What’s new in 2.2.2 (April 10, 2024)#
These are the changes in pandas 2.2.2. See Release notes for a full changelog including other versions of pandas.
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
DeprecationWarningwhen a customDataFrameorSeriessubclass method is called (GH 57553)Fixed regression in precision of
to_datetime()with string andunitinput (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 ofNone)'string[pyarrow]'without missing values (GH 57761)DataFrame.to_sql()was failing to find the right table when using the schema argument (GH 57539)