Software Alternatives, Accelerators & Startups

Jupyter VS nteract

Compare Jupyter VS nteract and see what are their differences

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.

nteract logo nteract

nteract is a desktop application that allows you to develop rich documents that contain prose...
  • Jupyter Landing page
    Landing page //
    2023-06-22
  • nteract Landing page
    Landing page //
    2022-06-29

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.

nteract features and specs

  • Ease of Use
    nteract offers a user-friendly interface that is simple to set up and use, making it accessible to both beginners and experienced users in data science environments.
  • Interactivity
    The tool provides an interactive experience for running live code, displaying text, and visualizing data efficiently within a single notebook interface.
  • Multi-language Support
    nteract supports multiple programming languages, thanks to Jupyter kernels, which allows flexibility and integration within various data science workflows.
  • Open Source
    Being open source, nteract encourages community contributions and improvements, offering a level of transparency and customization to its users.
  • Extensibility
    The presence of numerous plugins and extensions enables users to enhance the functionality of nteract based on their specific requirements.

Possible disadvantages of nteract

  • Dependency Management
    Managing dependencies can be complex, as users need to handle different libraries and packages to ensure compatibility within their projects.
  • Limited Advanced Features
    Compared to other IDEs, nteract may lack some advanced features required by professional developers for large, intricate projects.
  • Performance Issues
    nteract may experience performance issues when managing large datasets or complex computations due to the resource-intensive nature of notebooks.
  • Learning Curve for Extensions
    While extensibility is a pro, understanding and integrating numerous plugins and extensions can present a learning curve for new users.
  • Community and Documentation
    Although growing, the nteract community and available documentation might not be as extensive as more established platforms like Jupyter Notebook.

Jupyter videos

What is Jupyter Notebook?

More videos:

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

nteract videos

nteract weekly August 16, 2018

More videos:

  • Review - nteract weekly November 5, 2018
  • Review - nteract weekly October 1, 2018

Category Popularity

0-100% (relative to Jupyter and nteract)
Data Science And Machine Learning
Data Dashboard
100 100%
0% 0
Data Science Notebooks
0 0%
100% 100
Database Tools
100 100%
0% 0

User comments

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Reviews

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

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.
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.

nteract 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

Social recommendations and mentions

Based on our record, Jupyter seems to be a lot more popular than nteract. While we know about 216 links to Jupyter, we've tracked only 4 mentions of nteract. 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.

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 / about 1 month 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 / 3 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 / 4 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 / 8 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 / 11 months ago
View more

nteract mentions (4)

  • Best Python IDEs for Data Science!
    At the same time that already established and widely used IDEs like RStudio are renewed and provide support for new languages, other solutions appear almost out of nowhere and are adopted by the market as is the case of nteract, an open-source project to be the next interactive development experience adopted by Netflix, in practice it has support for Python, node.JS, R, Julia, C ++, Scala and .NET, in addition to... - Source: dev.to / over 3 years ago
  • Python IDE similar to Jupyter Notebook but not web based?
    Sounds like you're looking for nteract. Source: about 4 years ago
  • Installing Jupyter Notebook
    If you reach infuriation levels you can always cop out and use https://nteract.io/ Ultimately I would suggest jupyterlab over jupyter. Source: about 4 years ago
  • How to open .ipynb files with Jupyter Notebook by double-clicking from windows explorer?
    You can also try the software nteract (https://nteract.io). Source: about 4 years ago

What are some alternatives?

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

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.

iPython - iPython provides a rich toolkit to help you make the most out of using Python interactively.

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

BeakerX - Open Source Polyglot Data Science Tool

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

Polynote - The polyglot notebook with first-class Scala support.