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51c5bd8d7d
* Created a testing suite for the spotipy client.Spotify class * Delete pyvenv.cfg * Corrected error in description * Added some advanced usage examples, some of which involve utilizing basic data analysis methods using other libraries such as Pandas * Minor formatting fixes --------- Co-authored-by: Stéphane Bruckert <stephane.bruckert@gmail.com>
46 lines
1.5 KiB
Python
46 lines
1.5 KiB
Python
import spotipy
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from spotipy.oauth2 import SpotifyOAuth
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import random
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# Set up Spotify credentials
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sp = spotipy.Spotify(auth_manager=SpotifyOAuth(
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client_id="YOUR_APP_CLIENT_ID",
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client_secret="YOUR_APP_CLIENT_SECRET",
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redirect_uri="YOUR_APP_REDIRECT_URI",
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scope="user-top-read,user-library-read,user-read-recently-played"))
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# Get the user's top tracks
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top_tracks = sp.current_user_top_tracks(limit=10, time_range='long_term')
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# Get the user's liked tracks
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liked_tracks = sp.current_user_saved_tracks(limit=10)
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# Get the user's listening history
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history = sp.current_user_recently_played(limit=10)
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# Extract a list of the top track IDs
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top_track_ids = [track['id'] for track in top_tracks['items']]
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# Extract a list of the liked track IDs
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liked_track_ids = [track['track']['id'] for track in liked_tracks['items']]
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# Extract a list of the history track IDs
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history_track_ids = [track['track']['id'] for track in history['items']]
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# Combine the three lists and shuffle them randomly
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seed_track_ids = top_track_ids + liked_track_ids + history_track_ids
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random.shuffle(seed_track_ids)
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# Use the IDs to get some recommendations
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# Note: the seed_tracks parameter can accept up to 5 tracks
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recommendations = sp.recommendations(
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seed_tracks=seed_track_ids[0:5], limit=10, country='US')
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# Display the recommendations
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for i, track in enumerate(recommendations['tracks']):
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print(
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"{}. {} by {}"
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.format(i+1, track['name'], ', '
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.join([artist['name'] for artist in track['artists']]))
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)
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