- Update to 2.2.2
* Pandas 2.2.2 is now compatible with numpy 2.0
* Pandas 2.2.2 is the first version of pandas that is generally
compatible with the upcoming numpy 2.0 release, and wheels for
pandas 2.2.2 will work with both numpy 1.x and 2.x. One major
caveat is that arrays created with numpy 2.0’s new StringDtype
will convert to object dtyped arrays upon Series/DataFrame
creation. Full support for numpy 2.0’s StringDtype is expected
to land in pandas 3.0.
* As usual please report any bugs discovered to our issue tracker
## Fixed regressions
* DataFrame.__dataframe__() was producing incorrect data buffers
when the a column’s type was a pandas nullable on with missing
values (GH 56702)
* DataFrame.__dataframe__() was producing incorrect data buffers
when the a column’s type was a pyarrow nullable on with missing
values (GH 57664)
* Avoid issuing a spurious DeprecationWarning when a custom
DataFrame or Series subclass method is called (GH 57553)
* Fixed regression in precision of to_datetime() with string and
unit input (GH 57051)
## Bug fixes
* DataFrame.__dataframe__() was producing incorrect data buffers
when the column’s type was nullable boolean (GH 55332)
* DataFrame.__dataframe__() was showing bytemask instead of
bitmask for 'string[pyarrow]' validity buffer (GH 57762)
* DataFrame.__dataframe__() was showing non-null validity buffer
(instead of None) 'string[pyarrow]' without missing values (GH
57761)
* DataFrame.to_sql() was failing to find the right table when
using the schema argument (GH 57539)
- Remove obsolete python39 multibuild
- Add pandas-pr58269-pyarrow16xpass.patch
gh#pandas-dev/pandas#58269