How to Apply Custom Functions in Polars (.map_elements())
You’ve mastered the fast Polars Expression API. But what if you need to run a complex Python function that Polars doesn’t have?, so it’s Polars…

You’ve mastered the fast Polars Expression API. But what if you need to run a complex Python function that Polars doesn’t have?, so it’s Polars…

In data analysis, you’re constantly reshaping data. we used melt() to turn “wide” data into “long” data. Today, we’re doing the opposite. pivot() is the…

Let’s say you have a 10GB file with a “Country” column. The string “United States of America” might appear 50 million times, using a massive…

We’ve used the Polars Expression API a lot. But what is an expression? An expression, or pl.Expr, is a recipe for a calculation. It’s not…

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…

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….
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