Data Analysis with Polars

In this course I show you how to take advantage of Polars - the fast-growing open source dataframe library that is becoming the go-to dataframe library for data scientists in python. I am a Polars contributor with a focus on making Polars accessible to new users.

"A thorough introduction to Polars" - Ritchie Vink, creator of Polars

"Thank you for your great work with this course - I've optimized some code thanks to it already!" Maiia Bocharova

The course is for data scientists who have some familiarity with a dataframe library like Pandas but who want to move to Polars because it is easier to write and faster to run. The core materials are Jupyter notebooks that examine each topic in depth. Each notebook comes with a set of exercises to help you develop your understanding of the core concepts.

For many key topics this course is the only source of documentation. I have focused on producing Jupyter notebooks to allow anyone taking the course to start using the full power of Polars. As a consequence the video content is limited. More videos that go beyond the notebooks will be added in the coming months once the core functionality has been documented in the notebooks.

The course introduces the syntax of Polars and shows you the many ways that Polars allows you to produce queries that are easy to read and write. However, the course also delves deeper to help you understand and exploit the algorithms that drive the outstanding performance of Polars.

By the end of the course you will have optimised ways to:

• load and transform your data from CSV, Excel, Parquet, cloud storage or a database

• run your analysis in parallel

• work with larger-than-memory datasets

• carry out aggregations on your data

• combine your datasets

• visualise your outputs with Matplotlib, Seaborn, Plotly & Altair and

• prepare your data for machine learning pipelines