This project is based on a Spotify dataset available on Kaggle.com
Data from this source were used to construct tables in a SQLite database. Using the Python libraries sqlalchemy and Flask, data can be called from the SQLite database via the API at https://zmyd1nzqug.execute-api.us-west-1.amazonaws.com/dev using the routes below.
Data by Year - /api/v1.0/data_by_year
Averaged attributes for tracks by year. 1920-2021 (102 rows of data)
Data by Top Genres - /api/v1.0/top_51_genres
Averaged attributes for genres. 51 genres of data (51 rows)
Data by Artist - /api/v1.0/data_by_artist_clean/<artist>
Artists data can be requested from the api by adding an artist name to the end of the API url. Mind the capitalization and punctuation!
28,680 Artists (28,680 rows of data, one artist returned per API request).
Here are some of our favorites as examples:
This project uses the statistics.js JavaScript library to calculate the correlation coefficients on the Year vs Attribute and Genre Scatter Plot pages.