How to Read JSON Files in Polars (read_json)
You’ve learned to read CSVs, Parquet, and Excel. But many APIs and modern databases (like MongoDB) output JSON files. In this tutorial, you’ll learn how…

You’ve learned to read CSVs, Parquet, and Excel. But many APIs and modern databases (like MongoDB) output JSON files. In this tutorial, you’ll learn how…

In our Polars string guide, we covered basic text cleaning. When your data demands pattern-level precision, Polars regex delivers — it builds Regular Expression (Regex)…

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,…

Today we’re covering two powerful Polars Expressions: shift and rank. These are essential for financial analysis, ranking, and finding trends. In this article you’ll learn…

You’ve learned all the individual Polars methods. Now, let’s put them together in one “A-to-Z” project to clean a messy dataset and look at effective…

Matplotlib and Seaborn create static, non-interactive images. In 2026, data exploration is interactive. hvplot is a library that provides a .hvplot() method for Pandas and…

We’ve used groupby().agg(), which collapses your data (e.g., 100 rows become 3 rows). In contrast, Polars window functions allow you to compute calculations across groups…

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…

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…

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…