Liveness: Detects the presence of an audience in the recording. Instrumentalness: Predicts whether a track contains no vocals. 1.0 represents high confidence the track is acoustic.Įnergy: Energy is a measure from 0.0 to 1.0 and represents a perceptual measure of intensity and activity. A value of 0.0 is least danceable and 1.0 is most danceable.Īcousticness: A confidence measure from 0.0 to 1.0 of whether the track is acoustic. Popularity: Song Popularity (0–100) where higher is betterĭanceability: Danceability describes how suitable a track is for dancing based on a combination of musical elements. It have 16 columns describing the various features of songs.įollowing is the summary of all the variables in the data set. The data frame df consists of songs as well as their features values. You can choose features of your interest to work upon the analysis. The data fetched using API is in the form of JSON that is key-value data format. I used Spotipy and pandas libraries to create a data frame and save my data, and time library to pause the execution of the loop for the track. Install Spotipy and import all the required libraries. You can also check GitHub examples to better understand the working of their code. I highly recommend going through Spotipy’s documentation to understand the libraries and their work. Using Spotipy, we get full access to all of the music libraries provided by the Spotify platform. Spotipy is “a lightweight Python library for the Spotify Web API ”. Spotify’s API allows us to extract various “audio features” or categories such as danceability, energy, instrumentalness, liveness, loudness, speechiness, acousticness, and tempo, etc. To analyze each track from an album, I fetched track metadata such as track name, album, release date, length, and popularity. You can use any public playlist of your choice if you want to work on any particular artist or playlist. I decided to analyze this playlist because there was a change in music taste during this period and the playlist contains a huge number of songs which would help in analysis. The playlist provides the data for songs from the year 2010 to 2020, for various artists which were liked by the listeners. STEP 2: Data Extractionįor the following analysis, I’m using the playlist “ Top songs of the decade” from user “ Emanuelwhatelse ”. These values are unique for every new application. These two environment variables will help in gaining the access to the Spotify data and helps in querying the API endpoints. When you access your App, you will find your Client ID and Client Secret Key. The below picture shows a simple architecture of how to request APIs from the Web server and their databases through the internet. Various websites offer an API as a service that provides a high-level interface to directly retrieve data from their repositories or databases at the backend for development or analysis purposes. Thinking about my variating music love, I decided to analyze music playlists from Spotify and understand how the features affect each other and what makes a song popular.ĪPI stands for “ Application Programming Interface ”. I realized that I just listen to songs so randomly like I could be listening to Taylor Swift one minute and after a while swiping to rock. I recently came across the Spotify API and got to know we can use Spotify API to analyze our own music playlist. There are few other music API sources like Itunes, Shazam, LastFm. In doing so, they gain a better understanding of musical behaviors when they listen to songs on Spotify. ![]() The motivation of this article is to enable anyone to discover patterns and insights about the music that they listen to. The way Spotify suggests music to listeners has a major influence on their listening habits. I have been using Spotify for the last two years, and it is my favorite music application for some obvious reasons like huge song collections and no ads. It has more than 50 million tracks which user can browse using various parameters like artists, album, genre, or via playlists. Spotify is an audio streaming platform that provides DRM-restricted music, videos, and podcasts from record labels and media companies.
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