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.

🎵 Worldwide Music Artists Dataset (with image) 🎤

Welcome to the Worldwide Music Artists Dataset—your go-to resource for exploring the global music scene! 🌍🎶

This dataset features 100,000+ music artists from around the world, complete with: 📝 Name: Discover artists from every genre and corner of the globe. 🎸 Genres: Whether it's pop, rock, jazz, or classical, find your favorite styles. 📸 Profile Image: Visualize each artist with their unique profile picture. 📍 Location: See where your favorite artists hail from.

Whether you're a music enthusiast, a data scientist or a developer this dataset is perfect for your next project! 🚀

Imagine this: You’re walking through your neighborhood, earbuds in, the world tuned out as your favorite playlist flows seamlessly from one song to the next. This is not a rare moment, it’s an everyday ritual for millions around the globe. Music streaming has woven itself into our lives, whether during...

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   |

The Dataset's Purpose: This dataset's goal is to give a complete collection of music facts and lyrics for study and development. It aspires to be a useful resource for a variety of applications such as music analysis, natural language processing, sentiment analysis, recommendation systems, and others. This dataset, which combines song information and lyrics, can help academics, developers, and music fans examine and analyse the link between listeners' preferences and lyrical content.

Dataset Description:

The music dataset contains around 660 songs, each with its own set of characteristics. The following characteristics are included in the dataset:

Name: The title of the song. Lyrics: The lyrics of the song. Singer: The name of the singer or artist who performed the song. Movie: The movie or album associated with the song (if applicable). Genre: The genre or genres to which the song belongs. Rating: The rating or popularity score of the song from Spotify.

The dataset is intended to give a wide variety of songs from various genres, performers, and films. It includes popular songs from numerous ages and places, as well as a wide spectrum of musical styles. The lyrics were obtained from publically accessible services such as Spotify and Soundcloud, and were converted from audio to text using speech recognition algorithms. While every attempt has been taken to assure correctness, please keep in mind that owing to the limits of the data sources and voice recognition algorithms, there may be inaccuracies or missing lyrics encountered upon transcribing.

Use Cases in Research and Development:

This music dataset has several research and development applications. Among the possible applications are:

1. Music Analysis: By analysing the links between song elements such as genre, vocalist, and rating, researchers can acquire insights into the features and patterns of various music genres.
2. Natural Language Processing (NLP): NLP researchers may use the lyrics to create language models, sentiment analysis algorithms, topic modelling approaches, and other text-based music studies.
3. Recommendation Systems: Using the information, developers may create recommendation systems that offer music based on user preferences, lyrics sentiment, or genre similarities.
4. Music Generating Machine Learning Models: The dataset may be used to train machine learning models for generating new lyrics or making music compositions.
5. Music Sentiment Analysis: To get insights into the emotional components of music and its influence on listeners, researchers might analyse the feelings conveyed in song lyrics.
6. Movie Soundtracks Analysis: Researchers can explore the association between song attributes and their use in movie soundtracks by investigating the movie attribute.

Overall, the goal of this music dataset is to provide a rich resource for academics, developers, and music fans to investigate the complicated relationships between song features, lyrics, and numerous research and development applications in the music domain.

This dataset provides a list of lyrics from 1950 to 2019 describing music metadata as sadness, danceability, loudness, acousticness, etc. We also provide some informations as lyrics which can be used to natural language processing.

The audio data was scraped using Echo Nest® API integrated engine with spotipy Python’s package. The spotipy API permits the user to search for specific genres, artists,songs, release date, etc. To obtain the lyrics we used the Lyrics Genius® API as baseURL for requesting data based on the song title and artist name.

Key Music Streaming App StatisticsTop Music Streaming AppsMusic Streaming RevenueMusic Revenue by FormatMusic Streaming MarketshareMusic Streaming Subscribers by AppMusic Streaming Users by AppMusic...

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.

When Apple Music launched in 2015, it was Apple’s ambitious leap into the fiercely competitive world of streaming. Fast forward to 2025, and it's no longer just another streaming service, it’s a global audio ecosystem influencing how we discover music, connect with artists, and personalize our soundtracks. From hip-hop heads...

Music streaming revenue has increased astronomically in the last ten years alone – growing from 1.9 billion U.S. dollars in 2014 to 14.9 billion in 2024. Streaming has become a popular pastime for U.S. music fans and a major source of revenue for the industry, though many traditional consumers lament the resulting decline of physical music formats. Physical CD shipments have dwindled, whilst digital music platforms are flourishing.  The world of digital music   Platforms like Spotify have millions of users worldwide, and millions of tracks are streamed via the service each week. In the U.S., more than 25 percent of adults under the age of 35 are Spotify users, as well as almost 20 percent of adults aged 55 or above. Unsurprisingly, the vast majority of digital music revenue in the United States is derived from subscriptions and streaming, and successful musicians like Drake, Eminem, and Ariana Grande  amass billions of streams each year.  Whilst many artists in the music industry generate most of their income from touring, streaming is also incredibly lucrative, generating millions of dollars in earnings.  

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 summary statistics by North American Industry Classification System (NAICS) which include: operating revenue (dollars x 1,000,000), operating expenses (dollars x 1,000,000), salaries wages and benefits (dollars x 1,000,000), and operating profit margin (by percent), of record production and integrated record production/distribution (NAICS 512210 & 512220), music publishers (NAICS 512230), sound recording studios (NAICS 512240), and other sound recording industries (NAICS 512290), annual, for five years of data.

According to data gathered in the United States in April 2024, Generation Z is much more open to AI being used in the production of music. Nearly **** of the Gen Z respondents stated that they would be interested in listening to music that has been produced with the help of AI. Older generations seem more hesitant towards this topic, as only ** percent stated the same.

💁‍♀️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 is a volatile industry, where its dynamic nature can directly influence artist career behavior. That is, musical careers can suffer ups and downs depending on the current market moment. This dataset provides data about hot streak periods in musical careers, which are defined by high-impact bursts occurring in sequence.

Success in the music industry has a temporal structure, as the audience tastes change over time. Here, we use the Billboard Hot 100 charts with Spotify data to represent success over time. For musical careers, we build their time series from the debut date (i.e., date of the first release obtained from Spotify) to the last chart collected. Thus, each point in the time series represents the success of such an artist in a given week, according to the Hot 100 chart.

Therefore, we present MUHSIC (Music-oriented Hot Streak Information Collection), which contains:

Charts: enhanced data on all weekly Hot 100 Charts

Artists: artist success time series with hot streak information

Genres: genre success time series with hot streak information (the genre is the aggregated of all its artists)

Hot Streaks: summarized hot streak information

This data set was taken from Spotify's Top 50 Playlists (by country). Surprisingly, the Spotify API does not have an endpoint for collecting a track's genres - so what I did was extract each track's URI using Python, get the corresponding Artist URI, and then make a dataframe out of each Artist's genre(s). Lastly, I grouped them by country to see which countries preferred which genres of music.

A couple of things to note about this dataset: 1. An artist may have multiple genres or even 0. My code only takes into account the list of genres generated from the API. In other words, the genre count for each country will NOT equal 50.

1. Many music genres overlap with each other (i.e. electropop = EDM or pop??). For simplicity, I only classified these into one genre based off my own order (you can see it in the code below)

2. There are so many different genres that I couldn't cover every type in my code. For simplicity, I classified these into one genre called "Other". Also keep in mind that this group will include some genres that should belong to other groups because of the genres' unique name (you can see it in the code below).

3. I didn't get every country, only the countries Spotify covered on their Top 50s.

Classification code in Python: df3[~df3['Genres'].str.contains('hip hop|rap|r&b|edm|electronic|house|dubstep|electro|alternative|trance|pop|dance|rock|metal|thrash|emo|latin|reggaeton')] = 'Other' df3.loc[df3['Genres'].str.contains('hip hop|rap|r&b')] = 'Hip hop/Rap/R&b' df3.loc[df3['Genres'].str.contains('edm|electronic|house|dubstep|trance|electro')] = 'EDM' df3.loc[df3['Genres'].str.contains('pop|dance')] = 'Pop' df3.loc[df3['Genres'].str.contains('rock|metal|thrash|emo|alternative')] = 'Rock/Metal' df3.loc[df3['Genres'].str.contains('latin|reggaeton')] = 'Latin/Reggaeton'

In 2024, the total number of on-demand audio music streams in the United States hit an astronomical *** trillion. This is an increase of nearly *** billion from the previous year, reaching the highest number of streams.

In 2024, revenue from digital album downloads amounted to 166.7 million U.S. dollars, less than half the figure recorded in 2018. Meanwhile, subscription and streaming revenues have been increasing annually and reached 14.88 billion that same year, making up the vast majority of revenues for the entire music industry. Digital music – additional informationThe increase in digital music revenue, more specifically subscription and streaming services, may be down to the accessibility and availability of digital music. For legal downloads, consumers can pick between services such as Spotify, Apple Music, and Pandora. Of course, there are countless numbers of illegal sites which distribute digital music also. In 2019, approximately 34 percent of global internet users aged 16 to 24 admitted to accessing music through music ripping, the most popular method being copyright infringement.Digital music sales have made a huge impact on the listings of best-selling lists with digital sales either being combined with physical sales or set apart. The list of the top-selling digital songs in the United States in 2020 features artists such as The Weeknd, Tones and I, and Megan Thee Stallion. The title for the top selling digital song goes to ‘Blinding Lights’ by The Weeknd. The song sold over 372 thousand units in the United States from January to July 2020. Nevertheless, the share of the digital music market does not always directly correspond to the value of the digital market. The value of digital music singles downloads in 2024 amounted to 162.4 million U.S. dollars, which marked a drop in value from 678.5 million in 2017. The value of digital album sales also saw a decrease from 668.5 million in 2017 to 166.7 million in 2024.

This dataset is built by using data from Spotify. It provides a daily 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 March 2022 (downloaded from CSV files), considering 68 distinct markets.

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

Artist Networks: Success-based artist collaboration networks

Artists: Some artist data

Hit Songs: Hit Song data and features

Charts: Enhanced data from Spotify Daily Top 200 Charts

This dataset is a collection of mel-spectrogram features extracted from Indian folk music containing the following 15 folk styles: Bauls, Bhavageethe, Garba, Kajri, Maand, Sohar, Tamang Selo, Veeragase, Bhatiali, Bihu, Gidha, Lavani, Naatupura Paatu, Sufi, Uttarakhandi.

The number of recordings varies from 16 to 50 in the mentioned folk styles representing the scarcity of availability of given folk styles on the Internet. There are at least 4 artists and a maximum of 22. Overall there are 125 artists (34 female + 91 male) in these 15 folk styles.

There is a total of 606 recordings in the dataset, with a total duration of 54.45 hrs. Mel-spectrogram is extracted from a 3-second segment with each song's 1/2 second sliding window. Extracted mel-spectrogram for each segment is annotated with folk_style, state, artist, gender, song, source, no_of_artists, folk_style_id, state_id, artist_id, gender_id.

This project was funded under the grant number: ECR/2018/000204 by the Science & Engineering Research Board (SERB).