I’m going to be honest with you. I’m bad at writing Pandas data transformation code. Throughout the years I mostly focussed on the Spark APIs in Scala and PySpark, SQL, dbt, and some others, but I find the Pandas APIs usually just confusing and hard to read. I don’t like the black box magic and lock-in of low-code solutions either. Did you know that Microsoft Fabric has the perfect middle ground for this? It’s called Data Wrangler. Let’s dive in!

Actually, Data Wrangler is not a new thing. It was built as an extension for Visual Studio Code. It assists data engineers to wrangle their data with an intuitive interface that helps you write Pandas code. Microsoft nicely integrated this into the Fabric experience to make it even easier to use.

To get started with Data Wangler, create a new notebook in your Fabric Workspace. A notebook needs a Fabric Lakehouse and in our tutorial, we’re going to need some data to wrangle. I uploaded this CSV file with the most popular movies & series on IMDB to my Lakehouse and linked the Lakehouse to my notebook. To get started with wrangling data, we need to load our CSV into a Pandas DataFrame. The syntax to read a CSV file with Pandas is pretty straightforward, but you can save yourself the hassle by right-clicking on the CSV file.

This puts the code to load the CSV file in the