How many paid subscribers does Spotify have? As of the first quarter of 2025, Spotify had 268 million premium subscribers worldwide, up from 239 million in the corresponding quarter of 2024. Spotify’s subscriber base has increased dramatically in the last few years and has more than doubled since early 2019. Spotify and competitors Spotify is a music streaming service originally founded in 2006 in Sweden. The platform can be used from various devices and allows users to browse through a catalogue of music licensed through multiple record labels, as well as creating and sharing playlists with other users. Additionally, listeners are able to enjoy music for free with advertisements or are also given the option to purchase a subscription to allow for unlimited ad-free music streaming. Spotify’s largest competitors are Pandora, a company that offers a similar service and remains popular in the United States, and Apple Music, which was launched in 2015. While Pandora was once among the highest-grossing music apps in the Apple App Store, recent rankings show that global services like QQ Music, NetEase Cloud Music, and YouTube Music now generate higher monthly revenues.Users are also able to register Spotify accounts using Facebook directly through the website using an app. This enables them to connect with other Facebook friends and explore their music tastes and playlists. Spotify is a popular source for keeping up-to-date with music, and the ability to enjoy Spotify anywhere at any time allows consumers to shape their music consumption around their lifestyles and preferences.

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

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

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

💁‍♀️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 Spotify's official "Top Tracks of 2023" playlist, showcasing the most popular and influential music of the year according to Spotify's streaming data. It represents a wide range array of genres, artists, and musical styles that have defined the musical landscapes of 2023. 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 music trends or develop music recommendation systems based on empirical data.

Data Collection and Processing:

Obtaining the Data:

The data was obtained directly from the Spotify Web API, specifically from the "Top Tracks of 2023" official playlist curated by Spotify. The Spotify API provides detailed information about tracks, artists, and albums through various endpoints.

Data Processing:

To process and structure the data, I developed Python scripts using data science libraries such as pandas for data manipulation and spotipy for API interactions specifically for Spotify data retrieval.

Workflow:

1. Authentification
2. API Requests
3. Data Cleaning and Transformation
4. Saving the Data

Attribute Descriptions:

• artist_name: the artist name
• 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 the track's artist(s)
• 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

• Trends Analysis
• Genre Popularity
• Mood and Music
• Comparison with other tracks

Disclaimer and Responsible Use:

• This dataset, derived from Spotify's "Top Tracks of 2023" playlist, is intended for educational, research, and analysis purposes only. Users are urged to use this data responsibly and ethically.
• Users should comply with Spotify's Terms of Service and Developer Policies when using this dataset.
• The dataset includes music track information such as names and artist details, which are subject to copyright. While the dataset presents this information for analytical purposes, it does not convey any rights to the music itself.
• Users of the dataset must ensure that their use does not infringe on the rights of copyright holders. Any analysis, distribution, or derivative work should respect the intellectual property rights of all parties and comply with applicable laws.
• The dataset is provided "as is," without warranty, and the creator disclaims any legal liability for the use of the dataset by others. Users are responsible for ensuring their use of the dataset is legal and ethical.
• For the most accurate and up-to-date information regarding Spotify's music, playlists, and policies, users are encouraged to refer directly to Spotify's official website. This ensures that users have access to the latest details directly from the source.
• The creator/maintainer of this dataset is not affiliated with Spotify, any third-party entities, or artists mentioned within the dataset. This project is independent and has not been authorized, sponsored, or otherwise approved by Spotify or any other mentioned entities.

Contribution

I encourage users who discover new insights, propose dataset enhancements, or craft analytics that illuminate aspects of the dataset's focus to share their findings with the community. - Kaggle Notebooks: To facilitate sharing and collaboration, users are encouraged to create and share their analyses through Kaggle notebooks. For ease of use, start your notebook by clicking "New Notebook" atop this dataset’s page on K...

This dataset is based on the subset of users in the #nowplaying dataset who publish their #nowplaying tweets via Spotify. In principle, the dataset holds users, their playlists and the tracks contained in these playlists.

The csv-file holding the dataset contains the following columns: "user_id", "artistname", "trackname", "playlistname", where

• user_id is a hash of the user's Spotify user name
• artistname is the name of the artist
• trackname is the title of the track and
• playlistname is the name of the playlist that contains this track.

The separator used is , each entry is enclosed by double quotes and the escape character used is \.

A description of the generation of the dataset and the dataset itself can be found in the following paper:

Pichl, Martin; Zangerle, Eva; Specht, Günther: "Towards a Context-Aware Music Recommendation Approach: What is Hidden in the Playlist Name?" in 15th IEEE International Conference on Data Mining Workshops (ICDM 2015), pp. 1360-1365, IEEE, Atlantic City, 2015.

Gain valuable insights into music trends, artist popularity, and streaming analytics with our comprehensive Spotify Dataset. Designed for music analysts, marketers, and businesses, this dataset provides structured and reliable data from Spotify to enhance market research, content strategy, and audience engagement.

Dataset Features

Track Information: Access detailed data on songs, including track name, artist, album, genre, and release date. Streaming Popularity: Extract track popularity scores, listener engagement metrics, and ranking trends. Artist & Album Insights: Analyze artist performance, album releases, and genre trends over time. Related Searches & Recommendations: Track related search terms and suggested content for deeper audience insights. Historical & Real-Time Data: Retrieve historical streaming data or access continuously updated records for real-time trend analysis.

Customizable Subsets for Specific Needs Our Spotify Dataset is fully customizable, allowing you to filter data based on track popularity, artist, genre, release date, or listener engagement. Whether you need broad coverage for industry analysis or focused data for content optimization, we tailor the dataset to your needs.

Popular Use Cases

Market Analysis & Trend Forecasting: Identify emerging music trends, genre popularity, and listener preferences. Artist & Label Performance Tracking: Monitor artist rankings, album success, and audience engagement. Competitive Intelligence: Analyze competitor music strategies, playlist placements, and streaming performance. AI & Machine Learning Applications: Use structured music data to train AI models for recommendation engines, playlist curation, and predictive analytics. Advertising & Sponsorship Insights: Identify high-performing tracks and artists for targeted advertising and sponsorship opportunities.

Whether you're optimizing music marketing, analyzing streaming trends, or enhancing content strategies, our Spotify Dataset provides the structured data you need. Get started today and customize your dataset to fit your business objectives.

About Dataset

Overview:

This dataset provides detailed metadata and audio analysis for a wide collection of Spotify music tracks across various genres. It includes track-level information such as popularity, tempo, energy, danceability, and other musical features that can be used for music recommendation systems, genre classification, or trend analysis. The dataset is a rich source for exploring music consumption patterns and user preferences based on song characteristics.

Dataset Details:

This dataset contains rows of individual music tracks, each described by both metadata (such as track name, artist, album, and genre) and quantitative audio features. These features reflect different musical attributes such as energy, acousticness, instrumentalness, valence, and more, making it ideal for audio machine learning projects and exploratory data analysis.

Schema and Column Descriptions:

Key Features:

• Comprehensive Track Data: Metadata combined with detailed audio analysis.
• Genre Diversity: Includes tracks from various music genres.
• Audio Feature Rich: Suitable for audio classification, recommendation engines, or clustering.
• Machine Learning Friendly: Clean and numerical format ideal for ML models.

Usage:

This dataset is valuable for:

• 🎵 Music Recommendation Systems: Building collaborative or content-based recommenders.
• 📊 Data Visualization & Dashboards: Analyzing genre or mood trends over time.
• 🤖 Machine Learning Projects: Predicting song popularity or clustering similar tracks.
• 🧠 Music Psychology & Behavioral Studies: Exploring how music features relate to emotions or behavior.

Data Maintenance:

• Source: https://huggingface.co/datasets/ConquestAce/spotify-songs
• Last Updated: 2025/04/26
• License: Check original source for usage terms.

Additional Notes:

• This dataset can be enhanced by merging it with user listening behavior data, lyrics datasets, or chart positions for more advanced analysis.
• Some columns like key, mode, and explicit may need to be mapped for better readability in visualization.

1) Data Introduction • The Spotify Playlist-ORIGINS Dataset is a dataset of Spotify playlists called ORIGINS, which individuals have made with their favorite songs since 2014.

2) Data Utilization (1) Spotify Playlist-ORIGINS Dataset has characteristics that: • This dataset contains detailed music information for each playlist, including song name, artist, album, genre, release year, track ID, and structured metadata such as name, description, and song order for each playlist. (2) Spotify Playlist-ORIGINS Dataset can be used to: • Playlist-based music recommendation and user preference analysis: It can be used to develop a machine learning/deep learning-based music recommendation system or to study user preference analysis using playlist and song information. • Music Trend and Genre Popularity Analysis: It analyzes release year, genre, and artist data and can be used to study the music industry and culture, including music trends by period and genre, and changes in popular artists and songs.

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.

This dataset is a processed version of a popular Spotify tracks dataset. It includes musical features such as energy, valence, danceability, acousticness, and instrumentalness for each track, along with a newly added user preference score. The user_preference column represents a continuous value indicating how much a user likes each track, making this dataset ideal for regression tasks. The original dataset contained over 100,000 tracks; this version contains 9,999 labeled entries with user preferences added. The goal of this dataset is to enable analysis of how musical features influence user tastes, support building predictive models, and demonstrate practical applications of linear regression and other regression-based machine learning techniques. This dataset is well-suited for educational purposes, music analytics, and building simple recommendation systems.

A dataset comparing Spotify Premium plans including Individual, Duo, Family, and Student with pricing and user limits.

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} }

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.

Spotify Revenue, Expenses and Its Premium Users contains the number of premium users, number of Ad-supported users and total monthly active users (MAUs).

MAUs include number of premium users as well as number of Ad-supported users.

Note : Sum of Premium Users and Ad-supported users can have some difference from MAUs. Note : All money figures are in Euro millions except ARPU which is in Euro and as it is. **Note : All users figures are in millions. ** Note : Kindly Ignore the last row.

Following definitions: MAUs : It is defined as the total count of Ad-Supported Users and Premium Subscribers that have consumed content for greater than zero milliseconds in the last thirty days from the period-end indicated. Premium MAUs : It is defined as users that have completed registration with Spotify and have activated a payment method for Premium Service. Ad MAUs : It is defined as the total count of Ad-Supported Users that have consumed content for greater than zero milliseconds in the last thirty days from the period-end indicated. Premium ARPU : It is average revenue per user which is monthly measure defined as Premium subscription revenue recognized in the quarter indicated divided by the average daily Premium Subscribers in such quarter, which is then divided by three months. Cost of Revenue : It is expenses done by company.

Photo by Alexander Shatov on Unsplash

This dataset provides insights into user music listening behavior on the Spotify platform. It captures key information about individual music streaming sessions, including:

Track Information:

• spotify_track_uri: A unique identifier for each track on Spotify.
• track_name: The name of the track.
• artist_name: The name of the artist who performed the track.
• album_name: The name of the album the track belongs to.

Playback Details:

• ts: Timestamp indicating when the playback of the track ended.
• platform: The device or platform used to stream the track (e.g., mobile, desktop, web player).
• ms_played: The duration of the track played in milliseconds.

Playback Behavior:

• reason_start: The reason why the track started playing (e.g., user selection, autoplay, recommendation).
• reason_end: The reason why the track stopped playing (e.g., natural end, user skip, interruption).
• shuffle: Indicates whether shuffle mode was active during playback (TRUE or FALSE).
• skipped: Indicates whether the user manually skipped to the next track (TRUE or FALSE).

Potential Uses

This dataset can be valuable for various analyses

• Understanding user listening habits: Identifying popular tracks, artists, and albums; analyzing listening patterns across different platforms and times of day; understanding the impact of shuffle mode on listening behavior.
• Improving music recommendations: Analyzing user skips and playback durations to refine recommendation algorithms.
• Evaluating platform performance: Identifying and addressing issues related to playback quality and interruptions.
• Developing personalized music experiences: Tailoring features and recommendations based on individual listening preferences.

Analysis of ‘Spotify Recommendation’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/bricevergnou/spotify-recommendation on 28 January 2022.

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

Spotify Recommandation

( You can check how I used this dataset on my github repository )

I am basically a HUGE fan of music ( mostly French rap though with some exceptions but I love music ). And someday , while browsing stuff on Internet , I found the Spotify's API . I knew I had to use it when I found out you could get information like danceability about your favorite songs just with their id's.

https://user-images.githubusercontent.com/86613710/127216769-745ac143-7456-4464-bbe3-adc53872c133.png" alt="image">

Once I saw that , my machine learning instincts forced me to work on this project.

1. Data Collection

1.1 Playlist creation

I collected 100 liked songs and 95 disliked songs

For those I like , I made a playlist of my favorite 100 songs. It is mainly French Rap , sometimes American rap , rock or electro music.

For those I dislike , I collected songs from various kind of music so the model will have a broader view of what I don't like

There is : - 25 metal songs ( Cannibal Corps ) - 20 " I don't like " rap songs ( PNL ) - 25 classical songs - 25 Disco songs

I didn't include any Pop song because I'm kinda neutral about it

1.2 Getting the ID's

1. From the Spotify's API "Get a playlist's Items" , I turned the playlists into json formatted data which cointains the ID and the name of each track ( ids/yes.py and ids/no.py ). NB : on the website , specify "items(track(id,name))" in the fields format , to avoid being overwhelmed by useless data.

2. With a script ( ids/ids_to_data.py ) , I turned the json data into a long string with each ID separated with a comma.

1.3 Getting the statistics

Now I just had to enter the strings into the Spotify API "Get Audio Features from several tracks" and get my data files ( data/good.json and data/dislike.json )

2. Data features

From Spotify's API documentation :

• acousticness : A confidence measure from 0.0 to 1.0 of whether the track is acoustic. 1.0 represents high confidence the track is acoustic.
• 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.
• duration_ms : The duration of the track in milliseconds.
• 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. For example, death metal has high energy, while a Bach prelude scores low on the scale. Perceptual features contributing to this attribute include dynamic range, perceived loudness, timbre, onset rate, and general entropy.
• instrumentalness : Predicts whether a track contains no vocals. “Ooh” and “aah” sounds are treated as instrumental in this context. Rap or spoken word tracks are clearly “vocal”. The closer the instrumentalness value is to 1.0, the greater likelihood the track contains no vocal content. Values above 0.5 are intended to represent instrumental tracks, but confidence is higher as the value approaches 1.0.
• key : The key the track is in. Integers map to pitches using standard Pitch Class notation . E.g. 0 = C, 1 = C♯/D♭, 2 = D, and so on.
• liveness : Detects the presence of an audience in the recording. Higher liveness values represent an increased probability that the track was performed live. A value above 0.8 provides strong likelihood that the track is live.
• loudness : The overall loudness of a track in decibels (dB). Loudness values are averaged across the entire track and are useful for comparing relative loudness of tracks. Loudness is the quality of a sound that is the primary psychological correlate of physical strength (amplitude). Values typical range between -60 and 0 db.
• mode : Mode indicates the modality (major or minor) of a track, the type of scale from which its melodic content is derived. Major is represented by 1 and minor is 0.
• 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. Values above 0.66 describe tracks that are probably made entirely of spoken words. Values between 0.33 and 0.66 describe tracks that may contain both music and speech, either in sections or layered, including such cases as rap music. Values below 0.33 most likely represent music and other non-speech-like tracks.
• 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.
• time_signature : An estimated overall time signature of a track. The time signature (meter) is a notational convention to specify how many beats are in each bar (or measure).
• 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).

And the variable that has to be predicted :

• liked : 1 for liked songs , 0 for disliked songs

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

These are the published date of music videos of every song in

https://www.kaggle.com/edumucelli/spotifys-worldwide-daily-song-ranking

Most of the time, music videos published dates are same as music themselves.

It would be valid to use the dates as release dates.

There are no other sources better than youtube to cover as much songs as possible.

• The file contains no header

• 20 songs remained Nan (unavailable to find related videos)

• This data was retrieved by Youtube API

This dataset was constructed based on the data found in Kaggle from Spotify.

The files here reported can be used to build a property graph in Neo4J:

• song.csv - contains all the data for the Song nodes.
• artist.csv - contains the data for the Artist nodes.
• playlist.csv - contains the data for the Playlist nodes.
• user.csv - contains the data for the Playlist nodes (those creating Playlists).
• genre.csv - contains the data for the Genre nodes (a category for the Artists).
• type.csv - contains the data for the Type nodes (a category for the Playlists).
• sing.csv - contains the data for the SING relationship from Artist to Song nodes.
• created.csv - contains the data for the CREATED relationship from User to Playlist nodes.
• in.csv - contains the data for the IN relationship from Song to Playlist nodes.
• of_type.csv - contains the data for the OFTYPE relationship from Playlist to Type nodes.
• labelled.csv - contains the data for the LABELLED relationship from Artist to Genre nodes.

This data was used as test dataset in the paper "MINE GRAPH RULE: A New GQL Operator for Mining Association Rules in Property Graph Databases".

Many people used musical media via music streaming service providers to cope with the limitations of the COVID-19 pandemic. Accounting for such behavior from the perspective of uses-and-gratifications theory and situated cognition yields reliable explanations regarding people’s active and goal-oriented use of musical media. We accessed Spotify’s daily top 200 charts and their audio features from the DACH countries for the period during the first lockdown in 2020 and a comparable non-pandemic period situation in 2019 to support those theoretical explanations quantitatively with open data. After exploratory data analyses, applying a k-means clustering algorithm across the DACH countries allowed us to reduce the dimensionality of selected audio features. Following these clustering results, we discuss how these clusters are explainable using the arousal-valence-circumplex model and possibly be understood as (gratification) potentials that listeners can interact with to modulate their moods and thus emotionally cope with the stress of the pandemic. Then, we modeled a cross-validated binary SVM classifier to classify the two periods based on the extracted clusters and the remaining manifest variables (e.g., chart position) as input variables. The final test scenario of the classification task yielded high overall accuracy in classifying the periods as distinguishable classes. We conclude that these demonstrated approaches are generally suitable to classify the two periods based on the extracted mood clusters and the other input variables, and furthermore to interpret, by considering the model-related caveats, everyday music listening via those proxy variables as an emotion-focused coping strategy during the COVID-19 pandemic in DACH countries. Dataset for: Kalustian, K., & Ruth, N. (2021). “Evacuate the Dancefloor”: Exploring and Classifying Spotify Music Listening Before and During the COVID-19 Pandemic in DACH Countries. In: T. Fischinger, & C. Louven, C. (Eds.), Musikpsychologie – Empirische Forschungen - Ästhetische Experimente, Band 30.