Jupyter Notebook vs. Spyder

I would rate it 9/10 while recommending Jupyter Notebook as it offers me a wide range of functionality to operate. It is very well suited for someone who is new to python programming as the user interface helps you build code line by line. I personally have written multiple programs in Python using Jupyter Notebook as it helps me organize long code by breaking it in a structure. Also the ability to write comments using '#' helps a lot to a reader understand the code.

Incentivized

Spyder is well suited if you're limited on hardware. You have to work with single code file. You need to quickly write some code and test it. Apart from this if you want to have a look at your variables then you can make use of Spyder. If you're working with Anaconda navigator then this can be the best to start with as it can be installed with single click there.

Incentivized

• Coding and error correction line by line
• Simple and Effectiveness
• Easy to use for visualisation and presentation of code
• Could be used at any place any time without hassle

Incentivized

• Debugging of your existing code
• Generates figures very quickly as part of a figures tab which lets users understand results quickly
• Different layouts are available for the software which will give the users freedom to decide what layout works best for them

Incentivized

• Need more Hotkeys for creating a beautiful notebook. Sometimes we need to download other plugins which messes [with] its default settings.
• Not as powerful as IDE, which sometimes makes [the] job difficult and allows duplicate code as it get confusing when the number of lines increases. Need a feature where [an] error comes if duplicate code is found or [if a] developer tries the same function name.

Incentivized

• Colors in code format
• Add a broadcast to share the project with friends
• Contains more than one important language such as Python

Jupyter is highly simplistic. It took me about 5 mins to install and create my first "hello world" without having to look for help. The UI has minimalist options and is quite intuitive for anyone to become a pro in no time. The lightweight nature makes it even more likeable.

Incentivized

It is fairly straightforward to use. Pretty much good to go as soon as you install it. The IDE itself is very user friendly, and it is only limited by whatever limitations Python has as a language. Great for those who want to run their scripts quickly or do some Python programming without fussing.

Incentivized

I haven't had a need to contact support. However, all required help is out there in public forums.

Incentivized

Most of data scientists or data engineers are either using ec2 on the cloud or Atom or PyCharm locally. It is a bit hard to find people who are still using Spyder and have the sight of the IDE and can help you to answer your question.

Incentivized

Jupyter Notebook is unique in that it offers a flexible, lightweight, easy-to-replicate way of organizing your code in a visually intuitive fashion that can be exported in a number of formats. I've found that the broad functionalities available within the notebooks suit a lot of needs I have for EDA, modeling, and data export that makes other software products fairly redundant.

Incentivized

I have chosen Spyder because it's free and open-source that comes with properly documented comments in the code. I have been using Spyder for more than 2 years and it always feels good to work with Spyder every time start my work. In Spyder, we have three windows one for man code window, idle window, and the other is for running your code and analyze. So to test a particular code I use the idle window to see what is going to be the result when I use this set of codes. That the main reason, I use Spyder.

• Positive impact: flexible implementation on any OS, for many common software languages
• Positive impact: straightforward duplication for adaptation of workflows for other projects
• Negative impact: sometimes encourages pigeonholing of data science work into notebooks versus extending code capability into software integration

Incentivized

• Less time spent on employee training.
• Limited integration with Git.
• No tools for repository.

Incentivized