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.

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

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

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

Highest Charting Position: The highest position that the song has been on in the Spotify Top 200 Weekly Global Charts in 2020 & 2021. Number of Times Charted: The number of times that the song has been on in the Spotify Top 200 Weekly Global Charts in 2020 & 2021. Week of Highest Charting: The week when the song had the Highest Position in the Spotify Top 200 Weekly Global Charts in 2020 & 2021. Song Name: Name of the song that has been on in the Spotify Top 200 Weekly Global Charts in 2020 & 2021. Song iD: The song ID provided by Spotify (unique to each song). Streams: Approximate number of streams the song has. Artist: The main artist/ artists involved in making the song. Artist Followers: The number of followers the main artist has on Spotify. Genre: The genres the song belongs to. Release Date: The initial date that the song was released. Weeks Charted: The weeks that the song has been on in the Spotify Top 200 Weekly Global Charts in 2020 & 2021. Popularity:The popularity of the track. The value will be between 0 and 100, with 100 being the most popular. Danceability: Danceability describes how suitable a track is for dancing based on a combination of musical elements including tempo, rhythm stability, beat strength, and overall regularity. A value of 0.0 is least danceable and 1.0 is most danceable. Acousticness: A measure from 0.0 to 1.0 of whether the track is acoustic. Energy: Energy is a measure from 0.0 to 1.0 and represents a perceptual measure of intensity and activity. Typically, energetic tracks feel fast, loud, and noisy. Instrumentalness: Predicts whether a track contains no vocals. The closer the instrumentalness value is to 1.0, the greater likelihood the track contains no vocal content. Liveness: Detects the presence of an audience in the recording. Higher liveness values represent an increased probability that the track was performed live. Loudness: The overall loudness of a track in decibels (dB). Loudness values are averaged across the entire track. Values typical range between -60 and 0 db. Speechiness: Speechiness detects the presence of spoken words in a track. The more exclusively speech-like the recording (e.g. talk show, audio book, poetry), the closer to 1.0 the attribute value. Tempo: The overall estimated tempo of a track in beats per minute (BPM). In musical terminology, tempo is the speed or pace of a given piece and derives directly from the average beat duration. Valence: A measure from 0.0 to 1.0 describing the musical positiveness conveyed by a track. Tracks with high valence sound more positive (e.g. happy, cheerful, euphoric), while tracks with low valence sound more negative (e.g. sad, depressed, angry). Chord: The main chord of the song instrumental.

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

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.

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} }

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.

This dataset is a CSV format information that contains names of musical artist trending globally between May 30 -June 05, 2025 as recorded by Spotify along with their ranking. The other information such as the countries, cities and continent of the various musical artist residence and type of artist were manually filled up from personal research over the internet. It contains 200 rows and 6 columns.

Wiki-MID Dataset Wiki-MID is a LOD compliant multi-domain interests dataset to train and test Recommender Systems. Our English dataset includes an average of 90 multi-domain preferences per user on music, books, movies, celebrities, sport, politics and much more, for about half million Twitter users traced during six months in 2017. Preferences are either extracted from messages of users who use Spotify, Goodreads and other similar content sharing platforms, or induced from their "topical" friends, i.e., followees representing an interest rather than a social relation between peers. In addition, preferred items are matched with Wikipedia articles describing them. This unique feature of our dataset provides a mean to categorize preferred items, exploiting available semantic resources linked to Wikipedia such as the Wikipedia Category Graph, DBpedia, BabelNet and others. Data model: Our resource is designed on top of the Semantically-Interlinked Online Communities (SIOC) core ontology. The SIOC ontology favors the inclusion of data mined from social networks communities into the Linked Open Data (LOD) cloud.We represent Twitter users as instances of the SIOC UserAccount class.Topical users and message based user interests are then associated, through the usage of the Simple Knowledge Organization System Namespace Document (SKOS) predicate relatedMatch, to a corresponding Wikipedia page as a result of our automated mapping methodology.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

SPOT Spotify Technology S.A. Ordinary Shares

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Spotify's (SPOT) Growth Potential Fuels Bullish Analyst Outlook.

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

App Reviews

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F1842206%2Fd4a6033b6bd31af45d5175d02e697934%2FAPPLEAPPS2.png?generation=1700357122842963&alt=media" alt="">

1. uber-request-a-ride-us- 73787 rows
2. waze-navigation-live-traffic-us- 26260 rows
3. facebook-us- 24200 rows
4. spotify-music-and-podcasts-us- 15580 rows
5. netflix-us- 11760 rows
6. pinterest-us- 10860 rows
7. X-us- 8160 rows
8. tiktok-us- 2542 rows
9. tinder-dating-chat-friends-us- 1060 rows
10. instagram-us- 300 rows

These reviews are from Apple App Store

Usage

This dataset should paint a good picture on what is the public's perception of the apps over the years. Using this dataset, we can do the following

1. Extract sentiments and trends
2. Identify which version of an app had the most positive feedback, the worst.
3. Use topic modelling to identify the pain points of the application.

(AND MANY MORE!)

Note

Images generated using Bing Image Generator

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.

Context

The dataset was extracted from the wonderful portal of Project Guttenberg to develop a short story recommendation engine . The idea that was being explored was, that we have Spotify for music and Netflix for movies but can we develop a great recommendation application for reading. The dataset is supposed to be used to experiment with various similarity algorithms and metrics to help develop a great user short story reading recommendation and open it for free to a wider audience. The project is open on Github at https://github.com/shubchat/Readnet.

Acknowledgements

My deep thanks and respect for the team of volunteers at https://www.gutenberg.org/ .It is their inspiring effort to aggregate and maintain the open literature that is serving millions of people who love reading but don't have access to resources. If you are using this dataset please consider donating to this wonderful cause at https://www.gutenberg.org/wiki/Gutenberg:Project_Gutenberg_Needs_Your_Donation

Permissions on how to

Please refer to the guidance from project Gutenberg at https://www.gutenberg.org/wiki/Gutenberg:Permission_How-To for details.

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.