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Popular Songs Dataset
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About
This dataset provides detailed insights into music tracks and their performance across various digital platforms [1]. It includes core information such as the track name, artist(s), and release date, alongside their presence in popular playlists and charts on platforms like Spotify, Apple Music, Deezer, and Shazam [1]. Additionally, it features key musical metrics like BPM, key, mode, danceability, valence, energy, acousticness, instrumentalness, and liveness_speechiness [1]. This data allows for the analysis of popularity, genre characteristics, and audience engagement of music offerings across multiple streaming services [1].
Columns
- track_name: The title of the song [2]. It has 943 unique values out of 953 entries [3].
- artist(s)_name: The name(s) of the artist(s) who created the track [2]. Taylor Swift is the most common artist, accounting for 4% of entries [3, 4].
- artist_count: The number of artists associated with the track [2]. Values range from 1 to 8, with a mean of 1.56 [4].
- released_year: The year when the track was released [2]. Years span from 1930 to 2023, with most releases occurring between 2013 and 2023 [5].
- released_month: The month when the track was released [2]. Months are numbered 1 to 12, with a mean of 6.03 [5, 6].
- released_day: The day when the track was released [2]. Days range from 1 to 31, with a mean of 13.9 [6, 7].
- in_spotify_playlists: Identifies if the song is featured in Spotify playlists [2]. Values range from 31 to 52,900, with a mean of 5,200 [7, 8].
- in_spotify_charts: Specifies if the song ranks in popularity on Spotify's chart listings [2]. Values range from 0 to 147, with a mean of 12 [8].
- streams: The total number of streams the track has accumulated [9]. There are 949 unique stream counts [10].
- in_apple_playlists: Identifies if the song is featured in Apple Music playlists [9]. Values range from 0 to 672, with a mean of 67.8 [10].
- in_apple_charts: Specifies if the song ranks in popularity on Apple Music's chart listings [9]. Values range from 0 to 275, with a mean of 51.9 [11].
- in_deezer_playlists: Identifies if the song is featured in Deezer playlists [9]. Values range from 0 to 12,400, with a mean of 385 [12].
- in_deezer_charts: Specifies if the song ranks in popularity on Deezer's chart listings [9]. Values range from 0 to 58, with a mean of 2.67 [12, 13].
- in_shazam_charts: Specifies if the song ranks in popularity on Shazam's chart listings [9]. Values range from 0 to 1,450, with a mean of 60. Note that 5% of entries are missing for this column [13, 14].
- bpm: Beats per minute - a measure of tempo in music [9]. Values range from 65 to 206, with a mean of 123 [14].
- key: The musical key in which the track is composed [15]. C# is the most common key (13%), but 10% of entries are missing for this column [16].
- mode: Indicates whether the track is in a major or minor key [15]. 58% of tracks are in a Major key [16].
- danceability_%: A measure of how suitable a track is for dancing [15]. Values range from 23% to 96%, with a mean of 67% [16, 17].
- valence_%: The musical positiveness conveyed by a track [15]. Values range from 4% to 97%, with a mean of 51.4% [17, 18].
- energy_%: The perceived energy of a track [15]. Values range from 9% to 97%, with a mean of 64.3% [18].
- acousticness_%: A measure of how acoustic a track is [15]. Values range from 0% to 97%, with a mean of 27.1% [19].
- instrumentalness_%: A measure of whether a track contains vocals [15]. Values range from 0% to 91%, with a mean of 1.58% [20].
- liveness_%: A measure of presence of live elements in a track [20, 21]. Values range from 3% to 97%, with a mean of 18.2% [21].
- speechiness_%: Indicates the presence of spoken words in the song [21]. Values range from 2% to 64%, with a mean of 10.1% [22].
Distribution
The dataset is provided as a data file, typically in CSV format [23]. The sample file, named Popular_Spotify_Songs.csv, has a size of 106.27 kB and contains 24 distinct columns [3]. Most columns have 953 valid records, ensuring a solid base for analysis [3-8, 10-14, 16-22].
Usage
This dataset is ideal for analysts evaluating the popularity, genre trends, and audience engagement of music across various streaming platforms [1]. It can be used for data visualization and exploratory data analysis [3]. Specific use cases include:
- Analysing track performance across Spotify, Apple Music, Deezer, and Shazam [1].
- Understanding musical characteristics (BPM, key, danceability, energy) and their correlation with popularity [1, 15].
- Identifying trends in music releases over time [1, 2].
- Segmenting tracks by their presence in popular playlists and charts [1].
Coverage
The dataset's time range extends from 1930 to 2023, with a strong emphasis on releases between 2013 and 2023 [5]. It covers track performance and characteristics across multiple streaming platforms: Spotify, Apple Music, Deezer, and Shazam [1]. There is no explicit geographic or demographic scope specified, implying a general global coverage across these digital platforms. Notably, the 'in_shazam_charts' column has 5% missing values, and the 'key' column has 10% missing values, which users should be aware of [13, 16].
License
CC0: Public Domain
Who Can Use It
This dataset is primarily intended for data analysts, music researchers, and data scientists [1]. They can utilise it for:
- Performing statistical analysis on music trends and popularity.
- Creating visualisations to showcase patterns in track metrics and platform performance.
- Developing predictive models for music success or genre classification.
- Gaining insights into audience engagement with different musical offerings [1].
Dataset Name Suggestions
- Global Streaming Music Metrics
- Track Performance Across Platforms
- Spotify & Beyond: Music Insights
- Popular Songs Dataset
- Digital Music Track Analysis
Attributes
Original Data Source: Popular Songs Dataset
QUALITY