![]() In fact, this dependency management can play a vital role in accountability and accuracy. ![]() If you provide the exact versions of the libraries that you used in your scientific analysis, your results will be more verifiable. ![]() Python has a fantastic open source community, but that also means a proliferation of tools and methods for everything. ![]() I feel confident running this code in my user root directory, don’t you? Virtual environments aid reproducibility in data science. If you’re downloading everything into the same big environment, it’s inevitable that you’ll end up with inconsistent dependencies, and things will break. Packages from the different channels within conda aren’t even guaranteed not to conflict. Packages don’t always get upgraded at the same time, and many are not compatible with each other or even with the version of Python or Anaconda that you’re running. You don’t need them all at one time, and trying to figure out which ones are necessary for your project is frustrating to do by hand. If you’ve been using Python for any length of time, you’ve had the frustration of a cluttered development environment with too many packages installed. But what about for people doing data science who aren’t deploying to PyPI or conda-forge? Virtual environments can help you fix things when they break. We know the importance of dependency management for package development and software developers. Why use virtual environments in data science? Set up nearly-automatic Python virtual environments and create Jupyter notebooks and more in Visual Studio Code.
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