Time-Series in Polars: rolling() Window Functions
We’ve learned how to group time by month (using group_by_dynamic), but what about calculating a “7-day moving average”? This is where Polars rolling functions can…
AI Project: How to Translate Text with Hugging Face
You’ve used Hugging Face to understand text, generate it, and even transcribe audio. Now, let’s use it to break the language barrier with Hugging Face…
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…
AI Project: Fill-in-the-Blank with Hugging Face (BERT)
This is one of the original tasks that made models like BERT famous. A “Masked Language Model” (MLM) is trained by having words randomly “masked”…
Reshaping Data in Polars: The pivot() Method (Long to Wide)
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…
AI Project: How to Edit Images with AI (Inpainting with diffusers)
We’ve used Stable Diffusion to create images. Now, let’s use it to edit them. In this guide, we’ll explore Hugging Face Inpainting and how it…
Polars Performance: String Caching (Categorical Type)
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…
AI Project: Image Segmentation with Hugging Face
This is the next level of Computer Vision. Hugging Face Image Segmentation is an innovative approach transforming what computers see and understand. This is how…
A Deep Dive into the Polars Expression API (pl.Expr)
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…
AI Project: Quantization for Faster Models (Hugging Face optimum)
You’ve built amazing AI models, but they’re huge and slow. A model like gpt-2 can be 500MB+ and slow to run on a CPU. Hugging…
