News

Microsoft Data Analysis: .NET for Jupyter Notebooks, DataFrame for .NET

With the maturation of the open-source, cross-platform .NET Core initiative, Microsoft has been upping its data science analysis tooling lately, previewing .NET Core with Jupyter Notebooks functionality and a DataFrame type for .NET for easier data exploration.

Jupyter Notebooks, even though tightly tied to data science darling programming language Python, can now be done with .NET languages C# or F#. The popular notebooks provide interactive environments -- like documents or canvases -- that can feature source code and markdown, or rich text formatting, and other media.

Preview 1 was announced last month, requiring the .NET Core 3.0 SDK.

"When you think about Jupyter Notebooks, you probably think about writing your code in Python, R, Julia, or Scala and not .NET," Microsoft said. "Today we are excited to announce you can write .NET code in Jupyter Notebooks."

The post details working with ML.NET with Jupyter Notebooks for machine learning projects and .NET for Apache Spark for Big Data with projects in .NET.

"The initial set of features we released needed to be relevant to developers, with Notebook experience as well as give users new to the experience a useful set of tools they would be eager to try," Microsoft said.

Following up on that news, Microsoft on Dec. 16 announced the preview of the new DataFrame type for .NET.

"If you’ve used Python to manipulate data in notebooks, you’ll already be familiar with the concept of a DataFrame," Microsoft said. "At a high level, it is an in-memory representation of structured data." The post goes on to provide an overview of this new type and how developers can use it from Jupyter notebooks. Developers can interactively follow along here.

About the Author

David Ramel is an editor and writer at Converge 360.

comments powered by Disqus

Featured

  • Mastering Blazor Authentication and Authorization

    At the Visual Studio Live! @ Microsoft HQ developer conference set for August, Rockford Lhotka will explain the ins and outs of authentication across Blazor Server, WebAssembly, and .NET MAUI Hybrid apps, and show how to use identity and claims to customize application behavior through fine-grained authorization.

  • Linear Support Vector Regression from Scratch Using C# with Evolutionary Training

    Dr. James McCaffrey from Microsoft Research presents a complete end-to-end demonstration of the linear support vector regression (linear SVR) technique, where the goal is to predict a single numeric value. A linear SVR model uses an unusual error/loss function and cannot be trained using standard simple techniques, and so evolutionary optimization training is used.

  • Low-Code Report Says AI Will Enhance, Not Replace DIY Dev Tools

    Along with replacing software developers and possibly killing humanity, advanced AI is seen by many as a death knell for the do-it-yourself, low-code/no-code tooling industry, but a new report belies that notion.

  • Vibe Coding with Latest Visual Studio Preview

    Microsoft's latest Visual Studio preview facilitates "vibe coding," where developers mainly use GitHub Copilot AI to do all the programming in accordance with spoken or typed instructions.

  • Steve Sanderson Previews AI App Dev: Small Models, Agents and a Blazor Voice Assistant

    Blazor creator Steve Sanderson presented a keynote at the recent NDC London 2025 conference where he previewed the future of .NET application development with smaller AI models and autonomous agents, along with showcasing a new Blazor voice assistant project demonstrating cutting-edge functionality.

Most   Popular

Subscribe on YouTube