This is a practical technical course designed to help data teams build confidence in high-performance data wrangling using the Polars Python library. By learning a shared framework for transforming, analysing, and optimising datasets, participants reduce friction in collaborative workflows and strengthen their organisation’s data capability. The course focuses on real-world examples and equips teams with the skills needed to work efficiently with medium-to-large datasets.
Introduction to data wrangling with Polars: Short course
Introduction
WHAT YOU WILL LEARN
Upon completion of the course, participants will have the technical foundations and working fluency to use and read Polars effectively across key data-processing tasks.
Polars Fundamentals
Understand Polars’ core architecture, including eager vs lazy execution, expression and context-based transformations, schema awareness, and performance advantages over traditional tools.
Efficient Data Manipulation
Learn to select, filter, join, aggregate, and reshape data using Polars’ high-level contexts and expression system.
Advanced Performance & Optimisation
Gain experience with lazy pipelines, predicate and projection pushdown, parallelisation, and query optimisation, enabling scalable workflows for larger datasets.
COURSE MATERIALS
Provided is a HTML training document containing all examples, explanations, and reference material used throughout the course.
The course also includes a set of homework questions with an accompanying Jupyter notebook (.ipynb) and all required datasets. These materials allow participants to practise learned concepts independently through hands-on exercises.