Unwrapping Spotify Profile Preferences
Leveraging Python and Jupyter, created an analytical workflow to delve beyond Spotify Wrapped's top tracks. By tapping Spotify's API, I extracted audio features like danceability, acousticness, and energy on my listening history to showcase sonic preferences. After wrangling these granular datasets, my interactive visualizations spotlight hidden trends in musical characteristics across my favorite songs - essentially quantifying my aural personality over time. Through employing analytics at scale against the endless catalog of Spotify, we can gain tangible data-driven insights explaining our discrete musical tastes.
Leveraging Python and Jupyter, created an analytical workflow to delve beyond Spotify Wrapped's top tracks. By tapping Spotify's API, I extracted audio features like danceability, acousticness, and energy on my listening history to showcase sonic preferences. After wrangling these granular datasets, my interactive visualizations spotlight hidden trends in musical characteristics across my favorite songs - essentially quantifying my aural personality over time. Through employing analytics at scale against the endless catalog of Spotify, we can gain tangible data-driven insights explaining our discrete musical tastes.
Leveraging Python and Jupyter, created an analytical workflow to delve beyond Spotify Wrapped's top tracks. By tapping Spotify's API, I extracted audio features like danceability, acousticness, and energy on my listening history to showcase sonic preferences. After wrangling these granular datasets, my interactive visualizations spotlight hidden trends in musical characteristics across my favorite songs - essentially quantifying my aural personality over time. Through employing analytics at scale against the endless catalog of Spotify, we can gain tangible data-driven insights explaining our discrete musical tastes.
Track Trends Analysis
Audio Feature Data Plotting
The project demonstrates how to:
- Set up authentication with the Spotify API
- Get a user's top tracks for a time period
- Retrieve audio feature data on those tracks
- Analyze features like danceability, energy, acousticness etc.
- Visualize the audio features for comparison
This enables you to go beyond just a list of your top tracks and gain insights into your listening habits and music taste based on track audio characteristics.
