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.NET VS Jupyter

Compare .NET VS Jupyter and see what are their differences

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Contents:
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  3. » Reviews
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.NET logo .NET

.NET is a free, cross-platform, open source developer platform for building many different types of applications.

Jupyter logo Jupyter

Project Jupyter exists to develop open-source software, open-standards, and services for interactive computing across dozens of programming languages. Ready to get started? Try it in your browser Install the Notebook.
  • .NET Landing page
    Landing page //
    2023-09-19
  • Jupyter Landing page
    Landing page //
    2023-06-22

.NET features and specs

  • Cross-Platform
    The .NET platform supports Windows, macOS, and Linux, which allows for the development and deployment of applications across different operating systems.
  • Performance
    ASP.NET Core, a part of the .NET ecosystem, has high-performance benchmarks and is suitable for developing scalable and high-performance systems.
  • Large Ecosystem
    .NET has a vast library of pre-built components, frameworks, and APIs that speed up development and reduce the need for writing code from scratch.
  • Strong Community Support
    There is a large and active community of developers, providing resources such as forums, documentation, and third-party tools.
  • Integrated Development Environment (IDE)
    Visual Studio, the primary IDE for .NET, offers robust features like IntelliSense, debugging, and testing tools, making development easier and more efficient.
  • Security
    .NET provides a range of security features, including code access security, role-based security, and encryption, making it a reliable choice for secure applications.
  • Compatible with Modern Development
    .NET supports modern development practices like containerization with Docker and cloud-native applications, particularly with Azure.
  • Language Support
    .NET supports multiple programming languages like C#, F#, and VB.NET, allowing developers to choose the right one for their needs.

Possible disadvantages of .NET

  • Learning Curve
    Given its vast ecosystem and feature set, .NET can have a steep learning curve for beginners.
  • Memory Usage
    .NET applications can be more memory-intensive compared to applications built with some other frameworks, which can be a concern for resource-constrained environments.
  • Platform-Specific Issues
    While .NET is cross-platform, certain platform-specific issues can arise, requiring additional work to ensure compatibility.
  • Cost of Microsoft Tools
    Although .NET is open-source, some associated tools like Visual Studio Enterprise come with significant licensing costs.
  • Smaller Talent Pool
    Compared to more universally taught languages like Python or JavaScript, finding highly skilled .NET developers can be more challenging.

Jupyter features and specs

  • Interactive Computing
    Jupyter allows real-time interaction with the data and code, providing immediate feedback and making it easier to experiment and iterate.
  • Rich Media Output
    It supports output in various formats including HTML, images, videos, LaTeX, and more, enhancing the ability to visualize and interpret results.
  • Language Agnostic
    Jupyter supports multiple programming languages through its kernel system (e.g., Python, R, Julia), allowing flexibility in the choice of tools.
  • Collaborative Features
    It enables collaboration through shared notebooks, version control, and platform integrations like GitHub.
  • Educational Tool
    Jupyter is widely used for teaching, thanks to its easy-to-use interface and ability to combine narrative text with code, making it ideal for assignments and tutorials.
  • Extensibility
    Jupyter is highly extensible with a large ecosystem of plugins and extensions available for various functionalities.

Possible disadvantages of Jupyter

  • Performance Issues
    For larger datasets and more complex computations, Jupyter can be slower compared to running scripts directly in a dedicated IDE.
  • Version Control Challenges
    Managing version control for Jupyter notebooks can be cumbersome, as they are not plain text files and include metadata that can make diffing and merging complex.
  • Resource Intensive
    Running Jupyter notebooks can be resource-intensive, especially when working with multiple large notebooks simultaneously.
  • Security Concerns
    Because Jupyter allows code execution in the browser, it can be a potential security risk if notebooks from untrusted sources are run without restrictions.
  • Dependency Management
    Managing dependencies and ensuring that the notebook runs consistently across different environments can be challenging.
  • Less Suitable for Production
    Jupyter is often considered more as a research and educational tool rather than a production environment; transitioning from a notebook to production code can require significant refactoring.

Analysis of .NET

Overall verdict

  • Yes, .NET is considered a good and reliable choice for developers due to its robust features, cross-platform capabilities, and strong community support.

Why this product is good

  • Microsoft's .NET is a versatile and powerful open-source developer platform that supports building a wide range of applications, including web, mobile, desktop, gaming, cloud, and IoT applications. It offers strong language support for languages like C#, F#, and VB.NET and provides a rich ecosystem of libraries, tools, and frameworks such as ASP.NET for web development and Xamarin for mobile development. The platform is known for its performance, security, and the ability to work seamlessly across different operating systems, including Windows, macOS, and Linux.

Recommended for

  • Enterprise applications
  • Cross-platform development
  • Web developers using ASP.NET
  • Mobile app developers using Xamarin
  • Game developers utilizing Unity

.NET videos

+ Add

.NET Design Review: DataFrame

More videos:

  • Review - Truetrader.net | Loophole EXPOSED
  • Review - .NET Design Review: .NET Core 3.1

Jupyter videos

+ Add

What is Jupyter Notebook?

More videos:

  • Tutorial - Jupyter Notebook Tutorial: Introduction, Setup, and Walkthrough
  • Review - JupyterLab: The Next Generation Jupyter Web Interface

Category Popularity

0-100% (relative to .NET and Jupyter)

.NET

Jupyter

Ad Servers
100 100%
Ad Servers
0% 0
Data Science And Machine Learning
0 0%
Data Science And Machine Learning
100% 100
Developer Tools
100 100%
Developer Tools
0% 0
Data Dashboard
0 0%
Data Dashboard
100% 100

User comments

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Reviews

These are some of the external sources and on-site user reviews we've used to compare .NET and Jupyter

.NET Reviews

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Jupyter Reviews

Jupyter Notebook & 10 Alternatives: Data Notebook Review [2023]
Once you install nteract, you can open your notebook without having to launch the Jupyter Notebook or visit the Jupyter Lab. The nteract environment is similar to Jupyter Notebook but with more control and the possibility of extension via libraries like Papermill (notebook parameterization), Scrapbook (saving your notebook’s data and photos), and Bookstore (versioning).
Source: lakefs.io
7 best Colab alternatives in 2023
JupyterLab is the next-generation user interface for Project Jupyter. Like Colab, it's an interactive development environment for working with notebooks, code, and data. However, JupyterLab offers more flexibility as it can be self-hosted, enabling users to use their own hardware resources. It also supports extensions for integrating other services, making it a highly...
Source: deepnote.com
12 Best Jupyter Notebook Alternatives [2023] – Features, pros & cons, pricing
Jupyter Notebook is a widely popular tool for data scientists to work on data science projects. This article reviews the top 12 alternatives to Jupyter Notebook that offer additional features and capabilities.
Source: noteable.io
15 data science tools to consider using in 2021
Jupyter Notebook's roots are in the programming language Python -- it originally was part of the IPython interactive toolkit open source project before being split off in 2014. The loose combination of Julia, Python and R gave Jupyter its name; along with supporting those three languages, Jupyter has modular kernels for dozens of others.
Source: searchbusinessanalytics.techtarget.com
Top 4 Python and Data Science IDEs for 2021 and Beyond
Yep — it’s the most popular IDE among data scientists. Jupyter Notebooks made interactivity a thing, and Jupyter Lab took the user experience to the next level. It’s a minimalistic IDE that does the essentials out of the box and provides options and hacks for more advanced use.
Source: towardsdatascience.com

Social recommendations and mentions

Based on our record, Jupyter should be more popular than .NET. It has been mentiond 216 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.

.NET mentions (51)

  • The Delta Difference: Unleashing .NET, EF Core, and PostgreSQL Performance with Delta
    1.Dot net is the most performant framework 2.EF Core has gotten better and provides a slew of performance steps 3.PostgreSQL is a powerful, open source object-relational database that safely stores and scales the most complicated data workloads. 4.Delta An efficient approach to implementing a 304 Not Modified leveraging DB change tracking. - Source: dev.to / 1 day ago
  • How to Build a .NET PDF Editor (Developer Tutorial)
    Editing PDF files programmatically is a common requirement in enterprise applications — whether you're modifying invoices, generating reports, or enabling users to fill and save forms. The .NET ecosystem lacks native support for advanced PDF editing, which makes third-party libraries crucial. - Source: dev.to / about 1 month ago
  • dotnet cross-platform interop with C via Environment.ProcessId system call
    Dotnet (.NET 9 is used for this article) and C# decompiler. - Source: dev.to / 3 months ago
  • Why Does Everyone Forget Java and C# for Backend Development? Why Don’t Full-Stack Developers Learn Java and C#?
    C# was developed by Microsoft in the early 2000s as part of its .NET initiative, led by Anders Hejlsberg. Originally designed as an alternative to Java, C# evolved into a powerful language for Windows applications, backend services, game development (via Unity), and cloud computing. The introduction of .NET Core made C# fully cross-platform, allowing it to run on Windows, Linux, and macOS. - Source: dev.to / 4 months ago
  • Implementing Social Authentication in .NET Web API
    This blog post details how to implement social authentication and provide users with several social login options and how we can handle the users' data obtained as a result of these logins in our application. In this blog post, we’ll look at how we can integrate Google and Facebook login authentications. We will see how this can be implemented from the server side of an application using .NET 6; Microsoft's own... - Source: dev.to / 10 months ago
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Jupyter mentions (216)

  • The 3 Best Python Frameworks To Build UIs for AI Apps
    Showcase and share: Easily embed UIs in Jupyter Notebook, Google Colab or share them on Hugging Face using a public link. - Source: dev.to / 3 months ago
  • LangChain: From Chains to Threads
    LangChain wasn’t designed in isolation — it was built in the data pipeline world, where every data engineer’s tool of choice was Jupyter Notebooks. Jupyter was an innovative tool, making pipeline programming easy to experiment with, iterate on, and debug. It was a perfect fit for machine learning workflows, where you preprocess data, train models, analyze outputs, and fine-tune parameters — all in a structured,... - Source: dev.to / 4 months ago
  • Applied Artificial Intelligence & its role in an AGI World
    Leverage versatile resources to prototype and refine your ideas, such as Jupyter Notebooks for rapid iterations, Google Colabs for cloud-based experimentation, OpenAI’s API Playground for testing and fine-tuning prompts, and Anthropic's Prompt Engineering Library for inspiration and guidance on advanced prompting techniques. For frontend experimentation, tools like v0 are invaluable, providing a seamless way to... - Source: dev.to / 5 months ago
  • Jupyter Notebook for Java
    Lately I've been working on Langgraph4J which is a Java implementation of the more famous Langgraph.js which is a Javascript library used to create agent and multi-agent workflows by Langchain. Interesting note is that [Langchain.js] uses Javascript Jupyter notebooks powered by a DENO Jupiter Kernel to implement and document How-Tos. So, I faced a dilemma on how to use (or possibly simulate) the same approach in... - Source: dev.to / 9 months ago
  • JIRA Analytics with Pandas
    One of the most convenient ways to play with datasets is to utilize Jupyter. If you are not familiar with this tool, do not worry. I will show how to use it to solve our problem. For local experiments, I like to use DataSpell by JetBrains, but there are services available online and for free. One of the most well-known services among data scientists is Kaggle. However, their notebooks don't allow you to make... - Source: dev.to / 12 months ago
View more

What are some alternatives?

When comparing .NET and Jupyter, you can also consider the following products

WPMU DEV - WPMU offers WordPress Plugins, WordPress Themes, WordPress Multisite and BuddyPress Plugins and Themes.

Looker - Looker makes it easy for analysts to create and curate custom data experiences—so everyone in the business can explore the data that matters to them, in the context that makes it truly meaningful.

MAMP - MAMP is the abbreviation for Macintosh, Apache, MySQL, and PHP. It is a reliable application with its four components that allows you to access the local PHP server as well as the database server (SQL).

Databricks - Databricks provides a Unified Analytics Platform that accelerates innovation by unifying data science, engineering and business.‎What is Apache Spark?

Firefox Developer Edition - Built for those who build the Web. The only browser made for developers.

Google BigQuery - A fully managed data warehouse for large-scale data analytics.

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