Every time I listened to Martin Oliver’s album I did sequentially, so that probably explains it.Īll of the grey lines are songs in my actual top 20 and not in Spotify’s. df.groupby(by=).agg('count').sort_values(ascending=False)Įstocolmo and Los días eternos have exactly the same values everyday. Spotify list is as follows: Jorge Drexler, Radiohead, Arctic Monkeys, Vampire Weekend, Gustavo Cerati. I’m just gonna say: “Mexican? The that means?” Top Artists The barriers between one and the other get so blurry and arbitrary that I kind of don’t see the point. We are not gonna get into genres as I really don’t believe in them that much.
Of course Tints was nowhere to be found either. Paak and Kendrick Lamar - my god what an infectious beat. The thing is, I knew for a fact my biggest obsession track-wise this year belonged to Tints by Anderson. Cirrus from Bonobo, I listened in repeat everyday in January and February. Also, on Top Songs, 3 of them belonged to Martin Oliver’s sophomore album on which I had a humongous crush in April when it was released. As said above, Charly García and Serú Girán were nowhere to be seen. Looking at the Top Artists part, I realized everything didn’t seem as accurate. ranges = top_artists = set() for a_range in ranges: results = sp.current_user_top_artists(time_range=a_range, limit=50) top_artists =top_artists.union()īasically, the first artist not in the set would be my first discover reverse_df = df.iloc for i, row in reverse_df.iterrows(): if row not in top_artists: print("Song n# %d of the year" % (len(df) - i)) print(row) print("Played on %s" % str(row)) break Between the 100 artists received for each of this terms I created a set, so as not to get duplicates, and then if a new artist is not in this set, I’d consider it a ‘discovery’.
Basically, you can only access the first 100 for each term. There’s an offset parameter to balance with the limit so as to advance further in the list, but if you go further than 50 in the offset it returns an exception.
This gets you up to 50 artists per request and an interesting parameter of it lets you choose between long_term (calculated from several years of data and including all new data as it becomes available), medium_term (approximately last 6 months), and short_term (approximately last 4 weeks). How do we measure discovered artists? I used what I had in hand and decided to use the Spotify API’s function to get an user’s top artists. Now, this is the grey part where it starts to get not-so-scientificly-rigorous. Indeed, the first track that I listened to was Life Wasted ( AWESOME SONG TO START 2019, RIGHT?!). Mine, in particular, seemed odd like if my last months were not included at all. Just a quick look in r/spotify in Reddit is enough to realize most of the top 5 artists or top 5 songs were strange. Spotify Wrapped got leaked a couple of days before December and not every user was happy with the insights provided. Though not everything was as sweet this iteration. They are supposed to make you start the year listening to something new tailored to your liking by Spotify’s amazing recommendation algorithms. I need to confess, I’m in love with Tastebreakers ❤.
This year brought Spotify Wrapped which tells you lots of stats (which we’ll be analyzing) and two playlists: Top 100 and Tastebreakers. 2017 also brought the amount of minutes, the top songs playlist and a cool other playlist called “The ones that got away” featuring, maybe, cool recommendations from the past year. The amount of minutes spent listening to music, the number of different songs and a pretty cool insight about which was your favorite day of the week to listen to music. 2016 came with the usual top 5 of songs, artists and genres.
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