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

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. _

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🕹️ About Dataset

🎯 Context

What makes a song popular on Spotify?
Do artist popularity and follower count influence track success more than audio features?
How do album types and release dates shape listening trends?

These were the questions that inspired me to build this dataset.

Using Spotify’s API, I collected data on over 8,700 tracks, capturing detailed metadata about songs, artists, and albums. This dataset is ideal for exploring the intersection of music analytics, artist influence, and streaming behavior.

📦 Content

This dataset contains one CSV file with over 8,700 rows. Each row represents a unique track and includes metadata across three dimensions: track, artist, and album.

🙏 Acknowledgements

All data was collected using the Spotify Web API.
This dataset is intended for educational and research purposes only.

💡 Inspiration

You can use this dataset to:

• Analyze which artist traits correlate with track popularity
• Explore genre trends across different album types and release years
• Build machine learning models to predict song success
• Visualize music trends using Power BI or Python
• Compare artists, albums, or genres based on metadata

Cleaned Version

A cleaned version of the dataset (spotify_data_clean.csv) is now available. It includes:

Cleaning Process (SQL)

The cleaned dataset (spotify_data_clean.csv) was generated through a multi-step SQL pipeline designed to ensure consistency, completeness, and usability for analysis. Below is a summary of the transformations applied:

🔍 Null Handling & Imputation

• Identified and removed rows with missing track_name.
• Imputed missing artist_name, artist_popularity, artist_followers, and artist_genres using album-level joins (e.g., for albums like 1989).
• Replaced remaining nulls with default values:
  • 'N/A' for strings
  • 0 for numeric fields
  • '[]' for genre arrays (temporary placeholder)

✨ Standardization

• Trimmed whitespace from key fields: track_name, artist_name, album_name, album_type, explicit.
• Converted explicit values to uppercase (TRUE / FALSE).
• Cleaned artist_genres using regex to remove brackets and quotes.

📅 Release Date Normalization

• For year-only dates (e.g., 2020), appended -06-30 to estimate mid-year.
• For year-month formats (e.g., 2020-07), appended -01 to complete the date.
• Converted all dates to DATE format using STR_TO_DATE().

⏱ Duration Conversion

• Added a new column track_duration_min by converting track_duration_ms to minutes.
• Dropped the original track_duration_ms column after conversion.

🎵 Genre Enrichment

• Populated missing artist_genres for well-known artists using manual overrides:
  • Taylor Swift: country, pop, indie, folk
  • Olivia Rodrigo: pop rock, alternative pop, pop punk
  • Billie Eilish: alternative pop, electropop, dark pop
  • (...and more for 10+ artists)
• Remaining empty genres were replaced with 'N/A'.

🧹 Deduplication

• Used ROW_NUMBER() over track_name, artist_name, album_name, and album_release_date to identify duplicates.
• Removed duplicate rows and dropped the helper row_num column.

This SQL workflow ensures the dataset is clean, consistent, and ready for exploratory data analysis, genre modeling, and public sharing. All transformations were verified using sample queries and profiling tools.

Example Analysis

Explore genre trends and usage patterns in this companion notebook:
👉 Top Genres Using Pandas

🤝 Contribute

Feel free to fork the dataset or share your analyses!
If you clean, enrich, or expand the dataset, contributions are always welcome.

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Dataset Title: Top 5,000 Spotify Artists Metadata (2025)

Description:

This dataset contains metadata for up to 5,000 unique artists from Spotify, collected via the Spotify Web API in June 2025. The data includes key attributes such as artist name, Spotify ID, popularity score, genres, total followers, and profile image URLs, providing a comprehensive snapshot of prominent artists across various genres.

The dataset was created by querying the Spotify API for artists in popular genres (e.g., pop, rock, hip-hop, jazz, and more) and filtering for unique entries based on Spotify IDs. Artists are sorted by popularity to approximate the "top" artists on the platform, based on Spotify’s popularity metric (0–100), which reflects streaming activity and listener engagement.

Key Features:

Columns:

name: Artist’s name (string). id: Unique Spotify artist ID (string). popularity: Spotify popularity score (integer, 0–100). genres: Comma-separated list of genres associated with the artist (string). followers: Total number of followers on Spotify (integer). image_url: URL to the artist’s profile image, if available (string, nullable). Rows: Up to 5,000 unique artists. File Format: CSV (top_spotify_artists.csv). Data Source: Spotify Web API, accessed via the Spotipy Python library. Collection Date: June 2025.

Potential Uses:

Music Analytics: Analyze artist popularity trends, genre distributions, or follower demographics. Recommendation Systems: Build models to recommend artists based on genres or popularity. Visualization: Create visualizations of artist networks, genre overlaps, or popularity rankings. Market Research: Study the music industry’s top artists and their audience engagement. Notes:

The dataset is an approximation of "top" artists, as Spotify does not provide a direct "top artists" endpoint. Data was gathered by searching across multiple genres and sorting by popularity. Some artists may lack image URLs or genres due to incomplete Spotify profiles. Users should comply with Spotify’s API Terms of Use when utilizing this dataset. License: This dataset is shared under the CC BY-SA 4.0 license, with attribution to the Spotify Web API as the data source.

Acknowledgments:

Data sourced from the Spotify Web API. Collected using the Spotipy Python library.

Context 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 dataset contains 9,460 Spotify tracks with comprehensive audio features and metadata, specifically curated for music popularity classification and machine learning projects. The data has been filtered and processed to ensure high quality and completeness for analysis purposes.

Content Each track (row) contains 28 features including: Track Information: Artist name, track name, track ID, release date, and popularity score Audio Features: Danceability, energy, valence, acousticness, instrumentalness, liveness, speechiness, tempo, and loudness Technical Metadata: Musical key, mode, time signature, duration, and Spotify API references Additional Data: Genres, lyrics, preview URLs, and playlist information The popularity feature (0-100 scale) serves as the primary target variable for classification tasks.

Acknowledgements Credit goes entirely to Spotify for providing this data via their Web API. The audio features are calculated by Spotify's proprietary algorithms and represent the most comprehensive music analysis data available. Reference: https://developer.spotify.com/documentation/web-api

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Studies of music-evoked autobiographical memories (MEAMs) show that music is a potent cue for retrieving vivid and self-relevant memories. However, whether and how musical features are able to predict the qualities of MEAMs – including their emotional qualities, phenomenological characteristics and retrieval efficiency – remains unclear. In our study, a sample of 233 adult participants identified a piece of music that evoked an autobiographical memory (AM) before providing a written description of the memory, and then evaluating its emotional and phenomenological content. Participants were then presented with excerpts of ten songs that were popular during their childhood and early adulthood and reported the same details for any AMs evoked. Features of all songs were extracted using the Spotify Web API and subjected to principal components analysis for dimension reduction. This revealed a primary auditory feature component – characterised by low energeticness and high acousticness – that was found to predict several qualities of the memory. Specifically, results showed that low energetic – high acoustic songs were associated with AMs characterised emotionally by aesthetic appreciation, adoration, calmness, romance and sadness, while high energetic – low acoustic songs were associated with AMs high in memory energeticness, amusement and excitement. Phenomenologically, AMs associated with low energetic – high acoustic songs were described as less social, and more vivid, unique and important, and, in terms of retrieval efficacy, tended to be retrieved more slowly. Our findings show for the first time the extent to which the qualities of MEAMs can be predicted by music’s stimulus features. Further, by taking into account how the AMs were evoked, and subjective factors related to the memory-evoking music such as liking and familiarity, our study provides insights into possible mechanisms underlying music-assisted memory encoding and retrieval. We discuss the implications of our findings for understanding the links between perception, emotion and memory processes, and make suggestions for future work that can advance this research area.

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This dataset was generated with the use of spotipy library and contains basic information about artists, playlists and tracks. Besides basic information (names, popularities and release dates etc.) there are spotify generated audio features such as danceability, energy, acousticness and speechiness of each song. You can read more about them here: https://developer.spotify.com/documentation/web-api/reference/get-audio-features

Also, you can find audio analysis of each song by it's URI. Audio analyses are python dictionaries saved in a separate pickle files. They contain various information about each second of the track and may be interesting to you. Read more on https://developer.spotify.com/documentation/web-api/reference/get-audio-analysis

(Check out https://spotify-audio-analysis.glitch.me/ for colorful visualizations) https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F13032089%2F191c78773c9a877c662507576f53063d%2F2023-03-21%20220219.png?generation=1684434440641293&alt=media" alt="">

Example dimentionality reduction on tatums of "The Place Where He Inserted the Blade" by "Black Country, New Road". https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F13032089%2F0959cf76449f568cf816984e22d61a42%2FThe%20Place%20Where%20He%20Inserted%20The%20Blade.png?generation=1684435113369618&alt=media" alt="">

Have fun!

About

For more in-depth information about audio features provided by Spotify: https://developer.spotify.com/documentation/web-api/reference/#/operations/get-audio-features

I reposted my old dataset as many people requested. I don't consider updating the dataset further.

Meta-information

Title: Spotify Dataset 1921-2020, 600k+ Tracks Subtitle: Audio features of 600k+ tracks, popularity metrics of 1M+ artists Source: Spotify Web API Creator: Yamac Eren Ay Release Date (of Last Version): April 2021 Link to this dataset: https://www.kaggle.com/yamaerenay/spotify-dataset-19212020-600k-tracks Link to the old dataset: https://www.kaggle.com/yamaerenay/spotify-dataset-1921-2020-160k-tracks

Disclaimer

I am not posting here third-party Spotify data for arbitrary reasons or getting upvote.

The old dataset has been mentioned in tens of scientific papers using the old link which doesn't work anymore since July 2021, and most of the authors had some problems proving the validity of the dataset. You can cite the same dataset under the new link. I'll be posting more information regarding the old dataset.

If you have inquiries or complaints, please don't hesitate to reach out to me on LinkedIn or you can send me an email.

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/

Estimate, standard error, t-value and p-values of linear mixed effects models of E-A for each outcome variable.

Estimate, standard error, z-value and p-values of mixed effects logistic regressions of E-A on reporting each outcome variable.

I've been diving into the vibrant world of data for a solid two years, and guess what? I'm finally cracking the code on what it takes to soar in this industry! Early in my data adventures, I was like a kid on Limewire when I found Kaggle, downloading everything that caught my eye. But then, I stumbled upon Spotify's data and... let's just say, it was a bit of a reality check.

I found myself wrestling with duplicate records, scratching my head over inconsistent schemas, and feeling lost in the sauce without any guides. That experience was a game-changer for me. I made a promise to my future self: “When you've got the skills, create a dataset that's not just good, but legendary.” That time has come!

Introducing my unique Spotify dataset – a crystal clear reflection of dedication and clarity. What makes this set stand out? You're not just getting data; you're getting a story. You can literally trace my steps, unraveling the magic behind each table through my script on Github. It's like having a backstage pass to a data concert! (Yes, Swifties will love this dataset too 😉)

I'm all about transparency, and I believe it's the key to trust. With this dataset, I'm laying it all out there – no smoke and mirrors, just pure, unadulterated, CLEAN data. I want you to feel the same excitement I do when data just clicks into place. I encourage you all to checkout the Github repo I linked above to see how this dataset came to life!

If you have any questions, suggestions or simply want to network, reach out to me on LinkedIn

This dataset is created using data sourced from Spotify and adheres to their Terms of Use. The dataset is intended for non-commercial, academic purposes and does not infringe upon Spotify's intellectual property rights. For full details on Spotify's terms, please visit Spotify's Terms and Conditions of Use.

You can find documentation for Spotifys Web APIs here

As of 12/20/2023, this is V1 of my data and I'll most likely release a few more versions after working through kinks from former releases.

Other Datasets: - Zillow

Data relating to songs that feature on the studio and live albums of the band Muse.

Data was collected on 14/12/2022 and includes all Muse songs that feature on albums starting from Showbiz (1999) to Will of the People (2022).

Ideas for data analysis: - Which album has the longest songs? - Has the mood of songs gotten happier, sadder, or remained the same throughout Muse's career? - Is there any correlation between popularity and key, energy or danceability?

Data was scraped from Spotify using the Spotify Web API: https://developer.spotify.com/documentation/web-api/quick-start/

Eminem is one of the most influential hip-hop artists of all time, and the Rap God. I acquired this data using Spotify APs and supplemented it with other research to add to my own analysis. You can find my original analysis here: https://kaivalyapowale.com/2020/01/25/eminems-album-trends-and-music-to-be-murdered-by-2020/

My analysis was also published by top hip-hop websites: HipHop 24x7 - Data analysis reveals M2BMB is the most negative album Eminem Pro - Album's data analysis Eminem Pro - Eminem's albums are getting shorter

You can also check out visualizations on Tableau Public for some ideas: https://public.tableau.com/profile/kaivalya.powale#!/

Content

I have primarily used data from Spotify’s API using multiple endpoints for albums and tracks. I supplemented the data with stats from Billboard and calculations from this post.

Here's the explanation for all the audio features provided by Spotify!

I have researched data about album sales from multiple sources online. They are cited in my original analysis.

Acknowledgements

Here are the Spotify's Album endpoints. Charts data from Billboard. Swear data from this source.

Inspiration

I'd love to see new visualizations using this data or using the sales, swear, or duration for an analysis. It would be wonderful if someone compares this with other hip-hop greats.