq8rAA9WnW85ynwnxQkHUEz8+X81yx6oOl+z523i/cLM9c/rvlz/vFsBtwEPUFwv9C/ANIoJTD9Udb42Mg8B/kgx1+I/ytuzZduGVedbXjI7D1nnzImI7bOYpPQzlLNvZ+bb0biT9DRj5q8BN0bEvygGbu8PC69Nd0WVwdrQbHmhOfcLM9ef+3KdZTHFzDZRzM6/cFB/1pwJ32iymDNvNEBErFW2PV9lpiVpxsyVV4TNdAM2B24HXgRuBjYp2wcCX6463/KSucwXwNpV51iO8zbdfmHmbsvsvtw9ufu00tawnzuwOmXvacvPv+psy0tmx5CpqUTELllM+NgUmi2v1Bb35a4RzXk5u/0orojwLNAHOCjLMXBm7jqNOrt104mIRr5MTquaMTPFpVCaSbPlbcr9wszdwn25ayy4nN3awNkUl7NbcCmzhjvEWvo6xfi24RRXrPldRCyYod/MXcQesi4SDXpx8fY0auaIuKytRcDIzGyo65A1W94ladT9oj1m7hruy/UXEfdk5hY1jzcFLqKYWuRbjdhzExH3Zc3ZnxHxHooxhRMoep7M3AUc1N8J5enKrS4C1unOLB3VjJmBHYDPA6+1aA/gw90fZ4maLW9T7hdm7hbuy/X3ZkS8O8tB/Jk5NSJ2prycXbXR2vRqRLw337nY/Mzy0OvFwKbVRmtT02W2IOucdYBdKQaP1gqgES8UDM2ZeRIwN1uZGTwiHqogz5I0W15ozv3CzPXnvlx/x1NkXnhWZWbOiIiPAV+qLFX7jqDFYb7MfCUidqNxL4jedJktyDrnCmDVzJzSckFE3NTtaTqm6TJn5u7tLNuxO7N0RLPlLTXdfoGZ6859uf4y87qWbRGxVRbTYTTktYWzxbWQASLiU5l5BfD7CiItUTNmdgyZmkLND1JTaLa8Ulvcl+uvUc/6a4+Zu55nWS6jiDis6gyd1YyZgZOqDtBJzZa3KfcLM3cL9+X6a8iz/pbAzF3MgmzZjas6wFJoxswN/YPUimbLC825X5i5/tyX6+87VQdYCodXHWApNHRmx5Atu2b8ZdWMmRv6B6kVzZYXmnO/MHP9uS93sYhYA9iNmksnRUT/zHypulTti4gPAHuyaOZXM3NahbHa1WyZHUO2jCJiUGbOqDpHZzRD5tZ+kIDLGvYHqcnytqYZ9ouWzNz13JfrKyIOBL5NcVHxp8rmQcAuwHcy87dVZWtLRBwHjAEmAgs+10EU1zqdmJknV5WtLU2Z2YKscyJiV2AUi/6yujQzr64s1BI0W+Zm+0FqtrwLNNt+AWauN/fl+iunD9mmZW9YRAwAbsvMTSoJ1o6I+AewaWa+2aK9LzA1MzeuJlnbmjKzBVnHRcSPgU2A37LoL6sDgX9m5lEVRWtTk2Zuqh+kZssLTbtf/Bgz15X7cv2Vn/HWmflyi/Y1gMkN+hk/SHG5pydatG8A/CUz319NsrY1ZWYLso6LiH+09t9LRATwjwb9QWrGzE31g9RseaFp9wsz15n7cv1FxFjgWxSHLKeXzetTHLL8bmb+pqJobSonU/0Z8E8Wzfw+4EsN2hPZdJkd1N858yJi6yyvGF9ja2BeFYE6oBkzHw1cHxGt/iBVFaodR9NceaE59wsz19/RuC/XVWZOiOKaobvyziHWm4ATMrPl1QYaQmZeHRGbUFw+q/aw8B2Z+VZ1ydrWjJntIeuEiNgKOBNYjXe6xgcDLwPjM/POqrK1pRkzA0TECjTTD1Lz5W26/aJJM38I+DnNldl9uRtExDrUfMaZOavKPEsrIlbNzJbXPtVSsCBbChHxbhb9QXqmvfUbQbNlLv8okJlvl+NXNgMez8wXqk3WMRHxxcz8edU5lqTZ9gswc71FxPrAK5n5UkQMAUYA0zJzarXJ2tcsn3FEDAfOAtagKCCDYszbS8AXy0soNY2IeDIz1686R0sRsTlwNsU+cRVw3IIeyIi4PTM/XGW+1njIcimUP+iL/LBHxAcy88GKInXE8y1/QUXE2pn5XFWB2hIRo4BfAG9HxDjg68BrwPsj4ojMvLzKfC1FxFdaaf56RPQDyMzTujlShy3YlyNiVWCTiJjXqHMhlYX5mzWZPw7sFBFTG3E8CBR/FDLz3tZ+ZzSiiDieYt6xf0XED4D/BG4BvhMR5zbyvkxR1AwG3gLm0Lif92+AwzPzttrGiNgW+DWwRRWh2tPG7zgoislVuzNLJ/wcOBGYBHwBuDkiPpOZjwB9qgzWFmfq7zp/qTpAayLi4xExA5gZEX8p/+NdoCEzU8zRswXwUeB3wIGZuTOwXbms0XwH2IbiF9Nq5a1Xzf2GExE/r7m/PfAA8EPgvojYo7Jg7bsD6A8QEV+juBDzSsBXI+L7FeZqz90R8c+I+G5EfLDqMB1wAPBBip+1HwE7ZOYhFIcw/6PKYG2JiI9FxGTgZOBXwGHAuRFxU0QMrjZdq1ZpWYwBZOYkYJUK8nTEfwMDeOd32oLbqjRuHbFaZl6dmS9l5g8oxkBeXRa+DXlo0B6yToiI09taRPmHogGdSnHW1NSI2Ae4NiIOKH/4G3Y26wW9eWV3+ENl2xMLDmU2mE0piplVKCZ2nBsRYzOzkS+Hsm3N/e8CozLzrojYCDgfuLKaWO3qVTPoeTRFsfB6RJwM3AWcUF20Nt1LUeSMAS6LiDnAeRRzej1eZbA2vFV+pm8ArwPPA2TmnOKkxYb0Y+ATmTk7IjYETsvM7SJiF+Bc4BOVplvcVRHxZ4ppOhacODGYYpqOhuzppfj5uqS18XgR8YUK8nRIRKyxYHqRzLwxIvYGLgTWrDZZ6yzIOudg4KvAv1pZNqabs3RU3wVjPzLzgoiYBlwUxQSQDflfAhRjyDLzbWr+K4+IXkDf6lK1LjOfBPaNiD0pCt4fVZ2pk1ZfMG4lMx9t0KIX4JWI2Cwz7weeA/pRFA29adz/0rPM+w3gGxHxYYpJVm8u/9n4aLXxFnNXRPyB4p+L64EJEXE1MJKiF7UR9crM2eX9J4ENADLz2ijmKGsomfnliNidxa+GcEZmNuI/QlD87Wtr/O6I7gzSCacAQykOWQKQmfdGxM7A/6ssVTsc1N8JEXED8M3MvLWVZY9l5oYVxGpX2ZX/qdrxYxExCLgCeG9mNtwhtYjYGrgvM+e1aB8CbJ+Z/1tJsA4ox2J9m2Im7h2rztOWiJgLPEzRSzoEWD8zXyyLsXszc7Mq87WmHKT7O+Cesmk74P+AYRS9In+oKltbIuLuzNyylfYAdszMv1YQq00R0RvYl+KftQsoDsWPoSh0zsjMORXGa1VE/Ioi7w3AZygG9H8lIlYG7srMD1QaUOogC7JOiIg1gXmZObfqLB0VEf8GzM7Me1q096c4Jfx7lQRTpaKY6LPWzMx8IyLWpigULqoi15KUvaSfoJiZvTfFWWrXNPCJCJ9txEJxeRIRfYBDKca+3QP8KjPfioiVgHdli0luqxbFjPwnUPSQrUNRTD4LXAqc3Ij7ck3mUcC7MHNdWJCp4ZS9TMcCe1OcOfUG8AhwVjbmLNZNlVdqSzv78pmZOaHKbMuLiLiGojdvQs1Y2XcDBwEjM7PRxry1l3kssLOZu4YFWSc04x/eJs18KXAxcB2wH8V4lonANykOR3y9wniLaba80LT7hZnrrMn35X+nGBzf6J/xQ9nGJajaW1YlM3cPC7JOaNJfVs2Y+Z7M3KLm8R2ZuXU5vumBRhsT0mx5oWn3CzPXmfty/UXEXyiyTshydv4oZu0/CNglM/+twnitMnM3yUxvHbwB97R4fEf5dQXgwarzLUeZb6UYvA/FIN1rapY9VHW+Zs/bxPuFmeuf1325/nkHUJwB+CDwIsXZi9PKtjWrzmfm6m5Oe9E5cyJi+8y8OSI+Q3kacBaX92nUSXqaMfM44JyI2BiYSjn1RUQMBM6oMlgbmi0vNOd+Yeb6c1+usyzOZv41cC0wKWuuAxkRu9GAc5GZuZtUXRE20w3YHLidotq+GdikbB8IfLnqfMtL5iW8n4OrzrA85G3G/cLMlb8X9+Wuyftl4CHgEuBxYM+aZXdVnc/M1d3sIeuEzLyX4hIiLdtnR8SrFURaombMvATfobjeW7NoyLzNuF+YuXLuy13jUOBDmflaObfiBRExJDN/QuNePcXM3cBB/V0kGvSK9+1p1MwRcW9biyj++12xO/MsSbPlXZJG3S/aY+au4b5cfxExNTM3rXm8KsUkvA9QTHsxvKpsbTFz97CHrBOW8Mtqne7M0lHNmJki164UhyBqBcWg40bTbHmbcr8wc7dwX66/WRExPDOnAJQ9OJ+iuDD6sEqTtc3M3cCCrHOa7pcVzZn5CmDVBT9ItSLipm5Ps2TNlheac78wc/25L9ffgcD82obMnA8cGBG/qCbSEpm5G1iQdU4z/rJqusyZeUg7yz7bnVk6otnylppuv8DMdee+XH+ZOaOdZbd0Z5aOMnP3cAyZJElSxVaoOoAkSVJPZ0EmSZJUMQsySZKkilmQSZIkVez/A9fPVm112dTFAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(10, 8))\n",
    "width = 0.35 \n",
    "\n",
    "p_s = []\n",
    "p_s.append(plt.bar(sorted(df['decade'].unique()), df_merged.iloc[:,1], width))\n",
    "\n",
    "for i in range(1, len(df['rating'].unique())):\n",
    "    p_s.append(ax.bar(df_merged.iloc[:,0], df_merged.iloc[:,i+1], \n",
    "                      width, label = df['rating'].unique()[i], \n",
    "                      bottom = np.sum(df_merged.iloc[:,1:i+1], axis=1)\n",
    "                     ))\n",
    "\n",
    "ax.set_ylabel('Title count')\n",
    "ax.set_title('Number of titles by ratings & decades')\n",
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     },
     "metadata": {},
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   ],
   "source": [
    "import plotly.express as px\n",
    "\n",
    "fig = px.bar(df_groupby, x=\"decade\", y=\"counts\", color=\"rating\", title=\"Number of titles by ratings & decades\")\n",
    "fig.show()"
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