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IMDb Genre-wise Movies Dataset And Sparse Matrices

Contains 75k+ movies, collected from IMDb website: 22 genres + 1 master dataset.

@kaggle.soumyasacharya_imdb_movies_dataset

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About this Dataset

IMDb Genre-wise Movies Dataset And Sparse Matrices

Context

IMDb stores information related to more than 6 million titles (of which almost 500,000 are featured films) and it is owned by Amazon since 1998.

Content

The movies' Master dataset includes about 75k movies with attributes such as movie description, average rating, number of votes, genre, etc.

The movies are also divided according to genres, which makes a total of 22 genres. The master dataset, containing all the movies is the file all_df.csv

Each dataset has it's sparse matrix, containing all the Tf-idf scores after applying TfidfVectorizer[analyzer='word, ngram_range=(1,3), stopwords='english']

Data has been scraped from the publicly available website https://www.imdb.com .

Tables

Action Df

@kaggle.soumyasacharya_imdb_movies_dataset.action_df
  • 4.35 MB
  • 11562 rows
  • 24 columns
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CREATE TABLE action_df (
  "imdb_title_id" VARCHAR,
  "title" VARCHAR,
  "original_title" VARCHAR,
  "year" BIGINT,
  "date_published" TIMESTAMP,
  "genre" VARCHAR,
  "duration" BIGINT,
  "country" VARCHAR,
  "language" VARCHAR,
  "director" VARCHAR,
  "writer" VARCHAR,
  "production_company" VARCHAR,
  "actors" VARCHAR,
  "description" VARCHAR,
  "avg_vote" DOUBLE,
  "votes" BIGINT,
  "budget" VARCHAR,
  "usa_gross_income" VARCHAR,
  "worlwide_gross_income" VARCHAR,
  "metascore" DOUBLE,
  "reviews_from_users" DOUBLE,
  "reviews_from_critics" DOUBLE,
  "description_words" BIGINT,
  "movie_title" VARCHAR
);

Adventure Df

@kaggle.soumyasacharya_imdb_movies_dataset.adventure_df
  • 2.63 MB
  • 6748 rows
  • 24 columns
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CREATE TABLE adventure_df (
  "imdb_title_id" VARCHAR,
  "title" VARCHAR,
  "original_title" VARCHAR,
  "year" BIGINT,
  "date_published" TIMESTAMP,
  "genre" VARCHAR,
  "duration" BIGINT,
  "country" VARCHAR,
  "language" VARCHAR,
  "director" VARCHAR,
  "writer" VARCHAR,
  "production_company" VARCHAR,
  "actors" VARCHAR,
  "description" VARCHAR,
  "avg_vote" DOUBLE,
  "votes" BIGINT,
  "budget" VARCHAR,
  "usa_gross_income" VARCHAR,
  "worlwide_gross_income" VARCHAR,
  "metascore" DOUBLE,
  "reviews_from_users" DOUBLE,
  "reviews_from_critics" DOUBLE,
  "description_words" BIGINT,
  "movie_title" VARCHAR
);

All Df

@kaggle.soumyasacharya_imdb_movies_dataset.all_df
  • 28.1 MB
  • 74889 rows
  • 24 columns
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CREATE TABLE all_df (
  "imdb_title_id" VARCHAR,
  "title" VARCHAR,
  "original_title" VARCHAR,
  "year" VARCHAR,
  "date_published" VARCHAR,
  "genre" VARCHAR,
  "duration" BIGINT,
  "country" VARCHAR,
  "language" VARCHAR,
  "director" VARCHAR,
  "writer" VARCHAR,
  "production_company" VARCHAR,
  "actors" VARCHAR,
  "description" VARCHAR,
  "avg_vote" DOUBLE,
  "votes" BIGINT,
  "budget" VARCHAR,
  "usa_gross_income" VARCHAR,
  "worlwide_gross_income" VARCHAR,
  "metascore" DOUBLE,
  "reviews_from_users" DOUBLE,
  "reviews_from_critics" DOUBLE,
  "description_words" BIGINT,
  "movie_title" VARCHAR
);

Animation Df

@kaggle.soumyasacharya_imdb_movies_dataset.animation_df
  • 777.21 KB
  • 1881 rows
  • 24 columns
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CREATE TABLE animation_df (
  "imdb_title_id" VARCHAR,
  "title" VARCHAR,
  "original_title" VARCHAR,
  "year" BIGINT,
  "date_published" TIMESTAMP,
  "genre" VARCHAR,
  "duration" BIGINT,
  "country" VARCHAR,
  "language" VARCHAR,
  "director" VARCHAR,
  "writer" VARCHAR,
  "production_company" VARCHAR,
  "actors" VARCHAR,
  "description" VARCHAR,
  "avg_vote" DOUBLE,
  "votes" BIGINT,
  "budget" VARCHAR,
  "usa_gross_income" VARCHAR,
  "worlwide_gross_income" VARCHAR,
  "metascore" DOUBLE,
  "reviews_from_users" DOUBLE,
  "reviews_from_critics" DOUBLE,
  "description_words" BIGINT,
  "movie_title" VARCHAR
);

Biography Df

@kaggle.soumyasacharya_imdb_movies_dataset.biography_df
  • 773.2 KB
  • 1781 rows
  • 24 columns
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CREATE TABLE biography_df (
  "imdb_title_id" VARCHAR,
  "title" VARCHAR,
  "original_title" VARCHAR,
  "year" VARCHAR,
  "date_published" VARCHAR,
  "genre" VARCHAR,
  "duration" BIGINT,
  "country" VARCHAR,
  "language" VARCHAR,
  "director" VARCHAR,
  "writer" VARCHAR,
  "production_company" VARCHAR,
  "actors" VARCHAR,
  "description" VARCHAR,
  "avg_vote" DOUBLE,
  "votes" BIGINT,
  "budget" VARCHAR,
  "usa_gross_income" VARCHAR,
  "worlwide_gross_income" VARCHAR,
  "metascore" DOUBLE,
  "reviews_from_users" DOUBLE,
  "reviews_from_critics" DOUBLE,
  "description_words" BIGINT,
  "movie_title" VARCHAR
);

Comedy Df

@kaggle.soumyasacharya_imdb_movies_dataset.comedy_df
  • 9.75 MB
  • 25200 rows
  • 24 columns
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CREATE TABLE comedy_df (
  "imdb_title_id" VARCHAR,
  "title" VARCHAR,
  "original_title" VARCHAR,
  "year" VARCHAR,
  "date_published" VARCHAR,
  "genre" VARCHAR,
  "duration" BIGINT,
  "country" VARCHAR,
  "language" VARCHAR,
  "director" VARCHAR,
  "writer" VARCHAR,
  "production_company" VARCHAR,
  "actors" VARCHAR,
  "description" VARCHAR,
  "avg_vote" DOUBLE,
  "votes" BIGINT,
  "budget" VARCHAR,
  "usa_gross_income" VARCHAR,
  "worlwide_gross_income" VARCHAR,
  "metascore" DOUBLE,
  "reviews_from_users" DOUBLE,
  "reviews_from_critics" DOUBLE,
  "description_words" BIGINT,
  "movie_title" VARCHAR
);

Crime Df

@kaggle.soumyasacharya_imdb_movies_dataset.crime_df
  • 3.67 MB
  • 9715 rows
  • 24 columns
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CREATE TABLE crime_df (
  "imdb_title_id" VARCHAR,
  "title" VARCHAR,
  "original_title" VARCHAR,
  "year" VARCHAR,
  "date_published" VARCHAR,
  "genre" VARCHAR,
  "duration" BIGINT,
  "country" VARCHAR,
  "language" VARCHAR,
  "director" VARCHAR,
  "writer" VARCHAR,
  "production_company" VARCHAR,
  "actors" VARCHAR,
  "description" VARCHAR,
  "avg_vote" DOUBLE,
  "votes" BIGINT,
  "budget" VARCHAR,
  "usa_gross_income" VARCHAR,
  "worlwide_gross_income" VARCHAR,
  "metascore" DOUBLE,
  "reviews_from_users" DOUBLE,
  "reviews_from_critics" DOUBLE,
  "description_words" BIGINT,
  "movie_title" VARCHAR
);

Documentary Df

@kaggle.soumyasacharya_imdb_movies_dataset.documentary_df
  • 18.14 KB
  • 1 row
  • 24 columns
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CREATE TABLE documentary_df (
  "imdb_title_id" VARCHAR,
  "title" VARCHAR,
  "original_title" VARCHAR,
  "year" BIGINT,
  "date_published" TIMESTAMP,
  "genre" VARCHAR,
  "duration" BIGINT,
  "country" VARCHAR,
  "language" VARCHAR,
  "director" VARCHAR,
  "writer" VARCHAR,
  "production_company" VARCHAR,
  "actors" VARCHAR,
  "description" VARCHAR,
  "avg_vote" DOUBLE,
  "votes" BIGINT,
  "budget" VARCHAR,
  "usa_gross_income" VARCHAR,
  "worlwide_gross_income" VARCHAR,
  "metascore" VARCHAR,
  "reviews_from_users" DOUBLE,
  "reviews_from_critics" DOUBLE,
  "description_words" BIGINT,
  "movie_title" VARCHAR
);

Drama Df

@kaggle.soumyasacharya_imdb_movies_dataset.drama_df
  • 15.54 MB
  • 41004 rows
  • 24 columns
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CREATE TABLE drama_df (
  "imdb_title_id" VARCHAR,
  "title" VARCHAR,
  "original_title" VARCHAR,
  "year" BIGINT,
  "date_published" TIMESTAMP,
  "genre" VARCHAR,
  "duration" BIGINT,
  "country" VARCHAR,
  "language" VARCHAR,
  "director" VARCHAR,
  "writer" VARCHAR,
  "production_company" VARCHAR,
  "actors" VARCHAR,
  "description" VARCHAR,
  "avg_vote" DOUBLE,
  "votes" BIGINT,
  "budget" VARCHAR,
  "usa_gross_income" VARCHAR,
  "worlwide_gross_income" VARCHAR,
  "metascore" DOUBLE,
  "reviews_from_users" DOUBLE,
  "reviews_from_critics" DOUBLE,
  "description_words" BIGINT,
  "movie_title" VARCHAR
);

Family Df

@kaggle.soumyasacharya_imdb_movies_dataset.family_df
  • 1.42 MB
  • 3487 rows
  • 24 columns
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CREATE TABLE family_df (
  "imdb_title_id" VARCHAR,
  "title" VARCHAR,
  "original_title" VARCHAR,
  "year" BIGINT,
  "date_published" TIMESTAMP,
  "genre" VARCHAR,
  "duration" BIGINT,
  "country" VARCHAR,
  "language" VARCHAR,
  "director" VARCHAR,
  "writer" VARCHAR,
  "production_company" VARCHAR,
  "actors" VARCHAR,
  "description" VARCHAR,
  "avg_vote" DOUBLE,
  "votes" BIGINT,
  "budget" VARCHAR,
  "usa_gross_income" VARCHAR,
  "worlwide_gross_income" VARCHAR,
  "metascore" DOUBLE,
  "reviews_from_users" DOUBLE,
  "reviews_from_critics" DOUBLE,
  "description_words" BIGINT,
  "movie_title" VARCHAR
);

Fantasy Df

@kaggle.soumyasacharya_imdb_movies_dataset.fantasy_df
  • 1.36 MB
  • 3356 rows
  • 24 columns
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CREATE TABLE fantasy_df (
  "imdb_title_id" VARCHAR,
  "title" VARCHAR,
  "original_title" VARCHAR,
  "year" BIGINT,
  "date_published" TIMESTAMP,
  "genre" VARCHAR,
  "duration" BIGINT,
  "country" VARCHAR,
  "language" VARCHAR,
  "director" VARCHAR,
  "writer" VARCHAR,
  "production_company" VARCHAR,
  "actors" VARCHAR,
  "description" VARCHAR,
  "avg_vote" DOUBLE,
  "votes" BIGINT,
  "budget" VARCHAR,
  "usa_gross_income" VARCHAR,
  "worlwide_gross_income" VARCHAR,
  "metascore" DOUBLE,
  "reviews_from_users" DOUBLE,
  "reviews_from_critics" DOUBLE,
  "description_words" BIGINT,
  "movie_title" VARCHAR
);

Film Noir Df

@kaggle.soumyasacharya_imdb_movies_dataset.film_noir_df
  • 196.77 KB
  • 578 rows
  • 24 columns
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CREATE TABLE film_noir_df (
  "imdb_title_id" VARCHAR,
  "title" VARCHAR,
  "original_title" VARCHAR,
  "year" BIGINT,
  "date_published" TIMESTAMP,
  "genre" VARCHAR,
  "duration" BIGINT,
  "country" VARCHAR,
  "language" VARCHAR,
  "director" VARCHAR,
  "writer" VARCHAR,
  "production_company" VARCHAR,
  "actors" VARCHAR,
  "description" VARCHAR,
  "avg_vote" DOUBLE,
  "votes" BIGINT,
  "budget" VARCHAR,
  "usa_gross_income" VARCHAR,
  "worlwide_gross_income" VARCHAR,
  "metascore" DOUBLE,
  "reviews_from_users" DOUBLE,
  "reviews_from_critics" DOUBLE,
  "description_words" BIGINT,
  "movie_title" VARCHAR
);

History Df

@kaggle.soumyasacharya_imdb_movies_dataset.history_df
  • 845.19 KB
  • 1970 rows
  • 24 columns
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CREATE TABLE history_df (
  "imdb_title_id" VARCHAR,
  "title" VARCHAR,
  "original_title" VARCHAR,
  "year" BIGINT,
  "date_published" TIMESTAMP,
  "genre" VARCHAR,
  "duration" BIGINT,
  "country" VARCHAR,
  "language" VARCHAR,
  "director" VARCHAR,
  "writer" VARCHAR,
  "production_company" VARCHAR,
  "actors" VARCHAR,
  "description" VARCHAR,
  "avg_vote" DOUBLE,
  "votes" BIGINT,
  "budget" VARCHAR,
  "usa_gross_income" VARCHAR,
  "worlwide_gross_income" VARCHAR,
  "metascore" DOUBLE,
  "reviews_from_users" DOUBLE,
  "reviews_from_critics" DOUBLE,
  "description_words" BIGINT,
  "movie_title" VARCHAR
);

Horror Df

@kaggle.soumyasacharya_imdb_movies_dataset.horror_df
  • 3.17 MB
  • 8611 rows
  • 24 columns
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CREATE TABLE horror_df (
  "imdb_title_id" VARCHAR,
  "title" VARCHAR,
  "original_title" VARCHAR,
  "year" BIGINT,
  "date_published" TIMESTAMP,
  "genre" VARCHAR,
  "duration" BIGINT,
  "country" VARCHAR,
  "language" VARCHAR,
  "director" VARCHAR,
  "writer" VARCHAR,
  "production_company" VARCHAR,
  "actors" VARCHAR,
  "description" VARCHAR,
  "avg_vote" DOUBLE,
  "votes" BIGINT,
  "budget" VARCHAR,
  "usa_gross_income" VARCHAR,
  "worlwide_gross_income" VARCHAR,
  "metascore" DOUBLE,
  "reviews_from_users" DOUBLE,
  "reviews_from_critics" DOUBLE,
  "description_words" BIGINT,
  "movie_title" VARCHAR
);

Musical Df

@kaggle.soumyasacharya_imdb_movies_dataset.musical_df
  • 676.7 KB
  • 1755 rows
  • 24 columns
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CREATE TABLE musical_df (
  "imdb_title_id" VARCHAR,
  "title" VARCHAR,
  "original_title" VARCHAR,
  "year" BIGINT,
  "date_published" TIMESTAMP,
  "genre" VARCHAR,
  "duration" BIGINT,
  "country" VARCHAR,
  "language" VARCHAR,
  "director" VARCHAR,
  "writer" VARCHAR,
  "production_company" VARCHAR,
  "actors" VARCHAR,
  "description" VARCHAR,
  "avg_vote" DOUBLE,
  "votes" BIGINT,
  "budget" VARCHAR,
  "usa_gross_income" VARCHAR,
  "worlwide_gross_income" VARCHAR,
  "metascore" DOUBLE,
  "reviews_from_users" DOUBLE,
  "reviews_from_critics" DOUBLE,
  "description_words" BIGINT,
  "movie_title" VARCHAR
);

Music Df

@kaggle.soumyasacharya_imdb_movies_dataset.music_df
  • 209.18 KB
  • 460 rows
  • 24 columns
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CREATE TABLE music_df (
  "imdb_title_id" VARCHAR,
  "title" VARCHAR,
  "original_title" VARCHAR,
  "year" BIGINT,
  "date_published" TIMESTAMP,
  "genre" VARCHAR,
  "duration" BIGINT,
  "country" VARCHAR,
  "language" VARCHAR,
  "director" VARCHAR,
  "writer" VARCHAR,
  "production_company" VARCHAR,
  "actors" VARCHAR,
  "description" VARCHAR,
  "avg_vote" DOUBLE,
  "votes" BIGINT,
  "budget" VARCHAR,
  "usa_gross_income" VARCHAR,
  "worlwide_gross_income" VARCHAR,
  "metascore" DOUBLE,
  "reviews_from_users" DOUBLE,
  "reviews_from_critics" DOUBLE,
  "description_words" BIGINT,
  "movie_title" VARCHAR
);

Mystery Df

@kaggle.soumyasacharya_imdb_movies_dataset.mystery_df
  • 1.8 MB
  • 4701 rows
  • 24 columns
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CREATE TABLE mystery_df (
  "imdb_title_id" VARCHAR,
  "title" VARCHAR,
  "original_title" VARCHAR,
  "year" BIGINT,
  "date_published" TIMESTAMP,
  "genre" VARCHAR,
  "duration" BIGINT,
  "country" VARCHAR,
  "language" VARCHAR,
  "director" VARCHAR,
  "writer" VARCHAR,
  "production_company" VARCHAR,
  "actors" VARCHAR,
  "description" VARCHAR,
  "avg_vote" DOUBLE,
  "votes" BIGINT,
  "budget" VARCHAR,
  "usa_gross_income" VARCHAR,
  "worlwide_gross_income" VARCHAR,
  "metascore" DOUBLE,
  "reviews_from_users" DOUBLE,
  "reviews_from_critics" DOUBLE,
  "description_words" BIGINT,
  "movie_title" VARCHAR
);

Romance Df

@kaggle.soumyasacharya_imdb_movies_dataset.romance_df
  • 4.67 MB
  • 12376 rows
  • 24 columns
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CREATE TABLE romance_df (
  "imdb_title_id" VARCHAR,
  "title" VARCHAR,
  "original_title" VARCHAR,
  "year" BIGINT,
  "date_published" TIMESTAMP,
  "genre" VARCHAR,
  "duration" BIGINT,
  "country" VARCHAR,
  "language" VARCHAR,
  "director" VARCHAR,
  "writer" VARCHAR,
  "production_company" VARCHAR,
  "actors" VARCHAR,
  "description" VARCHAR,
  "avg_vote" DOUBLE,
  "votes" BIGINT,
  "budget" VARCHAR,
  "usa_gross_income" VARCHAR,
  "worlwide_gross_income" VARCHAR,
  "metascore" DOUBLE,
  "reviews_from_users" DOUBLE,
  "reviews_from_critics" DOUBLE,
  "description_words" BIGINT,
  "movie_title" VARCHAR
);

Sci Fi Df

@kaggle.soumyasacharya_imdb_movies_dataset.sci_fi_df
  • 1.26 MB
  • 3261 rows
  • 24 columns
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CREATE TABLE sci_fi_df (
  "imdb_title_id" VARCHAR,
  "title" VARCHAR,
  "original_title" VARCHAR,
  "year" BIGINT,
  "date_published" TIMESTAMP,
  "genre" VARCHAR,
  "duration" BIGINT,
  "country" VARCHAR,
  "language" VARCHAR,
  "director" VARCHAR,
  "writer" VARCHAR,
  "production_company" VARCHAR,
  "actors" VARCHAR,
  "description" VARCHAR,
  "avg_vote" DOUBLE,
  "votes" BIGINT,
  "budget" VARCHAR,
  "usa_gross_income" VARCHAR,
  "worlwide_gross_income" VARCHAR,
  "metascore" DOUBLE,
  "reviews_from_users" DOUBLE,
  "reviews_from_critics" DOUBLE,
  "description_words" BIGINT,
  "movie_title" VARCHAR
);

Sport Df

@kaggle.soumyasacharya_imdb_movies_dataset.sport_df
  • 393.4 KB
  • 913 rows
  • 24 columns
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CREATE TABLE sport_df (
  "imdb_title_id" VARCHAR,
  "title" VARCHAR,
  "original_title" VARCHAR,
  "year" BIGINT,
  "date_published" TIMESTAMP,
  "genre" VARCHAR,
  "duration" BIGINT,
  "country" VARCHAR,
  "language" VARCHAR,
  "director" VARCHAR,
  "writer" VARCHAR,
  "production_company" VARCHAR,
  "actors" VARCHAR,
  "description" VARCHAR,
  "avg_vote" DOUBLE,
  "votes" BIGINT,
  "budget" VARCHAR,
  "usa_gross_income" VARCHAR,
  "worlwide_gross_income" VARCHAR,
  "metascore" DOUBLE,
  "reviews_from_users" DOUBLE,
  "reviews_from_critics" DOUBLE,
  "description_words" BIGINT,
  "movie_title" VARCHAR
);

Thriller Df

@kaggle.soumyasacharya_imdb_movies_dataset.thriller_df
  • 3.88 MB
  • 10248 rows
  • 24 columns
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CREATE TABLE thriller_df (
  "imdb_title_id" VARCHAR,
  "title" VARCHAR,
  "original_title" VARCHAR,
  "year" BIGINT,
  "date_published" TIMESTAMP,
  "genre" VARCHAR,
  "duration" BIGINT,
  "country" VARCHAR,
  "language" VARCHAR,
  "director" VARCHAR,
  "writer" VARCHAR,
  "production_company" VARCHAR,
  "actors" VARCHAR,
  "description" VARCHAR,
  "avg_vote" DOUBLE,
  "votes" BIGINT,
  "budget" VARCHAR,
  "usa_gross_income" VARCHAR,
  "worlwide_gross_income" VARCHAR,
  "metascore" DOUBLE,
  "reviews_from_users" DOUBLE,
  "reviews_from_critics" DOUBLE,
  "description_words" BIGINT,
  "movie_title" VARCHAR
);

War Df

@kaggle.soumyasacharya_imdb_movies_dataset.war_df
  • 814.19 KB
  • 1998 rows
  • 24 columns
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CREATE TABLE war_df (
  "imdb_title_id" VARCHAR,
  "title" VARCHAR,
  "original_title" VARCHAR,
  "year" BIGINT,
  "date_published" TIMESTAMP,
  "genre" VARCHAR,
  "duration" BIGINT,
  "country" VARCHAR,
  "language" VARCHAR,
  "director" VARCHAR,
  "writer" VARCHAR,
  "production_company" VARCHAR,
  "actors" VARCHAR,
  "description" VARCHAR,
  "avg_vote" DOUBLE,
  "votes" BIGINT,
  "budget" VARCHAR,
  "usa_gross_income" VARCHAR,
  "worlwide_gross_income" VARCHAR,
  "metascore" DOUBLE,
  "reviews_from_users" DOUBLE,
  "reviews_from_critics" DOUBLE,
  "description_words" BIGINT,
  "movie_title" VARCHAR
);

Western Df

@kaggle.soumyasacharya_imdb_movies_dataset.western_df
  • 511.31 KB
  • 1438 rows
  • 24 columns
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CREATE TABLE western_df (
  "imdb_title_id" VARCHAR,
  "title" VARCHAR,
  "original_title" VARCHAR,
  "year" BIGINT,
  "date_published" TIMESTAMP,
  "genre" VARCHAR,
  "duration" BIGINT,
  "country" VARCHAR,
  "language" VARCHAR,
  "director" VARCHAR,
  "writer" VARCHAR,
  "production_company" VARCHAR,
  "actors" VARCHAR,
  "description" VARCHAR,
  "avg_vote" DOUBLE,
  "votes" BIGINT,
  "budget" VARCHAR,
  "usa_gross_income" VARCHAR,
  "worlwide_gross_income" VARCHAR,
  "metascore" DOUBLE,
  "reviews_from_users" DOUBLE,
  "reviews_from_critics" DOUBLE,
  "description_words" BIGINT,
  "movie_title" VARCHAR
);

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