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.

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 ---

Description

This dataset presents a comprehensive compilation of the most streamed songs on Spotify in 2024. It provides extensive insights into each track's attributes, popularity, and presence on various music platforms, offering a valuable resource for music analysts, enthusiasts, and industry professionals. The dataset includes information such as track name, artist, release date, ISRC, streaming statistics, and presence on platforms like YouTube, TikTok, and more.

Here is the link for the 2023 data: "https://www.kaggle.com/datasets/nelgiriyewithana/top-spotify-songs-2023">Most Streamed Spotify Songs 2023 🟢

Key Features

• Track Name: Name of the song.
• Album Name: Name of the album the song belongs to.
• Artist: Name of the artist(s) of the song.
• Release Date: Date when the song was released.
• ISRC: International Standard Recording Code for the song.
• All Time Rank: Ranking of the song based on its all-time popularity.
• Track Score: Score assigned to the track based on various factors.
• Spotify Streams: Total number of streams on Spotify.
• Spotify Playlist Count: Number of Spotify playlists the song is included in.
• Spotify Playlist Reach: Reach of the song across Spotify playlists.
• Spotify Popularity: Popularity score of the song on Spotify.
• YouTube Views: Total views of the song's official video on YouTube.
• YouTube Likes: Total likes on the song's official video on YouTube.
• TikTok Posts: Number of TikTok posts featuring the song.
• TikTok Likes: Total likes on TikTok posts featuring the song.
• TikTok Views: Total views on TikTok posts featuring the song.
• YouTube Playlist Reach: Reach of the song across YouTube playlists.
• Apple Music Playlist Count: Number of Apple Music playlists the song is included in.
• AirPlay Spins: Number of times the song has been played on radio stations.
• SiriusXM Spins: Number of times the song has been played on SiriusXM.
• Deezer Playlist Count: Number of Deezer playlists the song is included in.
• Deezer Playlist Reach: Reach of the song across Deezer playlists.
• Amazon Playlist Count: Number of Amazon Music playlists the song is included in.
• Pandora Streams: Total number of streams on Pandora.
• Pandora Track Stations: Number of Pandora stations featuring the song.
• Soundcloud Streams: Total number of streams on Soundcloud.
• Shazam Counts: Total number of times the song has been Shazamed.
• TIDAL Popularity: Popularity score of the song on TIDAL.
• Explicit Track: Indicates whether the song contains explicit content.

Potential Use Cases

• Music Analysis: Analyze trends in audio features to understand popular song characteristics.
• Platform Comparison: Compare song popularity across different music platforms.
• Artist Impact: Study the relationship between artist attributes and song success.
• Temporal Trends: Identify changes in music attributes and preferences over time.
• Cross-Platform Presence: Investigate song performance across various streaming services.

Your support through an upvote would be greatly appreciated if you find this dataset useful! ❤️🙂 Thank you.

Analysis of ‘Top 50 Spotify songs BY EACH COUNTRY’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/leonardopena/top-50-spotify-songs-by-each-country on 28 January 2022.

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

Context

The top songs BY COUNTRY by spotify. This dataset has several variables about the songs and is based on Billboard. The extraction was done at Christmas time, so the most played songs should be related to Christmas

Content

There are the most popular songs by country and 13 variables to be explored. Data were stracted from: http://organizeyourmusic.playlistmachinery.com/

Inspiration

What can we know about the genre? What is the mean of minutes that a top music has? And what about the cenario by country?

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

Analysis of ‘Top 50 Spotify Tracks- 2020’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/atillacolak/top-50-spotify-tracks-2020 on 28 January 2022.

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

# Context

Top 50 most streamed tracks on Spotify in 2020. This dataset has various variables regarding these songs.

Content

50 songs 16 variables

Numerical Variables

• energy (Ranges from 0 to 1)
• danceability (Ranges from 0 to 1)
• instrumentalness (Ranges from 0 to 1)
• valence (Ranges from 0 to 1)
• acousticness (Ranges from 0 to 1)
• duration_ms( Generally has a range of 200k-400k)
• liveness (Ranges from 0 to 1)
• speechiness (Ranges from 0 to 1)
• tempo
• loudness

Categorical Variables

• artist
• track_name
• key
• genre
• album

Inspiration

Which genres are most popular? In 2020, which features of these fifty tracks made them hit songs?

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

This dataset contains the latest global Spotify streaming data, including song names, artist names, total streams, and daily stream counts. The data provides insights into the performance of songs on Spotify, reflecting trends in music consumption.

Columns:

• Songs & Artist: The name of the song along with the associated artist(s).
• Streams: The total number of streams the song has accumulated globally.
• Daily: The number of streams the song received within the past 24 hours.

Use Cases:

• Music Trend Analysis: Discover popular artists and songs based on their streaming counts.
• Market Research: Analyze streaming trends for insights into music consumption habits.
• Predictive Modeling: Create models for predicting future streaming success based on past trends.
• Music Recommendation Systems: Train recommendation algorithms to suggest trending songs or artists.

Analysis of ‘Top 100 Most Streamed Songs on Spotify’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/pavan9065/top-100-most-streamed-songs-on-spotify on 28 January 2022.

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

Context The top 100 most-streamed songs in the world by spotify. This dataset has several variables about the songs.

Content 100 songs 14 variables Data were stracted from: http://organizeyourmusic.playlistmachinery.com/

Inspiration What can we know about the genre? What is the mean of minutes that a top music has?

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

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

👍 If this dataset was useful to you, leave your vote at the top of the page 👍

The dataset provides information on the daily top 200 tracks listened to by users of the Spotify digital platform around the world.

I put together this dataset because I really love music (I listen to it for several hours a day) and have not found a similar dataset with track genres on kaggle.

The dataset can be useful for beginners in the field of working with data. It contains missing values, arrays in columns, and so on, which can be great practice when conducting an EDA phase.

Soon, my example will appear here as possible, based on the specified dataset, go on a musical journey around the world and understand how the musical tastes of humanity have changed around the world)))

In addition, I will be very happy to see the work of the community on this dataset.

Also, in case of interest in data by country, I am ready to place it upon request.

You can contact me through: telegram @natarov_ivan

Analysis of ‘Top Spotify songs from 2010-2019 - BY YEAR’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/leonardopena/top-spotify-songs-from-20102019-by-year on 20 November 2021.

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

Context

The top songs BY YEAR in the world by spotify. This dataset has several variables about the songs and is based on Billboard

Content

There are the most popular songs in the world by year and 13 variables to be explored. Data were stracted from: http://organizeyourmusic.playlistmachinery.com/

Inspiration

What can we know about the genre? What is the mean of minutes that a top music has? And what about the cenario by year?

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

This dataset provides insights into the most popular tracks of renowned Tamil music artists on Spotify, and popularity over time. The data includes information about artists such as Ilayaraja, A.R. Rahman, Yuvan Shankar Raja, Harris Jayaraj, Vijay Antony, G.V. Prakash, Santhosh Narayanan, Anirudh Ravichander, Hiphop Tamizha, Sean Roldan, Sam C.S., Devi Sri Prasad, Ghibhran and Thaman.

The dataset includes:

• Top Tracks: Track names, popularity scores, album details, and release dates.
• Artist Popularity Over Time: Aggregated popularity scores for each artist per year, allowing analysis of trends and the identification of the top trending artist for each year.
• Track Duration and Popularity: Explore the relationship between the duration of tracks and their popularity.

This dataset is ideal for:

• Data Analysis: Analyze trends in Tamil music over the years, identify top-performing artists, and examine the factors contributing to the popularity of specific tracks.
• Visualization Projects: Create visualizations of artist popularity trends, track performance, and other key insights.
• Music Industry Insights: Gain a better understanding of the success of Tamil music artists on global streaming platforms like Spotify.

This dataset was collected using the Spotify API and is intended to support projects in data science, music analytics, and industry trend analysis.

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

Numerical Variables

Beats Per Minute (BPM): The tempo of the song.
Energy: The energy of a song - the higher the value, the more energetic the song
Danceability: The higher the value, the easier it is to dance to this song.
Loudness: The higher the value, the louder the song.
Valence: The higher the value, the more positive mood for the song.
Duration: Naturally, this is the duration of the song, in minutes.
Acoustic: The higher the value the more acoustic the song is.
Popularity: The higher the value the more popular the song is.

Categorical Variables

Artist Title Release Year

Dataset created thanks to an updated-weekly playlist from Ray Fontaine on Spotify. https://open.spotify.com/playlist/2YRe7HRKNRvXdJBp9nXFza?si=f3ce572764424629

Analysis of ‘Top 50 Spotify Songs - 2019’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/leonardopena/top50spotify2019 on 28 January 2022.

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

• Check the data extracted by year: https://www.kaggle.com/leonardopena/top-spotify-songs-from-20102019-by-year
• And by country: https://www.kaggle.com/leonardopena/top-50-spotify-songs-by-each-country

Context

The top 50 most listened songs in the world by spotify. This dataset has several variables about the songs.

Content

50 songs 13 variables Data were stracted from: http://organizeyourmusic.playlistmachinery.com/

Inspiration

What can we know about the genre? What is the mean of minutes that a top music has?

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

This dataset, "Spotify's Long Hits (2014-2024) 🎶," offers a unique collection of over 800 tracks, each standing out for its extended playtime, marking the years from 2014 to 2024. It serves as a unique lens through which the evolution of musical duration and listener preferences can be observed over a significant period. Each track in this dataset not only surpasses the conventional lengths but also encapsulates the essence of its time, making it a valuable resource for in-depth musical analysis.

Data Science Applications: The dataset's structure lends itself to various analytical pursuits within the data science realm. Researchers and enthusiasts can delve into trend analysis to uncover shifts in musical durations over the years, perform genre-based studies to explore the relationship between genre and track length, or even train machine learning models to predict track popularity based on various features. However, make sure to use the dataset only for educational purposes as per Spotify guidelines.

Column Descriptors: - ID: The unique identifier for each track on Spotify, facilitating direct access to the track. - Name: The title of the track, revealing its identity. - Duration (Minutes): The length of each track, provided in minutes, highlighting the extended nature of these compositions. - Artists: The names of the artists involved, offering insights into the collaborative landscape of each piece.

Ethically Mined Data: This dataset has been compiled with strict adherence to ethical data mining practices, utilizing Spotify's public API in full compliance with their guidelines. It represents a harmonious blend of technology and creativity, showcasing the vast musical archive that Spotify offers.

Gratitude is extended to Spotify for the data provided and the usage of their logo in the dataset thumbnail, which adds a recognizable visual cue to this academic resource. This dataset stands as a testament to the power of music and data combined, inviting exploration into the depths of musical analysis.

Analysis of ‘Spotify Top 2020 Songs’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/heminp16/spotify-top-2020-songs on 28 January 2022.

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

Context

The top 50 songs from the year 2020, totaling 50 songs in the dataset.

Content

The attributes were scraped from this website. Top Genre - genre of the song Year - release year of the song BPM – Beats Per Minute - The tempo of the song. Energy - The energy of a song; the higher the value, the more energetic. Danceability – Describes how suitable a track is for dancing; the higher the value, the easier it is to dance. Loudness (dB) – The loudness level in decibels, higher the value, the louder the song Liveness - the higher the value, the more likely the song is a live recording. Valence - A measure of musical positiveness of the track. The tracks with the highest number give a sense of positive moods. Duration (sec) - The duration of the song in seconds. Acousticness - A measure of how acoustic the track is. Speechiness - The higher the value that tells how many spoken words were in the track. Popularity - The higher the value, the more popular the song is.

Acknowledgements

I got inspired by Leonardo Henrique in this dataset, and found the amazing website that helped me scrap this data!

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

Analysis of ‘Spotify Top 200 Charts (2020-2021)’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/sashankpillai/spotify-top-200-charts-20202021 on 13 February 2022.

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

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

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

In the first quarter of 2025, the music streaming service Spotify reached an all-time high with 678 million active users worldwide. This marked an increase of around ten percent in just one year. What is Spotify? Spotify is a music streaming service that offers digital audio content. Basic audio content can be accessed for free whereas premium user subscriptions enable users to access offline mobile content as well as listen to music without advertising. In the first quarter of 2025, the company reported 268 million paying subscribers. Launched in 2008, Spotify originated in Sweden before expanding to European markets and the United States in 2011. Spotify’s U.S. launch was strongly marketed through Facebook, with the music streaming app profiting from the social listening integration via social media. Part of Spotify’s appeal can be attributed to the user- and brand-curated playlists, which can be shared publicly or between friends. Fans may choose what to listen to based on their current mood or preference, and the ability to share such content provides an element of social connectivity ordinarily reserved for networking sites.

💁‍♀️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 provides a comprehensive collection of setlists from Taylor Swift’s official era tours, curated expertly by Spotify. The playlist, available on Spotify under the title "Taylor Swift The Eras Tour Official Setlist," encompasses a diverse range of songs that have been performed live during the tour events of this global artist. Each dataset entry corresponds to a song featured in the playlist.

Taylor Swift, a pivotal figure in both country and pop music scenes, has had a transformative impact on the music industry. Her tours are celebrated not just for their musical variety but also for their theatrical elements, narrative style, and the deep emotional connection they foster with fans worldwide. This dataset aims to provide fans and researchers an insight into the evolution of Swift's musical and performance style through her tours, capturing the essence of what makes her tour unique.

Data Collection and Processing:

Obtaining the Data: The data was obtained directly from the Spotify Web API, specifically focusing on the setlist tracks by the artist. 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 (Taylor Swift)
• 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

Note: Popularity score reflects the score recorded on the day that retrieves this dataset. The popularity score could fluctuate daily.

Potential Applications:

• Predictive Analytics: Researchers might use this dataset to predict future setlist choices for tours based on album success, song popularity, and fan feedback.

Disclaimer and Responsible Use:

This dataset, derived from Spotify focusing on Taylor Swift's The Eras Tour setlist data, 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 on artists and the broader community.
• Data Accuracy and Timeliness: The dataset reflects a snapshot in time and may not represent the most current information available. Users are encouraged to verify the data's accuracy and timeliness.
• Source Verification: For the most accurate and up-to-date information, users are encouraged to refer directly to Spotify's official website.
• Independence Declaration: ...

Analysis of ‘Spotify Top 2018 & 2019 Songs’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/heminp16/spotify-top-2018-2019-songs on 28 January 2022.

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

Context

The top 50 songs from the years 2018 and 2019, totaling 100 songs in the dataset. Note: Better Now by Post Malone repeats twice, as it was popular in both 2018 and 2019.

Content

The attributes were scraped from this website. Top Genre - genre of the song Year - release year of the song BPM – Beats Per Minute - The tempo of the song. Energy - The energy of a song; the higher the value, the more energetic. Danceability – Describes how suitable a track is for dancing; the higher the value, the easier it is to dance. Loudness (dB) – The loudness level in decibels, higher the value, the louder the song Liveness - the higher the value, the more likely the song is a live recording. Valence - A measure of musical positiveness of the track. The tracks with the highest number give a sense of positive moods. Duration (sec) - The duration of the song in seconds. Acousticness - A measure of how acoustic the track is. Speechiness - The higher the value that tells how many spoken words were in the track. Popularity - The higher the value, the more popular the song is.

Acknowledgements

I got inspired by Leonardo Henrique in this dataset, and found the amazing website that helped me scrap this data!

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