I (Graham) spent some time sorting through my music collection to find my favorites for a “top 100 playlist”. Being a vis person, I immediately visualized the results, using Brunel. Here’s a look:
I took the data from my last post, aggregated up some fields and made a Chord Diagram for it, using RAVE. I was lazy and didn’t do a stellar job on rolling up years, so the year indicated is actually the center of a 4-year span — so 2007 is actually [2005.5, 2009.5] which is a little odd.
No big insights here — podcasts are all recent; alternative music is mostly recent too (Eels and Killers are artists with a large number of songs in my library). Interesting that I didn’t buy a lot of music form around 1999 …
I thought there were more packages that could do chord visualizations, but was only able to find some D3 examples.
The track information stored in iTunes is pretty interesting from a visualization point of view, as it contains dates, durations, categories, groupings — all the sorts of things that make for complex, interesting data to look at.The only issue is … it’s in iTunes, and I’d like to get a CSV version of it so I can use it in a bunch of tools.
So, here is the result; a couple of Python scripts that use standard libraries to read the XML file exported by iTunes and convert it to CSV. It’s not general or robust code, just some script that worked for me and should be pretty easy to modify for you. I’m not a Pythonista, mostly doing Java, so apologies for non-idiomatic usage. Feel free to correct or suggest in the comments as this is also a learning exercise for me.