This dataset was created using Spotify developer API. It consists of user-created as well as Spotify-curated playlists.
The dataset consists of 1 million playlists, 3 million unique tracks, 3 million unique albums, and 1.3 million artists.
The data is stored in a SQL database, with the primary entities being songs, albums, artists, and playlists.
Each of the aforementioned entities are represented by unique IDs (Spotify URI).
Data is stored into following tables:

• album
• artist
• track
• playlist
• track_artist1
• track_playlist1

album

| id | name | uri |

id: Album ID as provided by Spotify
name: Album Name as provided by Spotify
uri: Album URI as provided by Spotify

artist

| id | name | uri |

id: Artist ID as provided by Spotify
name: Artist Name as provided by Spotify
uri: Artist URI as provided by Spotify

track

| id | name | duration | popularity | explicit | preview_url | uri | album_id |

id: Track ID as provided by Spotify
name: Track Name as provided by Spotify
duration: Track Duration (in milliseconds) as provided by Spotify
popularity: Track Popularity as provided by Spotify
explicit: Whether the track has explicit lyrics or not. (true or false)
preview_url: A link to a 30 second preview (MP3 format) of the track. Can be null
uri: Track Uri as provided by Spotify
album_id: Album Id to which the track belongs

playlist

| id | name | followers | uri | total_tracks |

id: Playlist ID as provided by Spotify
name: Playlist Name as provided by Spotify
followers: Playlist Followers as provided by Spotify
uri: Playlist Uri as provided by Spotify
total_tracks: Total number of tracks in the playlist.

track_artist1

| track_id | artist_id |

Track-Artist association table

track_playlist1

| track_id | playlist_id |

Track-Playlist association table

- - - - - SETUP - - - - -

The data is in the form of a SQL dump. The download size is about 10 GB, and the database populated from it comes out to about 35GB.

spotifydbdumpschemashare.sql contains the schema for the database (for reference):
spotifydbdumpshare.sql is the actual data dump.

Setup steps:
1. Create database

- - - - - PAPER - - - - -

The description of this dataset can be found in the following paper:

Papreja P., Venkateswara H., Panchanathan S. (2020) Representation, Exploration and Recommendation of Playlists. In: Cellier P., Driessens K. (eds) Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2019. Communications in Computer and Information Science, vol 1168. Springer, Cham

📊 About the Dataset

This dataset captures the global music streaming trends on Spotify for the year 2024. It provides valuable insights into user preferences across various countries, top-performing artists and albums, streaming hours, and listener behavior patterns. It is designed to support data analysis, machine learning models, and business intelligence dashboards in the music and media industry.

With over 500 rows of clean, non-duplicated, and realistic entries from countries around the world, this dataset is ideal for uncovering:

• Global music popularity patterns
• Listener engagement across genres and demographics
• Artist performance across countries
• Revenue forecasting and content recommendations

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Gain valuable insights into music trends, artist popularity, and streaming analytics with our comprehensive Spotify Dataset. Designed for music analysts, marketers, and businesses, this dataset provides structured and reliable data from Spotify to enhance market research, content strategy, and audience engagement.

Dataset Features

Track Information: Access detailed data on songs, including track name, artist, album, genre, and release date. Streaming Popularity: Extract track popularity scores, listener engagement metrics, and ranking trends. Artist & Album Insights: Analyze artist performance, album releases, and genre trends over time. Related Searches & Recommendations: Track related search terms and suggested content for deeper audience insights. Historical & Real-Time Data: Retrieve historical streaming data or access continuously updated records for real-time trend analysis.

Customizable Subsets for Specific Needs Our Spotify Dataset is fully customizable, allowing you to filter data based on track popularity, artist, genre, release date, or listener engagement. Whether you need broad coverage for industry analysis or focused data for content optimization, we tailor the dataset to your needs.

Popular Use Cases

Market Analysis & Trend Forecasting: Identify emerging music trends, genre popularity, and listener preferences. Artist & Label Performance Tracking: Monitor artist rankings, album success, and audience engagement. Competitive Intelligence: Analyze competitor music strategies, playlist placements, and streaming performance. AI & Machine Learning Applications: Use structured music data to train AI models for recommendation engines, playlist curation, and predictive analytics. Advertising & Sponsorship Insights: Identify high-performing tracks and artists for targeted advertising and sponsorship opportunities.

Whether you're optimizing music marketing, analyzing streaming trends, or enhancing content strategies, our Spotify Dataset provides the structured data you need. Get started today and customize your dataset to fit your business objectives.

Description for Spotify Songs Dataset on Kaggle

Dataset Title: Spotify Songs Dataset

Description: This dataset contains a collection of songs fetched from the Spotify API, covering various genres including "acoustic", "afrobeat", "alt-rock", "alternative", "ambient", "anime", "black-metal", "bluegrass", "blues", "bossanova", "brazil", "breakbeat", "british", "cantopop", "chicago-house", "children", "chill", "classical", "club", "comedy", "country", "dance", "dancehall", "death-metal", "deep-house", "detroit-techno", "disco", "disney", "drum-and-bass", "dub", "dubstep", "edm", "electro", "electronic", "emo", "folk", "forro", "french", "funk", "garage", "german", "gospel", "goth", "grindcore", "groove", "grunge", "guitar", "happy", "hard-rock", "hardcore", "hardstyle", "heavy-metal", "hip-hop", "holidays", "honky-tonk", "house", "idm", "indian", "indie", "indie-pop", "industrial", "iranian", "j-dance", "j-idol", "j-pop", "j-rock", "jazz", "k-pop", "kids", "latin", "latino", "malay", "mandopop", "metal", "metal-misc", "metalcore", "minimal-techno", "movies", "mpb", "new-age", "new-release", "opera", "pagode", "party", "philippines-opm", "piano", "pop", "pop-film", "post-dubstep", "power-pop", "progressive-house", "psych-rock", "punk", "punk-rock", "r-n-b", "rainy-day", "reggae", "reggaeton", "road-trip", "rock", "rock-n-roll", "rockabilly", "romance", "sad", "salsa", "samba", "sertanejo", "show-tunes", "singer-songwriter", "ska", "sleep", "songwriter", "soul", "soundtracks", "spanish", "study", "summer", "swedish", "synth-pop", "tango", "techno", "trance", "trip-hop", "turkish", "work-out", "world-music". Each entry in the dataset provides detailed information about a song, including its name, artists, album, popularity, duration, and whether it is explicit.

Data Collection Method: The data was collected using the Spotify Web API through a Python script. The script performed searches for different genres and retrieved the top tracks for each genre. The fetched data was then compiled and saved into a CSV file.

Columns Description: id: Unique identifier for the track on Spotify. name: Name of the track. genre: genre of the song. artists: Names of the artists who performed the track, separated by commas if there are multiple artists. album: Name of the album the track belongs to. popularity: Popularity score of the track (0-100, where higher is more popular). duration_ms: Duration of the track in milliseconds. explicit: Boolean indicating whether the track contains explicit content.

Potential Uses: This dataset can be used for a variety of purposes, including but not limited to:

• Music Analysis: Analyze the popularity and characteristics of songs across different genres.
• Recommendation Systems: Develop and test music recommendation algorithms.
• Trend Analysis: Study trends in music preferences and popularity over time.
• Machine Learning: Train machine learning models for tasks like genre classification or popularity prediction. _ Acknowledgements: This dataset was created using the Spotify Web API. Special thanks to Spotify for providing access to their extensive music library through their API. _ License: This dataset is made available under the Creative Commons Attribution 4.0 International (CC BY 4.0) license. You are free to use, modify, and distribute this dataset, provided you give appropriate credit to the original creator. _

from kaggle

The dataset contains records of the user's Spotify streaming history, with each row representing a specific instance of a played track. The data includes various attributes providing insights into the user's music listening habits.

Columns:

1. ts (Timestamp):

   • The timestamp when the track was played.

2. platform:

   • The platform or device used for streaming (e.g., Windows 10).

3. ms_played:

   • The duration in milliseconds of how long the track was played.

4. conn_country:

   • The country code indicating the user's location during streaming (e.g., LK for Sri Lanka).

5. master_metadata_track_name:

   • The name of the track played.

6. master_metadata_album_artist_name:

   • The artist of the album to which the track belongs.

7. master_metadata_album_album_name:

   • The name of the album containing the track.

8. spotify_track_uri:

   • The unique Spotify URI for the track.

9. reason_start:

   • The reason for starting the track (e.g., play button clicked).

10. reason_end:

    • The reason for ending the track (e.g., track done).

11. shuffle:

    • Indicates whether shuffle mode was enabled (True/False).

12. offline:

    • Indicates whether the track was played offline (True/False).

13. offline_timestamp:

    • Timestamp indicating when the track was played offline (if applicable).

14. incognito_mode:

    • Indicates whether incognito mode was enabled (True/False).

Purpose:

This dataset is suitable for performing detailed Exploratory Data Analysis (EDA) to uncover patterns, trends, and insights into the user's music-listening behaviour. Potential analyses could include the distribution of listening durations, favourite artists and tracks, exploration of geographic listening patterns, and examination of usage patterns across different platforms.

Visualization tools such as Matplotlib and Seaborn could be utilized for a more in-depth analysis to create visual representations of the findings. This dataset aligns well with your interest in data science, offering opportunities to apply analytical techniques to real-world streaming data.

MusicOSet is an open and enhanced dataset of musical elements (artists, songs and albums) based on musical popularity classification. Provides a directly accessible collection of data suitable for numerous tasks in music data mining (e.g., data visualization, classification, clustering, similarity search, MIR, HSS and so forth). To create MusicOSet, the potential information sources were divided into three main categories: music popularity sources, metadata sources, and acoustic and lyrical features sources. Data from all three categories were initially collected between January and May 2019. Nevertheless, the update and enhancement of the data happened in June 2019.

The attractive features of MusicOSet include:

• Integration and centralization of different musical data sources
• Calculation of popularity scores and classification of hits and non-hits musical elements, varying from 1962 to 2018
• Enriched metadata for music, artists, and albums from the US popular music industry
• Availability of acoustic and lyrical resources
• Unrestricted access in two formats: SQL database and compressed .csv files

|    Data    | # Records |
|:-----------------:|:---------:|
| Songs       | 20,405  |
| Artists      | 11,518  |
| Albums      | 26,522  |
| Lyrics      | 19,664  |
| Acoustic Features | 20,405  |
| Genres      | 1,561   |

This dataset is based on the subset of users in the #nowplaying dataset who publish their #nowplaying tweets via Spotify. In principle, the dataset holds users, their playlists and the tracks contained in these playlists.

The csv-file holding the dataset contains the following columns: "user_id", "artistname", "trackname", "playlistname", where

• user_id is a hash of the user's Spotify user name
• artistname is the name of the artist
• trackname is the title of the track and
• playlistname is the name of the playlist that contains this track.

The separator used is , each entry is enclosed by double quotes and the escape character used is \.

A description of the generation of the dataset and the dataset itself can be found in the following paper:

Pichl, Martin; Zangerle, Eva; Specht, Günther: "Towards a Context-Aware Music Recommendation Approach: What is Hidden in the Playlist Name?" in 15th IEEE International Conference on Data Mining Workshops (ICDM 2015), pp. 1360-1365, IEEE, Atlantic City, 2015.

1) Data Introduction • The Spotify Tracks Dataset contains information on tracks from over 125 music genres, including both audio features (e.g., danceability, energy, valence) and metadata (e.g., title, artist, genre).

2) Data Utilization (1) Characteristics of the Spotify Tracks Dataset: • The data is structured in a tabular format at the track level, where each column represents numerical or categorical features based on musical properties. This makes it suitable for recommendation systems, genre classification, and emotion analysis. • It includes multi-dimensional attributes grounded in music theory such as track duration, time signature, energy, loudness, tempo, and speechiness—enabling its use in music classification and clustering tasks.

(2) Applications of the Spotify Tracks Dataset: • Design of Music Recommendation Systems: It can be used to build content-based filtering systems or hybrid recommendation algorithms based on user preferences.

Dataset Card for Spotify Million Song Dataset

  Dataset Summary

This is Spotify Million Song Dataset. This dataset contains song names, artists names, link to the song and lyrics. This dataset can be used for recommending songs, classifying or clustering songs.

  Supported Tasks and Leaderboards

[More Information Needed]

  Languages

[More Information Needed]

  Dataset Structure





  Data Instances

[More Information Needed]

  Data… See the full description on the dataset page: https://huggingface.co/datasets/vishnupriyavr/spotify-million-song-dataset.

This dataset was constructed based on the data found in Kaggle from Spotify.

The files here reported can be used to build a property graph in Neo4J:

• song.csv - contains all the data for the Song nodes.
• artist.csv - contains the data for the Artist nodes.
• playlist.csv - contains the data for the Playlist nodes.
• user.csv - contains the data for the Playlist nodes (those creating Playlists).
• genre.csv - contains the data for the Genre nodes (a category for the Artists).
• type.csv - contains the data for the Type nodes (a category for the Playlists).
• sing.csv - contains the data for the SING relationship from Artist to Song nodes.
• created.csv - contains the data for the CREATED relationship from User to Playlist nodes.
• in.csv - contains the data for the IN relationship from Song to Playlist nodes.
• of_type.csv - contains the data for the OFTYPE relationship from Playlist to Type nodes.
• labelled.csv - contains the data for the LABELLED relationship from Artist to Genre nodes.

This data was used as test dataset in the paper "MINE GRAPH RULE: A New GQL Operator for Mining Association Rules in Property Graph Databases".

Every week, Spotify updates its Top-50 playlists for each country. This dataset includes every country list of the 45th week of 2023 (6th November - 12th November). There are 73 available countries.

The dataset has a column for every musical aspect of each song, and also the name, country, artist and publication date of the track.

Data extracted from the Spotify Official API.

Columns

These features are created by Spotify to analyze tracks. Here I copy the definition of each column, based on Spotify's API documentation.

Danceability: Danceability describes how suitable a track is for dancing based on a combination of musical elements including tempo, rhythm stability, beat strength, and overall regularity. A value of 0.0 is least danceable and 1.0 is most danceable.

Acousticness: A confidence measure from 0.0 to 1.0 of whether the track is acoustic. 1.0 represents high confidence the track is acoustic.

Duration_ms: The duration of the track in milliseconds.

Energy: Energy is a measure from 0.0 to 1.0 and represents a perceptual measure of intensity and activity. Typically, energetic tracks feel fast, loud, and noisy.

Instrumentalness: Predicts whether a track contains no vocals. "Ooh" and "aah" sounds are treated as instrumental in this context. Rap or spoken word tracks are clearly "vocal".

Key: The key the track is in. Integers map to pitches using standard Pitch Class notation. E.g. 0 = C, 1 = C♯/D♭, 2 = D, and so on. If no key was detected, the value is -1.

Liveness: Detects the presence of an audience in the recording. Higher liveness values represent an increased probability that the track was performed live. A value above 0.8 provides strong likelihood that the track is live.

Loudness: The overall loudness of a track in decibels (dB). Loudness values are averaged across the entire track and are useful for comparing relative loudness of tracks.

Mode: Mode indicates the modality (major or minor) of a track, the type of scale from which its melodic content is derived. Major is represented by 1 and minor is 0.

Speechiness: Speechiness detects the presence of spoken words in a track. The more exclusively speech-like the recording (e.g. talk show, audio book, poetry), the closer to 1.0 the attribute value.

Tempo: The overall estimated tempo of a track in beats per minute (BPM). In musical terminology, tempo is the speed or pace of a given piece and derives directly from the average beat duration.

Time_signature: An estimated time signature. The time signature (meter) is a notational convention to specify how many beats are in each bar (or measure). The time signature ranges from 3 to 7 indicating time signatures of "3/4", to "7/4".

Valence: A measure from 0.0 to 1.0 describing the musical positiveness conveyed by a track. Tracks with high valence sound more positive (e.g. happy, cheerful, euphoric), while tracks with low valence sound more negative (e.g. sad, depressed, angry).

Introduction to the Spotify Dataset

Overview of the Dataset Source and Purpose Description of the Data Collection Process Data Exploration

Understanding the Structure and Size of the Dataset Overview of the Features and Columns Key Features in the Spotify Dataset

Explanation of Important Columns (e.g., track name, artist, album, duration, popularity) Genre and Music Category Analysis

Categorizing Songs by Genre and Music Type Most Popular Genres on Spotify **Artist Analysis ** Identifying Top Artists Based on Popularity and Number of Songs Relationship between Artist and Song Attributes Song Duration Analysis

Distribution of Song Durations Impact of Song Duration on Popularity and Listener Engagement Song Popularity and Listener Engagement

Analyzing the Popularity Scores of Songs Correlation between Popularity and Other Song Features Audio Features Analysis

Examination of Audio Features (danceability, energy, instrumentalness, etc.) Clustering Songs Based on Audio Features Time-Based Analysis

Seasonal Trends in Song Releases and Popularity Time Series Analysis of Listening Patterns Collaborations and Featured Artists

Frequency of Collaborations and Featured Artists Impact of Collaborations on Song Popularity Recommendation Systems

Overview of Spotify's Recommendation Algorithms Building Simple Recommendation Models User Behavior and Playlist Analysis

Analysis of User-Generated Playlists Common Song Additions and Removals

1) Data Introduction • The Spotify Playlist-ORIGINS Dataset is a dataset of Spotify playlists called ORIGINS, which individuals have made with their favorite songs since 2014.

2) Data Utilization (1) Spotify Playlist-ORIGINS Dataset has characteristics that: • This dataset contains detailed music information for each playlist, including song name, artist, album, genre, release year, track ID, and structured metadata such as name, description, and song order for each playlist. (2) Spotify Playlist-ORIGINS Dataset can be used to: • Playlist-based music recommendation and user preference analysis: It can be used to develop a machine learning/deep learning-based music recommendation system or to study user preference analysis using playlist and song information. • Music Trend and Genre Popularity Analysis: It analyzes release year, genre, and artist data and can be used to study the music industry and culture, including music trends by period and genre, and changes in popular artists and songs.

As of March 2018, Spotify’s user base was dominated by Millennials, with ** percent of its users aged 25 to 34 and ** percent aged between 18 and 24 years old. The streaming giant has permanently altered how consumers discover, engage with and share music, and according to a 2018 survey, Spotify reaches almost **** of 16 to 24 year olds in the United States each week. The power of SpotifySpotify’s popularity is undeniable, accumulating millions of premium subscribers worldwide each quarter and hundreds of millions of unique visitors to Spotify.com every month. In the United States, Spotify is one of the most commonly used apps for listening to podcasts, and despite being in constant competition with Apple Music, remains a large part of U.S. music listeners’ lives. A survey revealed that Spotify is also the preferred music streaming service among 18 to 29-year-olds, which may seem unremarkable given the data on Spotify’s user base, but serves as further evidence of Spotify’s popularity among younger users. Whether Spotify’s growth will last forever, only time will tell, particularly as Apple Music continues to put up a good fight and smaller but increasingly popular services such as Deezer begin to make their mark. But with the company recording a profit in early 2019 for the first time since its inception, Spotify remains very much a market leader and firmly on the path to future success.

My Spotify Data

Traffic analytics, rankings, and competitive metrics for spotify.com as of September 2025

💁‍♀️Please take a moment to carefully read through this description and metadata to better understand the dataset and its nuances before proceeding to the Suggestions and Discussions section.

Dataset Description:

This dataset compiles the tracks from all of Beyoncé's albums available on Spotify, showcasing the evolution of one of the most influential artists in the music industry. It represents a comprehensive array of genres, influences, and musical styles that Beyoncé has explored throughout her career. Each track in the dataset is detailed with a variety of features, popularity, and metadata. This dataset serves as an excellent resource for music enthusiasts, data analysts, and researchers aiming to explore the impact of Beyoncé's music, identify trends in her musical evolution, or develop music recommendation systems based on empirical data.

Scope of the Data:

The focus of this dataset is on providing a comprehensive view of Beyoncé's musical releases on Spotify, specifically tailored to showcase her creative output. To this end, the dataset includes tracks from the following album types: - Albums: Full-length albums released by Beyoncé, encapsulating a range of her musical styles and eras. - Singles: Standalone single releases, highlighting key songs that have been released independently of her full albums. It's important to note that this dataset deliberately excludes compilation albums. Compilations, which often contain a mixture of tracks from various artists or previously released tracks by Beyoncé, are not included to maintain a focus on her original releases and to provide a clearer picture of her artistic evolution.

Data Collection and Processing:

Obtaining the Data: The data was obtained directly from the Spotify Web API, specifically focusing on albums and tracks by Beyoncé. The Spotify API provides detailed information about tracks, artists, and albums through various endpoints.

Data Processing: To process and structure the data, Python scripts were developed using data science libraries such as pandas for data manipulation and spotipy for API interactions, specifically for Spotify data retrieval.

Workflow: - Authentication - API Requests - Data Cleaning and Transformation - Saving the Data

Attribute Descriptions:

• artist_name: the name of the artist (Beyoncé and collaborators)
• track_name: the title of the track
• is_explicit: Indicates whether the track contains explicit content
• album_release_date: The date when the track was released
• genres: A list of genres associated with Beyoncé
• danceability: A measure from 0.0 to 1.0 indicating how suitable a track is for - dancing based on a combination of musical elements
• valence: A measure from 0.0 to 1.0 indicating the musical positiveness conveyed by a track
• energy: A measure from 0.0 to 1.0 representing a perceptual measure of intensity and activity
• loudness: The overall loudness of a track in decibels (dB)
• acousticness: A measure from 0.0 to 1.0 whether the track is acoustic
• instrumentalness: Predicts whether a track contains no vocals
• liveness: Detects the presence of an audience in the recordings
• speechiness: Detects the presence of spoken words in a track
• key: The key the track is in. Integers map to pitches using standard Pitch Class notation
• tempo: The overall estimated tempo of a track in beats per minute (BPM)
• mode: Modality of the track
• duration_ms: The length of the track in milliseconds
• time_signature: An estimated overall time signature of a track
• popularity: A score between 0 and 100, with 100 being the most popular

Possible Data Projects:

• Trend Analysis in Beyonce's Musical Evolution
• Mood and Musical Elements in Beyonce's Tracks
• Beyonce's Influence on the Music Industry Analysis

Disclaimer and Responsible Use:

This dataset, derived from Spotify focusing on Beyoncé's albums and tracks, is intended for educational, research, and analysis purposes only. Users are urged to use this data responsibly, ethically, and within the bounds of legal stipulations. - Compliance with Terms of Service: Users should adhere to Spotify's Terms of Service and Developer Policies when utilizing this dataset. - Copyright Notice: The dataset presents music track information including names and artist details for analytical purposes and does not convey any rights to the music itself. Users must ensure that their use does not infringe on the copyright holders' rights. Any analysis, distribution, or derivative work should respect the intellectual property rights of all involved parties and comply with applicable laws. - No Warranty Disclaimer: The dataset is provided "as is," without warranty, and the creator disclaims any legal liability for its use by others. - Ethical Use: Users are encouraged to consider the ethical implications of their analyses and the potential impact...

18,403 music reviews scraped from Pitchfork, including relevant metadata such as author, review date, record release year, score, and genre, along with those album's audio features pulled from Spotify's API.

Spotify reported EUR6.24B in Current Liabilities for its fiscal quarter ending in September of 2025. Data for Spotify | SPOT - Current Liabilities including historical, tables and charts were last updated by Trading Economics this last December in 2025.