Merge branch 'main' of https://github.com/pandas-dev/pandas into implement_pdep17

Author: rhshadrach

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/development/maintaining.rst

@@ -388,8 +388,11 @@ Pre-release
 
 3. Make sure the CI is green for the last commit of the branch being released.
 
-4. If not a release candidate, make sure all backporting pull requests to the branch
-   being released are merged.
+4. If not a release candidate, make sure all backporting pull requests to the
+   branch being released are merged, and no merged pull requests are missing a
+   backport (check the
+   ["Still Needs Manual Backport"](https://github.com/pandas-dev/pandas/labels/Still%20Needs%20Manual%20Backport)
+   label for this).
 
 5. Create a new issue and milestone for the version after the one being released.
    If the release was a release candidate, we would usually want to create issues and
@@ -435,6 +438,9 @@ which will be triggered when the tag is pushed.
 
     scripts/download_wheels.sh <VERSION>
 
+   ATTENTION: this is currently not downloading *all* wheels, and you have to
+   manually download the remainings wheels and sdist!
+
 4. Create a `new GitHub release <https://github.com/pandas-dev/pandas/releases/new>`_:
 
    - Tag: ``<version>``
@@ -462,15 +468,22 @@ Post-Release
 ````````````
 
 1. Update symlinks to stable documentation by logging in to our web server, and
-   editing ``/var/www/html/pandas-docs/stable`` to point to ``version/<latest-version>``
-   for major and minor releases, or ``version/<minor>`` to ``version/<patch>`` for
+   editing ``/var/www/html/pandas-docs/stable`` to point to ``version/<X.Y>``
+   for major and minor releases, or ``version/<X.Y>`` to ``version/<patch>`` for
    patch releases. The exact instructions are (replace the example version numbers by
    the appropriate ones for the version you are releasing):
 
    - Log in to the server and use the correct user.
    - ``cd /var/www/html/pandas-docs/``
-   - ``ln -sfn version/2.1 stable`` (for a major or minor release)
-   - ``ln -sfn version/2.0.3 version/2.0`` (for a patch release)
+   - For a major or minor release (assuming the ``/version/2.1.0/`` docs have been uploaded to the server):
+
+     -  Create a new X.Y symlink to X.Y.Z: ``cd version; ln -sfn 2.1.0 2.1``
+     -  Update stable symlink to point to X.Y: ``ln -sfn version/2.1 stable``
+
+   - For a patch release (assuming the ``/version/2.1.3/`` docs have been uploaded to the server):
+
+     - Update the X.Y symlink to the new X.Y.Z patch version: ``cd version; ln -sfn 2.1.3 2.1``
+     - (the stable symlink should already be pointing to the correct X.Y version)
 
 2. If releasing a major or minor release, open a PR in our source code to update
    ``web/pandas/versions.json``, to have the desired versions in the documentation

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`)
+
+
+.. ---------------------------------------------------------------------------
+.. _whatsnew_232.contributors:
+
+Contributors
+~~~~~~~~~~~~

doc/source/whatsnew/v3.0.0.rst

@@ -1,6 +1,6 @@
 .. _whatsnew_300:
 
-What's new in 3.0.0 (Month XX, 2024)
+What's new in 3.0.0 (Month XX, 2025)
 ------------------------------------
 
 These are the changes in pandas 3.0.0. See :ref:`release` for a full changelog

pandas/_libs/groupby.pyi

@@ -67,6 +67,7 @@ def group_sum(
     result_mask: np.ndarray | None = ...,
     min_count: int = ...,
     is_datetimelike: bool = ...,
+    initial: object = ...,
     skipna: bool = ...,
 ) -> None: ...
 def group_prod(

pandas/_libs/groupby.pyx

@@ -707,6 +707,7 @@ def group_sum(
     uint8_t[:, ::1] result_mask=None,
     Py_ssize_t min_count=0,
     bint is_datetimelike=False,
+    object initial=0,
     bint skipna=True,
 ) -> None:
     """
@@ -725,9 +726,15 @@ def group_sum(
         raise ValueError("len(index) != len(labels)")
 
     nobs = np.zeros((<object>out).shape, dtype=np.int64)
-    # the below is equivalent to `np.zeros_like(out)` but faster
-    sumx = np.zeros((<object>out).shape, dtype=(<object>out).base.dtype)
-    compensation = np.zeros((<object>out).shape, dtype=(<object>out).base.dtype)
+    if initial == 0:
+        # the below is equivalent to `np.zeros_like(out)` but faster
+        sumx = np.zeros((<object>out).shape, dtype=(<object>out).base.dtype)
+        compensation = np.zeros((<object>out).shape, dtype=(<object>out).base.dtype)
+    else:
+        # in practice this path is only taken for strings to use empty string as initial
+        assert sum_t is object
+        sumx = np.full((<object>out).shape, initial, dtype=object)
+        # object code path does not use `compensation`
 
     N, K = (<object>values).shape
     if uses_mask: