MNT bump min pandas version to 1.2.0 #30613
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| else: | ||
| cat_column = cat_column.ravel() | ||
| # Insert extra column as a non-numpy-native dtype: | ||
| cat_column = pd.Categorical(cat_column.ravel()) |
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pd.Categorical was introduced in pandas version 0.15.0. :)
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Thanks!
Yeah that happens that we don't clean-up these ... these days I guess we would leave a TODO note with an explanation when to simplify the code again. Even then, I am pretty sure (especially in tests) there are a lot of stuff that we haven't cleaned up.
Part of me is like 😱, part of me is like 🤷. Generally 🤷 wins because it's less effort.
You could imagine that if it's easy to find these non-cleaned part of code, it could be a source for "good first issue" PRs maybe.
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I did enjoy that cleanup. It's very satisfying.
You could imagine that if it's easy to find these non-cleaned part of code, it could be a source for "good first issue" PRs maybe.
That's a good idea!
These parts might not be easy to identify with regex (without a TODO), but a few people might find something by looking by hand (eyes) in our next sprint in February. I will point them to it.
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I am fine with this, we'll bump up the dependencies for scikit-learn 1.7 and 1.2.0 happened ~20 days after 1.1.5, so it should not have too much impact.
In a case like this I would typically update only the lock-files I care about i.e. the one with min min_dependencies in their name.
You can do something like this:
python build_tools/update_environments_and_lock_files.py --select-build 'min_dependencies'`|
@StefanieSenger I wasn't sure whether I would ask you to revert the changes to the lock-files that are not related to min-dependencies. I ended up doing it because it makes it easier to review and is a bit safer. I also used a Basically in general only update the lock-files that you need for your PR and leave the rest to the weekly Monday automatic lock-files PRs. More details about the "a bit safer" part: if you update all of the lock-files you need to remember to use the "exotic builds" commit markers like |
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Yes, thanks @lesteve. Next time I will remember that. |
This PR sets the minimum pandas version to "1.2.0", since the previous minimal version ("1.1.5") was released over four years ago and was causing some issues for
_array_api._convert_to_numpy().See these problems roughly fixed in a commit and this comment..
(Note that these issues might be more complicated once we would test better for it, every time
_convert_to_numpy()would be called on an input beforecheck_array()comes into play.@adrinjalali, would you like to have a look?