Which Python IDE is used for data science?

Programming for Data Science Forum
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Hey Data Science Lovers! :innocent:

I am interested in knowing which Python integrated development environments (IDEs) are most helpful to you all in your job as data scientists. I am aware that there are numerous choices available; Jupyter Notebook, PyCharm, Visual Studio Code, and Spyder are just a few. However, I am curious if any IDEs are more popular or better adapted to the standard data science methodology. Depending on the particular job or activity at hand, do you usually utilize a combination or do you have a definite favorite? Please advise me on the best Python IDE(s) as I am relatively new to data science and want to make sure I have all the tools I need!

:pray:

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I prefer VScode, although PyCharm is another good alternative.

Chaotic neutral option: RStudio

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An extension of Jupyter Notebook with a more flexible and integrated environment.Allows working with notebooks, code, and data in a single interface

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I also use Jupyter Notebook because:

  • These are popular open-source notebook platforms for data science.
  • They allow you to combine code, text, and visualizations in a single document.
  • Features include data cleaning, transformation, statistical modeling, and machine learning.
  • JupyterLab is the next iteration, providing a more interactive and modular development environment.
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Hi, PyCharm excels as a robust IDE tailored for scientific computing, offering strong code navigation, refactoring tools, and seamless integration with data science libraries. However, it can be resource-heavy and may pose a learning challenge for newcomers

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PyCharm: Offers a comprehensive set of features for professional development, including code completion, debugging, and version control.

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I like Jupyter Notebook more.
One of the greatest Python notebooks and the most popular Python data science IDE is Jupyter Notebook. Because of its interactive features and compatibility with several programming languages, including Python, it is a well-liked option for data science.