In this lecture, we build a practical bridge between Python and PostgreSQL. You’ll learn how to connect to a database with `psycopg`, create tables, insert and query data, move SQL results into pandas for analysis, write transformed data back to PostgreSQL, and use SQLAlchemy for both Core and ORM workflows. By the end, you’ll understand when to use SQL, pandas, and ORM tools together in a real data workflow.
Topics covered:
Connecting Python to PostgreSQL with `psycopg3`
Loading database credentials from a .env file
Creating tables and inserting records programmatically
Reading SQL query results into pandas DataFrames
Doing EDA and feature engineering with SQL + pandas
Writing processed data back to PostgreSQL
Using SQLAlchemy Core and ORM for Python database access
If you’re learning data engineering, analytics, or backend data workflows, this lecture gives you the foundations for working confidently with Python and SQL together.
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