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Pros of Jupyter
Pros of Spyder
  • 18
    In-line code execution using blocks
  • 10
    In-line graphing support
  • 7
    Can be themed
  • 6
    Multiple kernel support
  • 3
    Best web-browser IDE for Python
  • 3
    Export to python code
  • 2
    LaTex Support
  • 1
    HTML export capability
  • 1
    Multi-user with Kubernetes
  • 5
    Variable Explorer
  • 2
    More tools for Python
  • 2
    Free with anaconda
  • 1

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Cons of Jupyter
Cons of Spyder
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    • 1
      Slow to fire up

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    What is Jupyter?

    The Jupyter Notebook is a web-based interactive computing platform. The notebook combines live code, equations, narrative text, visualizations, interactive dashboards and other media.

    What is Spyder?

    It is a powerful scientific environment written in Python, for Python, and designed by and for scientists, engineers and data analysts.

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    What companies use Jupyter?
    What companies use Spyder?
    See which teams inside your own company are using Jupyter or Spyder.
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    What tools integrate with Jupyter?
    What tools integrate with Spyder?

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    What are some alternatives to Jupyter and Spyder?
    Apache Zeppelin
    A web-based notebook that enables interactive data analytics. You can make beautiful data-driven, interactive and collaborative documents with SQL, Scala and more.
    PyCharm’s smart code editor provides first-class support for Python, JavaScript, CoffeeScript, TypeScript, CSS, popular template languages and more. Take advantage of language-aware code completion, error detection, and on-the-fly code fixes!
    It provides a rich architecture for interactive computing with a powerful interactive shell, a kernel for Jupyter. It has a support for interactive data visualization and use of GUI toolkits. Flexible, embeddable interpreters to load into your own projects. Easy to use, high performance tools for parallel computing.
    A free and open-source distribution of the Python and R programming languages for scientific computing, that aims to simplify package management and deployment. Package versions are managed by the package management system conda.
    An integrated development environment for R, with a console, syntax-highlighting editor that supports direct code execution. Publish and distribute data products across your organization. One button deployment of Shiny applications, R Markdown reports, Jupyter Notebooks, and more. Collections of R functions, data, and compiled code in a well-defined format. You can expand the types of analyses you do by adding packages.
    See all alternatives