Live version of the single page application.
TODO github pages not working because REDIRECT_URI hardcoded to 'http://127.0.0.1:5500/spotify_apidashboard_webpage.html and CLIENT_ID also hardcoded
I think I have to do verification more like mentioned in this bounty here https://replit.com/bounties/@AfricanFractal/build-a-web-app-for
NOTE spotify also provides big datasets for research https://research.atspotify.com/datasets/
NEXT TODO add also extracted artists
also very nice, ML with track features https://github.com/jmcabreira-zz/Data-Science-Projects/blob/master/A-Music-Taste-Analysis-Using-Spotify-API-and-Python./Playlist_analysis_%20.ipynb
TODO get all song attributes like here https://medium.com/@eelopez088/data-analysis-with-spotify-api-a1507f48e9b0 plot correlations, do a pcr to find user patterns, etc
TODO implement real login https://developer.spotify.com/documentation/web-api/concepts/authorization
Visualize song ranking data
- https://www.google.com/search?client=ubuntu-sn&channel=fs&q=ranking+data+visualization
- https://libguides.uvic.ca/pick-a-viz/ranking
- https://www.quanthub.com/data-storytelling-charts-for-displaying-ranks/
we know the ranking of the tracks based on ur usage (sort and long term) and we know track, artist, album, etc and popularity (0-100) of all those things myb convert gloabl-popularity to gloabl-popularity-rank to compare to my-ranks so i can compare popularity with my personal ranking in multiple dimensions (track, artist, album, etc.)
NOTE i can also play with the time_range API parameter 'long_term', 'mid_term', 'short_term' NOTE i can also use an offset as start index in API call to get from 50-100, etc. like described here spotify/web-api#1121
from google search https://www.google.com/search?client=ubuntu-sn&channel=fs&q=spotify+api+for+data+science
- https://towardsdatascience.com/spotify-api-audio-features-5d8bcbd780b2
- https://levelup.gitconnected.com/music-analysis-with-spotify-api-59c080734c6e
- https://github.com/jkvaternik/oye
- https://observablehq.com/@sandraviz/spotify-artists-d3--step2
- https://towardsdatascience.com/step-by-step-to-visualize-music-genres-with-spotify-api-ce6c273fb827
- https://github.com/iqbalbasyar/Spotify-for-Data-Science
here is also some nice stuff https://www.tableau.com/data-music
- https://public.tableau.com/app/profile/david.pires/viz/Spotify2/TracksAnalysis
- https://public.tableau.com/app/profile/robert.janezic/viz/ALTNATION/AltNation
- https://public.tableau.com/app/profile/pradeepkumar.g/viz/WhatsyourfavouriteMusicGenre/iron_viz_2019
- ...
Visulaisation suggetsions by Claude:
Here's what each visualization reveals:
Shows how many tracks each artist has in your top list Quickly identifies your most-played artists Rectangle size corresponds to number of tracks Good for seeing if you tend to listen deeply to specific artists or broadly across many
Compares your personal preferences against global popularity Reveals if you tend to favor mainstream hits or more niche tracks Trend line shows correlation between your taste and global trends Different colors for different artists show if certain artists consistently rank differently
Shows the release dates of albums in your top tracks Reveals if you prefer newer or older music Circle size shows number of tracks from each album Colors indicate different artists to show temporal patterns in your artist preferences
Would you like me to explore any of these concepts further or suggest additional visualization approaches? For example, we could create:
A genre distribution chart A listening time distribution (if you have that data) An artist collaboration network A seasonal listening pattern visualization
OFFTOPIC https://akirodic.com/p/jellyfish/