Spotify ETL Project

This project extracts data from the Spotify API, performs the necessary transformations, and stores the results as CSV files in a destination folder. Finally, it generates interactive dashboards using Streamlit.

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The problem this project solved

Before I discovered Streamlit, I built the project using Power BI for the dashboards. However, the dashboards couldn’t be automated — I had to create them manually for each playlist, which was very time-consuming.

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How to Run

1. Go to Spotify for Developers Dashboard

2. Create a new app. Once logged in, click “Create an App”, give it a name and description (anything you like), and save.

3. Copy your credentials from the app’s settings, copy the following values:

   • Client ID
   • Client Secret
   • Redirect URI (add http://127.0.0.1:8888/callback or any other URI you will use)

4. Create a virtual environment

   python -m venv spotify-env

5. Activate it

   .\spotify-env\Scripts\Activate.ps1

6. Clone repo

   https://github.com/fran-cornachione/SpotifyETL.git
   

7. Install requirements

   pip install -r requirements.txt
   

8. Create an .env file, and save the following credentials there:

   SPOTIFY_CLIENT_ID = 
   SPOTIFY_CLIENT_SECRET = 
   SPOTIFY_REDIRECT_URI = http://127.0.0.1:8888/callback (Or any other URI)
   

9. Run script main.py

   python main.py
   

You will now have all your playlists saved in the data folder, each playlist as a .csv file.

10. Run script dashboard.py

streamlit run dashboard.py

It will automatically take you to the Streamlit UI on your browser.

Now, you can see the analytics of any of your playlists.

• Newest Song (by release date)
• Oldest Song (by release date)
• Total Songs
• Average Duration (min)
• Shortest Song
• Longest Song
• Top 10 Most Popular Tracks
• Top 10 Artists by Song Count (artists with the most amount of tracks in the playlist)

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You can select a different playlist in the sidebar on the left.