Mini Project - Movie Analytics.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "7a8fa003",
   "metadata": {},
   "source": [
    "# Movie Analytics"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ca149b26",
   "metadata": {},
   "source": [
    "#### Dataset: https://grouplens.org/datasets/movielens/1m/\n",
    "#### Download, unzip dan masuk ke foder, lalu jalankan Jupyter dari folder tersebut"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c38a2019",
   "metadata": {},
   "source": [
    "## 1. Import Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "7cede300",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9af906fe",
   "metadata": {},
   "source": [
    "## 2. Reading and Exploring Data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b72d8e60",
   "metadata": {},
   "source": [
    "### Movies "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "4599d386",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "movie shape:  (3883, 3)\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 3883 entries, 0 to 3882\n",
      "Data columns (total 3 columns):\n",
      " #   Column   Non-Null Count  Dtype \n",
      "---  ------   --------------  ----- \n",
      " 0   movieid  3883 non-null   int64 \n",
      " 1   title    3883 non-null   object\n",
      " 2   genres   3883 non-null   object\n",
      "dtypes: int64(1), object(2)\n",
      "memory usage: 91.1+ KB\n"
     ]
    }
   ],
   "source": [
    "# movies name\n",
    "mnames = ['movieid', 'title', 'genres']\n",
    "movies_data = pd.read_table('movies.dat', sep='::', header=None, names=mnames, engine='python', encoding='latin-1')\n",
    "print('movie shape: ',movies_data.shape)\n",
    "movies_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "4f7d9f80",
   "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>movieid</th>\n",
       "      <th>title</th>\n",
       "      <th>genres</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Toy Story (1995)</td>\n",
       "      <td>Animation|Children's|Comedy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Jumanji (1995)</td>\n",
       "      <td>Adventure|Children's|Fantasy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Grumpier Old Men (1995)</td>\n",
       "      <td>Comedy|Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Waiting to Exhale (1995)</td>\n",
       "      <td>Comedy|Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Father of the Bride Part II (1995)</td>\n",
       "      <td>Comedy</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   movieid                               title                        genres\n",
       "0        1                    Toy Story (1995)   Animation|Children's|Comedy\n",
       "1        2                      Jumanji (1995)  Adventure|Children's|Fantasy\n",
       "2        3             Grumpier Old Men (1995)                Comedy|Romance\n",
       "3        4            Waiting to Exhale (1995)                  Comedy|Drama\n",
       "4        5  Father of the Bride Part II (1995)                        Comedy"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# display\n",
    "movies_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "8cfb57ac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3883"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check unique movies\n",
    "unique_movies = movies_data['movieid'].unique().tolist()\n",
    "len(unique_movies)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "86e8c2ae",
   "metadata": {},
   "source": [
    "### Ratings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "68a210e8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "movie shape:  (1000209, 4)\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1000209 entries, 0 to 1000208\n",
      "Data columns (total 4 columns):\n",
      " #   Column     Non-Null Count    Dtype\n",
      "---  ------     --------------    -----\n",
      " 0   userid     1000209 non-null  int64\n",
      " 1   movieid    1000209 non-null  int64\n",
      " 2   rating     1000209 non-null  int64\n",
      " 3   timestamp  1000209 non-null  int64\n",
      "dtypes: int64(4)\n",
      "memory usage: 30.5 MB\n"
     ]
    }
   ],
   "source": [
    "rnames = ['userid','movieid','rating','timestamp']\n",
    "ratings_data = pd.read_table('ratings.dat', sep='::', header=None, names=rnames, engine='python', encoding='latin-1')\n",
    "print('movie shape: ',ratings_data.shape)\n",
    "ratings_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "4d3463e6",
   "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>userid</th>\n",
       "      <th>movieid</th>\n",
       "      <th>rating</th>\n",
       "      <th>timestamp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1193</td>\n",
       "      <td>5</td>\n",
       "      <td>978300760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>661</td>\n",
       "      <td>3</td>\n",
       "      <td>978302109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>914</td>\n",
       "      <td>3</td>\n",
       "      <td>978301968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>3408</td>\n",
       "      <td>4</td>\n",
       "      <td>978300275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>2355</td>\n",
       "      <td>5</td>\n",
       "      <td>978824291</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1000204</th>\n",
       "      <td>6040</td>\n",
       "      <td>1091</td>\n",
       "      <td>1</td>\n",
       "      <td>956716541</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1000205</th>\n",
       "      <td>6040</td>\n",
       "      <td>1094</td>\n",
       "      <td>5</td>\n",
       "      <td>956704887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1000206</th>\n",
       "      <td>6040</td>\n",
       "      <td>562</td>\n",
       "      <td>5</td>\n",
       "      <td>956704746</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1000207</th>\n",
       "      <td>6040</td>\n",
       "      <td>1096</td>\n",
       "      <td>4</td>\n",
       "      <td>956715648</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1000208</th>\n",
       "      <td>6040</td>\n",
       "      <td>1097</td>\n",
       "      <td>4</td>\n",
       "      <td>956715569</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000209 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         userid  movieid  rating  timestamp\n",
       "0             1     1193       5  978300760\n",
       "1             1      661       3  978302109\n",
       "2             1      914       3  978301968\n",
       "3             1     3408       4  978300275\n",
       "4             1     2355       5  978824291\n",
       "...         ...      ...     ...        ...\n",
       "1000204    6040     1091       1  956716541\n",
       "1000205    6040     1094       5  956704887\n",
       "1000206    6040      562       5  956704746\n",
       "1000207    6040     1096       4  956715648\n",
       "1000208    6040     1097       4  956715569\n",
       "\n",
       "[1000209 rows x 4 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# display\n",
    "ratings_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "7509e95c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1000209, 4)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratings_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "57cf0a4e",
   "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>userid</th>\n",
       "      <th>movieid</th>\n",
       "      <th>rating</th>\n",
       "      <th>timestamp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1.000209e+06</td>\n",
       "      <td>1.000209e+06</td>\n",
       "      <td>1.000209e+06</td>\n",
       "      <td>1.000209e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.024512e+03</td>\n",
       "      <td>1.865540e+03</td>\n",
       "      <td>3.581564e+00</td>\n",
       "      <td>9.722437e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.728413e+03</td>\n",
       "      <td>1.096041e+03</td>\n",
       "      <td>1.117102e+00</td>\n",
       "      <td>1.215256e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>9.567039e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.506000e+03</td>\n",
       "      <td>1.030000e+03</td>\n",
       "      <td>3.000000e+00</td>\n",
       "      <td>9.653026e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.070000e+03</td>\n",
       "      <td>1.835000e+03</td>\n",
       "      <td>4.000000e+00</td>\n",
       "      <td>9.730180e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>4.476000e+03</td>\n",
       "      <td>2.770000e+03</td>\n",
       "      <td>4.000000e+00</td>\n",
       "      <td>9.752209e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>6.040000e+03</td>\n",
       "      <td>3.952000e+03</td>\n",
       "      <td>5.000000e+00</td>\n",
       "      <td>1.046455e+09</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             userid       movieid        rating     timestamp\n",
       "count  1.000209e+06  1.000209e+06  1.000209e+06  1.000209e+06\n",
       "mean   3.024512e+03  1.865540e+03  3.581564e+00  9.722437e+08\n",
       "std    1.728413e+03  1.096041e+03  1.117102e+00  1.215256e+07\n",
       "min    1.000000e+00  1.000000e+00  1.000000e+00  9.567039e+08\n",
       "25%    1.506000e+03  1.030000e+03  3.000000e+00  9.653026e+08\n",
       "50%    3.070000e+03  1.835000e+03  4.000000e+00  9.730180e+08\n",
       "75%    4.476000e+03  2.770000e+03  4.000000e+00  9.752209e+08\n",
       "max    6.040000e+03  3.952000e+03  5.000000e+00  1.046455e+09"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# statistics summary \n",
    "ratings_data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "38d3a711",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# minimum rating\n",
    "ratings_data['rating'].min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "63ef2026",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# maximum rating\n",
    "ratings_data['rating'].max()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae4f9a47",
   "metadata": {},
   "source": [
    "## 3. Data Cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "03f50ad8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "movieid    False\n",
       "title      False\n",
       "genres     False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies_data.isnull().any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "c355d1eb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "userid       False\n",
       "movieid      False\n",
       "rating       False\n",
       "timestamp    False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratings_data.isnull().any()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5b467206",
   "metadata": {},
   "source": [
    "## 4. Data Analytics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "0cfa0fa1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(3883,)\n",
      "<class 'pandas.core.series.Series'>\n",
      "RangeIndex: 3883 entries, 0 to 3882\n",
      "Series name: genres\n",
      "Non-Null Count  Dtype\n",
      "--------------  -----\n",
      "3883 non-null   bool \n",
      "dtypes: bool(1)\n",
      "memory usage: 3.9 KB\n"
     ]
    }
   ],
   "source": [
    "# filter movies with drama genre\n",
    "drama_movies = movies_data['genres'].str.contains('Drama')\n",
    "print(drama_movies.shape)\n",
    "drama_movies.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "fb4ae985",
   "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>movieid</th>\n",
       "      <th>title</th>\n",
       "      <th>genres</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Waiting to Exhale (1995)</td>\n",
       "      <td>Comedy|Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>American President, The (1995)</td>\n",
       "      <td>Comedy|Drama|Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>14</td>\n",
       "      <td>Nixon (1995)</td>\n",
       "      <td>Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>16</td>\n",
       "      <td>Casino (1995)</td>\n",
       "      <td>Drama|Thriller</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>17</td>\n",
       "      <td>Sense and Sensibility (1995)</td>\n",
       "      <td>Drama|Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3876</th>\n",
       "      <td>3946</td>\n",
       "      <td>Get Carter (2000)</td>\n",
       "      <td>Action|Drama|Thriller</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3879</th>\n",
       "      <td>3949</td>\n",
       "      <td>Requiem for a Dream (2000)</td>\n",
       "      <td>Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3880</th>\n",
       "      <td>3950</td>\n",
       "      <td>Tigerland (2000)</td>\n",
       "      <td>Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3881</th>\n",
       "      <td>3951</td>\n",
       "      <td>Two Family House (2000)</td>\n",
       "      <td>Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3882</th>\n",
       "      <td>3952</td>\n",
       "      <td>Contender, The (2000)</td>\n",
       "      <td>Drama|Thriller</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1603 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      movieid                           title                 genres\n",
       "3           4        Waiting to Exhale (1995)           Comedy|Drama\n",
       "10         11  American President, The (1995)   Comedy|Drama|Romance\n",
       "13         14                    Nixon (1995)                  Drama\n",
       "15         16                   Casino (1995)         Drama|Thriller\n",
       "16         17    Sense and Sensibility (1995)          Drama|Romance\n",
       "...       ...                             ...                    ...\n",
       "3876     3946               Get Carter (2000)  Action|Drama|Thriller\n",
       "3879     3949      Requiem for a Dream (2000)                  Drama\n",
       "3880     3950                Tigerland (2000)                  Drama\n",
       "3881     3951         Two Family House (2000)                  Drama\n",
       "3882     3952           Contender, The (2000)         Drama|Thriller\n",
       "\n",
       "[1603 rows x 3 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies_data[drama_movies]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "09f9b6ae",
   "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>movieid</th>\n",
       "      <th>title</th>\n",
       "      <th>genres</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Toy Story (1995)</td>\n",
       "      <td>Animation|Children's|Comedy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Jumanji (1995)</td>\n",
       "      <td>Adventure|Children's|Fantasy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Grumpier Old Men (1995)</td>\n",
       "      <td>Comedy|Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Waiting to Exhale (1995)</td>\n",
       "      <td>Comedy|Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Father of the Bride Part II (1995)</td>\n",
       "      <td>Comedy</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   movieid                               title                        genres\n",
       "0        1                    Toy Story (1995)   Animation|Children's|Comedy\n",
       "1        2                      Jumanji (1995)  Adventure|Children's|Fantasy\n",
       "2        3             Grumpier Old Men (1995)                Comedy|Romance\n",
       "3        4            Waiting to Exhale (1995)                  Comedy|Drama\n",
       "4        5  Father of the Bride Part II (1995)                        Comedy"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# merge movies dan rating\n",
    "# check movies\n",
    "movies_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "62a5fd92",
   "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>userid</th>\n",
       "      <th>movieid</th>\n",
       "      <th>rating</th>\n",
       "      <th>timestamp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1193</td>\n",
       "      <td>5</td>\n",
       "      <td>978300760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>661</td>\n",
       "      <td>3</td>\n",
       "      <td>978302109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>914</td>\n",
       "      <td>3</td>\n",
       "      <td>978301968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>3408</td>\n",
       "      <td>4</td>\n",
       "      <td>978300275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>2355</td>\n",
       "      <td>5</td>\n",
       "      <td>978824291</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   userid  movieid  rating  timestamp\n",
       "0       1     1193       5  978300760\n",
       "1       1      661       3  978302109\n",
       "2       1      914       3  978301968\n",
       "3       1     3408       4  978300275\n",
       "4       1     2355       5  978824291"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check ratings\n",
    "ratings_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "a8d3b9d3",
   "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>movieid</th>\n",
       "      <th>title</th>\n",
       "      <th>genres</th>\n",
       "      <th>userid</th>\n",
       "      <th>rating</th>\n",
       "      <th>timestamp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Toy Story (1995)</td>\n",
       "      <td>Animation|Children's|Comedy</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>978824268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Toy Story (1995)</td>\n",
       "      <td>Animation|Children's|Comedy</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>978237008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>Toy Story (1995)</td>\n",
       "      <td>Animation|Children's|Comedy</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>978233496</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>Toy Story (1995)</td>\n",
       "      <td>Animation|Children's|Comedy</td>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "      <td>978225952</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>Toy Story (1995)</td>\n",
       "      <td>Animation|Children's|Comedy</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>978226474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1000204</th>\n",
       "      <td>3952</td>\n",
       "      <td>Contender, The (2000)</td>\n",
       "      <td>Drama|Thriller</td>\n",
       "      <td>5812</td>\n",
       "      <td>4</td>\n",
       "      <td>992072099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1000205</th>\n",
       "      <td>3952</td>\n",
       "      <td>Contender, The (2000)</td>\n",
       "      <td>Drama|Thriller</td>\n",
       "      <td>5831</td>\n",
       "      <td>3</td>\n",
       "      <td>986223125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1000206</th>\n",
       "      <td>3952</td>\n",
       "      <td>Contender, The (2000)</td>\n",
       "      <td>Drama|Thriller</td>\n",
       "      <td>5837</td>\n",
       "      <td>4</td>\n",
       "      <td>1011902656</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1000207</th>\n",
       "      <td>3952</td>\n",
       "      <td>Contender, The (2000)</td>\n",
       "      <td>Drama|Thriller</td>\n",
       "      <td>5927</td>\n",
       "      <td>1</td>\n",
       "      <td>979852537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1000208</th>\n",
       "      <td>3952</td>\n",
       "      <td>Contender, The (2000)</td>\n",
       "      <td>Drama|Thriller</td>\n",
       "      <td>5998</td>\n",
       "      <td>4</td>\n",
       "      <td>1001781044</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000209 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         movieid                  title                       genres  userid  \\\n",
       "0              1       Toy Story (1995)  Animation|Children's|Comedy       1   \n",
       "1              1       Toy Story (1995)  Animation|Children's|Comedy       6   \n",
       "2              1       Toy Story (1995)  Animation|Children's|Comedy       8   \n",
       "3              1       Toy Story (1995)  Animation|Children's|Comedy       9   \n",
       "4              1       Toy Story (1995)  Animation|Children's|Comedy      10   \n",
       "...          ...                    ...                          ...     ...   \n",
       "1000204     3952  Contender, The (2000)               Drama|Thriller    5812   \n",
       "1000205     3952  Contender, The (2000)               Drama|Thriller    5831   \n",
       "1000206     3952  Contender, The (2000)               Drama|Thriller    5837   \n",
       "1000207     3952  Contender, The (2000)               Drama|Thriller    5927   \n",
       "1000208     3952  Contender, The (2000)               Drama|Thriller    5998   \n",
       "\n",
       "         rating   timestamp  \n",
       "0             5   978824268  \n",
       "1             4   978237008  \n",
       "2             4   978233496  \n",
       "3             5   978225952  \n",
       "4             5   978226474  \n",
       "...         ...         ...  \n",
       "1000204       4   992072099  \n",
       "1000205       3   986223125  \n",
       "1000206       4  1011902656  \n",
       "1000207       1   979852537  \n",
       "1000208       4  1001781044  \n",
       "\n",
       "[1000209 rows x 6 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# merging movies dan rating dataframe\n",
    "movie_ratings_data = movies_data.merge(ratings_data, on='movieid', how='inner')\n",
    "movie_ratings_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "3c5b9906",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "title\n",
       "American Beauty (1999)                                   3428\n",
       "Star Wars: Episode IV - A New Hope (1977)                2991\n",
       "Star Wars: Episode V - The Empire Strikes Back (1980)    2990\n",
       "Star Wars: Episode VI - Return of the Jedi (1983)        2883\n",
       "Jurassic Park (1993)                                     2672\n",
       "Saving Private Ryan (1998)                               2653\n",
       "Terminator 2: Judgment Day (1991)                        2649\n",
       "Matrix, The (1999)                                       2590\n",
       "Back to the Future (1985)                                2583\n",
       "Silence of the Lambs, The (1991)                         2578\n",
       "Men in Black (1997)                                      2538\n",
       "Raiders of the Lost Ark (1981)                           2514\n",
       "Fargo (1996)                                             2513\n",
       "Sixth Sense, The (1999)                                  2459\n",
       "Braveheart (1995)                                        2443\n",
       "Shakespeare in Love (1998)                               2369\n",
       "Princess Bride, The (1987)                               2318\n",
       "Schindler's List (1993)                                  2304\n",
       "L.A. Confidential (1997)                                 2288\n",
       "Groundhog Day (1993)                                     2278\n",
       "E.T. the Extra-Terrestrial (1982)                        2269\n",
       "Star Wars: Episode I - The Phantom Menace (1999)         2250\n",
       "Being John Malkovich (1999)                              2241\n",
       "Shawshank Redemption, The (1994)                         2227\n",
       "Godfather, The (1972)                                    2223\n",
       "dtype: int64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# most rated/watched movies (>< highly rated movies)\n",
    "# film yg paling banyak di-rating oleh users\n",
    "most_rated = movie_ratings_data.groupby('title').size().sort_values(ascending=False)\n",
    "most_rated.head(25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "e11228e8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Open Season (1996)                                          1\n",
       "Number Seventeen (1932)                                     1\n",
       "Song of Freedom (1936)                                      1\n",
       "Elstree Calling (1930)                                      1\n",
       "Juno and Paycock (1930)                                     1\n",
       "                                                         ... \n",
       "Jurassic Park (1993)                                     2672\n",
       "Star Wars: Episode VI - Return of the Jedi (1983)        2883\n",
       "Star Wars: Episode V - The Empire Strikes Back (1980)    2990\n",
       "Star Wars: Episode IV - A New Hope (1977)                2991\n",
       "American Beauty (1999)                                   3428\n",
       "Name: title, Length: 3706, dtype: int64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "most_rated = movie_ratings_data.title.value_counts(ascending=True)\n",
    "most_rated"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "bd0c3859",
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">rating</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>size</th>\n",
       "      <th>mean</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>title</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>$1,000,000 Duck (1971)</th>\n",
       "      <td>37</td>\n",
       "      <td>3.027027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>'Night Mother (1986)</th>\n",
       "      <td>70</td>\n",
       "      <td>3.371429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>'Til There Was You (1997)</th>\n",
       "      <td>52</td>\n",
       "      <td>2.692308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>'burbs, The (1989)</th>\n",
       "      <td>303</td>\n",
       "      <td>2.910891</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...And Justice for All (1979)</th>\n",
       "      <td>199</td>\n",
       "      <td>3.713568</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zed &amp; Two Noughts, A (1985)</th>\n",
       "      <td>29</td>\n",
       "      <td>3.413793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zero Effect (1998)</th>\n",
       "      <td>301</td>\n",
       "      <td>3.750831</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zero Kelvin (Kjærlighetens kjøtere) (1995)</th>\n",
       "      <td>2</td>\n",
       "      <td>3.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zeus and Roxanne (1997)</th>\n",
       "      <td>23</td>\n",
       "      <td>2.521739</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>eXistenZ (1999)</th>\n",
       "      <td>410</td>\n",
       "      <td>3.256098</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3706 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                           rating          \n",
       "                                             size      mean\n",
       "title                                                      \n",
       "$1,000,000 Duck (1971)                         37  3.027027\n",
       "'Night Mother (1986)                           70  3.371429\n",
       "'Til There Was You (1997)                      52  2.692308\n",
       "'burbs, The (1989)                            303  2.910891\n",
       "...And Justice for All (1979)                 199  3.713568\n",
       "...                                           ...       ...\n",
       "Zed & Two Noughts, A (1985)                    29  3.413793\n",
       "Zero Effect (1998)                            301  3.750831\n",
       "Zero Kelvin (Kjærlighetens kjøtere) (1995)      2  3.500000\n",
       "Zeus and Roxanne (1997)                        23  2.521739\n",
       "eXistenZ (1999)                               410  3.256098\n",
       "\n",
       "[3706 rows x 2 columns]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# highly rated movies\n",
    "highly_rated = movie_ratings_data.groupby('title').agg({'rating':[np.size, np.mean]})\n",
    "highly_rated"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "ef1b9806",
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">rating</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>size</th>\n",
       "      <th>mean</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>title</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>American Beauty (1999)</th>\n",
       "      <td>3428</td>\n",
       "      <td>4.317386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Star Wars: Episode IV - A New Hope (1977)</th>\n",
       "      <td>2991</td>\n",
       "      <td>4.453694</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Star Wars: Episode V - The Empire Strikes Back (1980)</th>\n",
       "      <td>2990</td>\n",
       "      <td>4.292977</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Star Wars: Episode VI - Return of the Jedi (1983)</th>\n",
       "      <td>2883</td>\n",
       "      <td>4.022893</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jurassic Park (1993)</th>\n",
       "      <td>2672</td>\n",
       "      <td>3.763847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Target (1995)</th>\n",
       "      <td>1</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>I Don't Want to Talk About It (De eso no se habla) (1993)</th>\n",
       "      <td>1</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>An Unforgettable Summer (1994)</th>\n",
       "      <td>1</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Never Met Picasso (1996)</th>\n",
       "      <td>1</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Full Speed (1996)</th>\n",
       "      <td>1</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3706 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                   rating          \n",
       "                                                     size      mean\n",
       "title                                                              \n",
       "American Beauty (1999)                               3428  4.317386\n",
       "Star Wars: Episode IV - A New Hope (1977)            2991  4.453694\n",
       "Star Wars: Episode V - The Empire Strikes Back ...   2990  4.292977\n",
       "Star Wars: Episode VI - Return of the Jedi (1983)    2883  4.022893\n",
       "Jurassic Park (1993)                                 2672  3.763847\n",
       "...                                                   ...       ...\n",
       "Target (1995)                                           1  4.000000\n",
       "I Don't Want to Talk About It (De eso no se hab...      1  4.000000\n",
       "An Unforgettable Summer (1994)                          1  3.000000\n",
       "Never Met Picasso (1996)                                1  2.000000\n",
       "Full Speed (1996)                                       1  3.000000\n",
       "\n",
       "[3706 rows x 2 columns]"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# highly rated movies (sorted)\n",
    "highly_rated_sorted = highly_rated.sort_values(('rating','size'), ascending=False)\n",
    "highly_rated_sorted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "5c8c93fd",
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">rating</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>size</th>\n",
       "      <th>mean</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>title</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Ulysses (Ulisse) (1954)</th>\n",
       "      <td>1</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lured (1947)</th>\n",
       "      <td>1</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Follow the Bitch (1998)</th>\n",
       "      <td>1</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bittersweet Motel (2000)</th>\n",
       "      <td>1</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Song of Freedom (1936)</th>\n",
       "      <td>1</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>One Little Indian (1973)</th>\n",
       "      <td>1</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Smashing Time (1967)</th>\n",
       "      <td>2</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Schlafes Bruder (Brother of Sleep) (1995)</th>\n",
       "      <td>1</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gate of Heavenly Peace, The (1995)</th>\n",
       "      <td>3</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Baby, The (1973)</th>\n",
       "      <td>1</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>I Am Cuba (Soy Cuba/Ya Kuba) (1964)</th>\n",
       "      <td>5</td>\n",
       "      <td>4.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lamerica (1994)</th>\n",
       "      <td>8</td>\n",
       "      <td>4.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Apple, The (Sib) (1998)</th>\n",
       "      <td>9</td>\n",
       "      <td>4.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sanjuro (1962)</th>\n",
       "      <td>69</td>\n",
       "      <td>4.608696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Seven Samurai (The Magnificent Seven) (Shichinin no samurai) (1954)</th>\n",
       "      <td>628</td>\n",
       "      <td>4.560510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Shawshank Redemption, The (1994)</th>\n",
       "      <td>2227</td>\n",
       "      <td>4.554558</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Godfather, The (1972)</th>\n",
       "      <td>2223</td>\n",
       "      <td>4.524966</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Close Shave, A (1995)</th>\n",
       "      <td>657</td>\n",
       "      <td>4.520548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Usual Suspects, The (1995)</th>\n",
       "      <td>1783</td>\n",
       "      <td>4.517106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Schindler's List (1993)</th>\n",
       "      <td>2304</td>\n",
       "      <td>4.510417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wrong Trousers, The (1993)</th>\n",
       "      <td>882</td>\n",
       "      <td>4.507937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dry Cleaning (Nettoyage à sec) (1997)</th>\n",
       "      <td>2</td>\n",
       "      <td>4.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Inheritors, The (Die Siebtelbauern) (1998)</th>\n",
       "      <td>2</td>\n",
       "      <td>4.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mamma Roma (1962)</th>\n",
       "      <td>2</td>\n",
       "      <td>4.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bells, The (1926)</th>\n",
       "      <td>2</td>\n",
       "      <td>4.500000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                   rating          \n",
       "                                                     size      mean\n",
       "title                                                              \n",
       "Ulysses (Ulisse) (1954)                                 1  5.000000\n",
       "Lured (1947)                                            1  5.000000\n",
       "Follow the Bitch (1998)                                 1  5.000000\n",
       "Bittersweet Motel (2000)                                1  5.000000\n",
       "Song of Freedom (1936)                                  1  5.000000\n",
       "One Little Indian (1973)                                1  5.000000\n",
       "Smashing Time (1967)                                    2  5.000000\n",
       "Schlafes Bruder (Brother of Sleep) (1995)               1  5.000000\n",
       "Gate of Heavenly Peace, The (1995)                      3  5.000000\n",
       "Baby, The (1973)                                        1  5.000000\n",
       "I Am Cuba (Soy Cuba/Ya Kuba) (1964)                     5  4.800000\n",
       "Lamerica (1994)                                         8  4.750000\n",
       "Apple, The (Sib) (1998)                                 9  4.666667\n",
       "Sanjuro (1962)                                         69  4.608696\n",
       "Seven Samurai (The Magnificent Seven) (Shichini...    628  4.560510\n",
       "Shawshank Redemption, The (1994)                     2227  4.554558\n",
       "Godfather, The (1972)                                2223  4.524966\n",
       "Close Shave, A (1995)                                 657  4.520548\n",
       "Usual Suspects, The (1995)                           1783  4.517106\n",
       "Schindler's List (1993)                              2304  4.510417\n",
       "Wrong Trousers, The (1993)                            882  4.507937\n",
       "Dry Cleaning (Nettoyage à sec) (1997)                   2  4.500000\n",
       "Inheritors, The (Die Siebtelbauern) (1998)              2  4.500000\n",
       "Mamma Roma (1962)                                       2  4.500000\n",
       "Bells, The (1926)                                       2  4.500000"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "highly_rated_sorted2 = highly_rated.sort_values(('rating','mean'), ascending=False)\n",
    "highly_rated_sorted2.head(25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "e8fb34a2",
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">rating</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>size</th>\n",
       "      <th>mean</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>title</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Gate of Heavenly Peace, The (1995)</th>\n",
       "      <td>3</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Smashing Time (1967)</th>\n",
       "      <td>2</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lured (1947)</th>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Song of Freedom (1936)</th>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Follow the Bitch (1998)</th>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>One Little Indian (1973)</th>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Baby, The (1973)</th>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Schlafes Bruder (Brother of Sleep) (1995)</th>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bittersweet Motel (2000)</th>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ulysses (Ulisse) (1954)</th>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                          rating     \n",
       "                                            size mean\n",
       "title                                                \n",
       "Gate of Heavenly Peace, The (1995)             3  5.0\n",
       "Smashing Time (1967)                           2  5.0\n",
       "Lured (1947)                                   1  5.0\n",
       "Song of Freedom (1936)                         1  5.0\n",
       "Follow the Bitch (1998)                        1  5.0\n",
       "One Little Indian (1973)                       1  5.0\n",
       "Baby, The (1973)                               1  5.0\n",
       "Schlafes Bruder (Brother of Sleep) (1995)      1  5.0\n",
       "Bittersweet Motel (2000)                       1  5.0\n",
       "Ulysses (Ulisse) (1954)                        1  5.0"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# display movie rating 5\n",
    "highly_rated_sorted3 = highly_rated_sorted.loc[highly_rated_sorted[('rating', 'mean')] == 5]\n",
    "highly_rated_sorted3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "08bdb1d9",
   "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>userid</th>\n",
       "      <th>gender</th>\n",
       "      <th>age</th>\n",
       "      <th>occupation</th>\n",
       "      <th>zipcode</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>F</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>48067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>M</td>\n",
       "      <td>56</td>\n",
       "      <td>16</td>\n",
       "      <td>70072</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>M</td>\n",
       "      <td>25</td>\n",
       "      <td>15</td>\n",
       "      <td>55117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>M</td>\n",
       "      <td>45</td>\n",
       "      <td>7</td>\n",
       "      <td>02460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>M</td>\n",
       "      <td>25</td>\n",
       "      <td>20</td>\n",
       "      <td>55455</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   userid gender  age  occupation zipcode\n",
       "0       1      F    1          10   48067\n",
       "1       2      M   56          16   70072\n",
       "2       3      M   25          15   55117\n",
       "3       4      M   45           7   02460\n",
       "4       5      M   25          20   55455"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 1 users data\n",
    "unames = ['userid','gender','age','occupation','zipcode']\n",
    "users_data = pd.read_table('users.dat', sep='::', header=None, names=unames, engine='python', encoding='latin-1')\n",
    "users_data.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "c9895558",
   "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>userid</th>\n",
       "      <th>gender</th>\n",
       "      <th>age</th>\n",
       "      <th>occupation</th>\n",
       "      <th>zipcode</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>F</td>\n",
       "      <td>0-18</td>\n",
       "      <td>10</td>\n",
       "      <td>48067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>M</td>\n",
       "      <td>56+</td>\n",
       "      <td>16</td>\n",
       "      <td>70072</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>M</td>\n",
       "      <td>25-34</td>\n",
       "      <td>15</td>\n",
       "      <td>55117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>M</td>\n",
       "      <td>45-49</td>\n",
       "      <td>7</td>\n",
       "      <td>02460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>M</td>\n",
       "      <td>25-34</td>\n",
       "      <td>20</td>\n",
       "      <td>55455</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   userid gender    age  occupation zipcode\n",
       "0       1      F   0-18          10   48067\n",
       "1       2      M    56+          16   70072\n",
       "2       3      M  25-34          15   55117\n",
       "3       4      M  45-49           7   02460\n",
       "4       5      M  25-34          20   55455"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 2 replace age with intervals labels\n",
    "mapping = {\n",
    "    1: '0-18',\n",
    "    18: '18-24',\n",
    "    25: '25-34',\n",
    "    35: '35-44',\n",
    "    45: '45-49',\n",
    "    50: '50-55',\n",
    "    56: '56+'\n",
    "}\n",
    "users_data['age'] = users_data['age'].replace(mapping) \n",
    "users_data.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "fe35a481",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "25-34    2096\n",
       "35-44    1193\n",
       "18-24    1103\n",
       "45-49     550\n",
       "50-55     496\n",
       "56+       380\n",
       "0-18      222\n",
       "Name: age, dtype: int64"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 3 grafik histogram\n",
    "age_counts = users_data['age'].value_counts()\n",
    "age_counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "f98e4c32",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0-18      222\n",
       "18-24    1103\n",
       "25-34    2096\n",
       "35-44    1193\n",
       "45-49     550\n",
       "50-55     496\n",
       "56+       380\n",
       "Name: age, dtype: int64"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "age_counts_sorted = age_counts.sort_index()\n",
    "age_counts_sorted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "b769c9bc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.bar(age_counts_sorted.index, age_counts_sorted.values)\n",
    "# plt.hist(age_counts_sorted)\n",
    "plt.xlabel('Age')\n",
    "plt.ylabel('Count')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "221e82b8",
   "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>userid</th>\n",
       "      <th>gender</th>\n",
       "      <th>age</th>\n",
       "      <th>occupation</th>\n",
       "      <th>zipcode</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>F</td>\n",
       "      <td>0-18</td>\n",
       "      <td>10</td>\n",
       "      <td>48067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>M</td>\n",
       "      <td>56+</td>\n",
       "      <td>16</td>\n",
       "      <td>70072</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>M</td>\n",
       "      <td>25-34</td>\n",
       "      <td>15</td>\n",
       "      <td>55117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>M</td>\n",
       "      <td>45-49</td>\n",
       "      <td>7</td>\n",
       "      <td>02460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>M</td>\n",
       "      <td>25-34</td>\n",
       "      <td>20</td>\n",
       "      <td>55455</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   userid gender    age  occupation zipcode\n",
       "0       1      F   0-18          10   48067\n",
       "1       2      M    56+          16   70072\n",
       "2       3      M  25-34          15   55117\n",
       "3       4      M  45-49           7   02460\n",
       "4       5      M  25-34          20   55455"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 4 Merge users_data dan movie_ratings_data\n",
    "users_data.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "e60f7ef7",
   "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>movieid</th>\n",
       "      <th>title</th>\n",
       "      <th>genres</th>\n",
       "      <th>userid</th>\n",
       "      <th>rating</th>\n",
       "      <th>timestamp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Toy Story (1995)</td>\n",
       "      <td>Animation|Children's|Comedy</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>978824268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Toy Story (1995)</td>\n",
       "      <td>Animation|Children's|Comedy</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>978237008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>Toy Story (1995)</td>\n",
       "      <td>Animation|Children's|Comedy</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>978233496</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>Toy Story (1995)</td>\n",
       "      <td>Animation|Children's|Comedy</td>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "      <td>978225952</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>Toy Story (1995)</td>\n",
       "      <td>Animation|Children's|Comedy</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>978226474</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   movieid             title                       genres  userid  rating  \\\n",
       "0        1  Toy Story (1995)  Animation|Children's|Comedy       1       5   \n",
       "1        1  Toy Story (1995)  Animation|Children's|Comedy       6       4   \n",
       "2        1  Toy Story (1995)  Animation|Children's|Comedy       8       4   \n",
       "3        1  Toy Story (1995)  Animation|Children's|Comedy       9       5   \n",
       "4        1  Toy Story (1995)  Animation|Children's|Comedy      10       5   \n",
       "\n",
       "   timestamp  \n",
       "0  978824268  \n",
       "1  978237008  \n",
       "2  978233496  \n",
       "3  978225952  \n",
       "4  978226474  "
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movie_ratings_data.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "54ac4813",
   "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>movieid</th>\n",
       "      <th>title</th>\n",
       "      <th>genres</th>\n",
       "      <th>userid</th>\n",
       "      <th>rating</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>gender</th>\n",
       "      <th>age</th>\n",
       "      <th>occupation</th>\n",
       "      <th>zipcode</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Toy Story (1995)</td>\n",
       "      <td>Animation|Children's|Comedy</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>978824268</td>\n",
       "      <td>F</td>\n",
       "      <td>0-18</td>\n",
       "      <td>10</td>\n",
       "      <td>48067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>48</td>\n",
       "      <td>Pocahontas (1995)</td>\n",
       "      <td>Animation|Children's|Musical|Romance</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>978824351</td>\n",
       "      <td>F</td>\n",
       "      <td>0-18</td>\n",
       "      <td>10</td>\n",
       "      <td>48067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>150</td>\n",
       "      <td>Apollo 13 (1995)</td>\n",
       "      <td>Drama</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>978301777</td>\n",
       "      <td>F</td>\n",
       "      <td>0-18</td>\n",
       "      <td>10</td>\n",
       "      <td>48067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>260</td>\n",
       "      <td>Star Wars: Episode IV - A New Hope (1977)</td>\n",
       "      <td>Action|Adventure|Fantasy|Sci-Fi</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>978300760</td>\n",
       "      <td>F</td>\n",
       "      <td>0-18</td>\n",
       "      <td>10</td>\n",
       "      <td>48067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>527</td>\n",
       "      <td>Schindler's List (1993)</td>\n",
       "      <td>Drama|War</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>978824195</td>\n",
       "      <td>F</td>\n",
       "      <td>0-18</td>\n",
       "      <td>10</td>\n",
       "      <td>48067</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   movieid                                      title  \\\n",
       "0        1                           Toy Story (1995)   \n",
       "1       48                          Pocahontas (1995)   \n",
       "2      150                           Apollo 13 (1995)   \n",
       "3      260  Star Wars: Episode IV - A New Hope (1977)   \n",
       "4      527                    Schindler's List (1993)   \n",
       "\n",
       "                                 genres  userid  rating  timestamp gender  \\\n",
       "0           Animation|Children's|Comedy       1       5  978824268      F   \n",
       "1  Animation|Children's|Musical|Romance       1       5  978824351      F   \n",
       "2                                 Drama       1       5  978301777      F   \n",
       "3       Action|Adventure|Fantasy|Sci-Fi       1       4  978300760      F   \n",
       "4                             Drama|War       1       5  978824195      F   \n",
       "\n",
       "    age  occupation zipcode  \n",
       "0  0-18          10   48067  \n",
       "1  0-18          10   48067  \n",
       "2  0-18          10   48067  \n",
       "3  0-18          10   48067  \n",
       "4  0-18          10   48067  "
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.merge(movie_ratings_data, users_data)\n",
    "data.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "a0c7d1c3",
   "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>movieid</th>\n",
       "      <th>title</th>\n",
       "      <th>genres</th>\n",
       "      <th>userid</th>\n",
       "      <th>rating</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>gender</th>\n",
       "      <th>age</th>\n",
       "      <th>occupation</th>\n",
       "      <th>zipcode</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>1</td>\n",
       "      <td>Toy Story (1995)</td>\n",
       "      <td>Animation|Children's|Comedy</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>978237008</td>\n",
       "      <td>F</td>\n",
       "      <td>50-55</td>\n",
       "      <td>9</td>\n",
       "      <td>55117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>17</td>\n",
       "      <td>Sense and Sensibility (1995)</td>\n",
       "      <td>Drama|Romance</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>978236383</td>\n",
       "      <td>F</td>\n",
       "      <td>50-55</td>\n",
       "      <td>9</td>\n",
       "      <td>55117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>34</td>\n",
       "      <td>Babe (1995)</td>\n",
       "      <td>Children's|Comedy|Drama</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>978237444</td>\n",
       "      <td>F</td>\n",
       "      <td>50-55</td>\n",
       "      <td>9</td>\n",
       "      <td>55117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>48</td>\n",
       "      <td>Pocahontas (1995)</td>\n",
       "      <td>Animation|Children's|Musical|Romance</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>978237570</td>\n",
       "      <td>F</td>\n",
       "      <td>50-55</td>\n",
       "      <td>9</td>\n",
       "      <td>55117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>199</td>\n",
       "      <td>Umbrellas of Cherbourg, The (Parapluies de Che...</td>\n",
       "      <td>Drama|Musical</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>978237570</td>\n",
       "      <td>F</td>\n",
       "      <td>50-55</td>\n",
       "      <td>9</td>\n",
       "      <td>55117</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    movieid                                              title  \\\n",
       "53        1                                   Toy Story (1995)   \n",
       "54       17                       Sense and Sensibility (1995)   \n",
       "55       34                                        Babe (1995)   \n",
       "56       48                                  Pocahontas (1995)   \n",
       "57      199  Umbrellas of Cherbourg, The (Parapluies de Che...   \n",
       "\n",
       "                                  genres  userid  rating  timestamp gender  \\\n",
       "53           Animation|Children's|Comedy       6       4  978237008      F   \n",
       "54                         Drama|Romance       6       4  978236383      F   \n",
       "55               Children's|Comedy|Drama       6       4  978237444      F   \n",
       "56  Animation|Children's|Musical|Romance       6       5  978237570      F   \n",
       "57                         Drama|Musical       6       5  978237570      F   \n",
       "\n",
       "      age  occupation zipcode  \n",
       "53  50-55           9   55117  \n",
       "54  50-55           9   55117  \n",
       "55  50-55           9   55117  \n",
       "56  50-55           9   55117  \n",
       "57  50-55           9   55117  "
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 5 display user age > 50\n",
    "data_50 = data.loc[data['age'] == '50-55']\n",
    "data_50.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "47232c6e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(72490, 10)"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_50.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "2b1aeb79",
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">rating</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>size</th>\n",
       "      <th>mean</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>age</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0-18</th>\n",
       "      <td>27211</td>\n",
       "      <td>3.549520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18-24</th>\n",
       "      <td>183536</td>\n",
       "      <td>3.507573</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25-34</th>\n",
       "      <td>395556</td>\n",
       "      <td>3.545235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35-44</th>\n",
       "      <td>199003</td>\n",
       "      <td>3.618162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45-49</th>\n",
       "      <td>83633</td>\n",
       "      <td>3.638062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50-55</th>\n",
       "      <td>72490</td>\n",
       "      <td>3.714512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56+</th>\n",
       "      <td>38780</td>\n",
       "      <td>3.766632</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       rating          \n",
       "         size      mean\n",
       "age                    \n",
       "0-18    27211  3.549520\n",
       "18-24  183536  3.507573\n",
       "25-34  395556  3.545235\n",
       "35-44  199003  3.618162\n",
       "45-49   83633  3.638062\n",
       "50-55   72490  3.714512\n",
       "56+     38780  3.766632"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 6 jumlah user dan rata-rata rating tiap range umur\n",
    "# question: user dengan range umur mana yg lebih kritis? tua atau muda? \n",
    "data_stats = data.groupby('age').agg({'rating':[np.size, np.mean]})\n",
    "data_stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "14b00edb",
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">rating</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>size</th>\n",
       "      <th>mean</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>title</th>\n",
       "      <th>age</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">$1,000,000 Duck (1971)</th>\n",
       "      <th>18-24</th>\n",
       "      <td>6</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25-34</th>\n",
       "      <td>11</td>\n",
       "      <td>3.090909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35-44</th>\n",
       "      <td>15</td>\n",
       "      <td>3.133333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45-49</th>\n",
       "      <td>1</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50-55</th>\n",
       "      <td>4</td>\n",
       "      <td>2.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">eXistenZ (1999)</th>\n",
       "      <th>25-34</th>\n",
       "      <td>183</td>\n",
       "      <td>3.234973</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35-44</th>\n",
       "      <td>74</td>\n",
       "      <td>3.364865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45-49</th>\n",
       "      <td>27</td>\n",
       "      <td>3.222222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50-55</th>\n",
       "      <td>24</td>\n",
       "      <td>3.291667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56+</th>\n",
       "      <td>5</td>\n",
       "      <td>2.200000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>22457 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                             rating          \n",
       "                               size      mean\n",
       "title                  age                   \n",
       "$1,000,000 Duck (1971) 18-24      6  3.000000\n",
       "                       25-34     11  3.090909\n",
       "                       35-44     15  3.133333\n",
       "                       45-49      1  2.000000\n",
       "                       50-55      4  2.750000\n",
       "...                             ...       ...\n",
       "eXistenZ (1999)        25-34    183  3.234973\n",
       "                       35-44     74  3.364865\n",
       "                       45-49     27  3.222222\n",
       "                       50-55     24  3.291667\n",
       "                       56+        5  2.200000\n",
       "\n",
       "[22457 rows x 2 columns]"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 7 tampilkan film, range umur, rata-rata rating\n",
    "by_age = data.groupby(['title','age']).agg({'rating':[np.size, np.mean]})\n",
    "by_age\n",
    "# by_age.rating.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "9d537c9a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "title                   age  \n",
       "$1,000,000 Duck (1971)  18-24    3.000000\n",
       "                        25-34    3.090909\n",
       "                        35-44    3.133333\n",
       "                        45-49    2.000000\n",
       "                        50-55    2.750000\n",
       "                                   ...   \n",
       "eXistenZ (1999)         25-34    3.234973\n",
       "                        35-44    3.364865\n",
       "                        45-49    3.222222\n",
       "                        50-55    3.291667\n",
       "                        56+      2.200000\n",
       "Name: rating, Length: 22457, dtype: float64"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "by_age = data.groupby(['title','age'])\n",
    "by_age.rating.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "477283fb",
   "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>age</th>\n",
       "      <th>0-18</th>\n",
       "      <th>18-24</th>\n",
       "      <th>25-34</th>\n",
       "      <th>35-44</th>\n",
       "      <th>45-49</th>\n",
       "      <th>50-55</th>\n",
       "      <th>56+</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>title</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>$1,000,000 Duck (1971)</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.090909</td>\n",
       "      <td>3.133333</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.750000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>'Night Mother (1986)</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>4.666667</td>\n",
       "      <td>3.423077</td>\n",
       "      <td>2.904762</td>\n",
       "      <td>3.833333</td>\n",
       "      <td>3.555556</td>\n",
       "      <td>4.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>'Til There Was You (1997)</th>\n",
       "      <td>3.500000</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>2.666667</td>\n",
       "      <td>2.900000</td>\n",
       "      <td>2.333333</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>2.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>'burbs, The (1989)</th>\n",
       "      <td>4.500000</td>\n",
       "      <td>3.244444</td>\n",
       "      <td>2.652174</td>\n",
       "      <td>2.818182</td>\n",
       "      <td>2.545455</td>\n",
       "      <td>3.208333</td>\n",
       "      <td>2.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...And Justice for All (1979)</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.428571</td>\n",
       "      <td>3.724138</td>\n",
       "      <td>3.657143</td>\n",
       "      <td>4.100000</td>\n",
       "      <td>3.551724</td>\n",
       "      <td>3.928571</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>Zed &amp; Two Noughts, A (1985)</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.375000</td>\n",
       "      <td>3.777778</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zero Effect (1998)</th>\n",
       "      <td>4.125000</td>\n",
       "      <td>3.883333</td>\n",
       "      <td>3.715278</td>\n",
       "      <td>3.608696</td>\n",
       "      <td>3.764706</td>\n",
       "      <td>3.833333</td>\n",
       "      <td>3.625000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zero Kelvin (Kjærlighetens kjøtere) (1995)</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zeus and Roxanne (1997)</th>\n",
       "      <td>1.500000</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>2.833333</td>\n",
       "      <td>3.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>eXistenZ (1999)</th>\n",
       "      <td>3.142857</td>\n",
       "      <td>3.289157</td>\n",
       "      <td>3.234973</td>\n",
       "      <td>3.364865</td>\n",
       "      <td>3.222222</td>\n",
       "      <td>3.291667</td>\n",
       "      <td>2.200000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3706 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "age                                             0-18     18-24     25-34  \\\n",
       "title                                                                      \n",
       "$1,000,000 Duck (1971)                      0.000000  3.000000  3.090909   \n",
       "'Night Mother (1986)                        2.000000  4.666667  3.423077   \n",
       "'Til There Was You (1997)                   3.500000  2.500000  2.666667   \n",
       "'burbs, The (1989)                          4.500000  3.244444  2.652174   \n",
       "...And Justice for All (1979)               3.000000  3.428571  3.724138   \n",
       "...                                              ...       ...       ...   \n",
       "Zed & Two Noughts, A (1985)                 1.000000  3.000000  3.375000   \n",
       "Zero Effect (1998)                          4.125000  3.883333  3.715278   \n",
       "Zero Kelvin (Kjærlighetens kjøtere) (1995)  0.000000  0.000000  0.000000   \n",
       "Zeus and Roxanne (1997)                     1.500000  2.500000  2.833333   \n",
       "eXistenZ (1999)                             3.142857  3.289157  3.234973   \n",
       "\n",
       "age                                            35-44     45-49     50-55  \\\n",
       "title                                                                      \n",
       "$1,000,000 Duck (1971)                      3.133333  2.000000  2.750000   \n",
       "'Night Mother (1986)                        2.904762  3.833333  3.555556   \n",
       "'Til There Was You (1997)                   2.900000  2.333333  2.500000   \n",
       "'burbs, The (1989)                          2.818182  2.545455  3.208333   \n",
       "...And Justice for All (1979)               3.657143  4.100000  3.551724   \n",
       "...                                              ...       ...       ...   \n",
       "Zed & Two Noughts, A (1985)                 3.777778  4.000000  3.000000   \n",
       "Zero Effect (1998)                          3.608696  3.764706  3.833333   \n",
       "Zero Kelvin (Kjærlighetens kjøtere) (1995)  3.500000  0.000000  0.000000   \n",
       "Zeus and Roxanne (1997)                     3.500000  1.000000  0.000000   \n",
       "eXistenZ (1999)                             3.364865  3.222222  3.291667   \n",
       "\n",
       "age                                              56+  \n",
       "title                                                 \n",
       "$1,000,000 Duck (1971)                      0.000000  \n",
       "'Night Mother (1986)                        4.333333  \n",
       "'Til There Was You (1997)                   2.666667  \n",
       "'burbs, The (1989)                          2.666667  \n",
       "...And Justice for All (1979)               3.928571  \n",
       "...                                              ...  \n",
       "Zed & Two Noughts, A (1985)                 0.000000  \n",
       "Zero Effect (1998)                          3.625000  \n",
       "Zero Kelvin (Kjærlighetens kjøtere) (1995)  0.000000  \n",
       "Zeus and Roxanne (1997)                     0.000000  \n",
       "eXistenZ (1999)                             2.200000  \n",
       "\n",
       "[3706 rows x 7 columns]"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 8 tampilkan dalam bentuk tabel\n",
    "# kolom: range usia\n",
    "# baris: judul film\n",
    "by_age.rating.mean().unstack(1).fillna(0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2274db46",
   "metadata": {},
   "source": [
    "### Pivot Table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "ae63bc8a",
   "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>Region</th>\n",
       "      <th>Product</th>\n",
       "      <th>Sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>East</td>\n",
       "      <td>A</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>West</td>\n",
       "      <td>A</td>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>North</td>\n",
       "      <td>B</td>\n",
       "      <td>150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>South</td>\n",
       "      <td>B</td>\n",
       "      <td>250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>East</td>\n",
       "      <td>A</td>\n",
       "      <td>120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>West</td>\n",
       "      <td>A</td>\n",
       "      <td>180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>North</td>\n",
       "      <td>B</td>\n",
       "      <td>130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>South</td>\n",
       "      <td>B</td>\n",
       "      <td>220</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Region Product  Sales\n",
       "0   East       A    100\n",
       "1   West       A    200\n",
       "2  North       B    150\n",
       "3  South       B    250\n",
       "4   East       A    120\n",
       "5   West       A    180\n",
       "6  North       B    130\n",
       "7  South       B    220"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_sales = {\n",
    "    'Region': ['East', 'West', 'North', 'South', 'East', 'West', 'North', 'South'],\n",
    "    'Product': ['A', 'A', 'B', 'B', 'A', 'A', 'B', 'B'],\n",
    "    'Sales': [100, 200, 150, 250, 120, 180, 130, 220]\n",
    "}\n",
    "df_sales = pd.DataFrame(data_sales)\n",
    "df_sales"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "fd3745a3",
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">Sales</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Product</th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Region</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>East</th>\n",
       "      <td>220.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>North</th>\n",
       "      <td>0.0</td>\n",
       "      <td>280.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>South</th>\n",
       "      <td>0.0</td>\n",
       "      <td>470.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>West</th>\n",
       "      <td>380.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Sales       \n",
       "Product      A      B\n",
       "Region               \n",
       "East     220.0    0.0\n",
       "North      0.0  280.0\n",
       "South      0.0  470.0\n",
       "West     380.0    0.0"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# buatlah tabel baru\n",
    "# kolom: Product A, B\n",
    "# baris(index): Region East, West, South, West\n",
    "df_sales_pivot = df_sales.pivot_table(columns='Product', index='Region', aggfunc='sum').fillna(0)\n",
    "df_sales_pivot"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}