So I decided to visualize that by tracking my favorite artists scrobbles over time. When I look at my all time favorite artists, I am sometimes shocked about how newer artists are able to make their way up the list so quickly. I tend to obsess over songs and artists for periods of times, listening to albums or songs on repeat for days or even weeks. I ended up making about 16 graphs, with some exploring the best way to present the same data.ĭuring this whole process, I tried to think about how I listen to music and what is not represented in the data and graphs I already have available to me. I started with simple bar charts listing my favorite songs and artists, but quickly explored how I could add time into the mix. Since this was my first experience with Tableau and I enjoy learning by doing, I went to Tableau and started throwing data on every axis to see what I could create. I went about making the graphs by exploring the data in Tableau. However, for my visualizations, I wanted to experiment with graphs other than bar graphs. I liked Yitzhak’s visualization because it visualized the data compared to the lists I am used to and that it gave some summary stats at the very beginning.
LASTFM SCROBBLER SPOTIFY HOW TO
When searching Spotify visualizations I came across Yaron Yitzhak’s guide on how to visualize Spotify data with Google’s Data Studio. Mom who visualized every time her children said “Mom” for a week My inspiration for this comes from two main sources. While this is slightly inaccurate, featured artists are not a common feature in my music and would only account for a fraction of a percentage of my scrobbles. To simplify this, I only attributed the artist who released the track to make sure each scrobble of a song was only counted once. For example, songs with featured artists are often listed as “Artist1 & Artist2” rather than attributed to all the individual artists. In Tableau, I used groups to clean up some minor data inconsistencies for some of my most listened to artists. I used an online tool where you just give it your username and it exports your Last.fm data to a CSV file (along with other options). For example, I have 10 scrobbles on a song means I have listened to that song 10 times. In a similar fashion, a scrobble is one record or instance of someone listening to a track.
Scrobbling is a term that means an online service is recording data of a user’s listening history, with Last.FM being one the most popular. Last.fm is a popular service that you can hook up to your streaming services to automatically record what songs and artists you listen to.
There is a lot of nuance that is missed about how I listen to music despite the fact that Spotify has all the data. It’s one thing to know your who your favorite artist is, but to see you listen to them twice as much? A lot more exciting! However, Spotify Wrapped included, they all hide the magnitude of the data from you (not to mention Spotify Wrapped excludes November and December from its charts). While we wait for Spotify Wrapped to come around each year, my friends and I love trying out new and different sites that that give a high level overview of our relationship with music such as Stats for Spotify, Obscurify, or having an AI judge my music taste. The lights, the cold weather, the festivities, and Spotify Wrapped! It’s something my friends look forward to at the end of every year as we love to see who are favorite artists are backed by the data of our actual listening habits. One of my favorite times of the year is December.