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janakiev.com | ||
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jaketae.github.io
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| | | | | As a novice who just started learning Python just three months ago, I was clueless about what virtual environments were. All I knew was that Anaconda was purportedly a good way to download and use Python, in particular because it came with many scientific packages pre-installed. I faintly remember reading somewhere that Anaconda came with conda, a package manager, but I didn't really dig much into it because I was busy learning the Python language to begin with. I wasn't interested in the complicated details-I just wanted to learn how to use this language to start building and graphing and calculating. | |
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vickiboykis.com
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| | | | | When I'm working with Jupyter notebooks, I often want to work with them from within a virtual environment. The general best practice is that you should always use either virtual environments or Docker containers for working with Python, for reasons outlined in this post, or you're gonna have a bad time. I know I have. The workflow is a little long, so I thought I'd document it for future me here. | |
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www.ethanrosenthal.com
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| | | | | I make Python packages for everything. Big projects obviously get a package, but so does every tiny analysis. Spinning up a quick jupyter notebook to check something out? Build a package first. Oh yeah, and every package gets its own virtual environment. Let's back up a little bit so that I can tell you why I do this. After that, I'll show you how I do this. Notably, my workflow is set up to make it simple to stay consistent. | |
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www.mattlayman.com
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| | | At Python Frederick this month, I presented on features of pytest to use when testing in Python. We looked at parametrize, test file organization, and fixtures. The recording is up on YouTube. | ||
