by Kristen Beck
A data visualization project focusing on sound feature analysis for the user's Spotify playlists. Visualize trends in your own musical preferences and compare your top music with tracks on Spotify’s top playlists.
This project has two parts, each one found in its on Jupyter notebook.
This step uses the Spotipy API client to query the user's Spotify playlist data, extracting key audio features which are then cleaned and transformed before being loaded into a CSV file indexed by track_id and containing a playlist membership label. Before running this notebook, you will first need to follow these instructions to set up a Spotify Developer and get your own access tokens (SPOTIPY_CLIENT_ID and SPOTIPY_CLIENT_SECRET) which should be entered in the API client configuration/authorization step.
This step uses the Matplotlib and Seaborn libraries to visualize statistical trends of Spotify playlists. Users who were unable to run the ETL notebook and create their own customized dataset should upload the provided sample file track_df.csv instead.