MovieLens 25M Dataset.ipynb

{
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  {
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    "SUMMARY\n",
    "================================================================================\n",
    "\n",
    "These files contain 1,000,209 anonymous ratings of approximately 3,900 movies \n",
    "made by 6,040 MovieLens users who joined MovieLens in 2000.\n",
    "\n",
    "USAGE LICENSE\n",
    "================================================================================\n",
    "\n",
    "Neither the University of Minnesota nor any of the researchers\n",
    "involved can guarantee the correctness of the data, its suitability\n",
    "for any particular purpose, or the validity of results based on the\n",
    "use of the data set.  The data set may be used for any research\n",
    "purposes under the following conditions:\n",
    "\n",
    "     * The user may not state or imply any endorsement from the\n",
    "       University of Minnesota or the GroupLens Research Group.\n",
    "\n",
    "     * The user must acknowledge the use of the data set in\n",
    "       publications resulting from the use of the data set, and must\n",
    "       send us an electronic or paper copy of those publications.\n",
    "\n",
    "     * The user may not redistribute the data without separate\n",
    "       permission.\n",
    "\n",
    "     * The user may not use this information for any commercial or\n",
    "       revenue-bearing purposes without first obtaining permission\n",
    "       from a faculty member of the GroupLens Research Project at the\n",
    "       University of Minnesota.\n",
    "\n",
    "If you have any further questions or comments, please contact Sean McNee\n",
    "<mcnee@cs.umn.edu>. \n",
    "\n",
    "ACKNOWLEDGEMENTS\n",
    "================================================================================\n",
    "\n",
    "Thanks to Shyong Lam and Jon Herlocker for cleaning up and generating the data\n",
    "set.\n",
    "\n",
    "FURTHER INFORMATION ABOUT THE GROUPLENS RESEARCH PROJECT\n",
    "================================================================================\n",
    "\n",
    "The GroupLens Research Project is a research group in the Department of \n",
    "Computer Science and Engineering at the University of Minnesota. Members of \n",
    "the GroupLens Research Project are involved in many research projects related \n",
    "to the fields of information filtering, collaborative filtering, and \n",
    "recommender systems. The project is lead by professors John Riedl and Joseph \n",
    "Konstan. The project began to explore automated collaborative filtering in \n",
    "1992, but is most well known for its world wide trial of an automated \n",
    "collaborative filtering system for Usenet news in 1996. Since then the project \n",
    "has expanded its scope to research overall information filtering solutions, \n",
    "integrating in content-based methods as well as improving current collaborative \n",
    "filtering technology.\n",
    "\n",
    "Further information on the GroupLens Research project, including research \n",
    "publications, can be found at the following web site:\n",
    "        \n",
    "        http://www.grouplens.org/\n",
    "\n",
    "GroupLens Research currently operates a movie recommender based on \n",
    "collaborative filtering:\n",
    "\n",
    "        http://www.movielens.org/\n",
    "\n",
    "RATINGS FILE DESCRIPTION\n",
    "================================================================================\n",
    "\n",
    "All ratings are contained in the file \"ratings.dat\" and are in the\n",
    "following format:\n",
    "\n",
    "UserID::MovieID::Rating::Timestamp\n",
    "\n",
    "- UserIDs range between 1 and 6040 \n",
    "- MovieIDs range between 1 and 3952\n",
    "- Ratings are made on a 5-star scale (whole-star ratings only)\n",
    "- Timestamp is represented in seconds since the epoch as returned by time(2)\n",
    "- Each user has at least 20 ratings\n",
    "\n",
    "USERS FILE DESCRIPTION\n",
    "================================================================================\n",
    "\n",
    "User information is in the file \"users.dat\" and is in the following\n",
    "format:\n",
    "\n",
    "UserID::Gender::Age::Occupation::Zip-code\n",
    "\n",
    "All demographic information is provided voluntarily by the users and is\n",
    "not checked for accuracy.  Only users who have provided some demographic\n",
    "information are included in this data set.\n",
    "\n",
    "- Gender is denoted by a \"M\" for male and \"F\" for female\n",
    "- Age is chosen from the following ranges:\n",
    "\n",
    "\t*  1:  \"Under 18\"\n",
    "\t* 18:  \"18-24\"\n",
    "\t* 25:  \"25-34\"\n",
    "\t* 35:  \"35-44\"\n",
    "\t* 45:  \"45-49\"\n",
    "\t* 50:  \"50-55\"\n",
    "\t* 56:  \"56+\"\n",
    "\n",
    "- Occupation is chosen from the following choices:\n",
    "\n",
    "\t*  0:  \"other\" or not specified\n",
    "\t*  1:  \"academic/educator\"\n",
    "\t*  2:  \"artist\"\n",
    "\t*  3:  \"clerical/admin\"\n",
    "\t*  4:  \"college/grad student\"\n",
    "\t*  5:  \"customer service\"\n",
    "\t*  6:  \"doctor/health care\"\n",
    "\t*  7:  \"executive/managerial\"\n",
    "\t*  8:  \"farmer\"\n",
    "\t*  9:  \"homemaker\"\n",
    "\t* 10:  \"K-12 student\"\n",
    "\t* 11:  \"lawyer\"\n",
    "\t* 12:  \"programmer\"\n",
    "\t* 13:  \"retired\"\n",
    "\t* 14:  \"sales/marketing\"\n",
    "\t* 15:  \"scientist\"\n",
    "\t* 16:  \"self-employed\"\n",
    "\t* 17:  \"technician/engineer\"\n",
    "\t* 18:  \"tradesman/craftsman\"\n",
    "\t* 19:  \"unemployed\"\n",
    "\t* 20:  \"writer\"\n",
    "\n",
    "MOVIES FILE DESCRIPTION\n",
    "================================================================================\n",
    "\n",
    "Movie information is in the file \"movies.dat\" and is in the following\n",
    "format:\n",
    "\n",
    "MovieID::Title::Genres\n",
    "\n",
    "- Titles are identical to titles provided by the IMDB (including\n",
    "year of release)\n",
    "- Genres are pipe-separated and are selected from the following genres:\n",
    "\n",
    "\t* Action\n",
    "\t* Adventure\n",
    "\t* Animation\n",
    "\t* Children's\n",
    "\t* Comedy\n",
    "\t* Crime\n",
    "\t* Documentary\n",
    "\t* Drama\n",
    "\t* Fantasy\n",
    "\t* Film-Noir\n",
    "\t* Horror\n",
    "\t* Musical\n",
    "\t* Mystery\n",
    "\t* Romance\n",
    "\t* Sci-Fi\n",
    "\t* Thriller\n",
    "\t* War\n",
    "\t* Western\n",
    "\n",
    "- Some MovieIDs do not correspond to a movie due to accidental duplicate\n",
    "entries and/or test entries\n",
    "- Movies are mostly entered by hand, so errors and inconsistencies may exist"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3dfcc536",
   "metadata": {},
   "source": [
    "Links Data File Structure (links.csv)\n",
    "---------------------------------------\n",
    "\n",
    "Identifiers that can be used to link to other sources of movie data are contained in the file `links.csv`. Each line of this file after the header row represents one movie, and has the following format:\n",
    "\n",
    "    movieId,imdbId,tmdbId\n",
    "\n",
    "movieId is an identifier for movies used by <https://movielens.org>. E.g., the movie Toy Story has the link <https://movielens.org/movies/1>.\n",
    "\n",
    "imdbId is an identifier for movies used by <http://www.imdb.com>. E.g., the movie Toy Story has the link <http://www.imdb.com/title/tt0114709/>.\n",
    "\n",
    "tmdbId is an identifier for movies used by <https://www.themoviedb.org>. E.g., the movie Toy Story has the link <https://www.themoviedb.org/movie/862>.\n",
    "\n",
    "Use of the resources listed above is subject to the terms of each provider."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ecc0bb23",
   "metadata": {},
   "source": [
    "GroupLens Research provides a number of collections of movie ratings data collected from users of MovieLens in the late\n",
    "1990s and early 2000s. The data provides movie ratings, movie metadata (genres and year), and demographic data about the \n",
    "users (age, zip code, gender identification, and occupation). Such data is often of interest in the development of\n",
    "recommendation systems based on machine learning algorithms. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c3a840a",
   "metadata": {},
   "source": [
    "The MovieLens 1M dataset contains one million ratings collected from six thousand users on four thousand movies. It’s \n",
    "spread across three tables: ratings, user information, and movie information. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "027ee23d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8c9e9125",
   "metadata": {},
   "source": [
    "# Extracting data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "0bc437a3",
   "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>user_id</th>\n",
       "      <th>gender</th>\n",
       "      <th>age</th>\n",
       "      <th>occupation</th>\n",
       "      <th>zip</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>F</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>48067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>M</td>\n",
       "      <td>56</td>\n",
       "      <td>16</td>\n",
       "      <td>70072</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>M</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>M</td>\n",
       "      <td>25</td>\n",
       "      <td>20</td>\n",
       "      <td>55455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6035</th>\n",
       "      <td>6036</td>\n",
       "      <td>F</td>\n",
       "      <td>25</td>\n",
       "      <td>15</td>\n",
       "      <td>32603</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6036</th>\n",
       "      <td>6037</td>\n",
       "      <td>F</td>\n",
       "      <td>45</td>\n",
       "      <td>1</td>\n",
       "      <td>76006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6037</th>\n",
       "      <td>6038</td>\n",
       "      <td>F</td>\n",
       "      <td>56</td>\n",
       "      <td>1</td>\n",
       "      <td>14706</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6038</th>\n",
       "      <td>6039</td>\n",
       "      <td>F</td>\n",
       "      <td>45</td>\n",
       "      <td>0</td>\n",
       "      <td>01060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6039</th>\n",
       "      <td>6040</td>\n",
       "      <td>M</td>\n",
       "      <td>25</td>\n",
       "      <td>6</td>\n",
       "      <td>11106</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6040 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      user_id gender  age  occupation    zip\n",
       "0           1      F    1          10  48067\n",
       "1           2      M   56          16  70072\n",
       "2           3      M   25          15  55117\n",
       "3           4      M   45           7  02460\n",
       "4           5      M   25          20  55455\n",
       "...       ...    ...  ...         ...    ...\n",
       "6035     6036      F   25          15  32603\n",
       "6036     6037      F   45           1  76006\n",
       "6037     6038      F   56           1  14706\n",
       "6038     6039      F   45           0  01060\n",
       "6039     6040      M   25           6  11106\n",
       "\n",
       "[6040 rows x 5 columns]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unames=[\"user_id\", \"gender\", \"age\", \"occupation\", \"zip\"]\n",
    "users= pd.read_table(\"users.dat\", sep=\"::\", header=None, names=unames, engine=\"python\")\n",
    "users"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "c16c5e5b",
   "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>rating</th>\n",
       "      <th>timestamp</th>\n",
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       "      <td>5.0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
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       "      <td>899</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1147868510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
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       "      <td>4.0</td>\n",
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       "      <th>6</th>\n",
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       "      <td>1147868826</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
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       "      <th>8</th>\n",
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       "      <td>5.0</td>\n",
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       "      <th>9</th>\n",
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       "      <td>1147868414</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   userId  movieId  rating   timestamp\n",
       "0       1      296     5.0  1147880044\n",
       "1       1      306     3.5  1147868817\n",
       "2       1      307     5.0  1147868828\n",
       "3       1      665     5.0  1147878820\n",
       "4       1      899     3.5  1147868510\n",
       "5       1     1088     4.0  1147868495\n",
       "6       1     1175     3.5  1147868826\n",
       "7       1     1217     3.5  1147878326\n",
       "8       1     1237     5.0  1147868839\n",
       "9       1     1250     4.0  1147868414"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rnames=[\"user_id\", \"movie_id\", \"rating\", \"timestamp\"]\n",
    "ratings= pd.read_csv(\"ratings.csv\")\n",
    "ratings.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "a5ed0900",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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",
       "    <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>6</th>\n",
       "      <td>7</td>\n",
       "      <td>Sabrina (1995)</td>\n",
       "      <td>Comedy|Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>Tom and Huck (1995)</td>\n",
       "      <td>Adventure|Children</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",
       "  </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",
       "5        6                         Heat (1995)   \n",
       "6        7                      Sabrina (1995)   \n",
       "7        8                 Tom and Huck (1995)   \n",
       "8        9                 Sudden Death (1995)   \n",
       "9       10                    GoldenEye (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  \n",
       "5                        Action|Crime|Thriller  \n",
       "6                               Comedy|Romance  \n",
       "7                           Adventure|Children  \n",
       "8                                       Action  \n",
       "9                    Action|Adventure|Thriller  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mnames=[\"movie_id\", \"title\", \"genres\"]\n",
    "movies=pd.read_csv(\"movies.csv\")\n",
    "movies.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3b6fb845",
   "metadata": {},
   "source": [
    "# Cleaning the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "20d711ee",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>1147868414</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user_id  movie_id  rating   timestamp\n",
       "0        1       296     5.0  1147880044\n",
       "1        1       306     3.5  1147868817\n",
       "2        1       307     5.0  1147868828\n",
       "3        1       665     5.0  1147878820\n",
       "4        1       899     3.5  1147868510\n",
       "5        1      1088     4.0  1147868495\n",
       "6        1      1175     3.5  1147868826\n",
       "7        1      1217     3.5  1147878326\n",
       "8        1      1237     5.0  1147868839\n",
       "9        1      1250     4.0  1147868414"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratings.rename(columns = {'userId':'user_id', 'movieId':'movie_id'}, inplace = True)\n",
    "ratings.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "b1a46804",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>movie_id</th>\n",
       "      <th>title</th>\n",
       "      <th>genres</th>\n",
       "    </tr>\n",
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       "  <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",
       "    <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>6</th>\n",
       "      <td>7</td>\n",
       "      <td>Sabrina (1995)</td>\n",
       "      <td>Comedy|Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>Tom and Huck (1995)</td>\n",
       "      <td>Adventure|Children</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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   movie_id                               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",
       "5         6                         Heat (1995)   \n",
       "6         7                      Sabrina (1995)   \n",
       "7         8                 Tom and Huck (1995)   \n",
       "8         9                 Sudden Death (1995)   \n",
       "9        10                    GoldenEye (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  \n",
       "5                        Action|Crime|Thriller  \n",
       "6                               Comedy|Romance  \n",
       "7                           Adventure|Children  \n",
       "8                                       Action  \n",
       "9                    Action|Adventure|Thriller  "
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies.rename(columns = {'movieId':'movie_id'}, inplace = True)\n",
    "movies.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c4cf8634",
   "metadata": {},
   "source": [
    "Analyzing the data spread across three tables is not a simple task; for example, suppose you wanted to compute mean ratings\n",
    "for a particular movie by gender identity and age. As you will see, this is more convenient to do with all of the data \n",
    "merged together into a single table. Using pandas’s merge function, we first merge ratings with users and then merge that \n",
    "result with the movies data. pandas infers which columns to use as the merge (or join) keys based on overlapping names:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "4046d558",
   "metadata": {},
   "outputs": [
    {
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       "      <td>01060</td>\n",
       "      <td>Snowriders (1996)</td>\n",
       "      <td>Documentary</td>\n",
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       "      <td>Drama</td>\n",
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       "      <td>Bloody Child, The (1996)</td>\n",
       "      <td>Drama|Thriller</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>899106 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        user_id  movie_id  rating   timestamp gender  age  occupation    zip  \\\n",
       "0             1       296     5.0  1147880044      F    1          10  48067   \n",
       "1             3       296     5.0  1439474476      M   25          15  55117   \n",
       "2             4       296     4.0  1573938898      M   45           7  02460   \n",
       "3             5       296     4.0   830786155      M   25          20  55455   \n",
       "4             7       296     4.0   835444730      M   35           1  06810   \n",
       "...         ...       ...     ...         ...    ...  ...         ...    ...   \n",
       "899101     6039      1145     3.0   848774734      F   45           0  01060   \n",
       "899102     6039      1146     3.0   848774673      F   45           0  01060   \n",
       "899103     6039      1155     3.0   848774797      F   45           0  01060   \n",
       "899104     6039      1157     3.0   848774768      F   45           0  01060   \n",
       "899105     6039      1165     3.0   848774657      F   45           0  01060   \n",
       "\n",
       "                                       title                       genres  \n",
       "0                        Pulp Fiction (1994)  Comedy|Crime|Drama|Thriller  \n",
       "1                        Pulp Fiction (1994)  Comedy|Crime|Drama|Thriller  \n",
       "2                        Pulp Fiction (1994)  Comedy|Crime|Drama|Thriller  \n",
       "3                        Pulp Fiction (1994)  Comedy|Crime|Drama|Thriller  \n",
       "4                        Pulp Fiction (1994)  Comedy|Crime|Drama|Thriller  \n",
       "...                                      ...                          ...  \n",
       "899101                     Snowriders (1996)                  Documentary  \n",
       "899102                 Curtis's Charm (1995)                 Comedy|Drama  \n",
       "899103  Invitation, The (Zaproszenie) (1986)                        Drama  \n",
       "899104        Symphonie pastorale, La (1946)                        Drama  \n",
       "899105              Bloody Child, The (1996)               Drama|Thriller  \n",
       "\n",
       "[899106 rows x 10 columns]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.merge(pd.merge(ratings, users), movies)\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "cf550b5f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "user_id                                 1\n",
       "movie_id                              296\n",
       "rating                                5.0\n",
       "timestamp                      1147880044\n",
       "gender                                  F\n",
       "age                                     1\n",
       "occupation                             10\n",
       "zip                                 48067\n",
       "title                 Pulp Fiction (1994)\n",
       "genres        Comedy|Crime|Drama|Thriller\n",
       "Name: 0, dtype: object"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.iloc[0]"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "115b6d7a",
   "metadata": {},
   "source": [
    "# Transforming the data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "adf32ff3",
   "metadata": {},
   "source": [
    "To get mean movie ratings for each film grouped by gender, we can use the pivot_table method:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "9839d68e",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>gender</th>\n",
       "      <th>F</th>\n",
       "      <th>M</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>title</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>\"Great Performances\" Cats (1998)</th>\n",
       "      <td>2.00</td>\n",
       "      <td>3.681818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>#realityhigh (2017)</th>\n",
       "      <td>3.00</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>$ (Dollars) (1971)</th>\n",
       "      <td>NaN</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>$5 a Day (2008)</th>\n",
       "      <td>NaN</td>\n",
       "      <td>3.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>$9.99 (2008)</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>'71 (2014)</th>\n",
       "      <td>3.00</td>\n",
       "      <td>3.409091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>'Hellboy': The Seeds of Creation (2004)</th>\n",
       "      <td>3.75</td>\n",
       "      <td>2.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>'Neath the Arizona Skies (1934)</th>\n",
       "      <td>NaN</td>\n",
       "      <td>3.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>'R Xmas (2001)</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>'Round Midnight (1986)</th>\n",
       "      <td>4.50</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "gender                                      F         M\n",
       "title                                                  \n",
       "\"Great Performances\" Cats (1998)         2.00  3.681818\n",
       "#realityhigh (2017)                      3.00       NaN\n",
       "$ (Dollars) (1971)                        NaN  4.000000\n",
       "$5 a Day (2008)                           NaN  3.666667\n",
       "$9.99 (2008)                              NaN  2.666667\n",
       "'71 (2014)                               3.00  3.409091\n",
       "'Hellboy': The Seeds of Creation (2004)  3.75  2.250000\n",
       "'Neath the Arizona Skies (1934)           NaN  3.250000\n",
       "'R Xmas (2001)                            NaN  2.750000\n",
       "'Round Midnight (1986)                   4.50  4.000000"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_ratings= data.pivot_table(\"rating\", index=\"title\", columns=\"gender\", aggfunc=\"mean\")\n",
    "mean_ratings.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "71c78283",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 8711, 18492,  5263, ...,  5249,  9476, 11551], dtype=int64)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "indexer=mean_ratings.sum(axis=\"columns\").argsort()\n",
    "indexer.values[:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "2b115d68",
   "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>gender</th>\n",
       "      <th>F</th>\n",
       "      <th>M</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>title</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Man Who Sleeps, The (Un homme qui dort) (1974)</th>\n",
       "      <td>5.000000</td>\n",
       "      <td>4.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Woman in the Window, The (1944)</th>\n",
       "      <td>4.500000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kiler (1997)</th>\n",
       "      <td>4.500000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>I Was Born, But... (a.k.a. Children of Tokyo) (Otona no miru ehon - Umarete wa mita keredo) (1932)</th>\n",
       "      <td>4.500000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Twin Peaks (1989)</th>\n",
       "      <td>4.916667</td>\n",
       "      <td>4.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Death on the Staircase (Soupçons) (2004)</th>\n",
       "      <td>4.666667</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Life is a Miracle (Zivot je cudo) (2004)</th>\n",
       "      <td>5.000000</td>\n",
       "      <td>4.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Belladonna of Sadness (1973)</th>\n",
       "      <td>5.000000</td>\n",
       "      <td>4.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Satan's Tango (Sátántangó) (1994)</th>\n",
       "      <td>5.000000</td>\n",
       "      <td>4.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wow! A Talking Fish! (1983)</th>\n",
       "      <td>4.750000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vengo (2000)</th>\n",
       "      <td>5.000000</td>\n",
       "      <td>4.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cosmos: A Spacetime Odissey</th>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bury My Heart at Wounded Knee (2007)</th>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brother Minister: The Assassination of Malcolm X (1994)</th>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Liquidation (2007)</th>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Life+1 Day (2016)</th>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equinox Flower (Higanbana) (1958)</th>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Don't Look Now: We're Being Shot At (La grande vadrouille) (1966)</th>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jim Henson's The Storyteller (1989)</th>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Marwencol (2010)</th>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "gender                                                     F         M\n",
       "title                                                                 \n",
       "Man Who Sleeps, The (Un homme qui dort) (1974)      5.000000  4.500000\n",
       "Woman in the Window, The (1944)                     4.500000  5.000000\n",
       "Kiler (1997)                                        4.500000  5.000000\n",
       "I Was Born, But... (a.k.a. Children of Tokyo) (...  4.500000  5.000000\n",
       "Twin Peaks (1989)                                   4.916667  4.600000\n",
       "Death on the Staircase (Soupçons) (2004)            4.666667  5.000000\n",
       "Life is a Miracle (Zivot je cudo) (2004)            5.000000  4.666667\n",
       "Belladonna of Sadness (1973)                        5.000000  4.750000\n",
       "Satan's Tango (Sátántangó) (1994)                   5.000000  4.750000\n",
       "Wow! A Talking Fish! (1983)                         4.750000  5.000000\n",
       "Vengo (2000)                                        5.000000  4.750000\n",
       "Cosmos: A Spacetime Odissey                         5.000000  5.000000\n",
       "Bury My Heart at Wounded Knee (2007)                5.000000  5.000000\n",
       "Brother Minister: The Assassination of Malcolm ...  5.000000  5.000000\n",
       "Liquidation (2007)                                  5.000000  5.000000\n",
       "Life+1 Day (2016)                                   5.000000  5.000000\n",
       "Equinox Flower (Higanbana) (1958)                   5.000000  5.000000\n",
       "Don't Look Now: We're Being Shot At (La grande ...  5.000000  5.000000\n",
       "Jim Henson's The Storyteller (1989)                 5.000000  5.000000\n",
       "Marwencol (2010)                                    5.000000  5.000000"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_ratings_20 = mean_ratings.take(indexer[-20:])\n",
    "mean_ratings_20"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "f3743e90",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "title                                                                                               gender\n",
       "Man Who Sleeps, The (Un homme qui dort) (1974)                                                      F         5.000000\n",
       "                                                                                                    M         4.500000\n",
       "Woman in the Window, The (1944)                                                                     F         4.500000\n",
       "                                                                                                    M         5.000000\n",
       "Kiler (1997)                                                                                        F         4.500000\n",
       "                                                                                                    M         5.000000\n",
       "I Was Born, But... (a.k.a. Children of Tokyo) (Otona no miru ehon - Umarete wa mita keredo) (1932)  F         4.500000\n",
       "                                                                                                    M         5.000000\n",
       "Twin Peaks (1989)                                                                                   F         4.916667\n",
       "                                                                                                    M         4.600000\n",
       "Death on the Staircase (Soupçons) (2004)                                                            F         4.666667\n",
       "                                                                                                    M         5.000000\n",
       "Life is a Miracle (Zivot je cudo) (2004)                                                            F         5.000000\n",
       "                                                                                                    M         4.666667\n",
       "Belladonna of Sadness (1973)                                                                        F         5.000000\n",
       "                                                                                                    M         4.750000\n",
       "Satan's Tango (Sátántangó) (1994)                                                                   F         5.000000\n",
       "                                                                                                    M         4.750000\n",
       "Wow! A Talking Fish! (1983)                                                                         F         4.750000\n",
       "                                                                                                    M         5.000000\n",
       "Vengo (2000)                                                                                        F         5.000000\n",
       "                                                                                                    M         4.750000\n",
       "Cosmos: A Spacetime Odissey                                                                         F         5.000000\n",
       "                                                                                                    M         5.000000\n",
       "Bury My Heart at Wounded Knee (2007)                                                                F         5.000000\n",
       "                                                                                                    M         5.000000\n",
       "Brother Minister: The Assassination of Malcolm X (1994)                                             F         5.000000\n",
       "                                                                                                    M         5.000000\n",
       "Liquidation (2007)                                                                                  F         5.000000\n",
       "                                                                                                    M         5.000000\n",
       "Life+1 Day (2016)                                                                                   F         5.000000\n",
       "                                                                                                    M         5.000000\n",
       "Equinox Flower (Higanbana) (1958)                                                                   F         5.000000\n",
       "                                                                                                    M         5.000000\n",
       "Don't Look Now: We're Being Shot At (La grande vadrouille) (1966)                                   F         5.000000\n",
       "                                                                                                    M         5.000000\n",
       "Jim Henson's The Storyteller (1989)                                                                 F         5.000000\n",
       "                                                                                                    M         5.000000\n",
       "Marwencol (2010)                                                                                    F         5.000000\n",
       "                                                                                                    M         5.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_ratings_20 = mean_ratings_20.stack()\n",
    "mean_ratings_20"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "5495178d",
   "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>gender</th>\n",
       "      <th>rating_by_gender</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Man Who Sleeps, The (Un homme qui dort) (1974)</td>\n",
       "      <td>F</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Man Who Sleeps, The (Un homme qui dort) (1974)</td>\n",
       "      <td>M</td>\n",
       "      <td>4.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Woman in the Window, The (1944)</td>\n",
       "      <td>F</td>\n",
       "      <td>4.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Woman in the Window, The (1944)</td>\n",
       "      <td>M</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Kiler (1997)</td>\n",
       "      <td>F</td>\n",
       "      <td>4.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Kiler (1997)</td>\n",
       "      <td>M</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>I Was Born, But... (a.k.a. Children of Tokyo) ...</td>\n",
       "      <td>F</td>\n",
       "      <td>4.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>I Was Born, But... (a.k.a. Children of Tokyo) ...</td>\n",
       "      <td>M</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Twin Peaks (1989)</td>\n",
       "      <td>F</td>\n",
       "      <td>4.916667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Twin Peaks (1989)</td>\n",
       "      <td>M</td>\n",
       "      <td>4.600000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               title gender  rating_by_gender\n",
       "0     Man Who Sleeps, The (Un homme qui dort) (1974)      F          5.000000\n",
       "1     Man Who Sleeps, The (Un homme qui dort) (1974)      M          4.500000\n",
       "2                    Woman in the Window, The (1944)      F          4.500000\n",
       "3                    Woman in the Window, The (1944)      M          5.000000\n",
       "4                                       Kiler (1997)      F          4.500000\n",
       "5                                       Kiler (1997)      M          5.000000\n",
       "6  I Was Born, But... (a.k.a. Children of Tokyo) ...      F          4.500000\n",
       "7  I Was Born, But... (a.k.a. Children of Tokyo) ...      M          5.000000\n",
       "8                                  Twin Peaks (1989)      F          4.916667\n",
       "9                                  Twin Peaks (1989)      M          4.600000"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_ratings_20.name = \"rating_by_gender\"\n",
    "mean_ratings_20 = mean_ratings_20.reset_index()\n",
    "mean_ratings_20.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2c5563ca",
   "metadata": {},
   "source": [
    "# Visualizing the data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3fc68bcf",
   "metadata": {},
   "source": [
    "The 20 best rated films by gender"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "e2c8868c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='rating_by_gender', ylabel='title'>"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "sns.barplot(x=\"rating_by_gender\",y=\"title\",hue=\"gender\", data= mean_ratings_20)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "71cc8748",
   "metadata": {},
   "source": [
    "This produced another DataFrame with the most rated films containing mean ratings with movie titles as row labels (the \"index\") and gender as column labels. I first filter down to movies that received at least 250 ratings (an arbitrary number); to do this, I group  the data by title, and use size() to get a Series of group sizes for each title:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "37ac9c8e",
   "metadata": {},
   "source": [
    "# Transforming the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "16e46601",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "title\n",
       "\"Great Performances\" Cats (1998)              12\n",
       "#realityhigh (2017)                            1\n",
       "$ (Dollars) (1971)                             1\n",
       "$5 a Day (2008)                                3\n",
       "$9.99 (2008)                                   3\n",
       "'71 (2014)                                    15\n",
       "'Hellboy': The Seeds of Creation (2004)        4\n",
       "'Neath the Arizona Skies (1934)                2\n",
       "'R Xmas (2001)                                 2\n",
       "'Round Midnight (1986)                         6\n",
       "'Salem's Lot (2004)                            8\n",
       "'Til There Was You (1997)                     17\n",
       "'Tis the Season for Love (2015)                1\n",
       "'burbs, The (1989)                           116\n",
       "'night Mother (1986)                          10\n",
       "(500) Days of Summer (2009)                  397\n",
       "(Dis)Honesty: The Truth About Lies (2015)      2\n",
       "(Untitled) (2009)                              2\n",
       "*batteries not included (1987)                58\n",
       "+1 (2013)                                      2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratings_by_title=data.groupby(\"title\").size()\n",
    "ratings_by_title.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "d8b1c3f9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['(500) Days of Summer (2009)', '10 Things I Hate About You (1999)',\n",
       "       '101 Dalmatians (1996)',\n",
       "       '101 Dalmatians (One Hundred and One Dalmatians) (1961)',\n",
       "       '12 Angry Men (1957)', '2001: A Space Odyssey (1968)',\n",
       "       '21 Grams (2003)', '28 Days Later (2002)', '300 (2007)',\n",
       "       '40-Year-Old Virgin, The (2005)',\n",
       "       ...\n",
       "       'X-Men: Days of Future Past (2014)', 'X-Men: First Class (2011)',\n",
       "       'X-Men: The Last Stand (2006)', 'X2: X-Men United (2003)',\n",
       "       'You've Got Mail (1998)', 'Young Frankenstein (1974)', 'Zodiac (2007)',\n",
       "       'Zombieland (2009)', 'Zoolander (2001)', 'Zootopia (2016)'],\n",
       "      dtype='object', name='title', length=891)"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "most_rated_titles= ratings_by_title.index[ratings_by_title >= 250]\n",
    "most_rated_titles"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7ba867bb",
   "metadata": {},
   "source": [
    "The index of titles receiving at least 250 ratings can then be used to select rows from mean_ratings using .loc:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "220d8c48",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>gender</th>\n",
       "      <th>F</th>\n",
       "      <th>M</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>title</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>(500) Days of Summer (2009)</th>\n",
       "      <td>3.719626</td>\n",
       "      <td>3.812069</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10 Things I Hate About You (1999)</th>\n",
       "      <td>3.435374</td>\n",
       "      <td>3.615385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101 Dalmatians (1996)</th>\n",
       "      <td>3.131068</td>\n",
       "      <td>2.978261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101 Dalmatians (One Hundred and One Dalmatians) (1961)</th>\n",
       "      <td>3.494382</td>\n",
       "      <td>3.520089</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12 Angry Men (1957)</th>\n",
       "      <td>4.255495</td>\n",
       "      <td>4.199746</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Young Frankenstein (1974)</th>\n",
       "      <td>3.965116</td>\n",
       "      <td>3.977667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zodiac (2007)</th>\n",
       "      <td>3.560811</td>\n",
       "      <td>3.691038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zombieland (2009)</th>\n",
       "      <td>3.698473</td>\n",
       "      <td>3.816716</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zoolander (2001)</th>\n",
       "      <td>3.234043</td>\n",
       "      <td>3.244141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zootopia (2016)</th>\n",
       "      <td>3.913462</td>\n",
       "      <td>3.867713</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "gender                                                     F         M\n",
       "title                                                                 \n",
       "(500) Days of Summer (2009)                         3.719626  3.812069\n",
       "10 Things I Hate About You (1999)                   3.435374  3.615385\n",
       "101 Dalmatians (1996)                               3.131068  2.978261\n",
       "101 Dalmatians (One Hundred and One Dalmatians)...  3.494382  3.520089\n",
       "12 Angry Men (1957)                                 4.255495  4.199746\n",
       "...                                                      ...       ...\n",
       "Young Frankenstein (1974)                           3.965116  3.977667\n",
       "Zodiac (2007)                                       3.560811  3.691038\n",
       "Zombieland (2009)                                   3.698473  3.816716\n",
       "Zoolander (2001)                                    3.234043  3.244141\n",
       "Zootopia (2016)                                     3.913462  3.867713\n",
       "\n",
       "[891 rows x 2 columns]"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_ratings= mean_ratings.loc[most_rated_titles]\n",
    "mean_ratings"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a6850cbf",
   "metadata": {},
   "source": [
    "To see the top films among female viewers, we can sort by the F column in descending order:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "d070aa3c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>gender</th>\n",
       "      <th>F</th>\n",
       "      <th>M</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>title</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Godfather, The (1972)</th>\n",
       "      <td>4.443649</td>\n",
       "      <td>4.325385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Shawshank Redemption, The (1994)</th>\n",
       "      <td>4.428320</td>\n",
       "      <td>4.435016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Godfather: Part II, The (1974)</th>\n",
       "      <td>4.385224</td>\n",
       "      <td>4.292067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Usual Suspects, The (1995)</th>\n",
       "      <td>4.293874</td>\n",
       "      <td>4.312202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Schindler's List (1993)</th>\n",
       "      <td>4.293323</td>\n",
       "      <td>4.263596</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "gender                                   F         M\n",
       "title                                               \n",
       "Godfather, The (1972)             4.443649  4.325385\n",
       "Shawshank Redemption, The (1994)  4.428320  4.435016\n",
       "Godfather: Part II, The (1974)    4.385224  4.292067\n",
       "Usual Suspects, The (1995)        4.293874  4.312202\n",
       "Schindler's List (1993)           4.293323  4.263596"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_female_ratings= mean_ratings.sort_values(\"F\", ascending=False)\n",
    "top_female_ratings.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ffb5709c",
   "metadata": {},
   "source": [
    "Measuring Rating Disagreement"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e9239fb5",
   "metadata": {},
   "source": [
    "Suppose you wanted to find the movies that are most divisive between male and female viewers. One way is to add a column to mean_ratings containing the difference in means, then sort by that:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "b45cce0a",
   "metadata": {},
   "outputs": [],
   "source": [
    "mean_ratings[\"diff\"]= mean_ratings[\"M\"]-mean_ratings[\"F\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "d8afa38f",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>F</th>\n",
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       "      <th>title</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>(500) Days of Summer (2009)</th>\n",
       "      <td>3.719626</td>\n",
       "      <td>3.812069</td>\n",
       "      <td>0.092443</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10 Things I Hate About You (1999)</th>\n",
       "      <td>3.435374</td>\n",
       "      <td>3.615385</td>\n",
       "      <td>0.180010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101 Dalmatians (1996)</th>\n",
       "      <td>3.131068</td>\n",
       "      <td>2.978261</td>\n",
       "      <td>-0.152807</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101 Dalmatians (One Hundred and One Dalmatians) (1961)</th>\n",
       "      <td>3.494382</td>\n",
       "      <td>3.520089</td>\n",
       "      <td>0.025707</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12 Angry Men (1957)</th>\n",
       "      <td>4.255495</td>\n",
       "      <td>4.199746</td>\n",
       "      <td>-0.055749</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Young Frankenstein (1974)</th>\n",
       "      <td>3.965116</td>\n",
       "      <td>3.977667</td>\n",
       "      <td>0.012551</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zodiac (2007)</th>\n",
       "      <td>3.560811</td>\n",
       "      <td>3.691038</td>\n",
       "      <td>0.130227</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zombieland (2009)</th>\n",
       "      <td>3.698473</td>\n",
       "      <td>3.816716</td>\n",
       "      <td>0.118242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zoolander (2001)</th>\n",
       "      <td>3.234043</td>\n",
       "      <td>3.244141</td>\n",
       "      <td>0.010098</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zootopia (2016)</th>\n",
       "      <td>3.913462</td>\n",
       "      <td>3.867713</td>\n",
       "      <td>-0.045749</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "gender                                                     F         M  \\\n",
       "title                                                                    \n",
       "(500) Days of Summer (2009)                         3.719626  3.812069   \n",
       "10 Things I Hate About You (1999)                   3.435374  3.615385   \n",
       "101 Dalmatians (1996)                               3.131068  2.978261   \n",
       "101 Dalmatians (One Hundred and One Dalmatians)...  3.494382  3.520089   \n",
       "12 Angry Men (1957)                                 4.255495  4.199746   \n",
       "...                                                      ...       ...   \n",
       "Young Frankenstein (1974)                           3.965116  3.977667   \n",
       "Zodiac (2007)                                       3.560811  3.691038   \n",
       "Zombieland (2009)                                   3.698473  3.816716   \n",
       "Zoolander (2001)                                    3.234043  3.244141   \n",
       "Zootopia (2016)                                     3.913462  3.867713   \n",
       "\n",
       "gender                                                  diff  \n",
       "title                                                         \n",
       "(500) Days of Summer (2009)                         0.092443  \n",
       "10 Things I Hate About You (1999)                   0.180010  \n",
       "101 Dalmatians (1996)                              -0.152807  \n",
       "101 Dalmatians (One Hundred and One Dalmatians)...  0.025707  \n",
       "12 Angry Men (1957)                                -0.055749  \n",
       "...                                                      ...  \n",
       "Young Frankenstein (1974)                           0.012551  \n",
       "Zodiac (2007)                                       0.130227  \n",
       "Zombieland (2009)                                   0.118242  \n",
       "Zoolander (2001)                                    0.010098  \n",
       "Zootopia (2016)                                    -0.045749  \n",
       "\n",
       "[891 rows x 3 columns]"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_ratings"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ac0cbc21",
   "metadata": {},
   "source": [
    "Sorting by \"diff\" yields the movies with the greatest rating difference so that we can see which ones were preferred by women:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "cc84814c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>gender</th>\n",
       "      <th>F</th>\n",
       "      <th>M</th>\n",
       "      <th>diff</th>\n",
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       "    <tr>\n",
       "      <th>title</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Striptease (1996)</th>\n",
       "      <td>2.602941</td>\n",
       "      <td>2.201220</td>\n",
       "      <td>-0.401722</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Swordfish (2001)</th>\n",
       "      <td>3.298387</td>\n",
       "      <td>2.947090</td>\n",
       "      <td>-0.351297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cape Fear (1991)</th>\n",
       "      <td>3.801980</td>\n",
       "      <td>3.467249</td>\n",
       "      <td>-0.334731</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>River Wild, The (1994)</th>\n",
       "      <td>3.551402</td>\n",
       "      <td>3.271698</td>\n",
       "      <td>-0.279704</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Patton (1970)</th>\n",
       "      <td>4.256098</td>\n",
       "      <td>3.986301</td>\n",
       "      <td>-0.269796</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "gender                         F         M      diff\n",
       "title                                               \n",
       "Striptease (1996)       2.602941  2.201220 -0.401722\n",
       "Swordfish (2001)        3.298387  2.947090 -0.351297\n",
       "Cape Fear (1991)        3.801980  3.467249 -0.334731\n",
       "River Wild, The (1994)  3.551402  3.271698 -0.279704\n",
       "Patton (1970)           4.256098  3.986301 -0.269796"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted_by_diff= mean_ratings.sort_values(\"diff\")\n",
    "sorted_by_diff.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8385961c",
   "metadata": {},
   "source": [
    "Reversing the order of the rows and again slicing off the top 10 rows, we get the movies preferred by men that women didn’t rate as highly:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "5376fae8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>gender</th>\n",
       "      <th>F</th>\n",
       "      <th>M</th>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>title</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Aristocats, The (1970)</th>\n",
       "      <td>3.115000</td>\n",
       "      <td>3.451777</td>\n",
       "      <td>0.336777</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Star Trek V: The Final Frontier (1989)</th>\n",
       "      <td>2.620000</td>\n",
       "      <td>2.938776</td>\n",
       "      <td>0.318776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Showgirls (1995)</th>\n",
       "      <td>1.978022</td>\n",
       "      <td>2.279487</td>\n",
       "      <td>0.301465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dr. Dolittle (1998)</th>\n",
       "      <td>2.396341</td>\n",
       "      <td>2.680791</td>\n",
       "      <td>0.284449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rocky Horror Picture Show, The (1975)</th>\n",
       "      <td>3.127660</td>\n",
       "      <td>3.381844</td>\n",
       "      <td>0.254185</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "gender                                         F         M      diff\n",
       "title                                                               \n",
       "Aristocats, The (1970)                  3.115000  3.451777  0.336777\n",
       "Star Trek V: The Final Frontier (1989)  2.620000  2.938776  0.318776\n",
       "Showgirls (1995)                        1.978022  2.279487  0.301465\n",
       "Dr. Dolittle (1998)                     2.396341  2.680791  0.284449\n",
       "Rocky Horror Picture Show, The (1975)   3.127660  3.381844  0.254185"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted_by_diff[::-1].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "df4c1ba3",
   "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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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>gender</th>\n",
       "      <th>F</th>\n",
       "      <th>M</th>\n",
       "      <th>diff</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>title</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Rocky Horror Picture Show, The (1975)</th>\n",
       "      <td>3.127660</td>\n",
       "      <td>3.381844</td>\n",
       "      <td>0.254185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dr. Dolittle (1998)</th>\n",
       "      <td>2.396341</td>\n",
       "      <td>2.680791</td>\n",
       "      <td>0.284449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Showgirls (1995)</th>\n",
       "      <td>1.978022</td>\n",
       "      <td>2.279487</td>\n",
       "      <td>0.301465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Star Trek V: The Final Frontier (1989)</th>\n",
       "      <td>2.620000</td>\n",
       "      <td>2.938776</td>\n",
       "      <td>0.318776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Aristocats, The (1970)</th>\n",
       "      <td>3.115000</td>\n",
       "      <td>3.451777</td>\n",
       "      <td>0.336777</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "gender                                         F         M      diff\n",
       "title                                                               \n",
       "Rocky Horror Picture Show, The (1975)   3.127660  3.381844  0.254185\n",
       "Dr. Dolittle (1998)                     2.396341  2.680791  0.284449\n",
       "Showgirls (1995)                        1.978022  2.279487  0.301465\n",
       "Star Trek V: The Final Frontier (1989)  2.620000  2.938776  0.318776\n",
       "Aristocats, The (1970)                  3.115000  3.451777  0.336777"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted_by_diff.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4d0fe0ce",
   "metadata": {},
   "source": [
    "Suppose instead you wanted the movies that elicited the most disagreement among viewers, independent of gender identification. Disagreement can be measured by the variance or standard deviation of the ratings. To get this, we first compute the rating standard deviation by title and then filter down to the active titles:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "efd68473",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "title\n",
       "(500) Days of Summer (2009)                               0.902808\n",
       "10 Things I Hate About You (1999)                         0.938845\n",
       "101 Dalmatians (1996)                                     1.000802\n",
       "101 Dalmatians (One Hundred and One Dalmatians) (1961)    0.962350\n",
       "12 Angry Men (1957)                                       0.816667\n",
       "Name: rating, dtype: float64"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rating_std_by_title= data.groupby(\"title\")[\"rating\"].std()\n",
    "rating_std_by_title=rating_std_by_title.loc[most_rated_titles]\n",
    "rating_std_by_title.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cd853606",
   "metadata": {},
   "source": [
    "Then, we sort in descending order and select the first 10 rows, which are roughly the 10 most divisively rated movies:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "99e71817",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "title\n",
       "Blair Witch Project, The (1999)           1.254746\n",
       "Final Destination (2000)                  1.242084\n",
       "Natural Born Killers (1994)               1.217770\n",
       "Beavis and Butt-Head Do America (1996)    1.206011\n",
       "Dumb & Dumber (Dumb and Dumber) (1994)    1.196526\n",
       "Moulin Rouge (2001)                       1.191026\n",
       "Waterboy, The (1998)                      1.189866\n",
       "Saw (2004)                                1.179653\n",
       "Rocky Horror Picture Show, The (1975)     1.178029\n",
       "Event Horizon (1997)                      1.175879\n",
       "Name: rating, dtype: float64"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rating_std_by_title.sort_values(ascending=False)[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8891ac12",
   "metadata": {},
   "source": [
    "You may have noticed that movie genres are given as a pipe-separated (|) string, since a single movie can belong to multiple genres. To help us group the ratings data by genre, we can use the explode method on DataFrame. Let's take alook at how this works. First, we can split the genres string into a list of genres using the str.split method on the Series:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "764ecdd3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    Adventure|Animation|Children|Comedy|Fantasy\n",
       "1                     Adventure|Children|Fantasy\n",
       "2                                 Comedy|Romance\n",
       "3                           Comedy|Drama|Romance\n",
       "4                                         Comedy\n",
       "Name: genres, dtype: object"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies[\"genres\"].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "a8591e51",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    [Adventure, Animation, Children, Comedy, Fantasy]\n",
       "1                       [Adventure, Children, Fantasy]\n",
       "2                                    [Comedy, Romance]\n",
       "3                             [Comedy, Drama, Romance]\n",
       "4                                             [Comedy]\n",
       "Name: genres, dtype: object"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies[\"genres\"].head().str.split(\"|\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "d7860a6d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>movie_id</th>\n",
       "      <th>title</th>\n",
       "      <th>genre</th>\n",
       "    </tr>\n",
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       "  <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": [
       "   movie_id                               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",
       "                                               genre  \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": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies[\"genre\"]=movies.pop(\"genres\").str.split(\"|\")\n",
    "movies.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "224c7c9d",
   "metadata": {},
   "source": [
    "Now, calling movies.explode(\"genre\") generates a new DataFrame with one row for each \"inner\" element in each list of movie genres. For example, if a movie is classified as both a comedy and a romance, then there will be two rows in the result, one with just \"Comedy\" and the other with just \"Romance\":"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "7d054d27",
   "metadata": {},
   "outputs": [],
   "source": [
    "#explode()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "0ab212ae",
   "metadata": {},
   "outputs": [
    {
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       "      <td>Adventure</td>\n",
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       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Jumanji (1995)</td>\n",
       "      <td>Children</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Jumanji (1995)</td>\n",
       "      <td>Fantasy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Grumpier Old Men (1995)</td>\n",
       "      <td>Comedy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Grumpier Old Men (1995)</td>\n",
       "      <td>Romance</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   movie_id                    title      genre\n",
       "0         1         Toy Story (1995)  Adventure\n",
       "0         1         Toy Story (1995)  Animation\n",
       "0         1         Toy Story (1995)   Children\n",
       "0         1         Toy Story (1995)     Comedy\n",
       "0         1         Toy Story (1995)    Fantasy\n",
       "1         2           Jumanji (1995)  Adventure\n",
       "1         2           Jumanji (1995)   Children\n",
       "1         2           Jumanji (1995)    Fantasy\n",
       "2         3  Grumpier Old Men (1995)     Comedy\n",
       "2         3  Grumpier Old Men (1995)    Romance"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies_exploded=movies.explode(\"genre\")\n",
    "movies_exploded[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b6ba8fe7",
   "metadata": {},
   "source": [
    "Now, we can merge all three tables together and group by genre:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "ab4de39d",
   "metadata": {},
   "outputs": [
    {
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       "      <th>2440843</th>\n",
       "      <td>166024</td>\n",
       "      <td>Whiplash (2013)</td>\n",
       "      <td>(no genres listed)</td>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2440844 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         movie_id                 title               genre  user_id  rating  \\\n",
       "0               1      Toy Story (1995)           Adventure        2     3.5   \n",
       "1               1      Toy Story (1995)           Animation        2     3.5   \n",
       "2               1      Toy Story (1995)            Children        2     3.5   \n",
       "3               1      Toy Story (1995)              Comedy        2     3.5   \n",
       "4               1      Toy Story (1995)             Fantasy        2     3.5   \n",
       "...           ...                   ...                 ...      ...     ...   \n",
       "2440839    107069  Lone Survivor (2013)            Thriller      802     4.0   \n",
       "2440840    107069  Lone Survivor (2013)                 War      802     4.0   \n",
       "2440841    111622    Begin Again (2013)              Comedy      802     3.5   \n",
       "2440842    111622    Begin Again (2013)             Romance      802     3.5   \n",
       "2440843    166024       Whiplash (2013)  (no genres listed)      802     5.0   \n",
       "\n",
       "          timestamp gender  age  occupation    zip  \n",
       "0        1141415820      M   56          16  70072  \n",
       "1        1141415820      M   56          16  70072  \n",
       "2        1141415820      M   56          16  70072  \n",
       "3        1141415820      M   56          16  70072  \n",
       "4        1141415820      M   56          16  70072  \n",
       "...             ...    ...  ...         ...    ...  \n",
       "2440839  1569513457      M   25          16  22801  \n",
       "2440840  1569513457      M   25          16  22801  \n",
       "2440841  1569513473      M   25          16  22801  \n",
       "2440842  1569513473      M   25          16  22801  \n",
       "2440843  1569513539      M   25          16  22801  \n",
       "\n",
       "[2440844 rows x 10 columns]"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rating_with_genre= pd.merge(pd.merge(movies_exploded,ratings),users)\n",
    "rating_with_genre"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "c302e8f9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "movie_id                     1\n",
       "title         Toy Story (1995)\n",
       "genre                Adventure\n",
       "user_id                      2\n",
       "rating                     3.5\n",
       "timestamp           1141415820\n",
       "gender                       M\n",
       "age                         56\n",
       "occupation                  16\n",
       "zip                      70072\n",
       "Name: 0, dtype: object"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rating_with_genre.iloc[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "429e2b0f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>age</th>\n",
       "      <th>1</th>\n",
       "      <th>18</th>\n",
       "      <th>25</th>\n",
       "      <th>35</th>\n",
       "      <th>45</th>\n",
       "      <th>50</th>\n",
       "      <th>56</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>genre</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>(no genres listed)</th>\n",
       "      <td>3.429688</td>\n",
       "      <td>3.585443</td>\n",
       "      <td>3.511290</td>\n",
       "      <td>3.225806</td>\n",
       "      <td>3.659574</td>\n",
       "      <td>3.516667</td>\n",
       "      <td>3.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Action</th>\n",
       "      <td>3.544493</td>\n",
       "      <td>3.469939</td>\n",
       "      <td>3.435797</td>\n",
       "      <td>3.465197</td>\n",
       "      <td>3.486770</td>\n",
       "      <td>3.490140</td>\n",
       "      <td>3.392598</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Adventure</th>\n",
       "      <td>3.568612</td>\n",
       "      <td>3.529618</td>\n",
       "      <td>3.487130</td>\n",
       "      <td>3.507658</td>\n",
       "      <td>3.534325</td>\n",
       "      <td>3.552670</td>\n",
       "      <td>3.411225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Animation</th>\n",
       "      <td>3.627280</td>\n",
       "      <td>3.630225</td>\n",
       "      <td>3.607941</td>\n",
       "      <td>3.606697</td>\n",
       "      <td>3.662809</td>\n",
       "      <td>3.675837</td>\n",
       "      <td>3.475969</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Children</th>\n",
       "      <td>3.489120</td>\n",
       "      <td>3.452994</td>\n",
       "      <td>3.415821</td>\n",
       "      <td>3.419049</td>\n",
       "      <td>3.490708</td>\n",
       "      <td>3.453088</td>\n",
       "      <td>3.304276</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Comedy</th>\n",
       "      <td>3.459904</td>\n",
       "      <td>3.433717</td>\n",
       "      <td>3.415630</td>\n",
       "      <td>3.427799</td>\n",
       "      <td>3.469804</td>\n",
       "      <td>3.433527</td>\n",
       "      <td>3.298182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Crime</th>\n",
       "      <td>3.695202</td>\n",
       "      <td>3.678833</td>\n",
       "      <td>3.682524</td>\n",
       "      <td>3.695338</td>\n",
       "      <td>3.741078</td>\n",
       "      <td>3.725204</td>\n",
       "      <td>3.584225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Documentary</th>\n",
       "      <td>3.661830</td>\n",
       "      <td>3.700704</td>\n",
       "      <td>3.694459</td>\n",
       "      <td>3.716297</td>\n",
       "      <td>3.779652</td>\n",
       "      <td>3.813408</td>\n",
       "      <td>3.516051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Drama</th>\n",
       "      <td>3.679893</td>\n",
       "      <td>3.688643</td>\n",
       "      <td>3.685882</td>\n",
       "      <td>3.695796</td>\n",
       "      <td>3.730219</td>\n",
       "      <td>3.706313</td>\n",
       "      <td>3.568063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fantasy</th>\n",
       "      <td>3.536565</td>\n",
       "      <td>3.536642</td>\n",
       "      <td>3.481633</td>\n",
       "      <td>3.500940</td>\n",
       "      <td>3.541542</td>\n",
       "      <td>3.554892</td>\n",
       "      <td>3.383912</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "age                       1         18        25        35        45  \\\n",
       "genre                                                                  \n",
       "(no genres listed)  3.429688  3.585443  3.511290  3.225806  3.659574   \n",
       "Action              3.544493  3.469939  3.435797  3.465197  3.486770   \n",
       "Adventure           3.568612  3.529618  3.487130  3.507658  3.534325   \n",
       "Animation           3.627280  3.630225  3.607941  3.606697  3.662809   \n",
       "Children            3.489120  3.452994  3.415821  3.419049  3.490708   \n",
       "Comedy              3.459904  3.433717  3.415630  3.427799  3.469804   \n",
       "Crime               3.695202  3.678833  3.682524  3.695338  3.741078   \n",
       "Documentary         3.661830  3.700704  3.694459  3.716297  3.779652   \n",
       "Drama               3.679893  3.688643  3.685882  3.695796  3.730219   \n",
       "Fantasy             3.536565  3.536642  3.481633  3.500940  3.541542   \n",
       "\n",
       "age                       50        56  \n",
       "genre                                   \n",
       "(no genres listed)  3.516667  3.500000  \n",
       "Action              3.490140  3.392598  \n",
       "Adventure           3.552670  3.411225  \n",
       "Animation           3.675837  3.475969  \n",
       "Children            3.453088  3.304276  \n",
       "Comedy              3.433527  3.298182  \n",
       "Crime               3.725204  3.584225  \n",
       "Documentary         3.813408  3.516051  \n",
       "Drama               3.706313  3.568063  \n",
       "Fantasy             3.554892  3.383912  "
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "genre_ratings= (rating_with_genre.groupby([\"genre\", \"age\"])[\"rating\"].mean().unstack(\"age\"))\n",
    "genre_ratings[:10]"
   ]
  }
 ],
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