Data analysis project involving Python & SQL programming languages.
Summary: In this Jupyter notebook, I explained how I analyzed 2.5 years of my Spotify listenings data using Python (8 JSON files imports) and SQL queries. In the case study, I answered the following questions:
- Volumetry: What is the total volumetry of data involved? What period is covered?
- User retention metrics: Do I use Spotify services more and more over time? (evolution of the number of sessions and hours played)
- Consumption habits :
- Am I versatile or do I tend to listen to the same artists / songs over time? (number of distinct artists and tracks)
- What is the breakdown of my consumption between music tracks and podcasts?
- Listening behavior:
- Time: When do I listen mostly Spotify? In public transports, at work, at home ? During the work week or during weekends?
- Device: Which device do I use most when using Spotify?
- Skipping actions: Do I tend to skip a lot of music tracks? If so, when does the skipping part occur after the beginning of the song? Is it the same depending on the device?
- Top rankings: What are they and do they stay the same across years?
- What are my most favorite songs?
- Who are my favorite artists?
- Which podcasts do I listen to the most?
Technical learnings:
- This project allowed me to experiment the SQLITE3 Python package that allows to execute SQL code in a Jupyter notebook.
- I could also put into practice the panel and hvplot packages after watching the excellent Youtube video from Thu Vu as main inspiration
Final note: To serve the dashboard locally, execute the following command in the terminal :
panel serve Interactive_dashboard.ipynb
Panel / hvplot Dashboard preview: