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.

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 2. mysql -u -p < spotifydbdumpshare.sql

• - - - - 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

💁‍♀️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...

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   |

Introduction Spotify for Developers offers a wide range of possibilities to utilize the extensive catalog of Spotify data. One of them are the audio features calculated for each song and made available via the official Spotify Web API.

This is an attempt to retrieve the spotify data post the last extracted data. Haven't fully tested if this spotify allowed any other API full request post 2019

About Each song (row) has values for artist name, track name, track id and the audio features itself (for more information about the audio features check out this doc from Spotify).

Additionally, there is also a popularity feature included in this dataset. Please note that Spotify recalculates this value based on the number of plays the track receives so it might not be correct value anymore when you access the data.

Key Questions/Hypothesis that can be Answered 1. ARE SONGS IN MAJOR MODE ARE MORE POPULAR THAN ONES IN MINOR? 2. ARE SONGS WITH HIGH LOUDNESS ARE MOST POPULAR? 3. MOST PEOPLE LIKE LISTENING TO SONGS WITH SHORTER DURATION?

In addition more detailed analysis can be done to see what causes a song to be popular.

Credit Entire Credit goes to Spotify for providing this data via their Web API.

https://developer.spotify.com/documentation/web-api/reference/tracks/get-track/

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.

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.

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.

Content

This is a dataset of Spotify tracks over a range of 125 different genres. Each track has some audio features associated with it. The data is in CSV format which is tabular and can be loaded quickly.

  Usage

The dataset can be used for:

Building a Recommendation System based on some user input or preference Classification purposes based on audio features and available genres Any other application that you can think of. Feel free to discuss!

  Column… See the full description on the dataset page: https://huggingface.co/datasets/maharshipandya/spotify-tracks-dataset.

This is an analysis of the data on Spotify tracks from 1921-2020 with Jupyter Notebook and Python Data Science tools.

About the Dataset

The Spotify dataset (titled data.csv) consists of 160,000+ tracks sorted by name, from 1921-2020 found in Spotify as of June 2020. Collected by Kaggle user and Turkish Data Scientist Yamaç Eren Ay, the data was retrieved and tabulated from the Spotify Web API. Each row in the dataset corresponds to a track, with variables such as the title, artist, and year located in their respective columns. Aside from the fundamental variables, musical elements of each track, such as the tempo, danceability, and key, were likewise extracted; the algorithm for these values were generated by Spotify based on a range of technical parameters.

Exploratory Data Analysis (EDA)

1. Studying the correlations between the variables in the Spotify data.
2. The evolution of different musical elements through the years.
3. The divide between explicit and non-explicit songs through the years.

Further Investigation and Inference (FII)

1. Determining if there is a significant difference in popularity between explicit and non-explicit songs.
2. Finding the most frequent emotions in Spotify tracks and analyzing their musical elements based on the track's mode and key.
3. Determining the classifications of the Spotify tracks through K-Means Clustering.

Project Directory Guide

1. Spotify Data.ipynb is the main notebook where the data is imported for EDA and FII.
2. data.csv is the dataset downloaded from Kaggle.
3. spotify_eda.html is the HTML file for the comprehensive EDA done using the Pandas Profiling module.

Project Notes

1. This is in partial fulfillment of the course Statistical Modelling and Simulation (CSMODEL).

Credits to gabminamedez for the original dataset.

My Spotify Data. Visit https://dataone.org/datasets/sha256%3A3169844b43b503ccf02418d05fee5638e092e6e468fa0eb90b3b8ea21ab9c5a7 for complete metadata about this dataset.

This statistic shows the average data consumption of mobile Spotify users in Italy from January to May 2018, including both WiFi and mobile data. According to the data tracked by Walletsaver, the average data consumption of mobile users for Spotify increased from ** megabytes (MB) in February to ** MB in May 2018.

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.

from kaggle

In 2024, Taylor Swift was the artist with the most streamed album on Spotify. Her album "THE TORTURED POETS DEPARTMENT" was streamed over 6.6 billion times in 2024. She also entered the top 10 with her album "Lover" in sixth position, having registered nearly 3.3 billion streams on Spotify.

How many paid subscribers does Spotify have? As of the second quarter of 2025, Spotify had 276 million premium subscribers worldwide, up from 246 million in the corresponding quarter of 2024. Spotify’s subscriber base has increased dramatically in the last few years and has more than doubled since early 2019. Spotify and competitors Spotify is a music streaming service originally founded in 2006 in Sweden. The platform can be used from various devices and allows users to browse through a catalog of music licensed through multiple record labels, as well as create and share playlists with other users. Additionally, listeners are able to enjoy music for free with advertisements or are also given the option to purchase a subscription to allow for unlimited ad-free music streaming. Spotify’s largest competitors are Pandora, a company that offers a similar service and remains popular in the United States, and Apple Music, which was launched in 2015. While Pandora was once among the highest-grossing music apps in the Apple App Store, recent rankings show that global services like QQ Music, NetEase Cloud Music, and YouTube Music now generate higher monthly revenues.Users can also register Spotify accounts using Facebook directly through the website using an app. This enables them to connect with other Facebook friends and explore their music tastes and playlists. Spotify is a popular source for keeping up-to-date with music, and the ability to enjoy Spotify anywhere at any time allows consumers to shape their music consumption around their lifestyles and preferences.

In 2024, Taylor Swift was the most streamed artist on Spotify. Her songs were streamed over 28 billion times within the year. The second most streamed artist was The Weeknd with more than 13 billion streams in 2023.

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.

This dataset comprises 311 gospel tracks from 46 artists, spanning from 1959 to 2022. Each track is accompanied by Spotify audio features, allowing for an in-depth analysis of the musical elements that define gospel music. The dataset includes a diverse range of artists, reflecting the rich history and evolution of gospel over more than six decades.

This dataset is perfect for studying the musical characteristics of gospel, tracing its development over time, or for use in machine learning projects related to genre analysis or music recommendations.

This dataset consist of data from Spotify's API on all albums listed on Spotify for The Beatles.

The columns in this dataset are:

name - the name of the song

album - the name of the album

release_date - the day month and year the album was released

track number - the order the song appears on the album

id - the Spotify id for the song

uri - the Spotify uri for the song

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.

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.

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. For example, death metal has high energy, while a Bach prelude scores low on the scale. Perceptual features contributing to this attribute include dynamic range, perceived loudness, timbre, onset rate, and general entropy.

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". The closer the instrumentalness value is to 1.0, the greater likelihood the track contains no vocal content. Values above 0.5 are intended to represent instrumental tracks, but confidence is higher as the value approaches 1.0.

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. Loudness is the quality of a sound that is the primary psychological correlate of physical strength (amplitude). Values typically range between -60 and 0 db.

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. Values above 0.66 describe tracks that are probably made entirely of spoken words. Values between 0.33 and 0.66 describe tracks that may contain both music and speech, either in sections or layered, including such cases as rap music. Values below 0.33 most likely represent music and other non-speech-like tracks.

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.

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).

popularity - the popularity of the song from 0 to 100

duration_ms - The duration of the track in milliseconds.

Possible ways to use this data:

Data exploration Data visualization Recommendation systems Cluster analysis Popularity predictions

I hope you find this data to be useful, enjoy!