Merge branch 'main' into skonda29-issue-61311

Author: skonda29

.github/workflows/wheels.yml

@@ -162,7 +162,7 @@ jobs:
         run: echo "sdist_name=$(cd ./dist && ls -d */)" >> "$GITHUB_ENV"
 
       - name: Build wheels
-        uses: pypa/cibuildwheel@v2.23.3
+        uses: pypa/cibuildwheel@v3.1.1
         with:
          package-dir: ./dist/${{ startsWith(matrix.buildplat[1], 'macosx') && env.sdist_name || needs.build_sdist.outputs.sdist_file }}
         env:

README.md

@@ -115,7 +115,7 @@ details, see the commit logs at https://github.com/pandas-dev/pandas.
 ## Dependencies
 - [NumPy - Adds support for large, multi-dimensional arrays, matrices and high-level mathematical functions to operate on these arrays](https://www.numpy.org)
 - [python-dateutil - Provides powerful extensions to the standard datetime module](https://dateutil.readthedocs.io/en/stable/index.html)
-- [pytz - Brings the Olson tz database into Python which allows accurate and cross platform timezone calculations](https://github.com/stub42/pytz)
+- [tzdata - Provides an IANA time zone database](https://tzdata.readthedocs.io/en/latest/)
 
 See the [full installation instructions](https://pandas.pydata.org/pandas-docs/stable/install.html#dependencies) for minimum supported versions of required, recommended and optional dependencies.
 

ci/code_checks.sh

@@ -58,9 +58,7 @@ if [[ -z "$CHECK" || "$CHECK" == "doctests" ]]; then
 
     MSG='Python and Cython Doctests' ; echo "$MSG"
     python -c 'import pandas as pd; pd.test(run_doctests=True)'
-    # TEMP don't let doctests fail the build until all string dtype changes are fixed
-    # RET=$(($RET + $?)) ; echo "$MSG" "DONE"
-    echo "$MSG" "DONE"
+    RET=$(($RET + $?)) ; echo "$MSG" "DONE"
 
 fi
 

ci/deps/actions-311-downstream_compat.yaml

@@ -50,8 +50,7 @@ dependencies:
   - pytz>=2023.4
   - pyxlsb>=1.0.10
   - s3fs>=2023.12.2
-  # TEMP upper pin for scipy (https://github.com/statsmodels/statsmodels/issues/9584)
-  - scipy>=1.12.0,<1.16
+  - scipy>=1.12.0
   - sqlalchemy>=2.0.0
   - tabulate>=0.9.0
   - xarray>=2024.1.1

doc/source/user_guide/migration-3-strings.rst

@@ -118,12 +118,17 @@ through the ``str`` accessor will work the same:
 Overview of behavior differences and how to address them
 ---------------------------------------------------------
 
-The dtype is no longer object dtype
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+The dtype is no longer a numpy "object" dtype
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 When inferring or reading string data, the data type of the resulting DataFrame
 column or Series will silently start being the new ``"str"`` dtype instead of
-``"object"`` dtype, and this can have some impact on your code.
+the numpy ``"object"`` dtype, and this can have some impact on your code.
+
+The new string dtype is a pandas data type ("extension dtype"), and no longer a
+numpy ``np.dtype`` instance. Therefore, passing the dtype of a string column to
+numpy functions will no longer work (e.g. passing it to a ``dtype=`` argument
+of a numpy function, or using ``np.issubdtype`` to check the dtype).
 
 Checking the dtype
 ^^^^^^^^^^^^^^^^^^

doc/source/whatsnew/index.rst

@@ -24,6 +24,7 @@ Version 2.3
 .. toctree::
    :maxdepth: 2
 
+   v2.3.2
    v2.3.1
    v2.3.0
 

doc/source/whatsnew/v2.3.2.rst

@@ -0,0 +1,34 @@
+.. _whatsnew_232:
+
+What's new in 2.3.2 (August XX, 2025)
+-------------------------------------
+
+These are the changes in pandas 2.3.2. See :ref:`release` for a full changelog
+including other versions of pandas.
+
+{{ header }}
+
+.. ---------------------------------------------------------------------------
+.. _whatsnew_232.string_fixes:
+
+Improvements and fixes for the StringDtype
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+Most changes in this release are related to :class:`StringDtype` which will
+become the default string dtype in pandas 3.0. See
+:ref:`whatsnew_230.upcoming_changes` for more details.
+
+.. _whatsnew_232.string_fixes.bugs:
+
+Bug fixes
+^^^^^^^^^
+- Fix :meth:`~DataFrame.to_json` with ``orient="table"`` to correctly use the
+  "string" type in the JSON Table Schema for :class:`StringDtype` columns
+  (:issue:`61889`)
+- Boolean operations (``|``, ``&``, ``^``) with bool-dtype objects on the left and :class:`StringDtype` objects on the right now cast the string to bool, with a deprecation warning (:issue:`60234`)
+
+.. ---------------------------------------------------------------------------
+.. _whatsnew_232.contributors:
+
+Contributors
+~~~~~~~~~~~~

doc/source/whatsnew/v3.0.0.rst

@@ -731,6 +731,7 @@ Timezones
 
 Numeric
 ^^^^^^^
+- Bug in :func:`api.types.infer_dtype` returning "mixed" for complex and ``pd.NA`` mix (:issue:`61976`)
 - Bug in :func:`api.types.infer_dtype` returning "mixed-integer-float" for float and ``pd.NA`` mix (:issue:`61621`)
 - Bug in :meth:`DataFrame.corr` where numerical precision errors resulted in correlations above ``1.0`` (:issue:`61120`)
 - Bug in :meth:`DataFrame.cov` raises a ``TypeError`` instead of returning potentially incorrect results or other errors (:issue:`53115`)
@@ -851,6 +852,7 @@ Groupby/resample/rolling
 - Bug in :meth:`DataFrame.ewm` and :meth:`Series.ewm` when passed ``times`` and aggregation functions other than mean (:issue:`51695`)
 - Bug in :meth:`DataFrame.resample` and :meth:`Series.resample` were not keeping the index name when the index had :class:`ArrowDtype` timestamp dtype (:issue:`61222`)
 - Bug in :meth:`DataFrame.resample` changing index type to :class:`MultiIndex` when the dataframe is empty and using an upsample method (:issue:`55572`)
+- Bug in :meth:`DataFrameGroupBy.agg` and :meth:`SeriesGroupBy.agg` that was returning numpy dtype values when input values are pyarrow dtype values, instead of returning pyarrow dtype values. (:issue:`53030`)
 - Bug in :meth:`DataFrameGroupBy.agg` that raises ``AttributeError`` when there is dictionary input and duplicated columns, instead of returning a DataFrame with the aggregation of all duplicate columns. (:issue:`55041`)
 - Bug in :meth:`DataFrameGroupBy.agg` where applying a user-defined function to an empty DataFrame returned a Series instead of an empty DataFrame. (:issue:`61503`)
 - Bug in :meth:`DataFrameGroupBy.apply` and :meth:`SeriesGroupBy.apply` for empty data frame with ``group_keys=False`` still creating output index using group keys. (:issue:`60471`)
@@ -940,6 +942,7 @@ Other
 - Bug in Dataframe Interchange Protocol implementation was returning incorrect results for data buffers' associated dtype, for string and datetime columns (:issue:`54781`)
 - Bug in ``Series.list`` methods not preserving the original :class:`Index`. (:issue:`58425`)
 - Bug in ``Series.list`` methods not preserving the original name. (:issue:`60522`)
+- Bug in ``Series.replace`` when the Series was created from an :class:`Index` and Copy-On-Write is enabled (:issue:`61622`)
 - Bug in printing a :class:`DataFrame` with a :class:`DataFrame` stored in :attr:`DataFrame.attrs` raised a ``ValueError`` (:issue:`60455`)
 - Bug in printing a :class:`Series` with a :class:`DataFrame` stored in :attr:`Series.attrs` raised a ``ValueError`` (:issue:`60568`)
 - Fixed bug where the :class:`DataFrame` constructor misclassified array-like objects with a ``.name`` attribute as :class:`Series` or :class:`Index` (:issue:`61443`)

pandas/_libs/lib.pyx

@@ -1974,9 +1974,11 @@ cdef class ComplexValidator(Validator):
         return cnp.PyDataType_ISCOMPLEX(self.dtype)
 
 
-cdef bint is_complex_array(ndarray values):
+cdef bint is_complex_array(ndarray values, bint skipna=True):
     cdef:
-        ComplexValidator validator = ComplexValidator(values.size, values.dtype)
+        ComplexValidator validator = ComplexValidator(values.size,
+                                                      values.dtype,
+                                                      skipna=skipna)
     return validator.validate(values)
 
 

pandas/core/algorithms.py

@@ -391,11 +391,11 @@ def unique(values):
 
     >>> pd.unique(pd.Series(pd.Categorical(list("baabc"))))
     ['b', 'a', 'c']
-    Categories (3, object): ['a', 'b', 'c']
+    Categories (3, str): ['a', 'b', 'c']
 
     >>> pd.unique(pd.Series(pd.Categorical(list("baabc"), categories=list("abc"))))
     ['b', 'a', 'c']
-    Categories (3, object): ['a', 'b', 'c']
+    Categories (3, str): ['a', 'b', 'c']
 
     An ordered Categorical preserves the category ordering.
 
@@ -405,7 +405,7 @@ def unique(values):
     ...     )
     ... )
     ['b', 'a', 'c']
-    Categories (3, object): ['a' < 'b' < 'c']
+    Categories (3, str): ['a' < 'b' < 'c']
 
     An array of tuples
 
@@ -751,7 +751,7 @@ def factorize(
     array([0, 0, 1])
     >>> uniques
     ['a', 'c']
-    Categories (3, object): ['a', 'b', 'c']
+    Categories (3, str): ['a', 'b', 'c']
 
     Notice that ``'b'`` is in ``uniques.categories``, despite not being
     present in ``cat.values``.
@@ -764,7 +764,7 @@ def factorize(
     >>> codes
     array([0, 0, 1])
     >>> uniques
-    Index(['a', 'c'], dtype='object')
+    Index(['a', 'c'], dtype='str')
 
     If NaN is in the values, and we want to include NaN in the uniques of the
     values, it can be achieved by setting ``use_na_sentinel=False``.