Polars Data Types: How to cast and Convert Columns
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…
How to Apply Custom Functions in Polars (.map_elements())
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…
Polars Performance: String Caching (Categorical Type)
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…
A Deep Dive into the Polars Expression API (pl.Expr)
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…
AI Project: Quantization for Faster Models (Hugging Face optimum)
You’ve built amazing AI models, but they’re huge and slow. A model like gpt-2 can be 500MB+ and slow to run on a CPU. Hugging…
Polars Lazy API: collect(), fetch(), and describe_plan()
So far, we’ve used Polars in “Eager” mode (like Pandas), where df.filter() runs immediately. However, the Polars Lazy API offers a different approach to working…
The Fastest Way to Save Data: Polars and Parquet (2026 Guide)
You’ve been taught to use .csv files for everything. This is fine for small files, but for data science in 2026, it’s slow and inefficient….
Joining DataFrames in Polars: The Blazing Fast join() Method
In Pandas, you use pd.merge() to combine datasets. In Polars, you use the join() method, which is one of the fastest in any library. If…
Master Polars: A Guide to the Expression API (select, filter, with_columns)
In our Polars vs. Pandas article, we showed that Polars is faster. The reason it’s faster is its Expression API. In this article, we’ll take…
Python yield Explained: A Deep Dive into Generators
If you’ve ever worked with huge files or infinite sequences, you’ve needed a generator. The keyword that powers them is yield. In this article, you’ll…
