Merge branch 'main' into fix_docstring_pandas_styler

ci/code_checks.sh

@@ -142,8 +142,6 @@ if [[ -z "$CHECK" || "$CHECK" == "docstrings" ]]; then
         -i "pandas.Series.sparse.sp_values SA01" \
         -i "pandas.Series.sparse.to_coo PR07,RT03,SA01" \
         -i "pandas.Series.std PR01,RT03,SA01" \
-        -i "pandas.Series.str.wrap RT03,SA01" \
-        -i "pandas.Series.str.zfill RT03" \
         -i "pandas.Timedelta.asm8 SA01" \
         -i "pandas.Timedelta.ceil SA01" \
         -i "pandas.Timedelta.components SA01" \
@@ -175,14 +173,7 @@ if [[ -z "$CHECK" || "$CHECK" == "docstrings" ]]; then
         -i "pandas.Timestamp.tzinfo GL08" \
         -i "pandas.Timestamp.value GL08" \
         -i "pandas.Timestamp.year GL08" \
-        -i "pandas.api.extensions.ExtensionArray.duplicated RT03,SA01" \
-        -i "pandas.api.extensions.ExtensionArray.fillna SA01" \
-        -i "pandas.api.extensions.ExtensionArray.insert PR07,RT03,SA01" \
         -i "pandas.api.extensions.ExtensionArray.interpolate PR01,SA01" \
-        -i "pandas.api.extensions.ExtensionArray.isin PR07,RT03,SA01" \
-        -i "pandas.api.extensions.ExtensionArray.tolist RT03,SA01" \
-        -i "pandas.api.extensions.ExtensionArray.unique RT03,SA01" \
-        -i "pandas.api.extensions.ExtensionArray.view SA01" \
         -i "pandas.api.interchange.from_dataframe RT03,SA01" \
         -i "pandas.api.types.is_bool PR01,SA01" \
         -i "pandas.api.types.is_categorical_dtype SA01" \
@@ -235,7 +226,6 @@ if [[ -z "$CHECK" || "$CHECK" == "docstrings" ]]; then
         -i "pandas.core.groupby.DataFrameGroupBy.nunique SA01" \
         -i "pandas.core.groupby.DataFrameGroupBy.ohlc SA01" \
         -i "pandas.core.groupby.DataFrameGroupBy.plot PR02" \
-        -i "pandas.core.groupby.DataFrameGroupBy.prod SA01" \
         -i "pandas.core.groupby.DataFrameGroupBy.sem SA01" \
         -i "pandas.core.groupby.DataFrameGroupBy.sum SA01" \
         -i "pandas.core.groupby.SeriesGroupBy.__iter__ RT03,SA01" \
@@ -252,7 +242,6 @@ if [[ -z "$CHECK" || "$CHECK" == "docstrings" ]]; then
         -i "pandas.core.groupby.SeriesGroupBy.nth PR02" \
         -i "pandas.core.groupby.SeriesGroupBy.ohlc SA01" \
         -i "pandas.core.groupby.SeriesGroupBy.plot PR02" \
-        -i "pandas.core.groupby.SeriesGroupBy.prod SA01" \
         -i "pandas.core.groupby.SeriesGroupBy.sem SA01" \
         -i "pandas.core.groupby.SeriesGroupBy.sum SA01" \
         -i "pandas.core.resample.Resampler.__iter__ RT03,SA01" \

doc/source/whatsnew/v3.0.0.rst

@@ -43,6 +43,7 @@ Other enhancements
 - Users can globally disable any ``PerformanceWarning`` by setting the option ``mode.performance_warnings`` to ``False`` (:issue:`56920`)
 - :meth:`Styler.format_index_names` can now be used to format the index and column names (:issue:`48936` and :issue:`47489`)
 - :class:`.errors.DtypeWarning` improved to include column names when mixed data types are detected (:issue:`58174`)
+- :class:`Series` now supports the Arrow PyCapsule Interface for export (:issue:`59518`)
 - :func:`DataFrame.to_excel` argument ``merge_cells`` now accepts a value of ``"columns"`` to only merge :class:`MultiIndex` column header header cells (:issue:`35384`)
 - :meth:`DataFrame.corrwith` now accepts ``min_periods`` as optional arguments, as in :meth:`DataFrame.corr` and :meth:`Series.corr` (:issue:`9490`)
 - :meth:`DataFrame.cummin`, :meth:`DataFrame.cummax`, :meth:`DataFrame.cumprod` and :meth:`DataFrame.cumsum` methods now have a ``numeric_only`` parameter (:issue:`53072`)

pandas/core/arrays/base.py

@@ -1137,6 +1137,13 @@ def fillna(
         ExtensionArray
             With NA/NaN filled.
 
+        See Also
+        --------
+        api.extensions.ExtensionArray.dropna : Return ExtensionArray without
+            NA values.
+        api.extensions.ExtensionArray.isna : A 1-D array indicating if
+            each value is missing.
+
         Examples
         --------
         >>> arr = pd.array([np.nan, np.nan, 2, 3, np.nan, np.nan])
@@ -1220,6 +1227,15 @@ def duplicated(
         Returns
         -------
         ndarray[bool]
+            With true in indices where elements are duplicated and false otherwise.
+
+        See Also
+        --------
+        DataFrame.duplicated : Return boolean Series denoting
+            duplicate rows.
+        Series.duplicated : Indicate duplicate Series values.
+        api.extensions.ExtensionArray.unique : Compute the ExtensionArray
+            of unique values.
 
         Examples
         --------
@@ -1303,6 +1319,13 @@ def unique(self) -> Self:
         Returns
         -------
         pandas.api.extensions.ExtensionArray
+            With unique values from the input array.
+
+        See Also
+        --------
+        Index.unique: Return unique values in the index.
+        Series.unique: Return unique values of Series object.
+        unique: Return unique values based on a hash table.
 
         Examples
         --------
@@ -1436,10 +1459,18 @@ def isin(self, values: ArrayLike) -> npt.NDArray[np.bool_]:
         Parameters
         ----------
         values : np.ndarray or ExtensionArray
+            Values to compare every element in the array against.
 
         Returns
         -------
         np.ndarray[bool]
+            With true at indices where value is in `values`.
+
+        See Also
+        --------
+        DataFrame.isin: Whether each element in the DataFrame is contained in values.
+        Index.isin: Return a boolean array where the index values are in values.
+        Series.isin: Whether elements in Series are contained in values.
 
         Examples
         --------
@@ -1743,6 +1774,12 @@ def view(self, dtype: Dtype | None = None) -> ArrayLike:
         ExtensionArray or np.ndarray
             A view on the :class:`ExtensionArray`'s data.
 
+        See Also
+        --------
+        api.extensions.ExtensionArray.ravel: Return a flattened view on input array.
+        Index.view: Equivalent function for Index.
+        ndarray.view: New view of array with the same data.
+
         Examples
         --------
         This gives view on the underlying data of an ``ExtensionArray`` and is not a
@@ -2201,6 +2238,12 @@ def tolist(self) -> list:
         Returns
         -------
         list
+            Python list of values in array.
+
+        See Also
+        --------
+        Index.to_list: Return a list of the values in the Index.
+        Series.to_list: Return a list of the values in the Series.
 
         Examples
         --------
@@ -2223,11 +2266,18 @@ def insert(self, loc: int, item) -> Self:
         Parameters
         ----------
         loc : int
+            Index where the `item` needs to be inserted.
         item : scalar-like
+            Value to be inserted.
 
         Returns
         -------
-        same type as self
+        ExtensionArray
+            With `item` inserted at `loc`.
+
+        See Also
+        --------
+        Index.insert: Make new Index inserting new item at location.
 
         Notes
         -----

pandas/core/groupby/groupby.py

@@ -164,32 +164,6 @@ class providing the base-class of operations.
             to each row or column of a DataFrame.
 """
 
-_groupby_agg_method_template = """
-Compute {fname} of group values.
-
-Parameters
-----------
-numeric_only : bool, default {no}
-    Include only float, int, boolean columns.
-
-    .. versionchanged:: 2.0.0
-
-        numeric_only no longer accepts ``None``.
-
-min_count : int, default {mc}
-    The required number of valid values to perform the operation. If fewer
-    than ``min_count`` non-NA values are present the result will be NA.
-
-Returns
--------
-Series or DataFrame
-    Computed {fname} of values within each group.
-
-Examples
---------
-{example}
-"""
-
 _groupby_agg_method_engine_template = """
 Compute {fname} of group values.
 
@@ -3029,16 +3003,38 @@ def sum(
             return result
 
     @final
-    @doc(
-        _groupby_agg_method_template,
-        fname="prod",
-        no=False,
-        mc=0,
-        example=dedent(
-            """\
+    def prod(self, numeric_only: bool = False, min_count: int = 0) -> NDFrameT:
+        """
+        Compute prod of group values.
+
+        Parameters
+        ----------
+        numeric_only : bool, default False
+            Include only float, int, boolean columns.
+
+            .. versionchanged:: 2.0.0
+
+                numeric_only no longer accepts ``None``.
+
+        min_count : int, default 0
+            The required number of valid values to perform the operation. If fewer
+            than ``min_count`` non-NA values are present the result will be NA.
+
+        Returns
+        -------
+        Series or DataFrame
+            Computed prod of values within each group.
+
+        See Also
+        --------
+        Series.prod : Return the product of the values over the requested axis.
+        DataFrame.prod : Return the product of the values over the requested axis.
+
+        Examples
+        --------
         For SeriesGroupBy:
 
-        >>> lst = ['a', 'a', 'b', 'b']
+        >>> lst = ["a", "a", "b", "b"]
         >>> ser = pd.Series([1, 2, 3, 4], index=lst)
         >>> ser
         a    1
@@ -3054,8 +3050,11 @@ def sum(
         For DataFrameGroupBy:
 
         >>> data = [[1, 8, 2], [1, 2, 5], [2, 5, 8], [2, 6, 9]]
-        >>> df = pd.DataFrame(data, columns=["a", "b", "c"],
-        ...                   index=["tiger", "leopard", "cheetah", "lion"])
+        >>> df = pd.DataFrame(
+        ...     data,
+        ...     columns=["a", "b", "c"],
+        ...     index=["tiger", "leopard", "cheetah", "lion"],
+        ... )
         >>> df
                   a  b  c
           tiger   1  8  2
@@ -3066,10 +3065,8 @@ def sum(
              b    c
         a
         1   16   10
-        2   30   72"""
-        ),
-    )
-    def prod(self, numeric_only: bool = False, min_count: int = 0) -> NDFrameT:
+        2   30   72
+        """
         return self._agg_general(
             numeric_only=numeric_only, min_count=min_count, alias="prod", npfunc=np.prod
         )

pandas/core/series.py

@@ -34,6 +34,7 @@
 from pandas._libs.lib import is_range_indexer
 from pandas.compat import PYPY
 from pandas.compat._constants import REF_COUNT
+from pandas.compat._optional import import_optional_dependency
 from pandas.compat.numpy import function as nv
 from pandas.errors import (
     ChainedAssignmentError,
@@ -558,6 +559,32 @@ def _init_dict(
 
     # ----------------------------------------------------------------------
 
+    def __arrow_c_stream__(self, requested_schema=None):
+        """
+        Export the pandas Series as an Arrow C stream PyCapsule.
+
+        This relies on pyarrow to convert the pandas Series to the Arrow
+        format (and follows the default behaviour of ``pyarrow.Array.from_pandas``
+        in its handling of the index, i.e. to ignore it).
+        This conversion is not necessarily zero-copy.
+
+        Parameters
+        ----------
+        requested_schema : PyCapsule, default None
+            The schema to which the dataframe should be casted, passed as a
+            PyCapsule containing a C ArrowSchema representation of the
+            requested schema.
+
+        Returns
+        -------
+        PyCapsule
+        """
+        pa = import_optional_dependency("pyarrow", min_version="16.0.0")
+        ca = pa.chunked_array([pa.Array.from_pandas(self, type=requested_schema)])
+        return ca.__arrow_c_stream__(requested_schema)
+
+    # ----------------------------------------------------------------------
+
     @property
     def _constructor(self) -> type[Series]:
         return Series

pandas/core/strings/accessor.py

@@ -1853,6 +1853,7 @@ def zfill(self, width: int):
         Returns
         -------
         Series/Index of objects.
+            A Series or Index where the strings are prepended with '0' characters.
 
         See Also
         --------
@@ -2385,6 +2386,13 @@ def wrap(
         Returns
         -------
         Series or Index
+            A Series or Index where the strings are wrapped at the specified line width.
+
+        See Also
+        --------
+        Series.str.strip : Remove leading and trailing characters in Series/Index.
+        Series.str.lstrip : Remove leading characters in Series/Index.
+        Series.str.rstrip : Remove trailing characters in Series/Index.
 
         Notes
         -----

pandas/tests/series/test_arrow_interface.py

@@ -0,0 +1,23 @@
+import ctypes
+
+import pytest
+
+import pandas as pd
+
+pa = pytest.importorskip("pyarrow", minversion="16.0")
+
+
+def test_series_arrow_interface():
+    s = pd.Series([1, 4, 2])
+
+    capsule = s.__arrow_c_stream__()
+    assert (
+        ctypes.pythonapi.PyCapsule_IsValid(
+            ctypes.py_object(capsule), b"arrow_array_stream"
+        )
+        == 1
+    )
+
+    ca = pa.chunked_array(s)
+    expected = pa.chunked_array([[1, 4, 2]])
+    assert ca.equals(expected)