Enhance data prep with AI-powered capabilities in Data Wrangler
All Fabric notebook users have access to Data Wrangler, a code-generating tool with an immersive interface for exploring and transforming pandas or Spark DataFrames. Data Wrangler provides a library of common data-cleaning operations that you can browse and apply seamlessly, getting real-time previews and generating reusable code.
Thanks to new AI-powered capabilities in Data Wrangler, including automated suggestions and custom operations with Copilot, you can now do even more to accelerate exploratory analysis and data preparation in Fabric.
Get automated suggestions with rule-based AI
Unsure where to start? A new set of automated suggestions analyzes your data with rule-based AI from the Microsoft PROSE team, highlighting the most relevant Data Wrangler operations for you. Every time you apply a new operation, the suggestions refresh based on the new state of your data.
Ask Copilot to apply custom operations
Need an operation that you don’t see in Data Wrangler? You can now use Copilot to generate custom code. All you need to do is describe the desired operation. As with any Data Wrangler step, you get a preview before applying or discarding the code.
Translate custom code from pandas to PySpark
Working on big data? Data Wrangler converts Spark DataFrames to pandas for performance reasons, then translates all exported code back to PySpark. With GenAI in Data Wrangler, custom code is also translated to PySpark—whether you type it in yourself or generate it with Copilot.
Next steps
We’re eager for you to try out the new capabilities in Data Wrangler and let us know what you think.
- Submit your feedback on Fabric Ideas and join the conversation on the Fabric Community.
- Learn more about Data Wrangler from our documentation: Accelerate data prep with Data Wrangler.
- If you’re interested in using Data Wrangler to apply the new set of Fabric AI functions—for tasks like sentiment analysis, entity extraction, and custom text generation—sign up for the preview experience with this link: aka.ms/DataWranglerAIPreview.
