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Jupyter

1.4K
1.2K
+ 1
51
Spyder

101
142
+ 1
10
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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
    Intellisense

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Cons of Jupyter
Cons of Spyder
    Be the first to leave a con
    • 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
    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!
    IPython
    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.
    Anaconda
    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.
    RStudio
    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