Python IDLE vs. SwaggerHub

Scenarios where python IDLE is well suited 1-Quick scripting and prototyping 2-Education and training 3-small projects utilities 4-exploring python libraries and modules Scenarios where python is less appropriate 1 large scale projects 2 complex debugging and profiling 3 multi language development 4 Advanced code analysis and inspection

ReadyAPI and SwaggerHub are a match made in heaven. Being able to so easily import the API directly into ReadyAPI and then being able to generate a test suite from the API is my favorite part of these softwares. We have rather large services so building out the API can take some time

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• Firstly, I would say Python IDLE interface is user friendly.
• Easy to learn for the beginners.
• Syntax highlighting is nice features.
• Smart indent helps a lot.

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• Treating the design as documentation ensures that we have consistent and well defined APIs
• API consumers can export the swagger definition or code stubs to use within their development environment
• We can iterate through multiple versions of a definition privately before making a version publicly available

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• Too simplistic
• Could not find source revision management integration support
• Only basic debugging is available
• Does not have data-science-specific notebooks (but can be installed separately)

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• The user interface could be more improved to sustain with other similar tools in the market.
• Beneficial if we have better integration with third party softwares.
• Nice to have ability to download the documentation in usable formats.

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The IDE Python IDLE is a good place to start as it helps you become familiar with the way Python works and understand its syntax.

This IDE allows you to configure the environment, font, size, colors, .....

It also looks like any simple text editor for any operating system, I work with Windows or Linux interchangeably, and you don't have to learn to use the IDE before programming.

Once the IDE is executed you can start programming directly in it.

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Python IDLE support is what the community can give you. As it is free software, it does not have support provided by the manufacturer or by third-parties.

In any case, for most of the problems that normal users can find, the solution, or alternatives, can be found quickly online.

As this IDE is made in Python, the support is the same group of Python developers.

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It's easy to set up and run quick analysis in Python IDLE on my local machine. The output is direct and easy to read. But sometimes I prefer Jupyter Notebook when the datasets are large, since it would take too long to run on my local machine. It is easier to run Jupyter Notebook on my cloud desktop

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The only other product we looked at was Postman. But we liked the SwaggerHub UI & the integrations it has with the AWS API gateway. Though Postman has some other great features like the ability to write test cases & execute them, SwaggerUI was more convenient to document & share them.

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• In a short time, we were able to develop several ML models for various teams to make accurate decisions.
• Beginners can easily understand and adapt to GUI.
• We could automate several manual validation tasks and so could reduce human intervention.

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• Faster API development lifecycle.
• Reduced discussion time when building a new vital API endpoint.
• Complete documentation which makes the new team member onboarding process easier.

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Independently Invited by TrustRadius

A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info