movierating.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "6ca5def0-dd67-4058-9d9f-e50386a7d5fa",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "88db6509-9243-4487-bc21-dfcb61e9523f",
   "metadata": {},
   "outputs": [
    {
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       "  <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": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movie_dataset = pd.read_csv(r'C:\\Users\\Rahul S\\OneDrive\\Desktop\\Datasets_for_naresh_it_projects\\movie_dataset\\movie.csv')\n",
    "movie_dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "f7923478-f3be-4e9a-83f9-d9467390c1b9",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <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",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>65</td>\n",
       "      <td>208</td>\n",
       "      <td>dark hero</td>\n",
       "      <td>2013-05-10 01:41:18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>65</td>\n",
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       "      <td>dark hero</td>\n",
       "      <td>2013-05-10 01:41:19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>65</td>\n",
       "      <td>521</td>\n",
       "      <td>noir thriller</td>\n",
       "      <td>2013-05-10 01:39:43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>65</td>\n",
       "      <td>592</td>\n",
       "      <td>dark hero</td>\n",
       "      <td>2013-05-10 01:41:18</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "   userId  movieId            tag            timestamp\n",
       "0      18     4141    Mark Waters  2009-04-24 18:19:40\n",
       "1      65      208      dark hero  2013-05-10 01:41:18\n",
       "2      65      353      dark hero  2013-05-10 01:41:19\n",
       "3      65      521  noir thriller  2013-05-10 01:39:43\n",
       "4      65      592      dark hero  2013-05-10 01:41:18"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tags_dataset = pd.read_csv(r'C:\\Users\\Rahul S\\OneDrive\\Desktop\\Datasets_for_naresh_it_projects\\movie_dataset\\tag.csv')\n",
    "tags_dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "41ffdbbe-968b-41a8-8dd2-e24eb8ddad37",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <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",
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       "      <td>3.5</td>\n",
       "      <td>2005-04-02 23:33:39</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
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       "      <td>3.5</td>\n",
       "      <td>2005-04-02 23:32:07</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>50</td>\n",
       "      <td>3.5</td>\n",
       "      <td>2005-04-02 23:29:40</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "   userId  movieId  rating            timestamp\n",
       "0       1        2     3.5  2005-04-02 23:53:47\n",
       "1       1       29     3.5  2005-04-02 23:31:16\n",
       "2       1       32     3.5  2005-04-02 23:33:39\n",
       "3       1       47     3.5  2005-04-02 23:32:07\n",
       "4       1       50     3.5  2005-04-02 23:29:40"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratings_dataset = pd.read_csv(r'C:\\Users\\Rahul S\\OneDrive\\Desktop\\Datasets_for_naresh_it_projects\\movie_dataset\\rating.csv')\n",
    "ratings_dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "aa49d54a-d221-4974-9af5-7016dc99708d",
   "metadata": {},
   "outputs": [],
   "source": [
    "del ratings_dataset['timestamp']\n",
    "del tags_dataset['timestamp']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "0801dbca-77dd-41be-ba50-0d817b1f5cb7",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>userId</th>\n",
       "      <th>movieId</th>\n",
       "      <th>tag</th>\n",
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       "      <td>Mark Waters</td>\n",
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       "      <td>dark hero</td>\n",
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       "      <td>dark hero</td>\n",
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       "      <td>65</td>\n",
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       "      <td>noir thriller</td>\n",
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      "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": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tags_dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "aa53660b-762b-4907-af6b-27b7c3e1ce56",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>userId</th>\n",
       "      <th>movieId</th>\n",
       "      <th>rating</th>\n",
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       "      <td>3.5</td>\n",
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      "text/plain": [
       "   userId  movieId  rating\n",
       "0       1        2     3.5\n",
       "1       1       29     3.5\n",
       "2       1       32     3.5\n",
       "3       1       47     3.5\n",
       "4       1       50     3.5"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratings_dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "ad10b9c9-392f-4d09-a659-4dbbeed03aa8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "row_0 = tags_dataset.iloc[0]\n",
    "type(row_0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "c7b9811f-8474-4c41-bf20-43d22d343a26",
   "metadata": {},
   "outputs": [
    {
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       "  <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",
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       "      <td>18</td>\n",
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       "      <td>Mark Waters</td>\n",
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       "      <td>208</td>\n",
       "      <td>dark hero</td>\n",
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       "      <th>2</th>\n",
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       "      <td>353</td>\n",
       "      <td>dark hero</td>\n",
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       "    <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",
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       "      <td>65</td>\n",
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       "      <td>bollywood</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>65</td>\n",
       "      <td>898</td>\n",
       "      <td>screwball comedy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>65</td>\n",
       "      <td>1248</td>\n",
       "      <td>noir thriller</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>65</td>\n",
       "      <td>1391</td>\n",
       "      <td>mars</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>65</td>\n",
       "      <td>1617</td>\n",
       "      <td>neo-noir</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\n",
       "5      65      668         bollywood\n",
       "6      65      898  screwball comedy\n",
       "7      65     1248     noir thriller\n",
       "8      65     1391              mars\n",
       "9      65     1617          neo-noir"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tags_dataset[0:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "cfd4198d-1e06-48f8-9b5c-a17e2f54a54b",
   "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": 69,
   "id": "28d95bfb-effc-485d-b36a-4a1d908d25f0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['userId', 'movieId', 'tag'], dtype='object')"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "row_0.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "7612b667-7567-4d8d-825a-e5f118ed1e0d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "18"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "row_0['userId']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "834ab242-fa1e-4e3d-9364-b8998ed9601a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'rating' in row_0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "9714779d-fb29-47c8-8eca-357b344522e9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "row_0.name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "e4748657-70d4-437c-a6ba-812dc5437797",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "userId              18\n",
      "movieId           4141\n",
      "tag        Mark Waters\n",
      "Name: firstRow, dtype: object\n"
     ]
    }
   ],
   "source": [
    "row_0 = row_0.rename('firstRow')\n",
    "row_0.name\n",
    "print(row_0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "d13c432f-8595-4c6b-81ba-2e0945c70696",
   "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>userId</th>\n",
       "      <th>movieId</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>465564.000000</td>\n",
       "      <td>465564.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>68712.354263</td>\n",
       "      <td>32627.762920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>41877.674053</td>\n",
       "      <td>36080.241157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>18.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>28780.000000</td>\n",
       "      <td>2571.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>70201.000000</td>\n",
       "      <td>7373.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>107322.000000</td>\n",
       "      <td>62235.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>138472.000000</td>\n",
       "      <td>131258.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              userId        movieId\n",
       "count  465564.000000  465564.000000\n",
       "mean    68712.354263   32627.762920\n",
       "std     41877.674053   36080.241157\n",
       "min        18.000000       1.000000\n",
       "25%     28780.000000    2571.000000\n",
       "50%     70201.000000    7373.000000\n",
       "75%    107322.000000   62235.000000\n",
       "max    138472.000000  131258.000000"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tags_dataset.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "57c5abaf-b567-4400-b3e7-2c5a72fafb50",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['userId', 'movieId', 'tag'], dtype='object')"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tags_dataset.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "9f25a709-3abd-4b2c-bfc8-7fabb0d9978e",
   "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>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",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>121</td>\n",
       "      <td>8641</td>\n",
       "      <td>seen more than once</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\n",
       "80      121     8641  seen more than once"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tags_dataset.iloc[[0,11,500,80]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "4fb1fa95-0716-40d3-ad6a-adca2071f8bf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "        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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>27278.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>59855.480570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>44429.314697</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>6931.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>68068.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>100293.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>131262.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             movieId\n",
       "count   27278.000000\n",
       "mean    59855.480570\n",
       "std     44429.314697\n",
       "min         1.000000\n",
       "25%      6931.250000\n",
       "50%     68068.000000\n",
       "75%    100293.250000\n",
       "max    131262.000000"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movie_dataset.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "3f8497ad-0c34-4a91-b370-1c180d571e01",
   "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>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": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratings_dataset.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "1beb844f-3980-45e8-aa3b-af11f5d93d43",
   "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": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratings_dataset['rating'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "f34d4ce1-5e83-4ee4-9443-b0517dfafb19",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.5255285642993797"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratings_dataset['rating'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "5a3fd55e-e84d-4ae4-8387-74d3d6aa05b9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "userId     69045.872583\n",
       "movieId     9041.567330\n",
       "rating         3.525529\n",
       "dtype: float64"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratings_dataset.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "52bbea5a-cac7-460a-9b79-e128943dc0c1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratings_dataset['rating'].min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "490b8272-7a60-4baf-b009-85bcba4b72c8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5.0"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratings_dataset['rating'].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "bfafd09c-d777-421a-9642-e7f6ca1298e1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.051988919275684"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratings_dataset['rating'].std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "f953af37-07cc-436b-bea4-d92872b98b82",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    4.0\n",
       "Name: rating, dtype: float64"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratings_dataset['rating'].mode()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "afebd353-d538-498a-8c04-27cec1883022",
   "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_dataset.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "10e2c446-4ffe-4928-8331-0cba903bbbc7",
   "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": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filter1 = ratings_dataset['rating'] > 10\n",
    "print(filter1)\n",
    "filter1.any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "8960d72a-c143-4d0a-b755-7774ed007b31",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filter2 = ratings_dataset['rating'] > 0\n",
    "filter2.all()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "e4c11959-511b-484f-ae66-005bbaa11fb6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(27278, 3)"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movie_dataset.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "ed9a33fa-8466-42c4-9a35-b9869fa495c0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movie_dataset.isnull().any().any()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fb8f1720-39e4-4c24-abb0-6587d757edcc",
   "metadata": {},
   "source": [
    "## no null value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "05c25b9d-a1f6-4e1d-a893-89ea616ecc27",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(20000263, 3)"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratings_dataset.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "c7bd6d02-76eb-452e-9c92-7fc01c200801",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratings_dataset.isnull().any().any()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "815b19be-36dd-4542-842c-a44383eff00a",
   "metadata": {},
   "source": [
    "## no null value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "fa63b1eb-0ea8-4881-85db-af57802ce3a6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(465564, 3)"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tags_dataset.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "57bdd374-0f5e-499e-8143-5536b975bd6d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tags_dataset.isnull().any().any()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9712fb6f-d9ef-4d0b-8c40-499182a18b6c",
   "metadata": {},
   "source": [
    "## we have sum tags that have null value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "756d2eb7-5714-4ded-80ff-8dddd9e00ad9",
   "metadata": {},
   "outputs": [],
   "source": [
    "tags_dataset = tags_dataset.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "981bc37a-74c9-44ac-bda0-539c47faf6a1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tags_dataset.isnull().any().any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "f50be15e-829c-4a18-8224-1e925e30d2ff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(465548, 3)"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tags_dataset.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d0f378b9-81f5-4d2d-a997-a32d3a50e832",
   "metadata": {},
   "source": [
    "## 📊 Data Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "d35354bf-b686-4444-8160-9c048161ce51",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "4dc4b87e-0634-406f-8409-8e6dfb78b24a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<Axes: title={'center': 'rating'}>]], dtype=object)"
      ]
     },
     "execution_count": 103,
     "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_dataset.hist(column ='rating' ,figsize=(10,5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "5f3ca6c6-e96d-4a00-aecb-341760ff8de9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 104,
     "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_dataset.boxplot(column ='rating' ,figsize=(10,5))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "11f12ed9-d47f-4b7f-9c6f-05d37298e072",
   "metadata": {},
   "source": [
    "## 📤 Slicing Out Columns¶\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "8497e404-de84-4558-ae84-17a3bca58ccf",
   "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": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tags_dataset['tag'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "65cfc8f7-fdb1-4d0d-bf58-af139365a851",
   "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>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": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tags_dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "d8f91ace-c3ff-48d0-92d2-4974811fddf4",
   "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>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": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movie_dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "f0b07053-3860-430e-b779-8ba94a2e83db",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>Comedy|Romance</td>\n",
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       "</table>\n",
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      ],
      "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": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movie_dataset[['title','genres']].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "4bb4e2ec-5c51-41a5-a848-460fb996c82b",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "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": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratings_dataset[-10:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "6a4159b8-2464-4bbd-878d-27df64dc9bcd",
   "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": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tag_counts = tags_dataset['tag'].value_counts()\n",
    "tag_counts[-10:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "74a0d93b-72a1-4af4-a2a0-6e68ca8c75d4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='tag'>"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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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": "markdown",
   "id": "0160695e-0c1e-437e-a157-6aa5fd6734e7",
   "metadata": {},
   "source": [
    "## Filters for Selecting Rows¶\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "e6431b43-fcf2-46b6-b1f0-a6c61d54e5eb",
   "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": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "is_highly_rated = ratings_dataset['rating'] >= 5.0\n",
    "ratings_dataset[is_highly_rated][30:50]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cadf5d68-c257-4f65-918d-078417472a7d",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.12.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}