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Jupyter

1.4K
1.2K
+ 1
51
Replit

99
166
+ 1
16
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Pros of Jupyter
Pros of Replit
  • 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
  • 6
    Less Complicated
  • 3
    Continuous Deployment
  • 2
    Free base plan and Premium plan is cheap
  • 2
    Github integration
  • 2
    Supports a Reasonable amount of languages
  • 1
    Helpfull Community
  • 0
    Emmet support
  • 0
    Emmet support

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Cons of Jupyter
Cons of Replit
    Be the first to leave a con
    • 2
      Limited Storage, CPU, Ram
    • 2
      Server cannot stay 24/7
    • 2
      Very Limited Database API
    • 2
      Poor support

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    - No public GitHub repository available -

    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 Replit?

    It is a platform for creating and sharing software. You can write your code and host it all in the same place. It is also a place to learn how to code.

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

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    What are some alternatives to Jupyter and Replit?
    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.
    Spyder
    It is a powerful scientific environment written in Python, for Python, and designed by and for scientists, engineers and data analysts.
    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.
    See all alternatives