A tool for visualizing the hypergraphs in your Spotify playlists. A lot of these rely on (a) you having a folder system in place to organize your playlists and (b) you being obsessed enough to manually enter that folder config into a folder_mapping.json file.
The basic file structure should be as follows:
├── .env
├── .gitignore # You might wanna pop data/ in here
├── Readme.md
├── color_mapping.json # Edit these to the colors you want for your folders
├── folder_mapping.json # Spotify API doesn't allow pulling folders so I made a manual fuzzy match instead
├── module
│ ├── __init__.py
│ └── functions.py # TODO: break this up into data processing, visualization
├── requirements.txt
└── venn.ipynb
Authenticate using your Spotify Developer Credentials
auth_manager = f.auth_call()
This will give you a link to open in a browser. Sign in to your Spotify account. It will then route you to a seemingly broken page (localhost). Note that all your credentials and whatnot should be in a .env file like so (note no variables should have quotes around them):
CLIENT_ID=<YOUR CLIENT ID>
CLIENT_SECRET=<YOUR CLIENT SECRET>
REDIRECT_URI=http://localhost:8080
N.B.: Make sure the redirect URI matches whatever's in your developer dashboard settings at https://developer.spotify.com/dashboard/<YOUR CLIENT ID>/settings under "Redirect URIs"
Copy that localhost address from your browser (contains params with special codes) and paste into the box brought up by this command:
sp = f.auth_response(auth_manager)
Download & cache data. Genres and audio features are pending (Spotify doesn't like this many API calls, even with backoffs) so we keep these False for now.
df = f.download_data(sp, fetch_genres=False, fetch_audio_features=False)
Show tracks added over time:
f.make_timeline(df)
Show tracks added over time by folder:
f.make_folder_stacked_timeline(df)
Show track counts per folder, divided by playlist
f.make_folder_bars(df)
Show co-occurrence heatmap
f.plot_shared_count_heatmap(df=df)
Plot sankey:
f.plot_thresholded_parcats(df=df, show_folders=True).show()
Parallel Categories:
f.plot_parallel_categories(df=df).show()
Track Repeat Analysis:
f.track_repeat_analysis(df=df).show()
