How many paid subscribers does Spotify have? As of the fourth quarter of 2024, Spotify had 263 million premium subscribers worldwide, up from 236 million in the corresponding quarter of 2023. 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.

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

About

For more in-depth information about audio features provided by Spotify: https://developer.spotify.com/documentation/web-api/reference/#/operations/get-audio-features

I reposted my old dataset as many people requested. I don't consider updating the dataset further.

Meta-information

Title: Spotify Dataset 1921-2020, 600k+ Tracks Subtitle: Audio features of 600k+ tracks, popularity metrics of 1M+ artists Source: Spotify Web API Creator: Yamac Eren Ay Release Date (of Last Version): April 2021 Link to this dataset: https://www.kaggle.com/yamaerenay/spotify-dataset-19212020-600k-tracks Link to the old dataset: https://www.kaggle.com/yamaerenay/spotify-dataset-1921-2020-160k-tracks

Disclaimer

I am not posting here third-party Spotify data for arbitrary reasons or getting upvote.

The old dataset has been mentioned in tens of scientific papers using the old link which doesn't work anymore since July 2021, and most of the authors had some problems proving the validity of the dataset. You can cite the same dataset under the new link. I'll be posting more information regarding the old dataset.

If you have inquiries or complaints, please don't hesitate to reach out to me on LinkedIn or you can send me an email.

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}
}

Analysis of ‘K-Pop Hits Through The Years’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/sberj127/kpop-hits-through-the-years on 28 January 2022.

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

What is the data?

The datasets contain the top songs from the said era or year accordingly (as presented in the name of each dataset). Note that only the KPopHits90s dataset represents an era (1989-2001). Although there is a lack of easily available and reliable sources to show the actual K-Pop hits per year during the 90s, this era was still included as this time period was when the first generation of K-Pop stars appeared. Each of the other datasets represent a specific year after the 90s.

How was it obtained?

A song is considered to be a K-Pop hit during that era or year if it is included in the annual series of K-Pop Hits playlists, which is created officially by Apple Music. Note that for the dataset that represents the 90s, the playlist 90s K-Pop Essentials was used as the reference.

1. These playlists were transferred into Spotify through the Tune My Music site. After transferring, the site also presented all the missing songs from each Spotify playlist when compared to the original Apple Music playlists.
   • Any data besides the names and artists of the hit songs were not directly obtained from Apple Music since these other details of songs in this music service are only available for those enrolled as members of the Apple Developer Program.
2. The presented missing songs from each playlist was manually searched and, if found, added to the respective Spotify playlist.
   • For the songs that were found, there are three types: (1) the song by the original artist, (2) the instrumental of the original song and (3) a cover of the song. When the first type is not found, the two other types are searched and are compared to each other. The one that sounded the most like the original song (from the Apple Music playlist) is chosen as the substitute in the Spotify playlist.
   • Presented is a link containing all the missing data per playlist (when the initial Spotify playlists were compared to the original Apple Music playlists) and the action done to each one.
3. The necessary identification details and specific audio features of each track were obtained through the use of the Spotipy library and Spotify Web API documentation.

Why did you make this?

As someone who has a particular curiosity to the field of data science and a genuine love for the musicality in the K-Pop scene, this data set was created to make something out of the strong interest I have for these separate subjects.

Acknowledgements

I would like to express my sincere gratitude to Apple Music for creating the annual K-Pop playlists, Spotify for making their API very accessible, Spotipy for making it easier to get the desired data from the Spotify Web API, Tune My Music for automating the process of transferring one's library into another service's library and, of course, all those involved in the making of these songs and artists included in these datasets for creating such high quality music and concepts digestible even for the general public.

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

Analysis of ‘Top Spotify Tracks of 2017’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/nadintamer/top-tracks-of-2017 on 28 January 2022.

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

Top Spotify Tracks of 2017

At the end of each year, Spotify compiles a playlist of the songs streamed most often over the course of that year. This year's playlist (Top Tracks of 2017) included 100 songs. The question is: What do these top songs have in common? Why do people like them?

Original Data Source: The audio features for each song were extracted using the Spotify Web API and the spotipy Python library. Credit goes to Spotify for calculating the audio feature values.

Data Description: There is one .csv file in the dataset. (featuresdf.csv) This file includes:

• Spotify URI for the song
• Name of the song
• Artist(s) of the song
• Audio features for the song (such as danceability, tempo, key etc.)

A more detailed explanation of the audio features can be found in the Metadata tab.

Exploring the Data: Some suggestions for what to do with the data:

• Look for patterns in the audio features of the songs. Why do people stream these songs the most?

• Try to predict one audio feature based on the others

• See which features correlate the most

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

Dataset

This dataset was created by legal

Released under MIT

Contents

This repository contains the raw data used for a research study that examined university students' music listening habits while studying. There are two experiments in this research study. Experiment 1 is a retrospective survey, and Experiment 2 is a mobile experience sampling research study. This repository contains five Microsoft Excel files with data obtained from both experiments. The files are as follows:

onlineSurvey_raw_data.xlsx esm_raw_data.xlsx esm_music_features_analysis.xlsx esm_demographics.xlsx index.xlsx Files Description File: onlineSurvey_raw_data.xlsx This file contains the raw data from Experiment 1, including the (anonymised) demographic information of the sample. The sample characteristics recorded are:

studentship area of study country of study type of accommodation a participant was living in age self-identified gender language ability (mono- or bi-/multilingual) (various) personality traits (various) musicianship (various) everyday music uses (various) music capacity The file also contains raw data of responses to the questions about participants' music listening habits while studying in real life. These pieces of data are:

likelihood of listening to specific (rated across 23) music genres while studying and during everyday listening. likelihood of listening to music with specific acoustic features (e.g., with/without lyrics, loud/soft, fast/slow) music genres while studying and during everyday listening. general likelihood of listening to music while studying in real life. (verbatim) responses to participants' written responses to the open-ended questions about their real-life music listening habits while studying. File: esm_raw_data.xlsx This file contains the raw data from Experiment 2, including the following variables:

information of the music tracks (track name, artist name, and if available, Spotify ID of those tracks) each participant was listening to during each music episode (both while studying and during everyday-listening) level of arousal at the onset of music playing and the end of the 30-minute study period level of valence at the onset of music playing and the end of the 30-minute study period specific mood at the onset of music playing and the end of the 30-minute study period whether participants were studying their location at that moment (if studying) whether they were studying alone (if studying) the types of study tasks (if studying) the perceived level of difficulty of the study task whether participants were planning to listen to music while studying (various) reasons for music listening (various) perceived positive and negative impacts of studying with music Each row represents the data for a single participant. Rows with a record of a participant ID but no associated data indicate that the participant did not respond to the questionnaire (i.e., missing data). File: esm_music_features_analysis.xlsx This file presents the music features of each recorded music track during both the study-episodes and the everyday-episodes (retrieved from Spotify's "Get Track's Audio Features" API). These features are:

energy level loudness valence tempo mode The contextual details of the moments each track was being played are also presented here, which include:

whether the participant was studying their location (e.g., at home, cafe, university) whether they were studying alone the type of study tasks they were engaging with (e.g., reading, writing) the perceived difficulty level of the task File: esm_demographics.xlsx This file contains the demographics of the sample in Experiment 2 (N = 10), which are the same as in Experiment 1 (see above). Each row represents the data for a single participant. Rows with a record of a participant ID but no associated demographic data indicate that the participant did not respond to the questionnaire (i.e., missing data). File: index.xlsx Finally, this file contains all the abbreviations used in each document as well as their explanations.

In the end, you should only measure and look at the numbers that drive action, meaning that the data tells you what you should do next.🥰

Please do upvote if you love the work.♥️🥰 For more related datasets: https://www.kaggle.com/datasets/rajatsurana979/fifafcmobile24 https://www.kaggle.com/datasets/rajatsurana979/most-streamed-spotify-songs-2023 https://www.kaggle.com/datasets/rajatsurana979/comprehensive-credit-card-transactions-dataset https://www.kaggle.com/datasets/rajatsurana979/hotel-reservation-data-repository https://www.kaggle.com/datasets/rajatsurana979/percent-change-in-consumer-spending https://www.kaggle.com/datasets/rajatsurana979/fast-food-sales-report

Description: This dataset captures sales transactions from a local restaurant near my home. It includes details such as the order ID, date of the transaction, item names (representing various food and beverage items), item types (categorized as Fast-food or Beverages), item prices, quantities ordered, transaction amounts, transaction types (cash, online, or others), the gender of the staff member who received the order, and the time of the sale (Morning, Evening, Afternoon, Night, Midnight). The dataset offers a valuable snapshot of the restaurant's daily operations and customer behavior.

Columns: 1. order_id: a unique identifier for each order. 2. date: date of the transaction. 3. item_name: name of the food. 4. item_type: category of item (Fastfood or Beverages). 5. item_price: price of the item for 1 quantity. 6. Quantity: how much quantity the customer orders. 7. transaction_amount: the total amount paid by customers. 8. transaction_type: payment method (cash, online, others). 9. received_by: gender of the person handling the transaction. 10. time_of_sale: different times of the day (Morning, Evening, Afternoon, Night, Midnight).

Potential Uses: - Analyzing sales trends over time. - Understanding customer preferences for different items. - Evaluating the impact of payment methods on revenue. - Investigating the performance of staff members based on gender. - Exploring the popularity of items at different times of the day.

Makeyourhandsdirtyonit

Results of the two samples’ Welch’s t-tests, Cohen’s d and Kolmogorov-Smirnov tests between the dance and baseline music datasets.

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.

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.

uber-request-a-ride-us- 73787 rows waze-navigation-live-traffic-us- 26260 rows facebook-us- 24200 rows spotify-music-and-podcasts-us- 15580 rows netflix-us- 11760 rows pinterest-us- 10860 rows X-us- 8160 rows tiktok-us- 2542 rows tinder-dating-chat-friends-us- 1060 rows 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

Extract sentiments and trends Identify which version of an app had the most positive feedback, the worst. Use topic modelling to identify the pain points of the application. (AND MANY MORE!)

Note Images generated using Bing Image Generator

License

CC0

Original Data Source: 🏆Uber, FB, Waze, etc US Apple App Store Reviews

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.