Modern DataFrames in Python: A Hands-On Tutorial with Polars and DuckDB

Working with large datasets in Python can be slow and memory-intensive using the popular Pandas library. This tutorial introduces Polars and DuckDB as modern, high-performance alternatives to address these challenges. Polars, built in Rust, utilizes multi-threaded execution and lazy evaluation to significantly speed up operations, while DuckDB functions as an efficient in-memory analytical database that can query large files directly using SQL. Through hands-on code examples, the content compares the syntax and performance of Pandas, Polars, and DuckDB for common tasks like loading, filtering, aggregating, and joining data.