Fix docstring validation for Series (GH-59592)

doc/source/whatsnew/v3.0.0.rst

@@ -1001,6 +1001,7 @@ Numeric
 - Bug in :meth:`Series.dot` returning ``object`` dtype for :class:`ArrowDtype` and nullable-dtype data (:issue:`61375`)
 - Bug in :meth:`Series.std` and :meth:`Series.var` when using complex-valued data (:issue:`61645`)
 - Bug in ``np.matmul`` with :class:`Index` inputs raising a ``TypeError`` (:issue:`57079`)
+- Bug in arithmetic operations between objects with numpy-nullable dtype and :class:`ArrowDtype` incorrectly raising (:issue:`58602`)
 
 Conversion
 ^^^^^^^^^^

pandas/core/arrays/masked.py

@@ -37,7 +37,10 @@
     is_string_dtype,
     pandas_dtype,
 )
-from pandas.core.dtypes.dtypes import BaseMaskedDtype
+from pandas.core.dtypes.dtypes import (
+    ArrowDtype,
+    BaseMaskedDtype,
+)
 from pandas.core.dtypes.missing import (
     array_equivalent,
     is_valid_na_for_dtype,
@@ -767,6 +770,10 @@ def _arith_method(self, other, op):
         pd_op = ops.get_array_op(op)
         other = ensure_wrapped_if_datetimelike(other)
 
+        if isinstance(other, ExtensionArray) and isinstance(other.dtype, ArrowDtype):
+            # GH#58602
+            return NotImplemented
+
         if op_name in {"pow", "rpow"} and isinstance(other, np.bool_):
             # Avoid DeprecationWarning: In future, it will be an error
             #  for 'np.bool_' scalars to be interpreted as an index
@@ -843,7 +850,11 @@ def _cmp_method(self, other, op) -> BooleanArray:
 
         mask = None
 
-        if isinstance(other, BaseMaskedArray):
+        if isinstance(other, ExtensionArray) and isinstance(other.dtype, ArrowDtype):
+            # GH#58602
+            return NotImplemented
+
+        elif isinstance(other, BaseMaskedArray):
             other, mask = other._data, other._mask
 
         elif is_list_like(other):

pandas/core/series.py

@@ -237,105 +237,36 @@
 @set_module("pandas")
 class Series(base.IndexOpsMixin, NDFrame):  # type: ignore[misc]
     """
-    One-dimensional ndarray with axis labels (including time series).
-
-    Labels need not be unique but must be a hashable type. The object
-    supports both integer- and label-based indexing and provides a host of
-    methods for performing operations involving the index. Statistical
-    methods from ndarray have been overridden to automatically exclude
-    missing data (currently represented as NaN).
-
-    Operations between Series (+, -, /, \\*, \\*\\*) align values based on their
-    associated index values-- they need not be the same length. The result
-    index will be the sorted union of the two indexes.
-
-    Parameters
-    ----------
-    data : array-like, Iterable, dict, or scalar value
-        Contains data stored in Series. If data is a dict, argument order is
-        maintained. Unordered sets are not supported.
-    index : array-like or Index (1d)
-        Values must be hashable and have the same length as `data`.
-        Non-unique index values are allowed. Will default to
-        RangeIndex (0, 1, 2, ..., n) if not provided. If data is dict-like
-        and index is None, then the keys in the data are used as the index. If the
-        index is not None, the resulting Series is reindexed with the index values.
-    dtype : str, numpy.dtype, or ExtensionDtype, optional
-        Data type for the output Series. If not specified, this will be
-        inferred from `data`.
-        See the :ref:`user guide <basics.dtypes>` for more usages.
-    name : Hashable, default None
-        The name to give to the Series.
-    copy : bool, default False
-        Copy input data. Only affects Series or 1d ndarray input. See examples.
-
-    See Also
-    --------
-    DataFrame : Two-dimensional, size-mutable, potentially heterogeneous tabular data.
-    Index : Immutable sequence used for indexing and alignment.
-
-    Notes
-    -----
-    Please reference the :ref:`User Guide <basics.series>` for more information.
-
-    Examples
-    --------
-    Constructing Series from a dictionary with an Index specified
-
-    >>> d = {"a": 1, "b": 2, "c": 3}
-    >>> ser = pd.Series(data=d, index=["a", "b", "c"])
-    >>> ser
-    a   1
-    b   2
-    c   3
-    dtype: int64
-
-    The keys of the dictionary match with the Index values, hence the Index
-    values have no effect.
-
-    >>> d = {"a": 1, "b": 2, "c": 3}
-    >>> ser = pd.Series(data=d, index=["x", "y", "z"])
-    >>> ser
-    x   NaN
-    y   NaN
-    z   NaN
-    dtype: float64
-
-    Note that the Index is first built with the keys from the dictionary.
-    After this the Series is reindexed with the given Index values, hence we
-    get all NaN as a result.
-
-    Constructing Series from a list with `copy=False`.
-
-    >>> r = [1, 2]
-    >>> ser = pd.Series(r, copy=False)
-    >>> ser.iloc[0] = 999
-    >>> r
-    [1, 2]
-    >>> ser
-    0    999
-    1      2
-    dtype: int64
-
-    Due to input data type the Series has a `copy` of
-    the original data even though `copy=False`, so
-    the data is unchanged.
-
-    Constructing Series from a 1d ndarray with `copy=False`.
-
-    >>> r = np.array([1, 2])
-    >>> ser = pd.Series(r, copy=False)
-    >>> ser.iloc[0] = 999
-    >>> r
-    array([999,   2])
-    >>> ser
-    0    999
-    1      2
-    dtype: int64
-
-    Due to input data type the Series has a `view` on
-    the original data, so
-    the data is changed as well.
+  One-dimensional ndarray with axis labels (including time series).
+
+Labels need not be unique but must be hashable (e.g. 1, 'a', or tuple).
+Non-unique labels can be accessed multiple times.
+
+Parameters
+----------
+data : array-like, Iterable, dict, or scalar value
+    Contains data stored in Series. If data is a dict, argument order is
+    maintained if the Python version is >= 3.7 and python is not 3.6.
+
+    .. versionchanged:: 2.0.0
+        Argument order is maintained if the Python version is >= 3.7 and python is not 3.6.
+index : array-like or Index (1d)
+    Values must be hashable and have the same length as `data`. Non-unique
+    index values are allowed. Default is RangeIndex(0, ..., n).
+
+    .. versionchanged:: 2.0.0
+        Non-unique index values are allowed.
+dtype : str, numpy.dtype, or ExtensionDtype, optional
+    Data type for the Series. If None, dtype will be inferred.
+name : str, optional
+    The name to give to the Series.
+copy : bool, default False
+    Copy input data.
+
+Returns
+-------
+pandas.Series
+    The constructed Series object.
     """
 
     _typ = "series"

pandas/tests/extension/test_arrow.py

@@ -3702,6 +3702,28 @@ def test_pow_with_all_na_float():
     tm.assert_series_equal(result, expected)
 
 
+def test_mul_numpy_nullable_with_pyarrow_float():
+    # GH#58602
+    left = pd.Series(range(5), dtype="Float64")
+    right = pd.Series(range(5), dtype="float64[pyarrow]")
+
+    expected = pd.Series([0, 1, 4, 9, 16], dtype="float64[pyarrow]")
+
+    result = left * right
+    tm.assert_series_equal(result, expected)
+
+    result2 = right * left
+    tm.assert_series_equal(result2, expected)
+
+    # while we're here, let's check __eq__
+    result3 = left == right
+    expected3 = pd.Series([True] * 5, dtype="bool[pyarrow]")
+    tm.assert_series_equal(result3, expected3)
+
+    result4 = right == left
+    tm.assert_series_equal(result4, expected3)
+
+
 @pytest.mark.parametrize(
     "type_name, expected_size",
     [