Combining DataFrames in Polars: The concat Method
We’ve used .join() to combine data based on a key (like a SQL JOIN). But what if you just want to stack two DataFrames on…
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…
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…
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,…
How to Find and Remove Duplicate Rows in Polars (2026 Guide)
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…
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…
Time-Series in Polars: Filling Gaps with upsample and interpolate
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…
Time-Series in Polars: rolling() Window Functions
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…
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…
