Using data extracted from Kaggle on the top restaurants from 2020, this project utilized Python scripting in Jupyter Notebook to transform and clean the data and finally, load the cleaned data frames into a PostgreSQL database.
This project transforms data from a .csv file to PostgreSQL using an .ipynb notebook in Google Colab. Pandas cleans and formats the data, while psycopg2 and SQLAlchemy create tables and load it. Supabase can be used for scalable management. This approach optimizes interactive data transformation and analysis.
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