In 2008, a small startup in Sweden set out to tackle a big problem, how to give people easy access to music without turning to piracy. That company was Spotify. Fast forward to 2025, and Spotify isn't just a music platform, it’s a cultural cornerstone. Whether you’re discovering indie artists,...

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

A dataset covering Spotify usage and artist performance in 2025, including metrics like monthly active users, premium subscriber counts, demographic breakdowns, and playlist analytics.

As of March 2018, Spotify’s user base was dominated by Millennials, with ** percent of its users aged 25 to 34 and ** percent aged between 18 and 24 years old. The streaming giant has permanently altered how consumers discover, engage with and share music, and according to a 2018 survey, Spotify reaches almost **** of 16 to 24 year olds in the United States each week. The power of SpotifySpotify’s popularity is undeniable, accumulating millions of premium subscribers worldwide each quarter and hundreds of millions of unique visitors to Spotify.com every month. In the United States, Spotify is one of the most commonly used apps for listening to podcasts, and despite being in constant competition with Apple Music, remains a large part of U.S. music listeners’ lives. A survey revealed that Spotify is also the preferred music streaming service among 18 to 29-year-olds, which may seem unremarkable given the data on Spotify’s user base, but serves as further evidence of Spotify’s popularity among younger users. Whether Spotify’s growth will last forever, only time will tell, particularly as Apple Music continues to put up a good fight and smaller but increasingly popular services such as Deezer begin to make their mark. But with the company recording a profit in early 2019 for the first time since its inception, Spotify remains very much a market leader and firmly on the path to future success.

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.

It started with a pair of headphones and a curiosity, what's this song playing? In coffee shops, dorm rooms, and crowded subway rides, Spotify quietly transformed how we consume music. Over the years, it’s become more than just a streaming app. It’s a personal DJ, a data mirror of our...

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.

As of the first quarter of 2025, Europe accounted for ** percent of Spotify monthly active users. The popular Swedish streaming service had a strong user base in Latin America, accounting for ** percent of Spotify's total *** million MAUs at that time.
Spotify Since its launch in 2008, Spotify has grown into the most widely used music streaming platform in the world, controlling over a ***** of the industry’s global market share. Despite being in direct competition with some of the biggest names in the tech industry, the company has managed to accumulate over *********** million paying subscribers and millions more free and ad-supported users. Spotify has ensured that this massive userbase has led to increasing financial success. The company reported an annual revenue of around *** billion euros in 2018, outpacing its 2017 figure by well over a ******* dollars. Music streaming Music has always been an important form of human expression and entertainment, but never before has it been so easily accessible. Thanks to the growing popularity of smartphones and streaming services, hundreds of millions of people around the world have access to an almost unlimited library of music at the touch of a button. As of 2024, streaming accounted for ** percent of the total music industry revenue in the United States, up from ** percent six years earlier. This highlights the massive influence of companies like Spotify on the music industry as a whole.

💁‍♀️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...

Music streaming has its roots in the peer-to-peer file sharing industry. Napster, Limewire and BitTorrent were at the forefront of changing the way people thought about music, and while they only...

According to a survey about the music streaming industry in the Middle East and North Africa (MENA) region in the first half of 2020, ** percent of respondents in Saudi Arabia were aware of Spotify as a music streaming platform. In comparison, **** percent of respondents were loyal to the brand.

This dataset was created using Spotify developer API. It consists of user-created as well as Spotify-curated playlists. The dataset consists of 1 million playlists, 3 million unique tracks, 3 million unique albums, and 1.3 million artists. The data is stored in a SQL database, with the primary entities being songs, albums, artists, and playlists. Each of the aforementioned entities are represented by unique IDs (Spotify URI). Data is stored into following tables:

album

artist

track

playlist

track_artist1

track_playlist1

album

| id | name | uri |

id: Album ID as provided by Spotify name: Album Name as provided by Spotify uri: Album URI as provided by Spotify

artist

| id | name | uri |

id: Artist ID as provided by Spotify name: Artist Name as provided by Spotify uri: Artist URI as provided by Spotify

track

| id | name | duration | popularity | explicit | preview_url | uri | album_id |

id: Track ID as provided by Spotify name: Track Name as provided by Spotify duration: Track Duration (in milliseconds) as provided by Spotify popularity: Track Popularity as provided by Spotify explicit: Whether the track has explicit lyrics or not. (true or false) preview_url: A link to a 30 second preview (MP3 format) of the track. Can be null uri: Track Uri as provided by Spotify album_id: Album Id to which the track belongs

playlist

| id | name | followers | uri | total_tracks |

id: Playlist ID as provided by Spotify name: Playlist Name as provided by Spotify followers: Playlist Followers as provided by Spotify uri: Playlist Uri as provided by Spotify total_tracks: Total number of tracks in the playlist.

track_artist1

| track_id | artist_id |

Track-Artist association table

track_playlist1

| track_id | playlist_id |

Track-Playlist association table

• - - - - SETUP - - - - -

The data is in the form of a SQL dump. The download size is about 10 GB, and the database populated from it comes out to about 35GB.

spotifydbdumpschemashare.sql contains the schema for the database (for reference): spotifydbdumpshare.sql is the actual data dump.

Setup steps: 1. Create database 2. mysql -u -p < spotifydbdumpshare.sql

• - - - - PAPER - - - - -

The description of this dataset can be found in the following paper:

Papreja P., Venkateswara H., Panchanathan S. (2020) Representation, Exploration and Recommendation of Playlists. In: Cellier P., Driessens K. (eds) Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2019. Communications in Computer and Information Science, vol 1168. Springer, Cham

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.

In the second quarter of 2025, Spotify reported having ** percent of their total monthly active users (MAUs) located in Latin America, which translates to around ****** million MAUs in the region, up from around ****** million a year earlier – an annual increase of *** percent. Overall, the region accounts for over one-fifth of Spotify’s MAUs worldwide.

Introduction

Music streaming statistics: To put it simply, music streaming is one of the most popular industries today, with an amazing 713.4 million paid subscribers globally and a market generating over $28.6 billion in recorded music revenue. Streaming has fundamentally changed how we discover, consume, and interact with different songs and music. It now accounts for over 67% of the entire music industry's earnings, a figure that has multiplied more than 15 times over the past decade.

Leading the charge are giants like Spotify, which boasts over 626 million total users, and YouTube Music, which leverages a user base of over 2 billion (with YouTube). The overall market for music streaming apps was valued at $49.5 billion in 2025 and is on an aggressive upward trajectory, with forecasts predicting it will smash the $100 billion mark by 2030. Countries like Nigeria are at the forefront of this digital wave, with an incredible 91% of its population engaging with digital music. From Gen Z's listening habits to the per-stream payout rates for artists, the numbers tell a fascinating story of this in progress.

So, let's dive straight into the most comprehensive collection of music streaming statistics 2025. If you are a music artist, a developer, or a business person, this will help you out. Let’s get into it.

The statistic presents data on the frequency of Spotify Premium usage in the United States as of 2017, sorted by age group. During a survey, **** percent of respondents aged 45 to 54 stated that they listened to Spotify Premium about once a day.

1) Data Introduction • The Spotify Tracks Dataset contains information on tracks from over 125 music genres, including both audio features (e.g., danceability, energy, valence) and metadata (e.g., title, artist, genre).

2) Data Utilization (1) Characteristics of the Spotify Tracks Dataset: • The data is structured in a tabular format at the track level, where each column represents numerical or categorical features based on musical properties. This makes it suitable for recommendation systems, genre classification, and emotion analysis. • It includes multi-dimensional attributes grounded in music theory such as track duration, time signature, energy, loudness, tempo, and speechiness—enabling its use in music classification and clustering tasks.

(2) Applications of the Spotify Tracks Dataset: • Design of Music Recommendation Systems: It can be used to build content-based filtering systems or hybrid recommendation algorithms based on user preferences.

In the second quarter of 2025, Spotify had over ** million premium subscribers in Latin America, up from ***** million in the second quarter of 2024 – an annual increase of about ** percent. Meanwhile, the number of Spotify monthly active users (MAUs) in Latin America grew by about *****percent in the second quarter of 2025 compared to the second quarter of 2024. Brazil and Mexico spend the most time streaming music Recent data shows that internet users in Mexico and Brazil devote more than * hours of their daily time to streaming music. However, while Brazil experienced a year-on-year growth of * percent in this regard, in Mexico, music streaming time declined by a fraction of a percentage point in the same period and by close to * percent since 2020. It is yet too early to say whether this trend will be a lasting one. Spotify still the top streaming choice What is certain is that music listeners in both countries rely heavily on Spotify. The company’s dominance is especially prominent in Mexico, where more than ************** of music streamers subscribe to the service. Interestingly though, Spotify has lost some ground in favor of other services such as YouTube Music, Apple Music, and Amazon Prime Music since 2021. In Brazil, Spotify is the leading streaming service, however, the market there is more varied. Here, the difference in usage between Spotify and its competitors such as YouTube Premium, Deezer and Amazon Music is not so pronounced, indicating perhaps an audience more open to less obvious players.

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

About the Dataset

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

Exploratory Data Analysis (EDA)

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

Further Investigation and Inference (FII)

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

Project Directory Guide

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

Project Notes

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

Credits to gabminamedez for the original dataset.

The music streaming market refers to the digital distribution of music content, enabling users to access and listen to songs, albums, and playlists on demand via the internet, without the need for physical media. This market has evolved with the proliferation of mobile devices, high-speed internet, and cloud-based services, making it easier for consumers to enjoy music anytime, anywhere. Music streaming platforms such as Spotify, Apple Music, YouTube Music, and Amazon Music dominate the industry, providing users with subscription-based services, freemium models, and ad-supported options. The rise of artificial intelligence and data analytics also plays a significant role in music streaming, offering personalized recommendations and curated playlists based on user preferences and listening habits. Music streaming is revolutionizing the music industry by reducing piracy, offering a wider variety of music, and providing revenue-sharing opportunities for artists, which have become essential for the growth of the global market. Several factors drive the growth of the music streaming market, including increased smartphone penetration, faster internet connections, and the growing popularity of on-demand media consumption. Recent developments include: November 2022: Mercedes Benz automobiles now include Apple Music's highly acclaimed audio with support for Dolby Atmos as a natural experience, according to a joint announcement from Apple Music and Mercedes Benz. This fulfills a shared commitment to provide customers throughout the world with the best music experience., October2021: Amazon has announced that users of the unlimited tier of the service can now stream music blended in dynamic audio from more devices than ever before, including iOS (iPhone Operating System) and Android systems with their existing headphones and select devices that support Alexa.. Key drivers for this market are: Growing popularity of on-demand media consumption. Potential restraints include: licensing agreements with record labels and content providers can limit the availability . Notable trends are: Rising adoption in digital comic is driving the market growth.