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.

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.

These are the top 20 most followed playlists on Spotify right now.

Spotify has about 80 million individual tracks on the platform.

There are currently more than 4 million podcast titles on the platform today.

This is the statistics for the Top 10 songs of various spotify artists and their YouTube videos. The Creators above generated the data and uploaded it to Kaggle on February 6-7 2023. The license to use this data is "CC0: Public Domain", allowing the data to be copied, modified, distributed, and worked on without having to ask permission. The data is in numerical and textual CSV format as attached. This dataset contains the statistics and attributes of the top 10 songs of various artists in the world. As described by the creators above, it includes 26 variables for each of the songs collected from spotify. These variables are briefly described next:

Track: name of the song, as visible on the Spotify platform. Artist: name of the artist. Url_spotify: the Url of the artist. Album: the album in wich the song is contained on Spotify. Album_type: indicates if the song is relesead on Spotify as a single or contained in an album. Uri: a spotify link used to find the song through the API. 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: 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. 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. 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. 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. 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. 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). 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. Duration_ms: the duration of the track in milliseconds. Stream: number of streams of the song on Spotify. Url_youtube: url of the video linked to the song on Youtube, if it have any. Title: title of the videoclip on youtube. Channel: name of the channel that have published the video. Views: number of views. Likes: number of likes. Comments: number of comments. Description: description of the video on Youtube. Licensed: Indicates whether the video represents licensed content, which means that the content was uploaded to a channel linked to a YouTube content partner and then claimed by that partner. official_video: boolean value that indicates if the video found is the official video of the song. The data was last updated on February 7, 2023.

Description

This dataset presents a comprehensive compilation of the most streamed songs on Spotify in 2024. It provides extensive insights into each track's attributes, popularity, and presence on various music platforms, offering a valuable resource for music analysts, enthusiasts, and industry professionals. The dataset includes information such as track name, artist, release date, ISRC, streaming statistics, and presence on platforms like YouTube, TikTok, and more.

Here is the link for the 2023 data: "https://www.kaggle.com/datasets/nelgiriyewithana/top-spotify-songs-2023">Most Streamed Spotify Songs 2023 🟢

Key Features

• Track Name: Name of the song.
• Album Name: Name of the album the song belongs to.
• Artist: Name of the artist(s) of the song.
• Release Date: Date when the song was released.
• ISRC: International Standard Recording Code for the song.
• All Time Rank: Ranking of the song based on its all-time popularity.
• Track Score: Score assigned to the track based on various factors.
• Spotify Streams: Total number of streams on Spotify.
• Spotify Playlist Count: Number of Spotify playlists the song is included in.
• Spotify Playlist Reach: Reach of the song across Spotify playlists.
• Spotify Popularity: Popularity score of the song on Spotify.
• YouTube Views: Total views of the song's official video on YouTube.
• YouTube Likes: Total likes on the song's official video on YouTube.
• TikTok Posts: Number of TikTok posts featuring the song.
• TikTok Likes: Total likes on TikTok posts featuring the song.
• TikTok Views: Total views on TikTok posts featuring the song.
• YouTube Playlist Reach: Reach of the song across YouTube playlists.
• Apple Music Playlist Count: Number of Apple Music playlists the song is included in.
• AirPlay Spins: Number of times the song has been played on radio stations.
• SiriusXM Spins: Number of times the song has been played on SiriusXM.
• Deezer Playlist Count: Number of Deezer playlists the song is included in.
• Deezer Playlist Reach: Reach of the song across Deezer playlists.
• Amazon Playlist Count: Number of Amazon Music playlists the song is included in.
• Pandora Streams: Total number of streams on Pandora.
• Pandora Track Stations: Number of Pandora stations featuring the song.
• Soundcloud Streams: Total number of streams on Soundcloud.
• Shazam Counts: Total number of times the song has been Shazamed.
• TIDAL Popularity: Popularity score of the song on TIDAL.
• Explicit Track: Indicates whether the song contains explicit content.

Potential Use Cases

• Music Analysis: Analyze trends in audio features to understand popular song characteristics.
• Platform Comparison: Compare song popularity across different music platforms.
• Artist Impact: Study the relationship between artist attributes and song success.
• Temporal Trends: Identify changes in music attributes and preferences over time.
• Cross-Platform Presence: Investigate song performance across various streaming services.

Your support through an upvote would be greatly appreciated if you find this dataset useful! ❤️🙂 Thank you.

In this blog are the latest Spotify statistics that paint a picture of how the company has succeeded so far and what’s likely to happen in the future.

Context

This dataset contains audio statistics of the top 2000 tracks on Spotify. The data contains about 15 columns each describing the track and it's qualities. Songs released from 1956 to 2019 are included from some notable and famous artists like Queen, The Beatles, Guns N' Roses, etc. http://sortyourmusic.playlistmachinery.com/ by @plamere uses Spotify API to extract the audio features from the tracks given the Spotify Playlist URI. This data contains audio features like Danceability, BPM, Liveness, Valence(Positivity) and many more. Each feature's description has been given in detail below.

Content

• Index: ID
• Title: Name of the Track
• Artist: Name of the Artist
• Top Genre: Genre of the track
• Year: Release Year of the track
• Beats per Minute(BPM): The tempo of the song
• Energy: The energy of a song - the higher the value, the more energtic. song
• Danceability: The higher the value, the easier it is to dance to this song.
• Loudness: The higher the value, the louder the song.
• Valence: The higher the value, the more positive mood for the song.
• Length: The duration of the song.
• Acoustic: The higher the value the more acoustic the song is.
• Speechiness: The higher the value the more spoken words the song contains
• Popularity: The higher the value the more popular the song is.

Acknowledgements

This data is extracted from the Spotify playlist - Top 2000s on PlaylistMachinery(@plamere) using Selenium with Python. More specifically, it was scraped from http://sortyourmusic.playlistmachinery.com/. Thanks to Paul for providing a free and open source to extract features and do cool stuff with your Spotify playlists!

Inspiration

This is a very fun dataset to explore and find out unique links which land songs in the Top 2000s. With this dataset, I wanted to be able to answer some questions like:

1. Which genres were more popular coming through 1950s to 2000s?
2. Songs of which genre mostly saw themselves landing in the Top 2000s?
3. Which artists were more likely to make a top song?
4. Songs containing which words are more popular?
5. What is the average tempo of songs compared over the years?
6. Is there a trend of acoustic songs being popular back in 1960s than they are now?
7. Is there a trend in genres preferred back in the day vs now? ... and a lot more.

North American Spotify users spend the most time on the platform steaming an average of 140 minutes of content on the Spotify app daily.

Spotify Technology S.A. (Spotify) is a digital music streaming service provider that gives access to songs across various devices including mobiles, tablets, computers, game consoles, and home entertainment systems Read More

Analysis of ‘Spotify Past Decades Songs Attributes’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/cnic92/spotify-past-decades-songs-50s10s on 28 January 2022.

--- Dataset description provided by original source is as follows ---

Context

Why do we like some songs more than others? Is there something about a song that pleases out subconscious, making us listening to it on repeat? To understand this I collected various attributes from a selection of songs available in the Spotify's playlist "All out ..s" starting from the 50s up to the newly ended 10s. Can you find the secret sauce to make a song popular?

Content

This data repo contains 7 datasets (.csv files), each representing a Spotify's "All out ..s" type of playlist. Those playlists collect the most popular/iconic songs from the decade. For each song, a set of attributes have been reported in order to perform some data analysis. The attributes have been scraped from this amazing website. In particular, according to the website the attributes are:

• top genre: genre of the song
• year: year of the song (due to re-releases, the year might not correspond to the release year of the original song)
• bpm(beats per minute): beats per minute
• nrgy(energy): energy of a song, the higher the value the more energetic the song is
• dnce(danceability): the higher the value, the easier it is to dance to this song.
• dB(loudness): the higher the value, the louder the song.
• live(liveness): the higher the value, the more likely the song is a live recording.
• val(valence): the higher the value, the more positive mood for the song.
• dur(duration): the duration of the song.
• acous(acousticness): the higher the value the more acoustic the song is.
• spch(speechiness): the higher the value the more spoken word the song contains.
• pop(popularity): the higher the value the more popular the song is.

Acknowledgements

I got inspired by the top-notch work by Leonardo Henrique in this dataset. Thanks to him I discovered this website, from which all the data collected here have been scraped.

--- Original source retains full ownership of the source dataset ---

This dataset presents the top songs currently trending for over 70 countries.

Top 50 songs for each country is updated daily to provide the most up-to-date information on the popularity of songs in the world.

If you find this dataset helpful, don't forget to leave a upvote ❤️🎧

Interesting Task Ideas:

1. Identify the most popular genres of music in different countries over time.
2. Analyze the change in rankings of songs over time to identify trends and patterns.
3. Investigate whether there is a correlation between song popularity and its danceability or energy level.
4. Explore the relationship between the explicitness of songs and their popularity.
5. Analyze the relationship between song features (such as acousticness or instrumentalness) and their popularity.
6. Predict the future popularity of songs based on historical data and machine learning algorithms.
7. Compare the top songs in different countries to identify cultural music preferences.

NOTES

• Data may be missing / incomplete from 2024-11-27 to 2024-12-09 due to Spotify API changes.

Here are the full list of the 10 most popular artists on Spotify and how many total song streams they have.

Overview The dataset is built by exploiting the Spotify and SoundCloud APIs. It is composed of over 14,500 different songs of both famous and less famous Italian musicians. Each song in the dataset is identified by its Spotify id and its title. Tracks' metadata include also lemmatized and POS-tagged lyrics and, in the most of cases, ten musical features directly gathered from Spotify. Musical features include acousticness (float), danceability (float), duration_ms (int), energy (float), instrumentalness (float), liveness (float), loudness (float), speechiness (float), tempo (float) and valence (float). All features range from 0.0 to 1.0 except for loudness that typically ranges between -60 and 0 db, the tempo that represents beats per minute (BPM) and the duration that represents the track in milliseconds. For further information refer to the Spotify's documentation at Spotify Documentation For further information regarding the dataset and the related project visit SoBigData Catalogue {"references": [""The Italian Music Superdiversity. Geography, Emotion and Language: one resource to find them, one resource to rule them all.", L. Pollacci, R. Guidotti, G. Rossetti, F.Giannotti, D. Pedreschi, In Multimedia Tools and Applications."]}

How many paid subscribers does Spotify have? As of the first quarter of 2025, Spotify had 268 million premium subscribers worldwide, up from 239 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 catalogue of music licensed through multiple record labels, as well as creating and sharing 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 are also able to 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.

Analysis of ‘The Weeknd's Full Discography Dataset’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/paulbaek/theweeknddiscography on 13 February 2022.

--- Dataset description provided by original source is as follows ---

Context

As one of The Weeknd's diehard fans, information about all songs from all albums released over the last decade is extracted from Spotify API for non-commercial, educational purposes.

Content

Data from all tracks in all the albums released up to "Dawn FM" (2022). The dataset excludes tracks featuring the artist and live versions. Spotify provides data on the features of a song, such as mood, properties and more. To find out more, please visit: https://developer.spotify.com/discover/

Acknowledgements

Huge thanks to Steven Morse for providing instructions and Python codes to make this happen. Please visit https://stmorse.github.io/journal/spotify-api.html for full instructions on extracting data from Spotify using Python.

Inspiration

I created the dataset to conduct analysis for educational purposes. It's only fitting to give back love to the world - much like the artist has done for us. #XOTWOD

--- Original source retains full ownership of the source dataset ---

The latest Spotify statistics from the company’s annual report show that 69% of Spotify premium subscribers are located in Europe and North America.

In 2023, Spotify's sales and marketing costs amounted to around *** billion euros, a ***** percent decrease from the previous year. According to the company, the decrease was primarily to a decrease in legal fees of ** million euros, a decrease in share-based compensation of ** million, and a decrease in other administrative costs of ** million. Spotify in numbers Spotify is a Swedish audio streaming and media services provider founded in 2006. While the platform remains the undisputed champion of the European streaming landscape, Spotify’s popularity and influence also extend far beyond the continent. Data shows that Spotify outpaces competitors such as Apple Music and Pandora in the running for the leading music streaming service in the United States. Additionally, the global number of Spotify Premium subscribers reached a record *** million in late 2022. While Spotify’s revenue also peaked at **** billion euros in 2022, its operating loss has not failed to make headlines either. Branching out into the world of podcasts While Spotify’s extensive music catalog draws millions of new listeners each year, the platform’s appeal also stems from its commitment to podcasts and other digital audio formats. In 2020, Spotify overtook Apple Podcasts as the most popular podcast app in the United States. And while podcast fans can listen to a multitude of titles from various genres that Spotify has acquired over the years, they can also choose from an ever-expanding catalog of Spotify original podcasts. One of the most successful but perhaps equally as controversial titles in Spotify’s repertoire is the Joe Rogan Experience. Despite coming under fire in 2022 for allegedly spreading COVID-19 misinformation, Joe Rogan’s show remains one of Spotify’s most lucrative deals and biggest moneymakers in terms of ad sales.