Python IDLE vs. Shiny

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

Shiny is well suited where an organisation is looking to empower their analysts to minimise time spent on repetitive analysis by deploying repeatable analytical pipelines, but also looking for them to add greater value to the organisation by utilising more advanced analytical techniques. Ideally it is well suited where IT are on board and supportive of some of the more advanced features such as deploying R Shiny dashboards.

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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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• Data tables are appealing to look at.
• Enables us to create trend indexes in an effective way.
• Easy to integrate with the rest of my R syntax.

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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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• Easier ways to connect to data sources
• Better access control for different roles in the organization
• Video material that allows a better learning experience

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Independently Invited by TrustRadius

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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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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

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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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

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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- Faster response working with a large amount of data. - R Studio connection and flexibility. - Scenarios modelling.

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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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Independently Invited by TrustRadius

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• We saw a good involvement to researchers when showing their models in shiny.
• We can have a quicker review from the user when the model is in production.
• False positives can be found easily and they help the retraining of the model.

Incentivized

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