Data Analysis with Polars

"A thorough introduction to Polars" - Ritchie Vink, creator of Polars - over 3,000 learners to date!

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 and I keep this course up-to-date with new releases of Polars - updated to version 1.30.0

"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 for learners and comes from my time examining the Polars source code.

An important note about videos: this is a primarily a notebook course and not a video course. Not all of the lectures have videos and some of the videos may have components that are not up-to-date. Why? Because the Polars API has changed too often to allow me to keep videos up-to-date. Instead I focus on keeping the notebooks up-to-date with an extensive automated testing system that alerts me to changes in the API. I release an updated version of the course about twice a month in response to changes in Polars.

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

• understand optimal patterns for building queries

• work with larger-than-memory datasets

• carry out aggregations on your data

• combine your datasets with joins and concatenations

• work with nested dtypes including lists and structs

• optimise the speed and memory usage of your queries

• work with string and categorical data

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

• prepare your data for machine learning pipelines with sklearn