How to Read SQL into a DataFrame in Python — Tutorial

About this lesson

The single most common analyst pipeline in Python starts with a SQL query and ends with a DataFrame. The database holds the data, the SQL filters it, the DataFrame is where the real work happens. Both pandas and Polars wrap the database driver and materialize the result as a DataFrame in one or two lines. Source code: https://github.com/GoCelesteAI/read-sql-dataframe This tutorial covers the three modern methods every Python data person should know. Pandas read_sql with a sqlite3 connection — the path of least resistance for anyone already on pandas. Polars read_database with the same connection — for when you want a Polars frame downstream. Polars read_database_uri — the one-liner that handles connection management for you via the connectorx backend. Same query, same result, three different ergonomics. What You'll Build: - read_sql.py — read 367 AAPL-above-two-hundred rows from a SQLite prices database three different ways. Pandas, polars with a connection, polars with a URI. All three arrive at the same shape. - The pd.read_sql idiom — pass a DB-API connection and a query string. Works against SQLite, Postgres, MySQL, Oracle, anything with a DB-API driver. - The pl.read_database alternative — same shape, returns a polars DataFrame, faster on large result sets because it streams through Arrow buffers. - The pl.read_database_uri one-liner — no connection management. Pass a URI string like sqlite-colon-slash-slash-slash-prices-dot-db and polars opens, reads, and closes for you. - Parameterized queries to defend against SQL injection — pass params equals as a tuple, never f-string user input into the query. - The filter-at-SQL principle. The single most common mistake is pulling the whole table into Python and filtering with pandas. For a 100 million row table that is the difference between a five-second query and a five-minute Python crash. Timestamps: 0:00 - Intro — SQL query in, DataFrame out 0:18 - Preview — three methods, same result 0:54 - Open read_sql.py

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