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?”…
Handling Nested Data in Polars: explode() and unnest()
Real-world data from APIs often comes as nested JSON. Pandas struggles with this, but Polars has two powerful expressions built for it: explode and unnest….
Cleaning Text in Polars: The .str Expression Namespace
Text data is almost always messy. One of the most efficient ways to tackle this is with Polars string manipulation. In Pandas, you use .str…
Time-Series in Polars: group_by_dynamic Explained
One of the most common tasks in data analysis is “resampling” time data. For example, turning a list of daily sales into “Total Monthly Sales.”…
