Todoist reminding me that my Last.fm analysis is overdue
I downloaded my Last.fm scrobble data a week or so back using this excellent online tool. It contains almost every song I listened to on my PC (and some from my phone and iPod) between March 2011 and November 2014, 12784 scrobbles in total.
I will do a proper analysis soon (sorry, Todoist), but here are some descriptive stats that Pandas throws out:
Artist Album Title Timestamp
count 12784 12593 12784 12784
unique 354 752 2048 12709
top The Beatles Past Masters Sleeping Sun 09 Feb 2013 13:24
freq 1904 285 39 8
I want to do an analysis of how I discovered and fell in love with new bands over the course of these three years. I don't listen to music as much these days, but this is a good dataset and I promise not to let it go to waste.
Update: Added the frequencies and histograms for artists and song titles.
Artists
>>> df.Artist.value_counts()
The Beatles 1904
Coldplay 842
John Mayer 604
Oasis 499
Arctic Monkeys 447
The Who 345
The Rolling Stones 318
Snow Patrol 315
The Velvet Underground 297
Bob Marley & The Wailers 287
Noah and the Whale 280
The Kooks 246
Norah Jones 211
The Kinks 201
Badfinger 189
Paul Simon 179
The Jimi Hendrix Experience 160
Pink Floyd 159
John Lennon 146
Queen 141
The Wailers 140
Radiohead 127
U2 121
Cream 114
George Harrison 111
Lorde 100
Bee Gees 97
Simon & Garfunkel 92
Roy Orbison 92
Billy Joel 92
...
Song Titles
>>> df.Title.value_counts()
Sleeping Sun 39
Could You Be Loved 38
Parachutes 35
Stuck on the Puzzle 34
You've Got to Hide Your Love Away 34
Word 34
Brothers & Sisters 34
My Girl 32
One Day 32
Wild Thing 32
Bigger Than My Body 32
Junk of the Heart (Happy) 31
Stir It Up 31
Blue Skies 31
Day Tripper 31
Don't Look Back in Anger 30
No Woman No Cry 30
For You (Shiver single Version) 30
All Around the World 29
Strawberry Swing 29
Lifeboats 28
Let Me Go (acoustic) 28
Ruby 28
Baby You're a Rich Man 28
Bitter Sweet Symphony 28
In Repair 27
Getting Better 27
Rainy Day 27
Love Is All Around 27
Here Comes the Sun 27
..
