Time-Series in Polars: group_by_rolling for Window Analysis
We’ve covered two types of window functions in Polars. Now, we’ll look at how to use Polars group_by_rolling functionality. group_by_rolling() is the third type. It’s…

We’ve covered two types of window functions in Polars. Now, we’ll look at how to use Polars group_by_rolling functionality. group_by_rolling() is the third type. It’s…

What if you have a column that contains lists, and you want to perform an operation on every item inside every list? In these situations,…

Duplicate data is a silent killer for analysis and machine learning. Polars provides high-speed, easy-to-use methods for finding and removing duplicate rows. The Setup Let’s…

When you load a CSV, Polars (and Pandas) often guesses the data types. Sometimes, it guesses wrong, loading a number column (like 5.0) as a…

Real-world data is often “sparse.” You might have sales data for Monday and Friday, but nothing for Tuesday, Wednesday, or Thursday. This is where polars…

We’ve learned how to group time by month (using group_by_dynamic), but what about calculating a “7-day moving average”? This is where Polars rolling functions can…

You’ve mastered the fast Polars Expression API. But what if you need to run a complex Python function that Polars doesn’t have?, so it’s Polars…

In data analysis, you’re constantly reshaping data. we used melt() to turn “wide” data into “long” data. Today, we’re doing the opposite. pivot() is the…

Let’s say you have a 10GB file with a “Country” column. The string “United States of America” might appear 50 million times, using a massive…

We’ve used the Polars Expression API a lot. But what is an expression? An expression, or pl.Expr, is a recipe for a calculation. It’s not…