Jupyter Notebook vs. Spyder

I've created a number of daisy chain notebooks for different workflows, and every time, I create my workflows with other users in mind. Jupiter Notebook makes it very easy for me to outline my thought process in as granular a way as I want without using innumerable small. inline comments.

Incentivized

Spyder is an open-source Python IDE designed for the movement of data science work. Spyder comes with an Anaconda package manager distribution, so depending on your setup you may have installed it on your machine.

Spyder includes most of the "standard IDE" features you can expect, such as a strong syntax code editor, Python code rendering, and an integrated text browser.

Spyder is used when we want to develop a code that is useful and able to explore proper documentation of the code that has been written. We use Spyder to perform data-related operations like filtration, cleaning, and enhancing the data qualities. There some cases where it is less appropriate like working in an environment, creating dashboards of data visualizations and plots.

• Simple and elegant code writing ability. Easier to understand the code that way.
• The ability to see the output after each step.
• The ability to use ton of library functions in Python.
• Easy-user friendly interface.

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

With Jupyter Notebook besides doing data analysis and performing complex visualizations you can also write machine learning algorithms with a long list of libraries that it supports. You can make better predictions, observations etc. with it which can help you achieve better business decisions and save cost to the company. It stacks up better as we know Python is more widely used than R in the industry and can be learnt easily. Unlike PyCharm jupyter notebooks can be used to make documentations and exported in a variety of formats.

Incentivized

I think Spyder doesn't stack up as well as other IDEs due to its many limitations. But it is available for free and that is one advantage it has over its competitors

Incentivized

• 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

• Easy to use.
• Quick switch from R.
• Hard to manage env.

Incentivized