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I've tried a few ways of working with Python and I've not been happy with any of them. Recently, I thought I had the right answer for me, just using the latest version of python from the official website and using venv to spin up virtual environments on a project by project basis. Just last week though, I tried to do a tutorial that needs Python 3.10. Venv doesn't support multiple versions of Python so I tried miniconda. I'm not sure I like minicondaonda, at least, not with VSCode. Apparently, conda is a package manager as well as a version manager, but it doesn't have all packages, so you end up possibly using both conda and pip, which apparently gets complicated. By default, the miniconda base environment is activated when you start a terminal. That seems a bit intrusive somehow because I don't only use this machine for Python. Also the version that miniconda seems to install in the base environment is not the latest version of Python. You can turn this behaviour off, but if you do that, VSCode can't activate any conda environments for some reason. There's also this problem https://github.com/microsoft/vscode/issues/129979, to which the workaround is to change an obscure setting in VScode that apparently breaks other things (although I don't know what). So miniconda and VSCode don't seem to get along well. There is something in the VSCode Python plugin called 'microconda'. I'm not sure what that is and googleing it returns no answers.
As a side note, if you use GitHub Actions or a similar CI/CD system, you can use a matrix or equivalent to create builds or run tests on various different operating systems and Python versions, so you might not even need to worry about running multiple Python versions locally. For example, I use that to build releases for multiple platforms, and to run comprehensive test suites in all configurations.
As a side note, if you use GitHub Actions or a similar CI/CD system, you can use a matrix or equivalent to create builds or run tests on various different operating systems and Python versions, so you might not even need to worry about running multiple Python versions locally. For example, I use that to build releases for multiple platforms, and to run comprehensive test suites in all configurations.