Combining DataFrames in Polars: The concat Method
We’ve used .join() to combine data based on a key (like a SQL JOIN). But what if you just want to stack two DataFrames on…
AI Project: Visual Question Answering (VQA) with Hugging Face
This is a true “2026 Vision” project. Hugging Face VQA is at the core of what we’re building—we’re giving our AI eyes and a brain….
A Guide to Polars Data Types (pl.dtypes)
In Polars, choosing the correct data type (or “dtype”) is the most important step for performance and memory usage. Using a massive Int64 for a…
A Deep Dive into the Hugging Face datasets Library
This article serves as a Hugging Face datasets guide. We’ve used the datasets library to load data for fine-tuning, but what is it? It’s a…
Polars and Excel: The Fast Way to Read .xlsx Files (2026 Guide)
While Parquet is the fastest format, the business world runs on Excel. Polars read Excel Via a read_excel function to load these files directly into…
AI Project: Document Question Answering (Hugging Face LayoutLM)
The Hugging Face Document AI is one of the most commercially valuable AI tasks. We’re moving beyond simple OCR (which just dumps text) to an…
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…
Polars List Comprehensions: The .list.eval() Method
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,…
How to Find the Best Models on the Hugging Face Hub (A 2026 Guide)
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…
How to Find and Remove Duplicate Rows in Polars (2026 Guide)
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…
