What is the difference between Spyder and Jupyter?

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北恋
北恋 2021-02-12 04:50

Python community, I am learning Python for data science, but my problem is that I still don\'t understand the difference between Spyder and Jupyter!

I would like you guys

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  • 2021-02-12 05:28

    Spyder Pros:

    • Code completion
    • Code cells: You can create code cells using Spyder.
    • Scientific libraries
    • PDB debugger
    • Help feature

    cons:

    • Limited to python only.
    • Bad layout not customizable

    Jupyter pros:

    • Easy to learn
    • Secure and free server - The Jupyter server can be utilized free of charge.
    • Keyboard shortcuts makes it easy and fast
    • Share Notebook

    cons:

    • Not recommended for running long, nonconcurrent errands.
    • No IDE integration, no linting, and no code-style adjustment.

    Read more in detail https://ssiddique.info/pycharm-vs-spyder-vs-jupyter.html

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  • 2021-02-12 05:40

    Here's just a basic summary of the two tools.

    Jupyter is a very popular application used for data analysis. It's an IPython notebook ("interactive python"). You can run each block of code separately. For example, I can print a graph using matplotlib. Create a new block of code and print another graph. There are also cool functions like %timeit that test the speed of your code.

    Spyder is just an Integrated Development Environment (IDE) for python like atom, visual studio, etc. I use VS Code and I suggest you install it as well. It's easier to learn and get running. There's also tons of helpful youtube videos due to its popularity.

    I prefer to use jupyter notebook to analyze data whether it be in pandas dataframes or plots. When I'm developing a program or implementing new code on data I already analyzed, I use a text editor like VS Code.

    There's a lot more to it, but I think that's all you need to know for now. As you gain more experience you'll learn more about the tools and find your preferences. If you want to know more, there a ton of information about them online with people who can probably explain this much better than I can.

    I hope your journey into data science goes well! Just be patient and remember struggling is part of learning. Good luck!

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