KaggleProject.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "abadb49f-6169-4be9-9317-d26581f2fd41",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "671602a6-c95e-4207-902a-153cc86bb78c",
   "metadata": {},
   "outputs": [],
   "source": [
    "movies =pd.read_csv(r\"C:\\Users\\vemer\\Documents\\Naresh Technology\\New\\15th FEB Kaggle introduction\\archive\\movie.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "53b99a4d-c0d4-4642-b918-3e36c9ef6d7f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n"
     ]
    }
   ],
   "source": [
    "print(type(movies))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "09f5bb84-54d5-4283-ab2c-eae13cc92945",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>Adventure|Animation|Children|Comedy|Fantasy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Jumanji (1995)</td>\n",
       "      <td>Adventure|Children|Fantasy</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   movieId             title                                       genres\n",
       "0        1  Toy Story (1995)  Adventure|Animation|Children|Comedy|Fantasy\n",
       "1        2    Jumanji (1995)                   Adventure|Children|Fantasy"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "05320ee9-d3e4-4cad-8a9f-486424f5dff6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\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>tag</th>\n",
       "      <th>timestamp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>18</td>\n",
       "      <td>4141</td>\n",
       "      <td>Mark Waters</td>\n",
       "      <td>2009-04-24 18:19:40</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   userId  movieId          tag            timestamp\n",
       "0      18     4141  Mark Waters  2009-04-24 18:19:40"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tags = pd.read_csv(r'C:\\Users\\vemer\\Documents\\Naresh Technology\\New\\15th FEB Kaggle introduction\\archive\\tag.csv', sep=',')\n",
    "tags.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "76e83157-2f69-4d04-ae2e-4a3054e2c8e2",
   "metadata": {},
   "outputs": [],
   "source": [
    "#ratings = pd.read_csv(r'C:\\Users\\vemer\\Documents\\Naresh Technology\\New\\15th FEB Kaggle introduction\\archive\\rating.csv', sep=',')\n",
    "ratings = pd.read_csv(r'C:\\Users\\vemer\\Documents\\Naresh Technology\\New\\15th FEB Kaggle introduction\\archive\\rating.csv', sep=',', parse_dates=['timestamp'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "18d812ca-7a70-47c0-b478-97fb7e669ba6",
   "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>2</td>\n",
       "      <td>3.5</td>\n",
       "      <td>2005-04-02 23:53:47</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   userId  movieId  rating           timestamp\n",
       "0       1        2     3.5 2005-04-02 23:53:47"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratings.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "b2f0a467-5339-460f-983e-def8544b020a",
   "metadata": {},
   "outputs": [],
   "source": [
    "del ratings['timestamp']\n",
    "del tags['timestamp']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3639f83c-f8a0-44c9-9b15-5d0bac16b50d",
   "metadata": {},
   "source": [
    "# 📦 Data Structures:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "e44c7d04-4c56-46a2-820c-74788f71d820",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "row_0 = tags.iloc[0]\n",
    "type(row_0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "1093b561-da54-4401-8303-6c0d4f2a4246",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "userId              18\n",
      "movieId           4141\n",
      "tag        Mark Waters\n",
      "Name: 0, dtype: object\n"
     ]
    }
   ],
   "source": [
    "print(row_0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "edb48c3f-3d2f-40a0-86fd-dc19e5a598b0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['userId', 'movieId', 'tag'], dtype='object')"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "row_0.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "1a9dbc85-afe0-41b6-98b7-0efc639ed71f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "18"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "row_0['userId']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "dc8c8b3e-4e6f-40bb-8020-14d8ac614ef0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'rating' in row_0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "3a34ddf4-853b-43df-85f1-d0aab4760f1d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "row_0.name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "f3bb8cc6-5e04-4b1c-b96a-e790e6c2aa73",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'firstRow'"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "row_0 = row_0.rename('firstRow')\n",
    "row_0.name"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0d542b51-751d-4095-9830-a176ef1b749b",
   "metadata": {},
   "source": [
    "# ▩ DataFrames"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "d1db03bd-297c-4c24-96d0-d2b7c9a0d9f8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>userId</th>\n",
       "      <th>movieId</th>\n",
       "      <th>tag</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>18</td>\n",
       "      <td>4141</td>\n",
       "      <td>Mark Waters</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>65</td>\n",
       "      <td>208</td>\n",
       "      <td>dark hero</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>65</td>\n",
       "      <td>353</td>\n",
       "      <td>dark hero</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>65</td>\n",
       "      <td>521</td>\n",
       "      <td>noir thriller</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>65</td>\n",
       "      <td>592</td>\n",
       "      <td>dark hero</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   userId  movieId            tag\n",
       "0      18     4141    Mark Waters\n",
       "1      65      208      dark hero\n",
       "2      65      353      dark hero\n",
       "3      65      521  noir thriller\n",
       "4      65      592      dark hero"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tags.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "96aadaca-89c1-4d9b-b1a4-e75cb4a3abf5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RangeIndex(start=0, stop=465564, step=1)"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tags.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "e0b2b15b-a208-4222-aa9d-d899ab6b0f95",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['userId', 'movieId', 'tag'], dtype='object')"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tags.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "1c0a99fa-7fcd-4dfd-9c7d-f752ebb24f7f",
   "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>tag</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>18</td>\n",
       "      <td>4141</td>\n",
       "      <td>Mark Waters</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>65</td>\n",
       "      <td>1783</td>\n",
       "      <td>noir thriller</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>500</th>\n",
       "      <td>342</td>\n",
       "      <td>55908</td>\n",
       "      <td>entirely dialogue</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     userId  movieId                tag\n",
       "0        18     4141        Mark Waters\n",
       "11       65     1783      noir thriller\n",
       "500     342    55908  entirely dialogue"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tags.iloc[ [0,11,500] ]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4247a3b9-a8d7-4a47-9c72-6965401473d4",
   "metadata": {},
   "source": [
    "# 📈 📉 Descriptive Statistics"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9090d53e-72dd-4344-bafb-1d10f67deb0a",
   "metadata": {},
   "source": [
    "# Let's look how the ratings are distributed!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "573a2793-e6a2-4737-afcc-e97bdf24a40a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    2.000026e+07\n",
       "mean     3.525529e+00\n",
       "std      1.051989e+00\n",
       "min      5.000000e-01\n",
       "25%      3.000000e+00\n",
       "50%      3.500000e+00\n",
       "75%      4.000000e+00\n",
       "max      5.000000e+00\n",
       "Name: rating, dtype: float64"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratings['rating'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "beb5c1d3-3cf2-4c8b-8038-0eb2efaf6427",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>userId</th>\n",
       "      <th>movieId</th>\n",
       "      <th>rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2.000026e+07</td>\n",
       "      <td>2.000026e+07</td>\n",
       "      <td>2.000026e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>6.904587e+04</td>\n",
       "      <td>9.041567e+03</td>\n",
       "      <td>3.525529e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>4.003863e+04</td>\n",
       "      <td>1.978948e+04</td>\n",
       "      <td>1.051989e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>5.000000e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>3.439500e+04</td>\n",
       "      <td>9.020000e+02</td>\n",
       "      <td>3.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>6.914100e+04</td>\n",
       "      <td>2.167000e+03</td>\n",
       "      <td>3.500000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.036370e+05</td>\n",
       "      <td>4.770000e+03</td>\n",
       "      <td>4.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.384930e+05</td>\n",
       "      <td>1.312620e+05</td>\n",
       "      <td>5.000000e+00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             userId       movieId        rating\n",
       "count  2.000026e+07  2.000026e+07  2.000026e+07\n",
       "mean   6.904587e+04  9.041567e+03  3.525529e+00\n",
       "std    4.003863e+04  1.978948e+04  1.051989e+00\n",
       "min    1.000000e+00  1.000000e+00  5.000000e-01\n",
       "25%    3.439500e+04  9.020000e+02  3.000000e+00\n",
       "50%    6.914100e+04  2.167000e+03  3.500000e+00\n",
       "75%    1.036370e+05  4.770000e+03  4.000000e+00\n",
       "max    1.384930e+05  1.312620e+05  5.000000e+00"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratings.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "9e5874e4-f310-462e-ba3e-03354d0d92f6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.5255285642993797"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratings['rating'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "4712911e-e42e-483f-a2e0-5a15708c5d17",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "userId     69045.872583\n",
       "movieId     9041.567330\n",
       "rating         3.525529\n",
       "dtype: float64"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratings.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "d43eec4d-1ed7-492c-a631-b311070c680e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratings['rating'].min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "f5e4f989-da47-4099-b45f-2d43d33ef261",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5.0"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratings['rating'].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "b6c62712-d866-40f9-8f84-d19fda8af251",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.051988919275684"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratings['rating'].std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "a5dfef71-34c0-4383-9c29-bef7edd3fa01",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    4.0\n",
       "Name: rating, dtype: float64"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratings['rating'].mode()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "af4efae8-b057-4e12-a2aa-a12bf3de6f5c",
   "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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>userId</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.000850</td>\n",
       "      <td>0.001175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>movieId</th>\n",
       "      <td>-0.000850</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.002606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rating</th>\n",
       "      <td>0.001175</td>\n",
       "      <td>0.002606</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           userId   movieId    rating\n",
       "userId   1.000000 -0.000850  0.001175\n",
       "movieId -0.000850  1.000000  0.002606\n",
       "rating   0.001175  0.002606  1.000000"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratings.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "5c500289-5b61-4b45-8eaf-3323a2dd5ed9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0           False\n",
      "1           False\n",
      "2           False\n",
      "3           False\n",
      "4           False\n",
      "            ...  \n",
      "20000258    False\n",
      "20000259    False\n",
      "20000260    False\n",
      "20000261    False\n",
      "20000262    False\n",
      "Name: rating, Length: 20000263, dtype: bool\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filter1 = ratings['rating'] > 10\n",
    "print(filter1)\n",
    "filter1.any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "46ba6b92-0b9b-4414-8ae0-e05f4faeb0cb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filter2 = ratings['rating'] > 0\n",
    "filter2.all()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6fb0ad7f-a2e8-492d-a09d-c92f4fe28279",
   "metadata": {},
   "source": [
    "# 🔧 Data Cleaning: Handling Missing Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "d0ff84cc-4088-4159-81c1-8f3735a476bf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(27278, 3)"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "8603345d-f92d-429c-b33c-38a635f8adc1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies.isnull().any().any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "eb6bfc98-5711-474d-8e66-0c12aeb2ff78",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(20000263, 3)"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratings.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "7d0320b0-62eb-463d-bb97-a7441d338373",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratings.isnull().any().any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "d0a6a1b8-688a-4026-8c4b-7aaf1d6c691e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(465564, 3)"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tags.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "69f9a5f4-cacc-4ddf-a3ed-6214b53a026b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tags.isnull().any().any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "e403dcb0-4c6f-4b95-b09e-9044961bf030",
   "metadata": {},
   "outputs": [],
   "source": [
    "tags=tags.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "2e32f59e-2fe9-4630-88ef-3e1a44adf2a6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tags.isnull().any().any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "855e30ad-b6dc-4bd0-9fe6-794f324ce9c7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(465548, 3)"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tags.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "10ed134e-ee34-48c6-bb56-b31cc8bd5148",
   "metadata": {},
   "source": [
    "# 📊 Data Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "68921ba2-107f-4254-9801-0e0a7ed25a4b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<Axes: title={'center': 'rating'}>]], dtype=object)"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "ratings.hist(column='rating', figsize=(10,5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "190c6f8e-c68f-407e-a98c-e19ecfd077f9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ratings.boxplot(column='rating', figsize=(10,5))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "822d2cd1-f92a-4161-a2ed-1277973bfab6",
   "metadata": {},
   "source": [
    "# 📤 Slicing Out Columns¶"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "c1fafd76-22d9-4f2c-bcc7-dd52a283179c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      Mark Waters\n",
       "1        dark hero\n",
       "2        dark hero\n",
       "3    noir thriller\n",
       "4        dark hero\n",
       "Name: tag, dtype: object"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tags['tag'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "2528ebb5-4079-438c-b1f8-40289b17a3f4",
   "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>title</th>\n",
       "      <th>genres</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Toy Story (1995)</td>\n",
       "      <td>Adventure|Animation|Children|Comedy|Fantasy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Jumanji (1995)</td>\n",
       "      <td>Adventure|Children|Fantasy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Grumpier Old Men (1995)</td>\n",
       "      <td>Comedy|Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Waiting to Exhale (1995)</td>\n",
       "      <td>Comedy|Drama|Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Father of the Bride Part II (1995)</td>\n",
       "      <td>Comedy</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                title  \\\n",
       "0                    Toy Story (1995)   \n",
       "1                      Jumanji (1995)   \n",
       "2             Grumpier Old Men (1995)   \n",
       "3            Waiting to Exhale (1995)   \n",
       "4  Father of the Bride Part II (1995)   \n",
       "\n",
       "                                        genres  \n",
       "0  Adventure|Animation|Children|Comedy|Fantasy  \n",
       "1                   Adventure|Children|Fantasy  \n",
       "2                               Comedy|Romance  \n",
       "3                         Comedy|Drama|Romance  \n",
       "4                                       Comedy  "
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies[['title','genres']].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "c3e4cd4b-d361-45e4-a53a-58c39bd5b592",
   "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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>20000253</th>\n",
       "      <td>138493</td>\n",
       "      <td>60816</td>\n",
       "      <td>4.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20000254</th>\n",
       "      <td>138493</td>\n",
       "      <td>61160</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20000255</th>\n",
       "      <td>138493</td>\n",
       "      <td>65682</td>\n",
       "      <td>4.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20000256</th>\n",
       "      <td>138493</td>\n",
       "      <td>66762</td>\n",
       "      <td>4.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20000257</th>\n",
       "      <td>138493</td>\n",
       "      <td>68319</td>\n",
       "      <td>4.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20000258</th>\n",
       "      <td>138493</td>\n",
       "      <td>68954</td>\n",
       "      <td>4.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20000259</th>\n",
       "      <td>138493</td>\n",
       "      <td>69526</td>\n",
       "      <td>4.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20000260</th>\n",
       "      <td>138493</td>\n",
       "      <td>69644</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20000261</th>\n",
       "      <td>138493</td>\n",
       "      <td>70286</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20000262</th>\n",
       "      <td>138493</td>\n",
       "      <td>71619</td>\n",
       "      <td>2.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          userId  movieId  rating\n",
       "20000253  138493    60816     4.5\n",
       "20000254  138493    61160     4.0\n",
       "20000255  138493    65682     4.5\n",
       "20000256  138493    66762     4.5\n",
       "20000257  138493    68319     4.5\n",
       "20000258  138493    68954     4.5\n",
       "20000259  138493    69526     4.5\n",
       "20000260  138493    69644     3.0\n",
       "20000261  138493    70286     5.0\n",
       "20000262  138493    71619     2.5"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratings[-10:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "6420d492-b392-4765-9d78-d008df4cb6e9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tag\n",
       "missing child                    1\n",
       "Ron Moore                        1\n",
       "Citizen Kane                     1\n",
       "mullet                           1\n",
       "biker gang                       1\n",
       "Paul Adelstein                   1\n",
       "the wig                          1\n",
       "killer fish                      1\n",
       "genetically modified monsters    1\n",
       "topless scene                    1\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tag_counts = tags['tag'].value_counts()\n",
    "tag_counts[-10:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "1a472843-8c99-43e4-be7f-7cc3c9002cb0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='tag'>"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tag_counts[:10].plot(kind='bar', figsize=(10,5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "1bc70b33-c557-4e73-b3f3-44f53f0efe92",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "sns.set()\n",
    "tag_counts[:10].plot(kind='bar', figsize=(10,5), color=sns.color_palette('husl', 10))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b4ed2761-9457-48cb-b107-42abf50ae70c",
   "metadata": {},
   "source": [
    "# 🎣 Filters for Selecting Rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "4d5b02d0-7d74-4161-bb62-0bc1d94116c9",
   "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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>239</th>\n",
       "      <td>3</td>\n",
       "      <td>50</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>242</th>\n",
       "      <td>3</td>\n",
       "      <td>175</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>244</th>\n",
       "      <td>3</td>\n",
       "      <td>223</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>245</th>\n",
       "      <td>3</td>\n",
       "      <td>260</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>246</th>\n",
       "      <td>3</td>\n",
       "      <td>316</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>247</th>\n",
       "      <td>3</td>\n",
       "      <td>318</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>248</th>\n",
       "      <td>3</td>\n",
       "      <td>329</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>252</th>\n",
       "      <td>3</td>\n",
       "      <td>457</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>253</th>\n",
       "      <td>3</td>\n",
       "      <td>480</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>254</th>\n",
       "      <td>3</td>\n",
       "      <td>490</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>256</th>\n",
       "      <td>3</td>\n",
       "      <td>541</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>258</th>\n",
       "      <td>3</td>\n",
       "      <td>593</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>263</th>\n",
       "      <td>3</td>\n",
       "      <td>858</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>264</th>\n",
       "      <td>3</td>\n",
       "      <td>904</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>267</th>\n",
       "      <td>3</td>\n",
       "      <td>924</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>268</th>\n",
       "      <td>3</td>\n",
       "      <td>953</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>271</th>\n",
       "      <td>3</td>\n",
       "      <td>1060</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>272</th>\n",
       "      <td>3</td>\n",
       "      <td>1073</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>275</th>\n",
       "      <td>3</td>\n",
       "      <td>1084</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>276</th>\n",
       "      <td>3</td>\n",
       "      <td>1089</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     userId  movieId  rating\n",
       "239       3       50     5.0\n",
       "242       3      175     5.0\n",
       "244       3      223     5.0\n",
       "245       3      260     5.0\n",
       "246       3      316     5.0\n",
       "247       3      318     5.0\n",
       "248       3      329     5.0\n",
       "252       3      457     5.0\n",
       "253       3      480     5.0\n",
       "254       3      490     5.0\n",
       "256       3      541     5.0\n",
       "258       3      593     5.0\n",
       "263       3      858     5.0\n",
       "264       3      904     5.0\n",
       "267       3      924     5.0\n",
       "268       3      953     5.0\n",
       "271       3     1060     5.0\n",
       "272       3     1073     5.0\n",
       "275       3     1084     5.0\n",
       "276       3     1089     5.0"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "is_highly_rated = ratings['rating'] >= 5.0\n",
    "ratings[is_highly_rated][30:50]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "id": "4fd6071e-1d01-4104-8f57-8c7284201d79",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        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>22</th>\n",
       "      <td>23</td>\n",
       "      <td>Assassins (1995)</td>\n",
       "      <td>Action|Crime|Thriller</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>42</td>\n",
       "      <td>Dead Presidents (1995)</td>\n",
       "      <td>Action|Crime|Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>44</td>\n",
       "      <td>Mortal Kombat (1995)</td>\n",
       "      <td>Action|Adventure|Fantasy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>51</td>\n",
       "      <td>Guardian Angel (1994)</td>\n",
       "      <td>Action|Drama|Thriller</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>66</td>\n",
       "      <td>Lawnmower Man 2: Beyond Cyberspace (1996)</td>\n",
       "      <td>Action|Sci-Fi|Thriller</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>70</td>\n",
       "      <td>From Dusk Till Dawn (1996)</td>\n",
       "      <td>Action|Comedy|Horror|Thriller</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>71</td>\n",
       "      <td>Fair Game (1995)</td>\n",
       "      <td>Action</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>76</td>\n",
       "      <td>Screamers (1995)</td>\n",
       "      <td>Action|Sci-Fi|Thriller</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>78</td>\n",
       "      <td>Crossing Guard, The (1995)</td>\n",
       "      <td>Action|Crime|Drama|Thriller</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>86</td>\n",
       "      <td>White Squall (1996)</td>\n",
       "      <td>Action|Adventure|Drama</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    movieId                                      title  \\\n",
       "22       23                           Assassins (1995)   \n",
       "41       42                     Dead Presidents (1995)   \n",
       "43       44                       Mortal Kombat (1995)   \n",
       "50       51                      Guardian Angel (1994)   \n",
       "65       66  Lawnmower Man 2: Beyond Cyberspace (1996)   \n",
       "69       70                 From Dusk Till Dawn (1996)   \n",
       "70       71                           Fair Game (1995)   \n",
       "75       76                           Screamers (1995)   \n",
       "77       78                 Crossing Guard, The (1995)   \n",
       "85       86                        White Squall (1996)   \n",
       "\n",
       "                           genres  \n",
       "22          Action|Crime|Thriller  \n",
       "41             Action|Crime|Drama  \n",
       "43       Action|Adventure|Fantasy  \n",
       "50          Action|Drama|Thriller  \n",
       "65         Action|Sci-Fi|Thriller  \n",
       "69  Action|Comedy|Horror|Thriller  \n",
       "70                         Action  \n",
       "75         Action|Sci-Fi|Thriller  \n",
       "77    Action|Crime|Drama|Thriller  \n",
       "85         Action|Adventure|Drama  "
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "is_action= movies['genres'].str.contains('Action')\n",
    "movies[is_action][5:15]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "id": "86b469c6-2eaa-45a2-8d3a-2d1cdd572d38",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        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>5</th>\n",
       "      <td>6</td>\n",
       "      <td>Heat (1995)</td>\n",
       "      <td>Action|Crime|Thriller</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>Sudden Death (1995)</td>\n",
       "      <td>Action</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>GoldenEye (1995)</td>\n",
       "      <td>Action|Adventure|Thriller</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>15</td>\n",
       "      <td>Cutthroat Island (1995)</td>\n",
       "      <td>Action|Adventure|Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>20</td>\n",
       "      <td>Money Train (1995)</td>\n",
       "      <td>Action|Comedy|Crime|Drama|Thriller</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>23</td>\n",
       "      <td>Assassins (1995)</td>\n",
       "      <td>Action|Crime|Thriller</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>42</td>\n",
       "      <td>Dead Presidents (1995)</td>\n",
       "      <td>Action|Crime|Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>44</td>\n",
       "      <td>Mortal Kombat (1995)</td>\n",
       "      <td>Action|Adventure|Fantasy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>51</td>\n",
       "      <td>Guardian Angel (1994)</td>\n",
       "      <td>Action|Drama|Thriller</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>66</td>\n",
       "      <td>Lawnmower Man 2: Beyond Cyberspace (1996)</td>\n",
       "      <td>Action|Sci-Fi|Thriller</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>70</td>\n",
       "      <td>From Dusk Till Dawn (1996)</td>\n",
       "      <td>Action|Comedy|Horror|Thriller</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>71</td>\n",
       "      <td>Fair Game (1995)</td>\n",
       "      <td>Action</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>76</td>\n",
       "      <td>Screamers (1995)</td>\n",
       "      <td>Action|Sci-Fi|Thriller</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>78</td>\n",
       "      <td>Crossing Guard, The (1995)</td>\n",
       "      <td>Action|Crime|Drama|Thriller</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>86</td>\n",
       "      <td>White Squall (1996)</td>\n",
       "      <td>Action|Adventure|Drama</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    movieId                                      title  \\\n",
       "5         6                                Heat (1995)   \n",
       "8         9                        Sudden Death (1995)   \n",
       "9        10                           GoldenEye (1995)   \n",
       "14       15                    Cutthroat Island (1995)   \n",
       "19       20                         Money Train (1995)   \n",
       "22       23                           Assassins (1995)   \n",
       "41       42                     Dead Presidents (1995)   \n",
       "43       44                       Mortal Kombat (1995)   \n",
       "50       51                      Guardian Angel (1994)   \n",
       "65       66  Lawnmower Man 2: Beyond Cyberspace (1996)   \n",
       "69       70                 From Dusk Till Dawn (1996)   \n",
       "70       71                           Fair Game (1995)   \n",
       "75       76                           Screamers (1995)   \n",
       "77       78                 Crossing Guard, The (1995)   \n",
       "85       86                        White Squall (1996)   \n",
       "\n",
       "                                genres  \n",
       "5                Action|Crime|Thriller  \n",
       "8                               Action  \n",
       "9            Action|Adventure|Thriller  \n",
       "14            Action|Adventure|Romance  \n",
       "19  Action|Comedy|Crime|Drama|Thriller  \n",
       "22               Action|Crime|Thriller  \n",
       "41                  Action|Crime|Drama  \n",
       "43            Action|Adventure|Fantasy  \n",
       "50               Action|Drama|Thriller  \n",
       "65              Action|Sci-Fi|Thriller  \n",
       "69       Action|Comedy|Horror|Thriller  \n",
       "70                              Action  \n",
       "75              Action|Sci-Fi|Thriller  \n",
       "77         Action|Crime|Drama|Thriller  \n",
       "85              Action|Adventure|Drama  "
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies[is_action].head(15)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7938130b-e93e-46e1-8f8b-e13984d1c7fc",
   "metadata": {},
   "source": [
    "# 👥 Group By and Aggregate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "id": "1229aa64-2cc9-492e-89bf-2d1260eb1a59",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>movieId</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rating</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0.5</th>\n",
       "      <td>239125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.0</th>\n",
       "      <td>680732</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.5</th>\n",
       "      <td>279252</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.0</th>\n",
       "      <td>1430997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.5</th>\n",
       "      <td>883398</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3.0</th>\n",
       "      <td>4291193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3.5</th>\n",
       "      <td>2200156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.0</th>\n",
       "      <td>5561926</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.5</th>\n",
       "      <td>1534824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5.0</th>\n",
       "      <td>2898660</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        movieId\n",
       "rating         \n",
       "0.5      239125\n",
       "1.0      680732\n",
       "1.5      279252\n",
       "2.0     1430997\n",
       "2.5      883398\n",
       "3.0     4291193\n",
       "3.5     2200156\n",
       "4.0     5561926\n",
       "4.5     1534824\n",
       "5.0     2898660"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratings_count = ratings[['movieId','rating']].groupby('rating').count()\n",
    "ratings_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "id": "9e5f88e2-8f1d-4390-b868-efdf6ddac1c0",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>rating</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>movieId</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3.921240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.211977</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.151040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.861393</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3.064592</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           rating\n",
       "movieId          \n",
       "1        3.921240\n",
       "2        3.211977\n",
       "3        3.151040\n",
       "4        2.861393\n",
       "5        3.064592"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "average_rating = ratings[['movieId','rating']].groupby('movieId').mean()\n",
    "average_rating.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "id": "b46af230-15c6-4bcd-8c84-103d08bf7e41",
   "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",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rating</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>movieId</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>131254</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131256</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131258</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131260</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131262</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         rating\n",
       "movieId        \n",
       "131254        1\n",
       "131256        1\n",
       "131258        1\n",
       "131260        1\n",
       "131262        1"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movie_count = ratings[['movieId','rating']].groupby('movieId').count()\n",
    "movie_count.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "08850d6b-822c-444c-ab45-4307b7935e42",
   "metadata": {},
   "source": [
    "# 🔰 Merge Dataframes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "id": "5d26534f-d6c7-4dd7-9774-7e66217be517",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "    }\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>tag</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>18</td>\n",
       "      <td>4141</td>\n",
       "      <td>Mark Waters</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>65</td>\n",
       "      <td>208</td>\n",
       "      <td>dark hero</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>65</td>\n",
       "      <td>353</td>\n",
       "      <td>dark hero</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>65</td>\n",
       "      <td>521</td>\n",
       "      <td>noir thriller</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>65</td>\n",
       "      <td>592</td>\n",
       "      <td>dark hero</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   userId  movieId            tag\n",
       "0      18     4141    Mark Waters\n",
       "1      65      208      dark hero\n",
       "2      65      353      dark hero\n",
       "3      65      521  noir thriller\n",
       "4      65      592      dark hero"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tags.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "id": "1bcb0adb-ba9f-4fe5-bd06-6af106e771d5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        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>Adventure|Animation|Children|Comedy|Fantasy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Jumanji (1995)</td>\n",
       "      <td>Adventure|Children|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|Romance</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  \\\n",
       "0        1                    Toy Story (1995)   \n",
       "1        2                      Jumanji (1995)   \n",
       "2        3             Grumpier Old Men (1995)   \n",
       "3        4            Waiting to Exhale (1995)   \n",
       "4        5  Father of the Bride Part II (1995)   \n",
       "\n",
       "                                        genres  \n",
       "0  Adventure|Animation|Children|Comedy|Fantasy  \n",
       "1                   Adventure|Children|Fantasy  \n",
       "2                               Comedy|Romance  \n",
       "3                         Comedy|Drama|Romance  \n",
       "4                                       Comedy  "
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "id": "de808878-bff1-4e3c-8b12-fc637a52f30d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>movieId</th>\n",
       "      <th>title</th>\n",
       "      <th>genres</th>\n",
       "      <th>userId</th>\n",
       "      <th>tag</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>Adventure|Animation|Children|Comedy|Fantasy</td>\n",
       "      <td>1644</td>\n",
       "      <td>Watched</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Toy Story (1995)</td>\n",
       "      <td>Adventure|Animation|Children|Comedy|Fantasy</td>\n",
       "      <td>1741</td>\n",
       "      <td>computer animation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>Toy Story (1995)</td>\n",
       "      <td>Adventure|Animation|Children|Comedy|Fantasy</td>\n",
       "      <td>1741</td>\n",
       "      <td>Disney animated feature</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>Toy Story (1995)</td>\n",
       "      <td>Adventure|Animation|Children|Comedy|Fantasy</td>\n",
       "      <td>1741</td>\n",
       "      <td>Pixar animation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>Toy Story (1995)</td>\n",
       "      <td>Adventure|Animation|Children|Comedy|Fantasy</td>\n",
       "      <td>1741</td>\n",
       "      <td>TÃ©a Leoni does not star in this movie</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   movieId             title                                       genres  \\\n",
       "0        1  Toy Story (1995)  Adventure|Animation|Children|Comedy|Fantasy   \n",
       "1        1  Toy Story (1995)  Adventure|Animation|Children|Comedy|Fantasy   \n",
       "2        1  Toy Story (1995)  Adventure|Animation|Children|Comedy|Fantasy   \n",
       "3        1  Toy Story (1995)  Adventure|Animation|Children|Comedy|Fantasy   \n",
       "4        1  Toy Story (1995)  Adventure|Animation|Children|Comedy|Fantasy   \n",
       "\n",
       "   userId                                     tag  \n",
       "0    1644                                 Watched  \n",
       "1    1741                      computer animation  \n",
       "2    1741                 Disney animated feature  \n",
       "3    1741                         Pixar animation  \n",
       "4    1741  TÃ©a Leoni does not star in this movie  "
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t = movies.merge(tags, on='movieId', how='inner')\n",
    "t.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c95e5856-7a35-462d-9642-fdcae9b3bd56",
   "metadata": {},
   "source": [
    "# 📚 Combine aggreagation, merging, and filters to get useful analytics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "id": "8c4a6af0-a5ab-440b-9c00-171610172593",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>movieId</th>\n",
       "      <th>rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>3.921240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>3.211977</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>3.151040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2.861393</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>3.064592</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   movieId    rating\n",
       "0        1  3.921240\n",
       "1        2  3.211977\n",
       "2        3  3.151040\n",
       "3        4  2.861393\n",
       "4        5  3.064592"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "avg_ratings= ratings.groupby('movieId', as_index=False).mean()\n",
    "del avg_ratings['userId']\n",
    "avg_ratings.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "id": "12f4b104-a92b-40b2-9b9a-f473a4bce81b",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>26739</th>\n",
       "      <td>131254</td>\n",
       "      <td>Kein Bund für's Leben (2007)</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26740</th>\n",
       "      <td>131256</td>\n",
       "      <td>Feuer, Eis &amp; Dosenbier (2002)</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26741</th>\n",
       "      <td>131258</td>\n",
       "      <td>The Pirates (2014)</td>\n",
       "      <td>Adventure</td>\n",
       "      <td>2.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26742</th>\n",
       "      <td>131260</td>\n",
       "      <td>Rentun Ruusu (2001)</td>\n",
       "      <td>(no genres listed)</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26743</th>\n",
       "      <td>131262</td>\n",
       "      <td>Innocence (2014)</td>\n",
       "      <td>Adventure|Fantasy|Horror</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       movieId                          title                    genres  \\\n",
       "26739   131254   Kein Bund für's Leben (2007)                    Comedy   \n",
       "26740   131256  Feuer, Eis & Dosenbier (2002)                    Comedy   \n",
       "26741   131258             The Pirates (2014)                 Adventure   \n",
       "26742   131260            Rentun Ruusu (2001)        (no genres listed)   \n",
       "26743   131262               Innocence (2014)  Adventure|Fantasy|Horror   \n",
       "\n",
       "       rating  \n",
       "26739     4.0  \n",
       "26740     4.0  \n",
       "26741     2.5  \n",
       "26742     3.0  \n",
       "26743     4.0  "
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "box_office = movies.merge(avg_ratings, on='movieId', how='inner')\n",
    "box_office.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "id": "ab6b434a-cd7e-4ece-97e5-6b7d289a7a43",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>movieId</th>\n",
       "      <th>title</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>26737</th>\n",
       "      <td>131250</td>\n",
       "      <td>No More School (2000)</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26738</th>\n",
       "      <td>131252</td>\n",
       "      <td>Forklift Driver Klaus: The First Day on the Jo...</td>\n",
       "      <td>Comedy|Horror</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26739</th>\n",
       "      <td>131254</td>\n",
       "      <td>Kein Bund für's Leben (2007)</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26740</th>\n",
       "      <td>131256</td>\n",
       "      <td>Feuer, Eis &amp; Dosenbier (2002)</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26743</th>\n",
       "      <td>131262</td>\n",
       "      <td>Innocence (2014)</td>\n",
       "      <td>Adventure|Fantasy|Horror</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
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       "26739   131254                       Kein Bund für's Leben (2007)   \n",
       "26740   131256                      Feuer, Eis & Dosenbier (2002)   \n",
       "26743   131262                                   Innocence (2014)   \n",
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