Started out as a Spotify music recommendation app
NEW: Added features from music-generator 🎹
Content:
Streaming history can be retrieved from the Spotify profile as JSON, or through the spotipy functions current_user_recently_played or current_user_saved_tracks
with open("recently_played_20210306.json","r") as f:
results = json.load(f)
tracks = []
for idx, item in enumerate(results['items']):
track = item['track']
tracks.append([idx, track['artists'][0]['name'], track['name']])
trackDict = {"id":[], "artist":[],"name":[]}
for idx, item in enumerate(results['items']):
track = item['track']
trackDict["id"].append(idx)
trackDict["artist"].append(track['artists'][0]['name'])
trackDict["name"].append(track['name'])
import pandas as pd
trackDf = pd.DataFrame.from_dict(trackDict)Analysis of features to produce a polar plot
import spotifyAPI
from secret import clientId,clientSecret
token = spotifyAPI.get_token(clientId,clientSecret)
lucy_id = spotifyAPI.get_track_id2('Lucy in the Sky', token, artist = 'The Beatles')
url = "https://open.spotify.com/track/"+lucy_id
import webbrowser
webbrowser.open(url)
import pandas as pd
lucy_features = spotifyAPI.get_features(lucy_id,token)
df = pd.DataFrame(lucy_features, index=[0])
df_features = df.loc[: ,['acousticness', 'danceability', 'energy', 'instrumentalness', 'liveness', 'speechiness', 'valence']]
spotifyAPI.feature_plot(df_features)json_response = spotifyAPI.get_track_reco(lucy_id,token)
uris =[]
for i in json_response['tracks']:
uris.append(i)
print(f"\"{i['name']}\" by {i['artists'][0]['name']}")This notebook leverages the Spotipy module to access the Spotify API:
https://spotipy.readthedocs.io/
- Basic auth
- Search & Get track
- Get features
- Get recommendations
- Spotipy auth
- Artist albums
- Artist top tracks
- Advanced Search (help)
- Current user
- Access Scopes (detailed later)
- Images
- Spotify Connect
- Playback
- Users
- Playlists
- Library
- Listening History
- Follow
- https://dev.to/mxdws/using-python-with-the-spotify-api-1d02
- https://medium.com/python-in-plain-english/music-recommendation-system-for-djs-d253d472677e
- https://github.com/tgel0/spotify-data
- https://towardsdatascience.com/a-visual-look-at-my-taste-in-music-a8c197a728be
- https://medium.com/@maxtingle/getting-started-with-spotifys-api-spotipy-197c3dc6353b
- https://towardsdatascience.com/a-music-taste-analysis-using-spotify-api-and-python-e52d186db5fc
- https://github.com/jmcabreira/A-Music-Taste-Analysis-Using-Spotify-API-and-Python.
- https://medium.com/analytics-vidhya/build-your-own-playlist-generator-with-spotifys-api-in-python-ceb883938ce4
- https://towardsdatascience.com/get-your-spotify-streaming-history-with-python-d5a208bbcbd3
- https://towardsdatascience.com/how-to-utilize-spotifys-api-and-create-a-user-interface-in-streamlit-5d8820db95d5
- https://developer.spotify.com/community/showcase/spotify-audio-analysis/
- https://medium.com/deep-learning-turkey/build-your-own-spotify-playlist-of-best-playlist-recommendations-fc9ebe92826a
- https://www.theverge.com/platform/amp/tldr/2018/2/5/16974194/spotify-recommendation-algorithm-playlist-hack-nelson
- http://sortyourmusic.playlistmachinery.com/
- https://towardsdatascience.com/k-means-clustering-and-pca-to-categorize-music-by-similar-audio-features-df09c93e8b64
- https://towardsdatascience.com/interactive-machine-learning-and-data-visualization-with-streamlit-7108c5032144
- http://druckhaus-hofmann.de/gallery/36-wj-june-2020.pdf
- https://medium.com/swlh/music-genre-classification-using-transfer-learning-pytorch-ea1c23e36eb8
- https://towardsdatascience.com/music-genre-classification-with-tensorflow-3de38f0d4dbb
- https://towardsdatascience.com/machine-learning-and-recommender-systems-using-your-own-spotify-data-4918d80632e3
- https://github.com/anthonyli358/spotify-recommender-systems/blob/main/recommender_playlists.ipynb