Need advice about which tool to choose?Ask the StackShare community!

Jupyter

2.7K
1.4K
+ 1
57
Postman

96.4K
82.5K
+ 1
1.8K
Add tool
Manage your open source components, licenses, and vulnerabilities
Learn More
Pros of Jupyter
Pros of Postman
  • 19
    In-line code execution using blocks
  • 11
    In-line graphing support
  • 8
    Can be themed
  • 7
    Multiple kernel support
  • 3
    LaTex Support
  • 3
    Best web-browser IDE for Python
  • 3
    Export to python code
  • 2
    HTML export capability
  • 1
    Multi-user with Kubernetes
  • 490
    Easy to use
  • 369
    Great tool
  • 276
    Makes developing rest api's easy peasy
  • 156
    Easy setup, looks good
  • 144
    The best api workflow out there
  • 53
    It's the best
  • 53
    History feature
  • 44
    Adds real value to my workflow
  • 43
    Great interface that magically predicts your needs
  • 35
    The best in class app
  • 12
    Can save and share script
  • 10
    Fully featured without looking cluttered
  • 8
    Collections
  • 8
    Option to run scrips
  • 8
    Global/Environment Variables
  • 7
    Shareable Collections
  • 7
    Dead simple and useful. Excellent
  • 7
    Dark theme easy on the eyes
  • 6
    Awesome customer support
  • 6
    Great integration with newman
  • 5
    Documentation
  • 5
    Simple
  • 5
    The test script is useful
  • 4
    Saves responses
  • 4
    This has simplified my testing significantly
  • 4
    Makes testing API's as easy as 1,2,3
  • 4
    Easy as pie
  • 3
    API-network
  • 3
    I'd recommend it to everyone who works with apis
  • 3
    Mocking API calls with predefined response
  • 2
    Now supports GraphQL
  • 2
    Postman Runner CI Integration
  • 2
    Easy to setup, test and provides test storage
  • 2
    Continuous integration using newman
  • 2
    Pre-request Script and Test attributes are invaluable
  • 2
    Runner
  • 2
    Graph
  • 1
    <a href="http://fixbit.com/">useful tool</a>

Sign up to add or upvote prosMake informed product decisions

Cons of Jupyter
Cons of Postman
    Be the first to leave a con
    • 10
      Stores credentials in HTTP
    • 9
      Bloated features and UI
    • 8
      Cumbersome to switch authentication tokens
    • 7
      Poor GraphQL support
    • 5
      Expensive
    • 3
      Not free after 5 users
    • 3
      Can't prompt for per-request variables
    • 1
      Import swagger
    • 1
      Support websocket
    • 1
      Import curl

    Sign up to add or upvote consMake informed product decisions

    - 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 Postman?

    It is the only complete API development environment, used by nearly five million developers and more than 100,000 companies worldwide.

    Need advice about which tool to choose?Ask the StackShare community!

    What companies use Jupyter?
    What companies use Postman?
    Manage your open source components, licenses, and vulnerabilities
    Learn More

    Sign up to get full access to all the companiesMake informed product decisions

    What tools integrate with Jupyter?
    What tools integrate with Postman?

    Sign up to get full access to all the tool integrationsMake informed product decisions

    What are some alternatives to Jupyter and Postman?
    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