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johnfraney.ca | ||
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jiby.tech
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| | | | | The packaging workflow of Python has historically been a bit messy, starting with pip being just good enough, and virtualenvs being a great idea but a bit unwieldy. Installation was difficult, and tools like anaconda filled the gaps. Lately, there's been renaissance of package tooling in Python thanks to other languages innovating in the area, inspiring and pollinating the ecosystem, improving the comfort of development. I want to present here the tools that I use to work with Python, usually latest iterations on well known concepts, to show that Python can be both fun to play with, and safe enough if we use modern tools. | |
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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.integralist.co.uk
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| | | | | Introduction Virtual Environments Creating Virtual Environments Installing Python Versions Virtual Environments for multiple Pythons Shell Configuration Managing Dependencies Caching Dependencies Command Line Packages Introduction This blog post aims to demonstrate the most practical way to install multiple versions of Python, and of setting up 'virtual environments' for macOS userso We'll also dig into how to manage our project dependencies (e.g. we'll be discussing the classic Pip and requirements.txt ... | |
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snarky.ca
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| | | There are a couple of things I always want to be true when I install Python packages for a project: 1. I have a virtual environment 2. Pip is up-to-date For virtual environments, you would like them to be created as fast as possible and (usually) with the newest version | ||
