💁‍♀️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 Song Attributes’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/geomack/spotifyclassification on 28 January 2022.

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

Context

A dataset of 2017 songs with attributes from Spotify's API. Each song is labeled "1" meaning I like it and "0" for songs I don't like. I used this to data to see if I could build a classifier that could predict whether or not I would like a song.

I wrote an article about the project I used this data for. It includes code on how to grab this data from the Spotipy API wrapper and the methods behind my modeling. https://opendatascience.com/blog/a-machine-learning-deep-dive-into-my-spotify-data/

Content

Each row represents a song.

There are 16 columns. 13 of which are song attributes, one column for song name, one for artist, and a column called "target" which is the label for the song.

Here are the 13 track attributes: acousticness, danceability, duration_ms, energy, instrumentalness, key, liveness, loudness, mode, speechiness, tempo, time_signature, valence.

Information on what those traits mean can be found here: https://developer.spotify.com/web-api/get-audio-features/

Acknowledgements

I would like to thank Spotify for providing this readily accessible data.

Inspiration

I'm a music lover who's curious about why I love the music that I love.

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

Analysis of ‘Spotify Past Decades Songs Attributes’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/cnic92/spotify-past-decades-songs-50s10s on 28 January 2022.

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

Context

Why do we like some songs more than others? Is there something about a song that pleases out subconscious, making us listening to it on repeat? To understand this I collected various attributes from a selection of songs available in the Spotify's playlist "All out ..s" starting from the 50s up to the newly ended 10s. Can you find the secret sauce to make a song popular?

Content

This data repo contains 7 datasets (.csv files), each representing a Spotify's "All out ..s" type of playlist. Those playlists collect the most popular/iconic songs from the decade. For each song, a set of attributes have been reported in order to perform some data analysis. The attributes have been scraped from this amazing website. In particular, according to the website the attributes are:

• top genre: genre of the song
• year: year of the song (due to re-releases, the year might not correspond to the release year of the original song)
• bpm(beats per minute): beats per minute
• nrgy(energy): energy of a song, the higher the value the more energetic the song is
• dnce(danceability): the higher the value, the easier it is to dance to this song.
• dB(loudness): the higher the value, the louder the song.
• live(liveness): the higher the value, the more likely the song is a live recording.
• val(valence): the higher the value, the more positive mood for the song.
• dur(duration): the duration of the song.
• acous(acousticness): the higher the value the more acoustic the song is.
• spch(speechiness): the higher the value the more spoken word the song contains.
• pop(popularity): the higher the value the more popular the song is.

Acknowledgements

I got inspired by the top-notch work by Leonardo Henrique in this dataset. Thanks to him I discovered this website, from which all the data collected here have been scraped.

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

The dataset is composed by different musial genres and for each kind we have different features that caracterize it. Reference https://developer.spotify.com/documentation/web-api/reference/#endpoint-get-audio-features

• Acousticness: A confidence measure from 0.0 to 1.0 of whether the track is acoustic. 1.0 represents high confidence the track is acoustic.
• Danceability: Danceability describes how suitable a track is for dancing based on a combination of musical elements including tempo, rhythm stability, beat strength, and overall regularity. A value of 0.0 is least danceable and 1.0 is most danceable.
• Duration_ms: The duration of the track in milliseconds.
• Energy: Energy is a measure from 0.0 to 1.0 and represents a perceptual measure of intensity and activity. Typically, energetic tracks feel fast, loud, and noisy. For example, death metal has high energy, while a Bach prelude scores low on the scale. Perceptual features contributing to this attribute include dynamic range, perceived loudness, timbre, onset rate, and general entropy.
• Instrumentalness: Predicts whether a track contains no vocals. “Ooh” and “aah” sounds are treated as instrumental in this context. Rap or spoken word tracks are clearly “vocal”. The closer the instrumentalness value is to 1.0, the greater likelihood the track contains no vocal content. Values above 0.5 are intended to represent instrumental tracks, but confidence is higher as the value approaches 1.0.
• Key: The key the track is in. Integers map to pitches using standard Pitch Class notation. E.g. 0 = C, 1 = C♯/D♭, 2 = D, and so on.
• Liveness: Detects the presence of an audience in the recording. Higher liveness values represent an increased probability that the track was performed live. A value above 0.8 provides strong likelihood that the track is live.
• Loudness: The overall loudness of a track in decibels (dB). Loudness values are averaged across the entire track and are useful for comparing relative loudness of tracks. Loudness is the quality of a sound that is the primary psychological correlate of physical strength (amplitude). Values typical range between -60 and 0 db.
• Mode: Mode indicates the modality (major or minor) of a track, the type of scale from which its melodic content is derived. Major is represented by 1 and minor is 0.
• Popularity: The popularity of the track. The value will be between 0 and 100, with 100 being the most popular. The popularity is calculated by algorithm and is based, in the most part, on the total number of plays the track has had and how recent those plays are. Note: When applying track relinking via the market parameter, it is expected to find relinked tracks with popularities that do not match min_*, max_*and target_* popularities. These relinked tracks are accurate replacements for unplayable tracks with the expected popularity scores. Original, non-relinked tracks are available via the linked_from attribute of the relinked track response.
• Speechiness: Speechiness detects the presence of spoken words in a track. The more exclusively speech-like the recording (e.g. talk show, audio book, poetry), the closer to 1.0 the attribute value. Values above 0.66 describe tracks that are probably made entirely of spoken words. Values between 0.33 and 0.66 describe tracks that may contain both music and speech, either in sections or layered, including such cases as rap music. Values below 0.33 most likely represent music and other non-speech-like tracks.
• Tempo: The overall estimated tempo of a track in beats per minute (BPM). In musical terminology, tempo is the speed or pace of a given piece and derives directly from the average beat duration.
• 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).

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 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 ---