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…
