Plotify

Plotify expands upon spotify wrapped by letting you explore your tastes and preferences in music with data visualization.

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Inspiration

I always like going through my spotify wrapped but some years it has been underwhelming since I can often guess who my top artists were for the entire year. I wanted to experiment with javascript libraries and the spotify API seemed like it had enough data to work with.

What it does

Plotify lets users authorize their spotify accounts and displays relevant information about their top artists, tracks, genres, playlists and more.

How I built it

I developed this app with flask and python scripting as the backend. I'm running authorization through a python utility for handling api tokens, and storing api call results in json. Plotting was done using both D3 and chart.js

Challenges I ran into

I've used flask several times before, but there was a significant time crunch with troubleshooting the charting libraries and trying to wrangle and filter enough data to make meaningful visualizations.

Accomplishments that I'm proud of

In the end, I'm very satisfied with the result. It's not highly sophisticated, but the original spotify wrappeds weren't either. I'm confident there's at least one thing that everyone could find interesting, whether a new genre they didn't know they liked, or realizing that they often prefer older music.

What I learned

I learned that 24 hours isn't very much time to get something polished and that even the nicest looking API documentation still has its kinks to solve.

What's next for Plotify

In the future, I'd like to take advantage of the streaming data that you can request from Spotify and allow users to upload their listening history directly to the site. Having years worth of data could make for some very interesting visualizations with how music preferences shift over time.

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