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.

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

💁‍♀️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...

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

We released a dataset of one million user-created playlists from the Spotify platform, dubbed the Million Playlist Dataset (MPD). The dataset includes, for each playlist, its title as well as the list of tracks (including album and artist names), and some additional metadata such as Spotify URIs and the playlist's number of followers. 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.

Estimates suggest that Apple Music had 95 million subscribers worldwide in June 2024, up by 2 million from the previous year. Launched in 2015 by U.S. tech giant Apple, Apple Music is the second largest music streaming service worldwide, competing with market leader Spotify. Spotify remains market leader While Apple Music is a popular music streaming platform, accounting for 12.6 percent of subscribers worldwide, the 2008 founded streaming service Spotify remains the market leader with a subscriber share of nearly 32 percent. Financially this meant that the Swedish company generated a global revenue of 3.7 billion euros through its Premium accounts in the fourth quarter of 2024 alone.Music streaming overall increasesOverall, music streaming has experienced significant growth over the last decade. Even if the annual growth rate is gradually declining, it still stood at over 7 percent in 2024, becoming the music industry’s main revenue driver and reaching a revenue of 20 billion U.S. dollars worldwide in 2024.

Bulgarian popfolk as a phenomenon

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F4937078%2F2de9bee900e6599f080c396be3659cb4%2Fthe-face.jpg?generation=1590504436110094&alt=media%20=400x800" alt="People celebrating Students Day in Bulgarian chalga club The Face in Blagoevgrad." title="People celebrating Students Day in Bulgarian chalga club The Face in Blagoevgrad.">
Bulgarian pop-folk (hereinafter referred to as chalga) is a dance genre, stemming from ethno-pop, with strong hints of Oriental rhythms and instrumentals. Chalga is one of many branches of Balkan folk throughout the peninsula (turbofolk in Serbia, manele in Romania etc.) After the fall of communism in 1989 in Central and Eastern Europe, chalga rapidly found place in everyday life.
Chalga relies on provocativity, and tracks commonly contain sexually explicit lyrics. Because of this, it causes much controversy in society and there is sparse scientific work in the field. Nevertheless, chalga becomes an increasingly popular musical style. As such, we believe it must be subject to development. Finding its 'evolution' constitutes the main scientific motivation behind this study.

Choice of data

Payner LTD is a Bulgarian record label and production studio, founded in 1990. It is currently considered the largest record label in the country, producing mainly in both Bulgarian folk and chalga genres. The company has active presence in television, taking ownership of three channels: 'Planeta TV', 'Planeta Folk' and 'Planeta HD'.
Payner LTD also maintains activity in the Internet, particularly in YouTube. Their main channel in YouTube, 'PlanetaOfficial', publishes music content exclusively. 'PlanetaOfficial' can be also credited with holding the largest audience in Bulgaria - for the time being, it has got 2.1 million subscribers and 5.0 billion total video views, dominating on the national YouTube scene.
The top three YouTube accounts in Bulgaria, associated with chalga music, as of 4 Jan 2021, are: - PlanetaOfficial (Payner LTD), 2.12m subscribers, 4962m total views, - FEN TV, 0.76m subscribers, 699m total views, - Diapason Records, 0.53m subscribers, 580m total views
In all of those circumstances, 'PlanetaOfficial' was recognised as a pivotal source of data in the study.

Method

Data were acquired from MILKER, software specifically designed for this purpose.

Data

Content

The following data in payner.csv contains Spotify information of 679 resolved tracks, out of 638 detected in PlanetaOfficial, in the period 2014-2020. Every row is a track, and contains: - the unique Spotify ID of the song; - pre-processed names of the first three artists in a song (if such are present), according to their order of mention; - name of the track; - datetime of the video upload in PlanetaOfficial; - various Spotify audio features.

Purity

There are no missing values in this data. However, [MILKER] is not flawless. It includes tracks, not associated with PlanetaOfficial - for example works by Bach and Beethoven. In this context, data purity is defined as the fraction of songs with corresponding video uploads by PlanetaOfficial.

After random sampling (n=100), it may be inferred that the purity of this dataset is (0.91, 0.99), 95% C.I.

Context

Gathered this dataset as part of my work for the Information Retrieval and Text Mining course at the Faculty of Mathematics and Computer Science, University of Bucharest.

Content

The data is composed of four sources. The initial data was forwarded from Sparktech's 2018 Textract Hackathon. This was enhanced with data from other three kaggle datasets: 150K Lyrics Labeled with Spotify Valence, dataset lyrics musics and AZLyrics song lyrics.

Apart from the original Sparktech data, the other datasets did not provide a Genre feature. In order to deal with the lack of Genre labeling , I have built a labeling function using the spotipy library, which uses the Spotify API in order to retrieve the genre of an Artist. Please note that the Spotify API returns a list of genres for one artist, so I considered the most common genre to be said artists dominant genre.

Aditionally, the AZLyrics data was badly encoded, namely the column delimiter character, the comma, was also used as a verse delimiter in the Lyrics column. Fortunately, the dataset comes with two URL columns that conveniently separate the Artist, Song and Lyrics columns, so with a bit of regex magic I was able to extract the useful data using https:// as a delimiter.

On a last note, I used Nakatani Shuyo's langdetect library to automatically label the lyrics with a language. In total, the lyrics come in 34 languages.

Acknowledgements

I am greatful to the kaggle users edenbd, Italo Marcelo and Albert Suarez, as well as the Sparktech team who gathered the original data and to my professor who provided it for the project.

Inspiration

In case you stumble across this dataset in the wild, I encourage you to try the Genre classification task on it and different feature engineering approaches. I am excited to see how inventive you can get!

The most successful music streaming service in the United States was Apple Music as of September, with the most up to date information showing that 49.5 million users accessed the platform each month. Spotify closely followed, with a similarly impressive 47.7 million monthly users.

What is a music streaming service?

Music streaming services provide their users with a database compiled of songs, playlists, albums and videos, where content can be accessed online, downloaded, shared, bookmarked and organized.

The music streaming business is huge, and has sometimes been lauded as the savior of the music industry. The biggest two services are in constant competition for the monopoly of the market. Apple Music was launched in 2015, whereas Spotify has been around since 2008. Other popular streaming services include Deezer, SoundCloud and iHeartRadio.

Do artists make a lot of money from streaming services? 

In short, unfortunately not. Both Apple Music and Spotify have been frequently criticized for the tiny royalty payments they offer artists. Particularly for emerging talent, streaming services are far from a lucrative source of income. Bigger, established stars like Taylor Swift are more likely to regularly make a good amount of money this way. But either way, a track needs to go viral or be streamed several million times before it earns any real cash.