These are the changes in pandas 2.2.2. See :ref:`release` for a full changelog including other versions of pandas.
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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 :class:`Series`/:class:`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
- :meth:`DataFrame.__dataframe__` was producing incorrect data buffers when the a column's type was a pandas nullable on with missing values (:issue:`56702`)
- :meth:`DataFrame.__dataframe__` was producing incorrect data buffers when the a column's type was a pyarrow nullable on with missing values (:issue:`57664`)
- Avoid issuing a spurious
DeprecationWarningwhen a custom :class:`DataFrame` or :class:`Series` subclass method is called (:issue:`57553`) - Fixed regression in precision of :func:`to_datetime` with string and
unitinput (:issue:`57051`)
- :meth:`DataFrame.__dataframe__` was producing incorrect data buffers when the column's type was nullable boolean (:issue:`55332`)
- :meth:`DataFrame.__dataframe__` was showing bytemask instead of bitmask for
'string[pyarrow]'validity buffer (:issue:`57762`) - :meth:`DataFrame.__dataframe__` was showing non-null validity buffer (instead of
None)'string[pyarrow]'without missing values (:issue:`57761`) - :meth:`DataFrame.to_sql` was failing to find the right table when using the schema argument (:issue:`57539`)
.. contributors:: v2.2.1..v2.2.2