Bump pandas from 2.1.4 to 2.2.1

[dependabot]

Bumps pandas from 2.1.4 to 2.2.1.

Release notes

Sourced from pandas's releases.

Pandas 2.2.1

We are pleased to announce the release of pandas 2.2.1. This release includes some new features, bug fixes, and performance improvements. We recommend that all users upgrade to this version.

See the full whatsnew for a list of all the changes. Pandas 2.2.1 supports Python 3.9 and higher.

The release will be available on the defaults and conda-forge channels:

conda install pandas

Or via PyPI:

python3 -m pip install --upgrade pandas

Please report any issues with the release on the pandas issue tracker.

Thanks to all the contributors who made this release possible.

Pandas 2.2.0

We are pleased to announce the release of pandas 2.2.0. This release includes some new features, bug fixes, and performance improvements. We recommend that all users upgrade to this version.

See the full whatsnew for a list of all the changes. Pandas 2.2.0 supports Python 3.9 and higher.

The release will be available on the defaults and conda-forge channels:

conda install pandas

Or via PyPI:

python3 -m pip install --upgrade pandas

Please report any issues with the release on the pandas issue tracker.

Thanks to all the contributors who made this release possible.

Pandas 2.2.0rc0

We are pleased to announce a release candidate for pandas 2.2.0. If all goes well, we'll release pandas 2.2.0 in about two weeks.

See the whatsnew for a list of all the changes.

The release will be available on conda-forge and PyPI.

The release can be installed from PyPI

python -m pip install --upgrade --pre pandas==2.2.0rc0 Or from conda-forge

... (truncated)

Commits

• bdc79c1 RLS: 2.2.1
• 470b886 Backport PR #57582 on branch 2.2.x (DOC: Add contributors for 2.2.1) (#57583)
• 541448e RLS: 2.2.1
• bdbb179 Backport PR #57576 on branch 2.2.x (DOC: Add release date for 2.2.1) (#57579)
• 5521dc9 Backport PR #57314 on branch 2.2.x (BUG: Fix near-minimum timestamp handling)...
• ea56e0c Backport PR #57556 on branch 2.2.x (Remove PyArrow deprecation warning) (#57568)
• 3bfedfe Backport PR #57551: BLD: Add pyarrow extra for pip installation (#57557)
• 3a4033c Backport PR #57439 on branch 2.2.x (BUG: read_json returning Index instead of...
• c101d30 Backport PR #57510 on branch 2.2.x (DOC: Fix xarray example) (#57538)
• 0b49cf3 Backport PR #57536 on branch 2.2.x (BUG: dt64 + DateOffset with milliseconds)...
• Additional commits viewable in compare view

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[brews]

Ah, sorry, I marked you as reivewer @kemccusker because I got the update on the issue. I don't mind merging this but wasn't sure if you already wanted to handle this one. If you weren't already planning it, could you take 5 minutes and review this before the end of the week, @kemccusker?

[brews]

Appreciate the fix!

I might suggest adding a comment to each of the tests that needed changing explaining why all this sorting and index shuffling is needed. I feel like I wouldn't be able to grok what's going on (or the "why" of it) if I was reading through the tests.

[brews]

First, sorry for the spam.

If you haven't already tried it, you might be able to ditch some or all of the index/column ordering I've been complaining about by passing check_like=True to pandas.testing.assert_frame_equal() unless index/column sorting has some subtlety you need to enforce that I'm not seeing.

[JMGilbert]

First, sorry for the spam.

If you haven't already tried it, you might be able to ditch some or all of the index/column ordering I've been complaining about by passing check_like=True to pandas.testing.assert_frame_equal() unless index/column sorting has some subtlety you need to enforce that I'm not seeing.

This works, but requires setting the indices of both dataframes, something like:

e = expected.set_index(['ssp','gcm',...])
a = actual.set_index(['ssp','gcm',...])
assert_frame_equal(a, e, check_like = True)

Maybe we should be actually using the index dimensions (like ssp, gcm, etc) as pandas indices in the first place.

[kemccusker]

@JMGilbert I like the idea to set_index on index dimensions, assuming we know them ahead of time

[brews]

@JMGilbert Is the index information you're setting something already available in all the "expected" files used for the test, but not set as the index by open_zipped_results()? Any reason it wouldn't be possible to pass args/kwargs to open_zipped_results() (which get passed to pd.read_csv()) and so have it set the index to expected when the data is read in?

[brews]

Ahhhh. I see what's going on. I was totally misreading the issue. I tried your solution @JMGilbert and I think it works great. Thank you for being patient with me everyone.

I did it like:

    # Set index so can compare unsorted tables.
    idx = ["model", "ssp", "rcp", "gcm", "year"]
    actual = actual.set_index(idx)
    expected = expected.set_index(idx)

    assert_frame_equal(expected, actual, rtol=1e-4, atol=1e-4, check_like=True)