Data Analysis with Polars and Python: High-performance data analysis using Polars, Python, and modern DataFrame workflows

Boris Paskhaver

Can$94.99

Video Jan 2026 22hrs 13mins 1st Edition

Boris Paskhaver

Can$94.99

Video Jan 2026 22hrs 13mins 1st Edition

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Key benefits

Modern Polars workflows focused on performance and memory efficiency
Real-world datasets used across Series, DataFrames, joins, and GroupBy
Clear coverage of eager vs. lazy execution and query optimization

Description

This course teaches efficient, scalable data analysis with Polars and Python, a modern alternative to traditional DataFrame libraries. You begin by setting up your Python environment and revisiting key Python concepts. Early lessons introduce Polars Series and DataFrames, emphasizing performance and memory efficiency. You work in Jupyter Lab to gain hands-on experience with vectorized, declarative logic, replacing row-by-row thinking with expressions. As you progress, you'll learn to filter, sort, reshape, and join large datasets, working with text, categorical, datetime, and nested data. Advanced topics like GroupBy, window functions, and selectors help you create concise, scalable analysis pipelines. The course concludes with LazyFrames, teaching optimized query plans for large data without memory overload. You'll also explore Polars' integration with other libraries and how to extend its functionality. Ideal for analysts and developers, this course enhances your ability to work with large datasets more efficiently, reinforcing key concepts with quizzes throughout.

Who is this book for?

This course is for data analysts, data scientists, and Python developers who want high-performance data analysis. Basic Python knowledge is recommended.