Time-Series in Polars: group_by_rolling for Window Analysis
We’ve covered two types of window functions in Polars. Now, we’ll look at how to use Polars group_by_rolling functionality. group_by_rolling() is the third type. It’s…
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We’ve covered two types of window functions in Polars. Now, we’ll look at how to use Polars group_by_rolling functionality. group_by_rolling() is the third type. It’s…

This is one of the most powerful and flexible AI tasks, the visual version of Zero-Shot Text Classification. Zero-Shot Image Classification is revolutionising how AI…

What if you have a column that contains lists, and you want to perform an operation on every item inside every list? In these situations,…

We’ve used the Hugging Face pipeline many times. But how do you know which model name to use? How do you find a model to…

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…

In a previous Hugging Face project, we used the translation pipeline, which is fast and easy. But what if you need more control? Or want…

When you load a CSV, Polars (and Pandas) often guesses the data types. Sometimes, it guesses wrong, loading a number column (like 5.0) as a…

This is one of the most incredible “2026 Vision” projects. You’ve used Zero-Shot for text, but what about sound? Zero-Shot Audio Classification opens up fascinating…

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…

This is a core task in Natural Language Processing (NLP). When it comes to extracting entities from text, Hugging Face NER has become a popular…