Polars Performance Tuning: 3 Tricks to Make Your Code Faster
Polars is fast by default. But you can make it slower if you write “Pandas-style” code. Here are the top 3 optimisations for Polars Performance…
Big Data in Polars: How to Scan and Process Multiple Files at Once
In the real world, data is rarely in one big CSV. It’s usually split, and this is where you might want to use Polars to…
Polars vs. Pandas: Zero-Copy Conversion (The Arrow Revolution)
One of the biggest fears about switching to a new tool like Polars is: “What if I need a library that only works with Pandas?”…
How to Fix: MemoryError in Python
A MemoryError is one of Python’s most serious errors. If you’ve encountered a MemoryError Python problem before, you know it’s not a syntax issue; it’s…
Polars vs. Pandas: A 1GB Performance Showdown (2026 Guide)
We’ve told you Polars is faster than Pandas. When it comes to Polars vs Pandas Performance, now let’s prove it. We’ll create a 1GB (10…
How to Apply Custom Functions on Polars Groups (.group_by().apply())
We know that .map_elements() is slow because it runs row-by-row. We know that .group_by().agg() is super fast, but it’s limited to simple functions (like sum,…
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…
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,…
