Dataset: Spotify Multi-Genre Playlists Data

About this Dataset

Spotify Multi-Genre Playlists Data

Context

Once I heard Spotify sports its own public audio features available for each and every track and to anyone who wants to use them, I knew I wanted to analyize my own musical taste - and for that, we need some frame of reference. So here we are!

Content

7 Different datasets, one for each Spotify category including: rock,blues,metal,hiphop,indie,pop and indie (might add more when I got the time later on)..
Each row includes each track's audio features, its playlist of origin, the creating artist's genre, and more!

Acknowledgements

Wouldn't have been possible without Spotify for Developers and the Spotipy library for python, be sure to check both of these out if you'd like to try to get your own data yourself!

Also, while we're on the topic, I obviously claim no ownership of the audio features provided here - it's all Spotify!

Inspiration

I should note there are bigger, more comprehensive Spotify datasets in here such as this one, for example , however this dataset has everything you need to do some quality track analysis, with it divided into different categories (while still sporting a coherent data structure). Furthermore, since I gathered these tracks from Spotify's official playlists available at my region, you can easily do some interesting commercial playlist analysis using this data.

Tables

Alternative Music Data

@kaggle.siropo_spotify_multigenre_playlists_data.alternative_music_data

399.13 KB
2160 rows
22 columns


CREATE TABLE alternative_music_data (
  "artist_name" VARCHAR,
  "track_name" VARCHAR,
  "popularity" BIGINT,
  "genres" VARCHAR,
  "playlist" VARCHAR,
  "danceability" DOUBLE,
  "energy" DOUBLE,
  "key" BIGINT,
  "loudness" DOUBLE,
  "mode" BIGINT,
  "speechiness" DOUBLE,
  "acousticness" DOUBLE,
  "instrumentalness" DOUBLE,
  "liveness" DOUBLE,
  "valence" DOUBLE,
  "tempo" DOUBLE,
  "id" VARCHAR,
  "uri" VARCHAR,
  "track_href" VARCHAR,
  "analysis_url" VARCHAR,
  "duration_ms" BIGINT,
  "time_signature" BIGINT
);

Blues Music Data

@kaggle.siropo_spotify_multigenre_playlists_data.blues_music_data

347.82 KB
2050 rows
22 columns


CREATE TABLE blues_music_data (
  "artist_name" VARCHAR,
  "track_name" VARCHAR,
  "popularity" BIGINT,
  "genres" VARCHAR,
  "playlist" VARCHAR,
  "danceability" DOUBLE,
  "energy" DOUBLE,
  "key" BIGINT,
  "loudness" DOUBLE,
  "mode" BIGINT,
  "speechiness" DOUBLE,
  "acousticness" DOUBLE,
  "instrumentalness" DOUBLE,
  "liveness" DOUBLE,
  "valence" DOUBLE,
  "tempo" DOUBLE,
  "id" VARCHAR,
  "uri" VARCHAR,
  "track_href" VARCHAR,
  "analysis_url" VARCHAR,
  "duration_ms" BIGINT,
  "time_signature" BIGINT
);

Hiphop Music Data

@kaggle.siropo_spotify_multigenre_playlists_data.hiphop_music_data

391.18 KB
2581 rows
22 columns


CREATE TABLE hiphop_music_data (
  "artist_name" VARCHAR,
  "track_name" VARCHAR,
  "popularity" BIGINT,
  "genres" VARCHAR,
  "playlist" VARCHAR,
  "danceability" DOUBLE,
  "energy" DOUBLE,
  "key" BIGINT,
  "loudness" DOUBLE,
  "mode" BIGINT,
  "speechiness" DOUBLE,
  "acousticness" DOUBLE,
  "instrumentalness" DOUBLE,
  "liveness" DOUBLE,
  "valence" DOUBLE,
  "tempo" DOUBLE,
  "id" VARCHAR,
  "uri" VARCHAR,
  "track_href" VARCHAR,
  "analysis_url" VARCHAR,
  "duration_ms" BIGINT,
  "time_signature" BIGINT
);

Indie Alt Music Data

@kaggle.siropo_spotify_multigenre_playlists_data.indie_alt_music_data

703.76 KB
4338 rows
22 columns


CREATE TABLE indie_alt_music_data (
  "artist_name" VARCHAR,
  "track_name" VARCHAR,
  "popularity" BIGINT,
  "genres" VARCHAR,
  "playlist" VARCHAR,
  "danceability" DOUBLE,
  "energy" DOUBLE,
  "key" BIGINT,
  "loudness" DOUBLE,
  "mode" BIGINT,
  "speechiness" DOUBLE,
  "acousticness" DOUBLE,
  "instrumentalness" DOUBLE,
  "liveness" DOUBLE,
  "valence" DOUBLE,
  "tempo" DOUBLE,
  "id" VARCHAR,
  "uri" VARCHAR,
  "track_href" VARCHAR,
  "analysis_url" VARCHAR,
  "duration_ms" BIGINT,
  "time_signature" BIGINT
);

Metal Music Data

@kaggle.siropo_spotify_multigenre_playlists_data.metal_music_data

502.59 KB
3045 rows
22 columns


CREATE TABLE metal_music_data (
  "artist_name" VARCHAR,
  "track_name" VARCHAR,
  "popularity" BIGINT,
  "genres" VARCHAR,
  "playlist" VARCHAR,
  "danceability" DOUBLE,
  "energy" DOUBLE,
  "key" BIGINT,
  "loudness" DOUBLE,
  "mode" BIGINT,
  "speechiness" DOUBLE,
  "acousticness" DOUBLE,
  "instrumentalness" DOUBLE,
  "liveness" DOUBLE,
  "valence" DOUBLE,
  "tempo" DOUBLE,
  "id" VARCHAR,
  "uri" VARCHAR,
  "track_href" VARCHAR,
  "analysis_url" VARCHAR,
  "duration_ms" BIGINT,
  "time_signature" BIGINT
);

Pop Music Data

@kaggle.siropo_spotify_multigenre_playlists_data.pop_music_data

567.89 KB
3831 rows
22 columns


CREATE TABLE pop_music_data (
  "artist_name" VARCHAR,
  "track_name" VARCHAR,
  "popularity" BIGINT,
  "genres" VARCHAR,
  "playlist" VARCHAR,
  "danceability" DOUBLE,
  "energy" DOUBLE,
  "key" BIGINT,
  "loudness" DOUBLE,
  "mode" BIGINT,
  "speechiness" DOUBLE,
  "acousticness" DOUBLE,
  "instrumentalness" DOUBLE,
  "liveness" DOUBLE,
  "valence" DOUBLE,
  "tempo" DOUBLE,
  "id" VARCHAR,
  "uri" VARCHAR,
  "track_href" VARCHAR,
  "analysis_url" VARCHAR,
  "duration_ms" BIGINT,
  "time_signature" BIGINT
);

Rock Music Data

@kaggle.siropo_spotify_multigenre_playlists_data.rock_music_data

1.22 MB
8747 rows
22 columns


CREATE TABLE rock_music_data (
  "artist_name" VARCHAR,
  "track_name" VARCHAR,
  "popularity" BIGINT,
  "genres" VARCHAR,
  "playlist" VARCHAR,
  "danceability" DOUBLE,
  "energy" DOUBLE,
  "key" BIGINT,
  "loudness" DOUBLE,
  "mode" BIGINT,
  "speechiness" DOUBLE,
  "acousticness" DOUBLE,
  "instrumentalness" DOUBLE,
  "liveness" DOUBLE,
  "valence" DOUBLE,
  "tempo" DOUBLE,
  "id" VARCHAR,
  "uri" VARCHAR,
  "track_href" VARCHAR,
  "analysis_url" VARCHAR,
  "duration_ms" BIGINT,
  "time_signature" BIGINT
);