DuckDB and Python: Analyzing Data with SQL and DataFrames

Are you tired of slow data loading or complex setups just to run analytical SQL queries on medium-sized datasets in Python? DuckDB provides a fast, powerful, and embedded solution for modern data processing. By the end of this course, you will be able to set up DuckDB in any Python environment, write efficient analytical queries, connect SQL results directly to popular DataFrame libraries, and optimize your local data workflows for speed and simplicity. What you'll learn: Understand the architecture of DuckDB and its advantages as an in-process OLAP database for Python environments. Configure DuckDB connections and execute standard and analytical SQL queries directly from your Python scripts. Apply techniques for reading data efficiently from common formats like CSV and Parquet files without external tooling. Practice integrating query results into Pandas and Polars DataFrames for further manipulation and visualization. Learn how to extend DuckDB functionality by registering custom Python functions for use within your SQL queries. The course begins with foundational concepts and environment setup, moving quickly into practical data loading and querying exercises. We then explore advanced integration patterns, focusing on seamless interoperability with Python's data analysis ecosystem. This course is designed for beginner Python programmers and data analysts who want to significantly accelerate their local data analysis capabilities using modern tools. No prior experience with specialized databases or high-performance computing is required. Start streamlining your data analysis workflows today.