Update dependency pandas to v2.2.3

[renovate]

This PR contains the following updates:

Package	Change	Age	Adoption	Passing	Confidence
pandas (source)	==2.2.2 -> ==2.2.3
pandas (source)	==2.1.3 -> ==2.2.3

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Release Notes

pandas-dev/pandas (pandas)

v2.2.3: Pandas 2.2.3

Compare Source

We are pleased to announce the release of pandas 2.2.3.
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 2.2.3 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.

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[icfaust]

@ethanglaser So far I can see in the 2.2.2 pandas release notes (https://pandas.pydata.org/docs/whatsnew/v2.2.2.html) that this is the first version to support numpy 2.0. That would suggest that they have changed some python-C API interfaces in their backend, and may have introduced a memory leak. The memory loss between estimators for python 3.9, 3.10 windows and linux are the same (approximately 191000 bytes for an input data of 80800) for all estimators. This definitely looks to be an upstream issue with pandas, since all pandas dataframes are currently converted to numpy before being passed to onedal. I suspect that pandas isn't tracking the reference count correctly. I would like to do the following:

1. Merge [testing] refactor test_memory_usage.py #1776 and update this PR, see if it still persists
2. Modify the numpy versions for 3.9 and 3.10 in this PR and see what happens
3. Test locally, reproduce issue and then run valgrind (I suspect this would take some time to do)

[ethanglaser]

/intelci: run

[ethanglaser]

/azp run CI

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[Alexsandruss]

/intelci: run

[Alexsandruss]

/intelci: run