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…
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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…

This is the project you’ve been waiting for. We’re going to write a Python script that generates a unique image from a text prompt (e.g.,…

This is one of the most powerful concepts in modern AI. In particular, Hugging Face Text Embeddings are an innovative way to use embeddings today….

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….

You’ve built amazing AI models, but how do you let your friends or colleagues use them without running your script? One solution is to share…

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…

This is the final part of our fine-tuning series. In this article, we’ll explore Hugging Face Evaluate and Share to wrap up our journey. Now,…

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….

Welcome to Part 2! In Part 1 (The Data), we loaded the “imdb” dataset and prepared it with a tokenizer. Now, we’ll do the exciting…

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…