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remove obsolete numpy cap, skip failing PPC tests #163

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merged 2 commits into from
May 7, 2023

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@h-vetinari h-vetinari commented May 7, 2023

Fixes #150

In general, such hard caps in the run-requirements are a really bad idea, because they force every user of pandas (through conda-forge) to forego improvements in new numpy versions etc., even though it's about highly localized failures. The better way would be to add this cap in the test requirements.

In either case, such caps should be removed as soon as possible, in this case once the next version is released that contains the fix.

Also, CI on main is failing for PPY, which is not good. I added another commit to fix this.

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Hi! This is the friendly automated conda-forge-linting service.

I just wanted to let you know that I linted all conda-recipes in your PR (recipe) and found it was in an excellent condition.

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Hi! This is the friendly automated conda-forge-linting service.

I wanted to let you know that I linted all conda-recipes in your PR (recipe) and found some lint.

Here's what I've got...

For recipe:

  • Failed to even lint the recipe, probably because of a conda-smithy bug 😢. This likely indicates a problem in your meta.yaml, though. To get a traceback to help figure out what's going on, install conda-smithy and run conda smithy recipe-lint . from the recipe directory.

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Hi! This is the friendly automated conda-forge-linting service.

I just wanted to let you know that I linted all conda-recipes in your PR (recipe) and found it was in an excellent condition.

@h-vetinari h-vetinari changed the title remove obsolete numpy cap remove obsolete numpy cap, skip failing PPC tests May 7, 2023
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@conda-forge/pandas, PTAL

If desired, I can join as a maintainer as well.

@h-vetinari h-vetinari mentioned this pull request May 7, 2023
@lithomas1 lithomas1 merged commit 6eb13eb into conda-forge:main May 7, 2023
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thanks @h-vetinari. You're welcome to add yourself as a maintainer if you'd like.

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In general, such hard caps in the run-requirements are a really bad idea, because they force every user of pandas (through conda-forge) to forego improvements in new numpy versions etc., even though it's about highly localized failures. The better way would be to add this cap in the test requirements.

The cap you removed was in the test requirements, right? (and not in the run-requirements)

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The cap you removed was in the test requirements, right? (and not in the run-requirements)

My bad, not sure how I managed to confuse myself on that one. Sorry!

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Test failures with NumPy 1.24
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