Merge remote-tracking branch 'upstream/main' into fix-docstring

Author: fbourgey

.github/workflows/unit-tests.yml

@@ -380,7 +380,7 @@ jobs:
           fetch-depth: 0
 
       - name: Set up Python Free-threading Version
-        uses: deadsnakes/action@v3.1.0
+        uses: deadsnakes/action@v3.2.0
         with:
           python-version: 3.13-dev
           nogil: true

ci/code_checks.sh

@@ -73,12 +73,9 @@ if [[ -z "$CHECK" || "$CHECK" == "docstrings" ]]; then
         -i "pandas.NA SA01" \
         -i "pandas.Period.freq GL08" \
         -i "pandas.Period.ordinal GL08" \
-        -i "pandas.Period.to_timestamp SA01" \
         -i "pandas.PeriodDtype.freq SA01" \
         -i "pandas.RangeIndex.from_range PR01,SA01" \
-        -i "pandas.RangeIndex.start SA01" \
         -i "pandas.RangeIndex.step SA01" \
-        -i "pandas.RangeIndex.stop SA01" \
         -i "pandas.Series.cat.add_categories PR01,PR02" \
         -i "pandas.Series.cat.as_ordered PR01" \
         -i "pandas.Series.cat.as_unordered PR01" \
@@ -93,10 +90,8 @@ if [[ -z "$CHECK" || "$CHECK" == "docstrings" ]]; then
         -i "pandas.Series.dt.floor PR01,PR02" \
         -i "pandas.Series.dt.freq GL08" \
         -i "pandas.Series.dt.month_name PR01,PR02" \
-        -i "pandas.Series.dt.nanoseconds SA01" \
         -i "pandas.Series.dt.normalize PR01" \
         -i "pandas.Series.dt.round PR01,PR02" \
-        -i "pandas.Series.dt.seconds SA01" \
         -i "pandas.Series.dt.strftime PR01,PR02" \
         -i "pandas.Series.dt.to_period PR01,PR02" \
         -i "pandas.Series.dt.total_seconds PR01" \
@@ -112,26 +107,13 @@ if [[ -z "$CHECK" || "$CHECK" == "docstrings" ]]; then
         -i "pandas.Timedelta.max PR02" \
         -i "pandas.Timedelta.min PR02" \
         -i "pandas.Timedelta.resolution PR02" \
-        -i "pandas.Timedelta.to_numpy PR01" \
-        -i "pandas.TimedeltaIndex.components SA01" \
-        -i "pandas.TimedeltaIndex.microseconds SA01" \
-        -i "pandas.TimedeltaIndex.nanoseconds SA01" \
-        -i "pandas.TimedeltaIndex.seconds SA01" \
         -i "pandas.TimedeltaIndex.to_pytimedelta RT03,SA01" \
         -i "pandas.Timestamp.max PR02" \
         -i "pandas.Timestamp.min PR02" \
         -i "pandas.Timestamp.nanosecond GL08" \
         -i "pandas.Timestamp.resolution PR02" \
         -i "pandas.Timestamp.tzinfo GL08" \
         -i "pandas.Timestamp.year GL08" \
-        -i "pandas.api.extensions.ExtensionArray.interpolate PR01,SA01" \
-        -i "pandas.api.types.is_bool PR01,SA01" \
-        -i "pandas.api.types.is_categorical_dtype SA01" \
-        -i "pandas.api.types.is_complex PR01,SA01" \
-        -i "pandas.api.types.is_complex_dtype SA01" \
-        -i "pandas.api.types.is_datetime64_dtype SA01" \
-        -i "pandas.api.types.is_datetime64_ns_dtype SA01" \
-        -i "pandas.api.types.is_datetime64tz_dtype SA01" \
         -i "pandas.api.types.is_dict_like PR07,SA01" \
         -i "pandas.api.types.is_extension_array_dtype SA01" \
         -i "pandas.api.types.is_file_like PR07,SA01" \
@@ -165,7 +147,6 @@ if [[ -z "$CHECK" || "$CHECK" == "docstrings" ]]; then
         -i "pandas.core.groupby.DataFrameGroupBy.agg RT03" \
         -i "pandas.core.groupby.DataFrameGroupBy.aggregate RT03" \
         -i "pandas.core.groupby.DataFrameGroupBy.boxplot PR07,RT03,SA01" \
-        -i "pandas.core.groupby.DataFrameGroupBy.filter SA01" \
         -i "pandas.core.groupby.DataFrameGroupBy.get_group RT03,SA01" \
         -i "pandas.core.groupby.DataFrameGroupBy.groups SA01" \
         -i "pandas.core.groupby.DataFrameGroupBy.hist RT03" \
@@ -181,7 +162,6 @@ if [[ -z "$CHECK" || "$CHECK" == "docstrings" ]]; then
         -i "pandas.core.groupby.SeriesGroupBy.__iter__ RT03,SA01" \
         -i "pandas.core.groupby.SeriesGroupBy.agg RT03" \
         -i "pandas.core.groupby.SeriesGroupBy.aggregate RT03" \
-        -i "pandas.core.groupby.SeriesGroupBy.filter PR01,SA01" \
         -i "pandas.core.groupby.SeriesGroupBy.get_group RT03,SA01" \
         -i "pandas.core.groupby.SeriesGroupBy.groups SA01" \
         -i "pandas.core.groupby.SeriesGroupBy.indices SA01" \

doc/source/whatsnew/v2.3.0.rst

@@ -103,8 +103,9 @@ Conversion
 Strings
 ^^^^^^^
 - Bug in :meth:`Series.str.replace` when ``n < 0`` for :class:`StringDtype` with ``storage="pyarrow"`` (:issue:`59628`)
+- Bug in ``ser.str.slice`` with negative ``step`` with :class:`ArrowDtype` and :class:`StringDtype` with ``storage="pyarrow"`` giving incorrect results (:issue:`59710`)
 - Bug in the ``center`` method on :class:`Series` and :class:`Index` object ``str`` accessors with pyarrow-backed dtype not matching the python behavior in corner cases with an odd number of fill characters (:issue:`54792`)
-
+-
 
 Interval
 ^^^^^^^^

doc/source/whatsnew/v3.0.0.rst

@@ -55,6 +55,7 @@ Other enhancements
 - :meth:`Series.plot` now correctly handle the ``ylabel`` parameter for pie charts, allowing for explicit control over the y-axis label (:issue:`58239`)
 - :meth:`DataFrame.plot.scatter` argument ``c`` now accepts a column of strings, where rows with the same string are colored identically (:issue:`16827` and :issue:`16485`)
 - :meth:`pandas.concat` will raise a ``ValueError`` when ``ignore_index=True`` and ``keys`` is not ``None`` (:issue:`59274`)
+- :meth:`str.get_dummies` now accepts a  ``dtype`` parameter to specify the dtype of the resulting DataFrame (:issue:`47872`)
 - Multiplying two :class:`DateOffset` objects will now raise a ``TypeError`` instead of a ``RecursionError`` (:issue:`59442`)
 - Restore support for reading Stata 104-format and enable reading 103-format dta files (:issue:`58554`)
 - Support passing a :class:`Iterable[Hashable]` input to :meth:`DataFrame.drop_duplicates` (:issue:`59237`)
@@ -668,6 +669,7 @@ Reshaping
 - Bug in :meth:`DataFrame.join` when a :class:`DataFrame` with a :class:`MultiIndex` would raise an ``AssertionError`` when :attr:`MultiIndex.names` contained ``None``. (:issue:`58721`)
 - Bug in :meth:`DataFrame.merge` where merging on a column containing only ``NaN`` values resulted in an out-of-bounds array access (:issue:`59421`)
 - Bug in :meth:`DataFrame.unstack` producing incorrect results when ``sort=False`` (:issue:`54987`, :issue:`55516`)
+- Bug in :meth:`DataFrame.pivot_table` incorrectly subaggregating results when called without an ``index`` argument (:issue:`58722`)
 - Bug in :meth:`DataFrame.unstack` producing incorrect results when manipulating empty :class:`DataFrame` with an :class:`ExtentionDtype` (:issue:`59123`)
 
 Sparse

pandas/_libs/lib.pyx

@@ -733,7 +733,9 @@ cpdef ndarray[object] ensure_string_array(
     convert_na_value : bool, default True
         If False, existing na values will be used unchanged in the new array.
     copy : bool, default True
-        Whether to ensure that a new array is returned.
+        Whether to ensure that a new array is returned. When True, a new array
+        is always returned. When False, a new array is only returned when needed
+        to avoid mutating the input array.
     skipna : bool, default True
         Whether or not to coerce nulls to their stringified form
         (e.g. if False, NaN becomes 'nan').
@@ -762,11 +764,15 @@ cpdef ndarray[object] ensure_string_array(
 
     result = np.asarray(arr, dtype="object")
 
-    if copy and (result is arr or np.shares_memory(arr, result)):
-        # GH#54654
-        result = result.copy()
-    elif not copy and result is arr:
-        already_copied = False
+    if result is arr or np.may_share_memory(arr, result):
+        # if np.asarray(..) did not make a copy of the input arr, we still need
+        #  to do that to avoid mutating the input array
+        # GH#54654: share_memory check is needed for rare cases where np.asarray
+        #  returns a new object without making a copy of the actual data
+        if copy:
+            result = result.copy()
+        else:
+            already_copied = False
     elif not copy and not result.flags.writeable:
         # Weird edge case where result is a view
         already_copied = False
@@ -1123,10 +1129,21 @@ def is_bool(obj: object) -> bool:
     """
     Return True if given object is boolean.
 
+    Parameters
+    ----------
+    obj : object
+        Object to check.
+
     Returns
     -------
     bool
 
+    See Also
+    --------
+    api.types.is_scalar : Check if the input is a scalar.
+    api.types.is_integer : Check if the input is an integer.
+    api.types.is_float : Check if the input is a float.
+
     Examples
     --------
     >>> pd.api.types.is_bool(True)
@@ -1142,10 +1159,22 @@ def is_complex(obj: object) -> bool:
     """
     Return True if given object is complex.
 
+    Parameters
+    ----------
+    obj : object
+        Object to check.
+
     Returns
     -------
     bool
 
+    See Also
+    --------
+    api.types.is_complex_dtype: Check whether the provided array or
+                                dtype is of a complex dtype.
+    api.types.is_number: Check if the object is a number.
+    api.types.is_integer: Return True if given object is integer.
+
     Examples
     --------
     >>> pd.api.types.is_complex(1 + 1j)

pandas/_libs/tslibs/period.pyx

@@ -2001,6 +2001,12 @@ cdef class _Period(PeriodMixin):
         -------
         Timestamp
 
+        See Also
+        --------
+        Timestamp : A class representing a single point in time.
+        Period : Represents a span of time with a fixed frequency.
+        PeriodIndex.to_timestamp : Convert a `PeriodIndex` to a `DatetimeIndex`.
+
         Examples
         --------
         >>> period = pd.Period('2023-1-1', freq='D')

pandas/_libs/tslibs/timedeltas.pyx

@@ -1471,6 +1471,14 @@ cdef class _Timedelta(timedelta):
         """
         Array view compatibility.
 
+        This method allows you to reinterpret the underlying data of a Timedelta
+        object as a different dtype. The `view` method provides a way to reinterpret
+        the internal representation of the `Timedelta` object without modifying its
+        data. This is particularly useful when you need to work with the underlying
+        data directly, such as for performance optimizations or interfacing with
+        low-level APIs. The returned value is typically the number of nanoseconds
+        since the epoch, represented as an integer or another specified dtype.
+
         Parameters
         ----------
         dtype : str or dtype
@@ -1479,7 +1487,10 @@ cdef class _Timedelta(timedelta):
         See Also
         --------
         Timedelta.asm8 : Return a numpy timedelta64 array scalar view.
-
+        numpy.ndarray.view : Returns a view of an array with the same data.
+        Timedelta.to_numpy : Converts the Timedelta to a NumPy timedelta64.
+        Timedelta.total_seconds : Returns the total duration of the Timedelta
+            object in seconds.
         Examples
         --------
         >>> td = pd.Timedelta('3D')

pandas/conftest.py

@@ -1272,6 +1272,34 @@ def string_dtype(request):
     return request.param
 
 
+@pytest.fixture(
+    params=[
+        ("python", pd.NA),
+        pytest.param(("pyarrow", pd.NA), marks=td.skip_if_no("pyarrow")),
+        pytest.param(("pyarrow", np.nan), marks=td.skip_if_no("pyarrow")),
+        ("python", np.nan),
+    ],
+    ids=[
+        "string=string[python]",
+        "string=string[pyarrow]",
+        "string=str[pyarrow]",
+        "string=str[python]",
+    ],
+)
+def string_dtype_no_object(request):
+    """
+    Parametrized fixture for string dtypes.
+    * 'string[python]' (NA variant)
+    * 'string[pyarrow]' (NA variant)
+    * 'str' (NaN variant, with pyarrow)
+    * 'str' (NaN variant, without pyarrow)
+    """
+    # need to instantiate the StringDtype here instead of in the params
+    # to avoid importing pyarrow during test collection
+    storage, na_value = request.param
+    return pd.StringDtype(storage, na_value)
+
+
 @pytest.fixture(
     params=[
         "string[python]",