last.fm is one of my very favorite services. It's rough around the edges in some parts, but I've gotten incredible value from it. A couple of websites built on it that I check out from time to time:
- https://lastfmviz.netlify.app/ - shows what you've been listening to as a grid of album covers. You can scroll down as long as you want. It's cool to look back and remember where I was when listening to specific music.
- https://lastfmstats.com/ - generates tons of rankings, line charts, racing bar charts, etc. A couple I like: "Artist streaks" (I listened to Pavement tracks 122 times in a row in August 2023), "Unique artists in a single month" (225 in July 2025) and "Unique weeks per artist/album/track" (good to identify what you're always listening to vs. what you listened to heavily in a specific time)
- https://pmcdonough8133.github.io/last.timer/ - shows your listening rankings by hours, minutes instead of just scrobble count. This really should be a default feature in the site, as some artists have average track length 2-3x times of others.
If you use Spotify, another site I've had loads of fun with is https://explorify.link/.
Give LastWave a try! My buddy built it back in college and updated it recently.
https://savas.ca/lastwave/
Generates a groovy wave chart of all your past listening.
> shows your listening rankings by hours, minutes instead of just scrobble count
I've wanted to build something like this for a long time, cool (and unsurprising, really) to see it's already done!
Swans is my number 30 by scrobbles but 4 by playtime, which makes total sense.
If you're a Spotify user, you can get even more precise data by downloading your listening data. The website I linked gets data from MusicBrainz and tries to fill in the gaps with an average, but even then it gets some things wrong.
E.g. Fishmans - Long Season is a 40 minute song, but the website's considers it as divided into 4-5 parts. And you don't have to listen to the full song to get a scrobble.
In the Spotify data you get the exact number of seconds you listened to it. And it is surprisingly complete and easy to use too. With LLMs I bet you can load it into pandas and construct queries for any insight you want in seconds.
Nice tip, but I use YouTube Music. I just downloaded my listening history, looks like they don't include listening duration, alas.
The middle one is fascinating. The first track I ever scrobbled is by an artist I have yet to listen to again in 22 years. Much of the longest gaps is taken up by bands I found or started to like due to Rock Band which came out around that time. Man I miss that too, we had 30 or 40 people over right after it came out and turned the house into a karaoke dive, right down to having to kick them off the couch the next morning.
Thanks for sharing, these are awesome ways to explore your data.
Thanks for sharing lastfmstats, wasn't familiar with that one! Delightful and nostalgic seeing the listens sliced as they have
a while ago I created this one, for when you want to listen to a familiar album, but can't decide which one: https://what-to-listen.chef-labs.deno.net/