Explore my listening habits, as downloaded from Apple Music.
I have downloaded my Apple Music library and listening history dating back to 2016. Click on any of the artists below
Most Played Songs:
Top Album:
Good Kid, M.A.A.D City (134 hours)
Most Played Songs:
Top Album:
Un Verano Sin Ti (56 hours)
Most Played Songs:
Top Album:
Blonde (104 hours)
Most Played Songs:
Top Album:
Aquemini (61 hours)
Most Played Songs:
Top Album:
Faces (53 hours)
I got an idea... Let's make a word cloud out of all the song titles in my library, and see which words come up the most!
First, I need to break up all my song titles, word by word....
I have 44,000 rows now, surely that's too many to display on a wordcloud...
Ok, I have only included words that apper at least 4 times.
Here's my result!
Hmmm, that's not really what I was hoping for. There are still too many words included, and my most common word is "(feat."!
Let's remove some of these filler words
to see see what we get. Let's also up the minimum count for a word to be included to 5.
Here's my result!
Hmmmmm looks better but still not quite satisfying. We are starting to see some interesting words, such as Up, Down, Sunshine, Moonligh. But still too messy...
The main issue seems to be all the artists names that are appearing in this word cloud. I think the key is to remove all parts of a song title that ocurr after the "(feat." text...
Here's my result!
There we go! There are still some filler words in there, and some alternate spellings of (Feat. I could remove if I wanted to.
However, I feel as though I have spent enough time on this section, and after second thought, maybe creating a word cloud out of rap song titles isn't the best idea after all....
MOVING ON...
You've now seen what I listen to on Apple Music, but what about WHEN I listen to Apple Music?
After normalizing all of my listen times to local time, I got the results below.
I think the most interesting finding here was just how much I was listening to music while studying late night in college.
During my 3 full calendar years in college (2018-2020), an average of 10% of my plays were from 1:00 - 3:59 AM (!!)
Compare this with my first two full years in the workforce, where this number shrank down to 1%.
