A Guide to Polars Data Types (pl.dtypes)
In Polars, choosing the correct data type (or “dtype”) is the most important step for performance and memory usage. Using a massive Int64 for a…
Polars and Excel: The Fast Way to Read .xlsx Files (2026 Guide)
While Parquet is the fastest format, the business world runs on Excel. Polars read Excel Via a read_excel function to load these files directly into…
Polars List Comprehensions: The .list.eval() Method
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,…
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….
