While I'm developing (or just while I'm commuting to work) I usually love to hear some rock music.
I created some playlists on Spotify, but lately...
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Will you post this code on GitHub? I would like to test it against my own playlist :)
Hi Roger, I uploaded on github a Jupyter notebook:
github.com/eric-bonfadini/python-n...
I didn't realise Spotify has such an awesomely detailed API - you've put it to great use here Eric!
Just as an aside for anyone that likes the idea of tracking their music listening habits without writing code, there's a really cool service called last.fm (it's not as detailed as Eric's above analysis however)
The title of this post does not do justice to its awesomeness.
Thanx a lot Ben!
I even spent quite some time in finding a good title, but, after all, developers are not good in naming things! :-D
It's a very interesting concept. I make video tutorials for Packt Publishing. Would you be interested in discussing making a course?
Would be a great opportunity to showcase some of your awesome skills with Python.
Nice one! This is something I always had in mind but never got it done. I like the approach to use charts for visualization!
Also good that you linked the API documentations, makes it easier to implement the calls (saves some time on research).
Thanks for the post!
Super dooper nice! I like Pandas, too, and the ease it gives you for data analysis. If it comes to music I am also big fan of it so I will surely take a closer look to Spotify API.
BTW, why didn't you use pandas built-in charting capabilities? Was seaborn really needed here?
I usually like Pandas built-in capabilities for quick plots while I'm exploring data (therefore Seaborn wasn't strictly needed here).
Anyway I really love the APIs, configurations and plots offered by Seaborn, along with its simplicity and Pandas integration.
This is really awesome, dude.
Do you think it could be used to generate a playlist based on an analysis of multiple people to get a playlist with common interests between them?
It could help with some problems i've seen in parties.
Really liked this, i'm going to reread it later.
Awesome article :)
Im dipping my toes into data science for the first time since i took my intro class, but never could find a project that was interesting to do + scoped to my capabilities.
Ive created a programming music playlist and have set up the spotify api to get the track info.
Now the only part left is the data science analysis, so excited to tackle that today.
Playlist for anyone whos interested in Classical/Video Game music :
open.spotify.com/user/1250987938/p...
Hopefully the data is all in the same genre.
I knew Spotify has an API. I didn't know it could do "Tell me about the song", but considering the "Year in Music" stuff, I suppose I should.
I have Last.FM data going back almost a decade (I could've used the API, for which I have a key, but instead I used this web-based tool), and I could see adding "100 Most-Played Tracks in 2011" and the like, and tracking how often I played my favorites, but really, the "This is what a Fave Song looks like; give me more" is much more interesting.
I will have to dive into the APIs and your code.
But where is the fun in that? :)
dude, this post was awesome!
nice to know the detailed API Spotify has
Awesome idea ! Looks fun :)
This is absolutely wonderful Eric! Thanks for sharing.
And how are you gonna get anything similar with a YouTube search?
Awesome article.
@walker , I think you'll really enjoy reading this one.
Really cool article man! Something I definitely want to dive into now
Really interesting work, thanks !
Honestly Spotify's current "Recommended for you" algorithm doesn't really work for me so from my experience it's really not something reliable.
That's what and how personal playlist do.
Looks like it works the same way.
This is quite amazing. Awesome work!