While adding a section to my Udemy Polars course on making visualisations with Plotly and Matplotlib I was struck by how much work Matplotlib required. In this post I show a strategy for making plo...

Polars uses Apache Arrow to store its data in-memory. One of the big advantages of Arrow is that it supports a variety of nested data types (or “dtypes”). In this post we look at the pl.List dtype ...

I appeared on the Real Python podcast to talk Polars! We chatted about: why lazy mode in Polars is so important working with larger-than-memory datasets transitioning from Pandas to Polars ...

Polars now allows you to write Parquet files even when the file is too large to fit in memory. It does this by using streaming to process data in batches and then writing these batches to a Parquet...

In a previous post we saw that Polars has fast-track algorithms for calculating some statistics on sorted data. In this post we see that Polars also has a fast-track algorithm for getting groupby k...

I was consulting for a client recently who needs to process hundreds of Gb of CSV files. On their first pass with Polars they had read from their CSVs with a pattern like this (simplified) version....

Most new Polars users are familiar with Pandas so a mapping from Pandas code to Polars code might come in handy. As I show in my Polars quickstart notebook there are a number of important differenc...

In a recent post I showed how to use Polars in AWS Lambda using the smart_open library. There are a variety of ways that you can work with Polars in AWS Lambda, however. In this post we look at how...

This post was created while writing my Data Analysis with Polars course. Check it out on Udemy with a half price discount Working with cloud storage such as AWS S3 can be a pain with lots of ...

This post was created while writing my Data Analysis with Polars course. Check it out on Udemy with a half price discount Want to jump right into using Polars? I’ve released a quickstart not...