Reshaping Data in Polars: The pivot() Method (Long to Wide)
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…
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…
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….
Working with JSON in Polars: The .json Namespace (2026 Guide)
It’s very common to have a column in your data that contains a JSON string. In Pandas, this is slow and difficult to work with….
Polars and Databases: Reading from SQL (The 2026 Guide)
In the real world, data doesn’t just live in CSV files. It lives in SQL databases. If you’re looking for a simple way to use…
Reshaping Data in Polars: The melt() Method (Unpivoting)
Your data is often in a “wide” format, like a spreadsheet, but analysis tools (like plotting libraries) prefer “long” format. One useful function for this…
Conditional Logic in Polars: The when/then API (SQL Case When)
One of the most common data tasks is creating a new column based on a condition. In this tutorial, we’ll focus on using Polars when…
Working with Dates in Polars: The .dt Namespace (2026 Guide)
Just loading dates isn’t enough. For real analysis, you need to “engineer features” from them, like “What day of the week do most sales happen?”…
