Using Regex in Polars: .str.contains(), .str.replace_all(), .str.extract()
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)…
Polars Project: A-to-Z Data Cleaning (The 2026 Guide)
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…
How to Find and Remove Duplicate Rows in Polars (2026 Guide)
Duplicate data is a silent killer for analysis and machine learning. Polars provides high-speed, easy-to-use methods for finding and removing duplicate rows. The Setup Let’s…
High-Performance NLP: Pre-processing Text with Polars (2026 Guide)
When preparing text data for an AI model, you’re often working with millions of rows. For this reason, many practitioners are interested in Polars NLP…
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….
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…
Handling Missing Data in Polars (null, fill_null, drop_nulls)
Just like Pandas has NaN, Polars has null to represent missing or empty data. Before you can analyze a dataset, you must have a strategy…
Handling Missing Data in Pandas: A Guide to dropna() and fillna()
In the real world, your datasets will have holes. Users forget to fill out forms, sensors break, or data gets corrupted. as these Pandas Missing…
