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Reinstall old Python libraries after update
Recently, Python updated from 3.11 to 3.12 and now all my libraries are gone. Actually they're in the old 3.11 site-packages, but now that python points to 3.12 those are not active. Every time I run a program I'm used to, I get a bunch of import errors, and have to reinstall the dependencies.
I know how to do it, but it's tedious. Isn't there a way to carry over the 3rd party packages that were installed on the old Python, but not "internal" stuff like setuptools and pip? Those are usually part of the Python distribution and I don't want nor need to mess with them.
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Every time I run a program I'm used to, I get a bunch of import errors, and have to reinstall the dependencies.
As you're presumably aware, the fundamental problem is that each Python installation is treated as its own separate environment, with its own suite of third-party libraries. Since there still is an "old 3.11 site-packages" folder, we can deduce that your "update" to Python installed 3.12, but didn't remove 3.11. This is expected if the 3.11 distribution was included with your Linux distribution - since critical system scripts would have been written and tested for 3.11, and would also need to be updated in order to replace the system Python.
(On the other hand, as Hackysack correctly points out - if these Python installations are coming from your system package manager, you should check what packages they include - especially check what 3.12 already provides you, before making plans to reinstall everything. Note that distro-provided Python will often put third-party libraries in a separate dist-packages folder, rather than site-packages.)
As I explained in my answer on the other question, you can try hacking sys.path to include the old site-packages folder, but this is very brittle and likely to produce difficult-to-debug errors.
If you had set up a virtual environment, you could try using the --upgrade option for venv. But to the best of my knowledge, this does not attempt to reinstall third-party libraries; it only reconfigures the venv to use the new Python version.
The most robust option for setting up a new environment - virtual or not - is to freeze the old dependencies, create a new empty environment (if it doesn't already exist), and reinstall the frozen dependencies. But even this can fail, if the new Python version isn't supported by specific "pinned" versions of the installed libraries. You may need to edit the requirements.txt file to remove or modify version restrictions, which in turn may require doing a bit of research to figure out which versions of a library are acceptable for your use cases.
As for excluding things like setuptools and pip, this too can be done by editing the requirements.txt file from pip freeze etc. But there isn't really a good reason to exclude them - they'll be just as useful in the new environment as they were in the old one. You might consider updating both to the latest version, however. In general, the team responsible for these tools does a great job of ensuring that the newest releases of those tools work on the newest versions of Python; on the other hand, eventually they drop support for older Python versions, and older tool versions can't be guaranteed forwards-compatible (since nobody can predict the future).
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The following users marked this post as Works for me:
| User | Comment | Date |
|---|---|---|
| matthewsnyder |
Thread: Works for me I forgot about pip freeze. That's probably the best way to accomplish this. |
Jun 2, 2024 at 20:42 |
It depends.
Pure Python is usually forward-compatible with newer minor versions, but this is not always the case. Features/modules that were available in a previous minor version may no longer be available in the next minor version. Some features may not behave in the same way as in a previous minor version.
Packages that make use of C extensions, such as numpy, are even more likely to have compatibility issues between minor versions (and even micro/patch versions). Cython makes for a good example, here. Cython takes Python code and converts it to C code. If you use Cython to compile Python 3.11 code, it will work in all micro/patch versions of 3.11 (meaning 3.11.0, 3.11.1, 3.11.2, etc), but not for any other minor version. Then again, the same Python code that was compiled with Cython may be valid in multiple minor versions (in other words, the underlying Python 3.11 code may also be valid 3.12 code and compile with Cython just fine).
In short, it's difficult to know which specific packages will be forward-compatible with newer minor versions, meaning that simply copying the package directories from one installation of Python to another is risky, at best. Unless one is willing to scour the source code of each package in order to verify compatibility, then your best bet is to just use the traditional/conventional methods of reinstalling the packages.
I'm not sure how your Python is installed, but it sounds like it might be your system Python, since that is usually when a Python installation gets replaced with a newer version. If that's the case, and you installed your Python packages through your system's package manager (I would avoid doing this in the future), then your distro's package repos should already contain the updated packages to go along with the new Python version. If you are instead using pip's "user install" feature or a venv (created by python -m venv somevenv), then I'm afraid you'll have to use pip to reinstall the packages.
A quick and easy way to reinstall a list of specific packages and their versions is to use pip freeze to output a list of the installed packages and their versions:
pip freeze > /tmp/requirements.txt
And then use that file to install the same packages and the same versions of said packages:
pip install -r /tmp/requirements.txt
If you would like to avoid having your Python installation replaced/upgraded like this, you could always build one from source. Once built, it will never change. Personally, I keep multiple Python installations under ~/.pythons/, but I keep every version isolated from each other (even micro/patch versions).
Going into detail about the different methods of maintaining a Python built from source (or multiple) is beyond the scope of this post, though, so that's where I'll leave this answer.
The pip freeze command may output individual dependencies with format
zipp @ file:///Users/abcxyz/work/recipes/ci_py311/zipp_1677907997878/work
Using this output will not work (unless you happen to be that user abcxyz). To generate a requirements file that does not refer to local, non-existing or no-longer existing cache directories, you can instead use
pip list --format=freeze > requirements.txt
which will generate a requirements.txt file that only lists the specific versions, in the format zipp==3.11.0.
If you then run pip install -r requirements.txt for a newer version of Python, you may sometimes run into version conflicts. You can try to fix those by not pinning the dependency to a specific version (so simply by removing the ==3.11.0 in the example of zipp==3.11.0), but in some cases there may be no fix (if a dependency has no release yet for the newer Python and the older version somehow cannot be installed); in the worst case you'll have to temporarily remove the 3d party library.
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