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…
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…
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?”…
Handling Nested Data in Polars: explode() and unnest()
Real-world data from APIs often comes as nested JSON. Pandas struggles with this, but Polars has two powerful expressions built for it: explode and unnest….
Cleaning Text in Polars: The .str Expression Namespace
Text data is almost always messy. One of the most efficient ways to tackle this is with Polars string manipulation. In Pandas, you use .str…
PyScript Project: Load and Analyze a User’s CSV File in the Browser
This is the ultimate goal of PyScript for Data Science: building a tool that lets your users analyze their own data, all inside their browser….
Time-Series in Polars: group_by_dynamic Explained
One of the most common tasks in data analysis is “resampling” time data. For example, turning a list of daily sales into “Total Monthly Sales.”…
