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www.jennapederson.com | ||
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vxlabs.com
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| | | | | The new Python package and project manager uv is in fact amazing. I say that, because it's really fast, but more importantly because this single tool does a whole lot, really fast: Installing Python binaries, installing and running packages in self-contained environments like pipx, managing virtual environments. However, I've been avoiding it so far due to one flaw: uv defaults to installing its virtual environment and all dependencies into the .venv sub-directory of your project, almost exactly like the... | |
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jinyuz.dev
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| | | | | So, there was a time when I was only developing applications using Python. And so I found out about virtual environments. And then after a couple of months, I discovered pyenv. It also came to a time I had to work on multiple projects that uses different versions of nodejs and searched something similar so I installed nvm. Then, I was required to work on a Ruby project so I installed rbenv. | |
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valatka.dev
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| | | | | Uv was the best thing that had happened to Python. Used it for over one year now. Good time to reflect on what makes it stand out of the crowd. | |
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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. | ||
