How many paid subscribers does Spotify have? As of the fourth quarter of 2024, Spotify had 263 million premium subscribers worldwide, up from 236 million in the corresponding quarter of 2023. 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.

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.

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.

34% of Spotify’s monthly active users live in Europe. That means that Spotify has 147.22 million users in the EU regions alone. Here’s the breakdown of regions that contribute the most users to Spotify:

Spotify Million Playlist Dataset Challenge

Summary

The Spotify Million Playlist Dataset Challenge consists of a dataset and evaluation to enable research in music recommendations. It is a continuation of the RecSys Challenge 2018, which ran from January to July 2018. The dataset contains 1,000,000 playlists, including playlist titles and track titles, created by users on the Spotify platform between January 2010 and October 2017. The evaluation task is automatic playlist continuation: given a seed playlist title and/or initial set of tracks in a playlist, to predict the subsequent tracks in that playlist. This is an open-ended challenge intended to encourage research in music recommendations, and no prizes will be awarded (other than bragging rights).

Background

Playlists like Today’s Top Hits and RapCaviar have millions of loyal followers, while Discover Weekly and Daily Mix are just a couple of our personalized playlists made especially to match your unique musical tastes.

Our users love playlists too. In fact, the Digital Music Alliance, in their 2018 Annual Music Report, state that 54% of consumers say that playlists are replacing albums in their listening habits.

But our users don’t love just listening to playlists, they also love creating them. To date, over 4 billion playlists have been created and shared by Spotify users. People create playlists for all sorts of reasons: some playlists group together music categorically (e.g., by genre, artist, year, or city), by mood, theme, or occasion (e.g., romantic, sad, holiday), or for a particular purpose (e.g., focus, workout). Some playlists are even made to land a dream job, or to send a message to someone special.

The other thing we love here at Spotify is playlist research. By learning from the playlists that people create, we can learn all sorts of things about the deep relationship between people and music. Why do certain songs go together? What is the difference between “Beach Vibes” and “Forest Vibes”? And what words do people use to describe which playlists?

By learning more about nature of playlists, we may also be able to suggest other tracks that a listener would enjoy in the context of a given playlist. This can make playlist creation easier, and ultimately help people find more of the music they love.

Dataset

To enable this type of research at scale, in 2018 we sponsored the RecSys Challenge 2018, which introduced the Million Playlist Dataset (MPD) to the research community. Sampled from the over 4 billion public playlists on Spotify, this dataset of 1 million playlists consist of over 2 million unique tracks by nearly 300,000 artists, and represents the largest public dataset of music playlists in the world. The dataset includes public playlists created by US Spotify users between January 2010 and November 2017. The challenge ran from January to July 2018, and received 1,467 submissions from 410 teams. A summary of the challenge and the top scoring submissions was published in the ACM Transactions on Intelligent Systems and Technology.

In September 2020, we re-released the dataset as an open-ended challenge on AIcrowd.com. The dataset can now be downloaded by registered participants from the Resources page.

Each playlist in the MPD contains a playlist title, the track list (including track IDs and metadata), and other metadata fields (last edit time, number of playlist edits, and more). All data is anonymized to protect user privacy. Playlists are sampled with some randomization, are manually filtered for playlist quality and to remove offensive content, and have some dithering and fictitious tracks added to them. As such, the dataset is not representative of the true distribution of playlists on the Spotify platform, and must not be interpreted as such in any research or analysis performed on the dataset.

Dataset Contains

1000 examples of each scenario:

Title only (no tracks) Title and first track Title and first 5 tracks First 5 tracks only Title and first 10 tracks First 10 tracks only Title and first 25 tracks Title and 25 random tracks Title and first 100 tracks Title and 100 random tracks

Download Link

Full Details: https://www.aicrowd.com/challenges/spotify-million-playlist-dataset-challenge Download Link: https://www.aicrowd.com/challenges/spotify-million-playlist-dataset-challenge/dataset_files

In the fourth quarter of 2024, the music streaming service Spotify reached an all-time high with 675 million active users worldwide. This marked an increase of around 12 percent in just one year. What is Spotify? Spotify is a music streaming service that offers digital audio content. Basic audio content can be accessed for free whereas premium user subscriptions enable users to access offline mobile content as well as listen to music without advertising. In the fourth quarter of 2024, the company reported 263 million paying subscribers. Launched in 2008, Spotify originated in Sweden before expanding to European markets and the United States in 2011. Spotify’s U.S. launch was strongly marketed through Facebook, with the music streaming app profiting from the social listening integration via social media. Part of Spotify’s appeal can be attributed to the user- and brand-curated playlists, which can be shared publicly or between friends. Fans may choose what to listen to based on their current mood or preference, and the ability to share such content provides an element of social connectivity ordinarily reserved for networking sites.

As of January 2025, Spotify has over 640 million monthly active users. Here is the full breakdown of Spotify users by year since 2015:

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

Paying subscribers account for about half of Spotify’s monthly active users. This is the number of paying subscribers by year that Spotify has had since 2015.

On October 22, 2024, 'APT.' by ROSÉ and Bruno Mars was the most-streamed track on Spotify with 14.6 million streams worldwide, followed by 'Die With A Smile" by Lady Gaga and Bruno Mars, reaching over 11 million Spotify streams on Spotify that day. Billie Eilish's 'BIRDS OF A FEATHER' came third with just over 7.6 million streams. How do music artists get so many streams on Spotify? Firstly, Spotify is one of the most successful and popular music streaming services in the United States, and as of the first half of 2018 had the biggest share of music streaming subscribers in the world. With Spotify’s vast audience, featuring on the platform is a good start for emerging and popular artists hoping to make an impact. Secondly, there is no exact science to ‘going viral’. From the famous egg photo on Instagram posted in early 2019 to wildly successful music video ‘Gangnam Style’ released back in 2012, viral content comes in all shapes and sizes. Purposeful viral marketing is one way in which something could go viral, and is one of the reasons why some songs have so many streams in a short space of time. This type of marketing involves a tactical approach and pre-planning in an attempt to push the content into the public eye and encourage it to spread as quickly as possible. However, many artists who go viral do not expect to. Accessible, catchy content created by an already popular artist is already poised to do well, i.e. the latest song or album from U.S. singer Drake. This is an example of incidental viral marketing, when content spreads by itself partially as a result of an established and engaged audience. Indeed, Spotify’s most-streamed tracks generally originate from a well-known figure with a large following. But for smaller or entirely unknown content creators, going viral or experiencing their 15 minutes of fame can simply be a case of posting the right thing at the right time.

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

MGD: Music Genre Dataset

Over recent years, the world has seen a dramatic change in the way people consume music, moving from physical records to streaming services. Since 2017, such services have become the main source of revenue within the global recorded music market. Therefore, this dataset is built by using data from Spotify. It provides a weekly chart of the 200 most streamed songs for each country and territory it is present, as well as an aggregated global chart.

Considering that countries behave differently when it comes to musical tastes, we use chart data from global and regional markets from January 2017 to December 2019, considering eight of the top 10 music markets according to IFPI: United States (1st), Japan (2nd), United Kingdom (3rd), Germany (4th), France (5th), Canada (8th), Australia (9th), and Brazil (10th).

We also provide information about the hit songs and artists present in the charts, such as all collaborating artists within a song (since the charts only provide the main ones) and their respective genres, which is the core of this work. MGD also provides data about musical collaboration, as we build collaboration networks based on artist partnerships in hit songs. Therefore, this dataset contains:

Genre Networks: Success-based genre collaboration networks

Genre Mapping: Genre mapping from Spotify genres to super-genres

Artist Networks: Success-based artist collaboration networks

Artists: Some artist data

Hit Songs: Hit Song data and features

Charts: Enhanced data from Spotify Weekly Top 200 Charts

This dataset was originally built for a conference paper at ISMIR 2020. If you make use of the dataset, please also cite the following paper:

Gabriel P. Oliveira, Mariana O. Silva, Danilo B. Seufitelli, Anisio Lacerda, and Mirella M. Moro. Detecting Collaboration Profiles in Success-based Music Genre Networks. In Proceedings of the 21st International Society for Music Information Retrieval Conference (ISMIR 2020), 2020.

@inproceedings{ismir/OliveiraSSLM20, title = {Detecting Collaboration Profiles in Success-based Music Genre Networks}, author = {Gabriel P. Oliveira and Mariana O. Silva and Danilo B. Seufitelli and Anisio Lacerda and Mirella M. Moro}, booktitle = {21st International Society for Music Information Retrieval Conference} pages = {726--732}, year = {2020} }

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.

👍 If this dataset was useful to you, leave your vote at the top of the page 👍

The dataset provides information on the daily top 200 tracks listened to by users of the Spotify digital platform around the world.

I put together this dataset because I really love music (I listen to it for several hours a day) and have not found a similar dataset with track genres on kaggle.

The dataset can be useful for beginners in the field of working with data. It contains missing values, arrays in columns, and so on, which can be great practice when conducting an EDA phase.

Soon, my example will appear here as possible, based on the specified dataset, go on a musical journey around the world and understand how the musical tastes of humanity have changed around the world)))

In addition, I will be very happy to see the work of the community on this dataset.

Also, in case of interest in data by country, I am ready to place it upon request.

You can contact me through: telegram @natarov_ivan

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   |

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

Despite not being very profitable, Spotify has maintained strong subscriber and revenue growth. Here are the key Spotify Statistics you need to know.

Analysis of ‘K-Pop Hits Through The Years’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/sberj127/kpop-hits-through-the-years on 28 January 2022.

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

What is the data?

The datasets contain the top songs from the said era or year accordingly (as presented in the name of each dataset). Note that only the KPopHits90s dataset represents an era (1989-2001). Although there is a lack of easily available and reliable sources to show the actual K-Pop hits per year during the 90s, this era was still included as this time period was when the first generation of K-Pop stars appeared. Each of the other datasets represent a specific year after the 90s.

How was it obtained?

A song is considered to be a K-Pop hit during that era or year if it is included in the annual series of K-Pop Hits playlists, which is created officially by Apple Music. Note that for the dataset that represents the 90s, the playlist 90s K-Pop Essentials was used as the reference.

1. These playlists were transferred into Spotify through the Tune My Music site. After transferring, the site also presented all the missing songs from each Spotify playlist when compared to the original Apple Music playlists.
   • Any data besides the names and artists of the hit songs were not directly obtained from Apple Music since these other details of songs in this music service are only available for those enrolled as members of the Apple Developer Program.
2. The presented missing songs from each playlist was manually searched and, if found, added to the respective Spotify playlist.
   • For the songs that were found, there are three types: (1) the song by the original artist, (2) the instrumental of the original song and (3) a cover of the song. When the first type is not found, the two other types are searched and are compared to each other. The one that sounded the most like the original song (from the Apple Music playlist) is chosen as the substitute in the Spotify playlist.
   • Presented is a link containing all the missing data per playlist (when the initial Spotify playlists were compared to the original Apple Music playlists) and the action done to each one.
3. The necessary identification details and specific audio features of each track were obtained through the use of the Spotipy library and Spotify Web API documentation.

Why did you make this?

As someone who has a particular curiosity to the field of data science and a genuine love for the musicality in the K-Pop scene, this data set was created to make something out of the strong interest I have for these separate subjects.

Acknowledgements

I would like to express my sincere gratitude to Apple Music for creating the annual K-Pop playlists, Spotify for making their API very accessible, Spotipy for making it easier to get the desired data from the Spotify Web API, Tune My Music for automating the process of transferring one's library into another service's library and, of course, all those involved in the making of these songs and artists included in these datasets for creating such high quality music and concepts digestible even for the general public.

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

My Sirius XM now playing tracker has been running for six years without any major interruptions. I have gathered all of the data collected to date and am open sourcing the dataset for anyone to use.

The tracker has been recording data for The Highway country and The Heat hip-hop stations.

Dataset Details

Total Entries

The Heat Total: 643472

The Highway Total: 604861

Entry Date Ranges

The Heat First: Mon Jun 20 2016 18:49:38 (GMT)

The Heat Last: Fri Jul 15 2022 18:08:38 (GMT)

The Highway First: Tue Nov 29 2016 00:03:40 (GMT)

The Highway Last: Fri Jul 15 2022 18:11:09 (GMT)

Fields

artist -- Artist name

title -- Song title

start_time -- Song start timestamp in UNIX epoch (GMT)

spotify -- Matched song spotify metadata (may be unavailable)

spotify.artist -- Artist name

spotify.title -- Song title

spotify.url -- Spotify song URL

spotify.uri -- Spotify song URI

spotify.album -- Album title

spotify.album_image -- Album image URL

Analysis of ‘Spotify Top 200 Charts (2020-2021)’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/sashankpillai/spotify-top-200-charts-20202021 on 13 February 2022.

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

The dataset include all the songs that have been on the Top 200 Weekly (Global) charts of Spotify in 2020 & 2021. The dataset include the following features:

Highest Charting Position: The highest position that the song has been on in the Spotify Top 200 Weekly Global Charts in 2020 & 2021. Number of Times Charted: The number of times that the song has been on in the Spotify Top 200 Weekly Global Charts in 2020 & 2021. Week of Highest Charting: The week when the song had the Highest Position in the Spotify Top 200 Weekly Global Charts in 2020 & 2021. Song Name: Name of the song that has been on in the Spotify Top 200 Weekly Global Charts in 2020 & 2021. Song iD: The song ID provided by Spotify (unique to each song). Streams: Approximate number of streams the song has. Artist: The main artist/ artists involved in making the song. Artist Followers: The number of followers the main artist has on Spotify. Genre: The genres the song belongs to. Release Date: The initial date that the song was released. Weeks Charted: The weeks that the song has been on in the Spotify Top 200 Weekly Global Charts in 2020 & 2021. Popularity:The popularity of the track. The value will be between 0 and 100, with 100 being the most popular. 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 measure from 0.0 to 1.0 of whether the track is acoustic. 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. The closer the instrumentalness value is to 1.0, the greater likelihood the track contains no vocal content. Liveness: Detects the presence of an audience in the recording. Higher liveness values represent an increased probability that the track was performed live. Loudness: The overall loudness of a track in decibels (dB). Loudness values are averaged across the entire track. Values typical range between -60 and 0 db. 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. 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). Chord: The main chord of the song instrumental.

Acknowledgements- This dataset would not be possible without the help of spotifycharts.com and Spotipy Python Library

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