Like many people, my Spotify Wrapped has become an annual highlight. It has always bothered me how it misses most of December and when I learned that you could request your full Spotify data for free, I wanted to see what it missed from that last month of 2022 and what other insights I could pull. I did most of the cleaning and analysis in Python before using D3 to create the first interactive and Datawrapper/Adobe Illustrator to create the static graphics.

Song-related trends

The challenge with genres

From obscure categories like Metropopolis and bubblegrunge to broad classifications like pop and disco, top genres is always a Spotify Wrapped highlight. I wanted to see where my genre preferences fell overtime. To do this I set up a script using the api to obtain the artist id of every artist in my history and then merged that with my streaming data. I was surprised that genres are classified by artists rather than individual songs, meaning something like Taylor Swift's debut is still considered pop. The other major challenge to the genre data is that each artist has an array of genres assigned to them. Kylie Minogue returns: ['australian dance', 'australian pop', 'dance pop', 'electropop', 'eurodance', 'europop', 'new wave pop', 'pop']. The overlap made it hard to identify my top genres and compare them overtime. Nevertheless, I did find the it interesting to see how different album releases and discoveries influenced my listening moods.