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…
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…
