Merge branch 'pandas-dev:main' into main

Author: hasanrashid

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

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, str): [a, b, c]
+    Categories (3, str): ['a', 'b', 'c']
 
     Notice that ``'b'`` is in ``uniques.categories``, despite not being
     present in ``cat.values``.

pandas/core/arrays/base.py

@@ -1688,13 +1688,13 @@ def factorize(
         >>> cat = pd.Categorical(['a', 'b', 'c'])
         >>> cat
         ['a', 'b', 'c']
-        Categories (3, object): ['a', 'b', 'c']
+        Categories (3, str): ['a', 'b', 'c']
         >>> cat.repeat(2)
         ['a', 'a', 'b', 'b', 'c', 'c']
-        Categories (3, object): ['a', 'b', 'c']
+        Categories (3, str): ['a', 'b', 'c']
         >>> cat.repeat([1, 2, 3])
         ['a', 'b', 'b', 'c', 'c', 'c']
-        Categories (3, object): ['a', 'b', 'c']
+        Categories (3, str): ['a', 'b', 'c']
         """
 
     @Substitution(klass="ExtensionArray")