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….
AI Project: Fine-Tuning a Hugging Face Model (Part 2: The Trainer)
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…
Polars and Databases: Reading from SQL (The 2026 Guide)
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…
AI Project: Fine-Tuning a Hugging Face Model (Part 1: The Data)
You’ve used Hugging Face pipelines to run pre-trained models. If you want to get the most from these models, learning about Hugging Face Fine-Tuning is…
Reshaping Data in Polars: The melt() Method (Unpivoting)
Your data is often in a “wide” format, like a spreadsheet, but analysis tools (like plotting libraries) prefer “long” format. One useful function for this…
AI Project: Using Hugging Face Agents (Your Personal AI Assistant)
This is the cutting-edge of AI, far beyond a simple pipeline. An Agent is an LLM (like ChatGPT) that has been given tools and can…
AI Project: Zero-Shot Classification with Hugging Face
This is one of the most powerful and “magical” tasks in modern AI. In particular, Hugging Face Zero-Shot is a technique that demonstrates impressive versatility…
Conditional Logic in Polars: The when/then API (SQL Case When)
One of the most common data tasks is creating a new column based on a condition. In this tutorial, we’ll focus on using Polars when…
AI Project: Object Detection with Hugging Face (DETR)
We’ve taught our AI to classify an image (e.g., “This is a cat”). Now let’s teach it to find the cat. Object Detection is a…
Working with Dates in Polars: The .dt Namespace (2026 Guide)
Just loading dates isn’t enough. For real analysis, you need to “engineer features” from them, like “What day of the week do most sales happen?”…
