CLN: assorted, suppress test warnings

doc/source/whatsnew/v1.4.0.rst

@@ -985,7 +985,7 @@ ExtensionArray
 - Bug in :func:`array` failing to preserve :class:`PandasArray` (:issue:`43887`)
 - NumPy ufuncs ``np.abs``, ``np.positive``, ``np.negative`` now correctly preserve dtype when called on ExtensionArrays that implement ``__abs__, __pos__, __neg__``, respectively. In particular this is fixed for :class:`TimedeltaArray` (:issue:`43899`, :issue:`23316`)
 - NumPy ufuncs ``np.minimum.reduce`` ``np.maximum.reduce``, ``np.add.reduce``, and ``np.prod.reduce`` now work correctly instead of raising ``NotImplementedError`` on :class:`Series` with ``IntegerDtype`` or ``FloatDtype`` (:issue:`43923`, :issue:`44793`)
-- NumPy ufuncs with ``out`` keyword are now supported by arrays with ``IntegerDtype`` and ``FloatingDtype`` (:issue:`??`)
+- NumPy ufuncs with ``out`` keyword are now supported by arrays with ``IntegerDtype`` and ``FloatingDtype`` (:issue:`45122`)
 - Avoid raising ``PerformanceWarning`` about fragmented DataFrame when using many columns with an extension dtype (:issue:`44098`)
 - Bug in :class:`IntegerArray` and :class:`FloatingArray` construction incorrectly coercing mismatched NA values (e.g. ``np.timedelta64("NaT")``) to numeric NA (:issue:`44514`)
 - Bug in :meth:`BooleanArray.__eq__` and :meth:`BooleanArray.__ne__` raising ``TypeError`` on comparison with an incompatible type (like a string). This caused :meth:`DataFrame.replace` to sometimes raise a ``TypeError`` if a nullable boolean column was included (:issue:`44499`)

pandas/core/algorithms.py

@@ -108,8 +108,6 @@
         TimedeltaArray,
     )
 
-_shared_docs: dict[str, str] = {}
-
 
 # --------------- #
 # dtype access    #

pandas/core/arraylike.py

@@ -11,15 +11,13 @@
 import numpy as np
 
 from pandas._libs import lib
+from pandas._libs.ops_dispatch import maybe_dispatch_ufunc_to_dunder_op
 from pandas.util._exceptions import find_stack_level
 
 from pandas.core.dtypes.generic import ABCNDFrame
 
+from pandas.core import roperator
 from pandas.core.construction import extract_array
-from pandas.core.ops import (
-    maybe_dispatch_ufunc_to_dunder_op,
-    roperator,
-)
 from pandas.core.ops.common import unpack_zerodim_and_defer
 
 REDUCTION_ALIASES = {

pandas/core/arrays/base.py

@@ -68,7 +68,7 @@
 from pandas.core import (
     arraylike,
     missing,
-    ops,
+    roperator,
 )
 from pandas.core.algorithms import (
     factorize_array,
@@ -1644,21 +1644,23 @@ def _create_arithmetic_method(cls, op):
     @classmethod
     def _add_arithmetic_ops(cls):
         setattr(cls, "__add__", cls._create_arithmetic_method(operator.add))
-        setattr(cls, "__radd__", cls._create_arithmetic_method(ops.radd))
+        setattr(cls, "__radd__", cls._create_arithmetic_method(roperator.radd))
         setattr(cls, "__sub__", cls._create_arithmetic_method(operator.sub))
-        setattr(cls, "__rsub__", cls._create_arithmetic_method(ops.rsub))
+        setattr(cls, "__rsub__", cls._create_arithmetic_method(roperator.rsub))
         setattr(cls, "__mul__", cls._create_arithmetic_method(operator.mul))
-        setattr(cls, "__rmul__", cls._create_arithmetic_method(ops.rmul))
+        setattr(cls, "__rmul__", cls._create_arithmetic_method(roperator.rmul))
         setattr(cls, "__pow__", cls._create_arithmetic_method(operator.pow))
-        setattr(cls, "__rpow__", cls._create_arithmetic_method(ops.rpow))
+        setattr(cls, "__rpow__", cls._create_arithmetic_method(roperator.rpow))
         setattr(cls, "__mod__", cls._create_arithmetic_method(operator.mod))
-        setattr(cls, "__rmod__", cls._create_arithmetic_method(ops.rmod))
+        setattr(cls, "__rmod__", cls._create_arithmetic_method(roperator.rmod))
         setattr(cls, "__floordiv__", cls._create_arithmetic_method(operator.floordiv))
-        setattr(cls, "__rfloordiv__", cls._create_arithmetic_method(ops.rfloordiv))
+        setattr(
+            cls, "__rfloordiv__", cls._create_arithmetic_method(roperator.rfloordiv)
+        )
         setattr(cls, "__truediv__", cls._create_arithmetic_method(operator.truediv))
-        setattr(cls, "__rtruediv__", cls._create_arithmetic_method(ops.rtruediv))
+        setattr(cls, "__rtruediv__", cls._create_arithmetic_method(roperator.rtruediv))
         setattr(cls, "__divmod__", cls._create_arithmetic_method(divmod))
-        setattr(cls, "__rdivmod__", cls._create_arithmetic_method(ops.rdivmod))
+        setattr(cls, "__rdivmod__", cls._create_arithmetic_method(roperator.rdivmod))
 
     @classmethod
     def _create_comparison_method(cls, op):
@@ -1680,16 +1682,16 @@ def _create_logical_method(cls, op):
     @classmethod
     def _add_logical_ops(cls):
         setattr(cls, "__and__", cls._create_logical_method(operator.and_))
-        setattr(cls, "__rand__", cls._create_logical_method(ops.rand_))
+        setattr(cls, "__rand__", cls._create_logical_method(roperator.rand_))
         setattr(cls, "__or__", cls._create_logical_method(operator.or_))
-        setattr(cls, "__ror__", cls._create_logical_method(ops.ror_))
+        setattr(cls, "__ror__", cls._create_logical_method(roperator.ror_))
         setattr(cls, "__xor__", cls._create_logical_method(operator.xor))
-        setattr(cls, "__rxor__", cls._create_logical_method(ops.rxor))
+        setattr(cls, "__rxor__", cls._create_logical_method(roperator.rxor))
 
 
 class ExtensionScalarOpsMixin(ExtensionOpsMixin):
     """
-    A mixin for defining  ops on an ExtensionArray.
+    A mixin for defining ops on an ExtensionArray.
 
     It is assumed that the underlying scalar objects have the operators
     already defined.

pandas/core/arrays/datetimelike.py

@@ -14,6 +14,7 @@
     TypeVar,
     Union,
     cast,
+    final,
     overload,
 )
 import warnings
@@ -1138,6 +1139,7 @@ def _add_timedelta_arraylike(self, other):
 
         return type(self)(new_values, dtype=self.dtype)
 
+    @final
     def _add_nat(self):
         """
         Add pd.NaT to self
@@ -1153,6 +1155,7 @@ def _add_nat(self):
         result.fill(iNaT)
         return type(self)(result, dtype=self.dtype, freq=None)
 
+    @final
     def _sub_nat(self):
         """
         Subtract pd.NaT from self
@@ -1589,7 +1592,7 @@ def strftime(self, date_format: str) -> npt.NDArray[np.object_]:
               dtype='object')
         """
         result = self._format_native_types(date_format=date_format, na_rep=np.nan)
-        return result.astype(object)
+        return result.astype(object, copy=False)
 
 
 _round_doc = """

pandas/core/arrays/sparse/scipy_sparse.py

@@ -195,17 +195,17 @@ def coo_to_sparse_series(
     from pandas import SparseDtype
 
     try:
-        s = Series(A.data, MultiIndex.from_arrays((A.row, A.col)))
+        ser = Series(A.data, MultiIndex.from_arrays((A.row, A.col)))
     except AttributeError as err:
         raise TypeError(
             f"Expected coo_matrix. Got {type(A).__name__} instead."
         ) from err
-    s = s.sort_index()
-    s = s.astype(SparseDtype(s.dtype))
+    ser = ser.sort_index()
+    ser = ser.astype(SparseDtype(ser.dtype))
     if dense_index:
         # is there a better constructor method to use here?
         i = range(A.shape[0])
         j = range(A.shape[1])
         ind = MultiIndex.from_product([i, j])
-        s = s.reindex(ind)
-    return s
+        ser = ser.reindex(ind)
+    return ser

pandas/core/arrays/string_.py

@@ -413,7 +413,7 @@ def __setitem__(self, key, value):
 
         super().__setitem__(key, value)
 
-    def astype(self, dtype, copy=True):
+    def astype(self, dtype, copy: bool = True):
         dtype = pandas_dtype(dtype)
 
         if is_dtype_equal(dtype, self.dtype):

pandas/core/arrays/string_arrow.py

@@ -626,7 +626,7 @@ def value_counts(self, dropna: bool = True) -> Series:
 
         return Series(counts, index=index).astype("Int64")
 
-    def astype(self, dtype, copy=True):
+    def astype(self, dtype, copy: bool = True):
         dtype = pandas_dtype(dtype)
 
         if is_dtype_equal(dtype, self.dtype):

pandas/core/indexes/base.py

@@ -1000,7 +1000,7 @@ def view(self, cls=None):
             result._id = self._id
         return result
 
-    def astype(self, dtype, copy=True):
+    def astype(self, dtype, copy: bool = True):
         """
         Create an Index with values cast to dtypes.
 

pandas/core/indexes/numeric.py

@@ -240,7 +240,7 @@ def __contains__(self, key) -> bool:
             return False
 
     @doc(Index.astype)
-    def astype(self, dtype, copy=True):
+    def astype(self, dtype, copy: bool = True):
         dtype = pandas_dtype(dtype)
         if is_float_dtype(self.dtype):
             if needs_i8_conversion(dtype):

pandas/tests/extension/decimal/test_decimal.py

@@ -152,9 +152,6 @@ def test_value_counts(self, all_data, dropna, request):
 
         tm.assert_series_equal(result, expected)
 
-    def test_value_counts_with_normalize(self, data):
-        return super().test_value_counts_with_normalize(data)
-
 
 class TestCasting(base.BaseCastingTests):
     pass

pandas/tests/extension/test_sparse.py

@@ -208,7 +208,9 @@ def test_index_from_array(self, data):
     #  the sparse dtype
     @pytest.mark.xfail(reason="Index cannot yet store sparse dtype")
     def test_index_from_listlike_with_dtype(self, data):
-        super().test_index_from_listlike_with_dtype(data)
+        msg = "passing a SparseArray to pd.Index"
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            super().test_index_from_listlike_with_dtype(data)
 
 
 class TestMissing(BaseSparseTests, base.BaseMissingTests):

pandas/tests/indexes/datetimes/test_indexing.py

@@ -651,6 +651,7 @@ def test_get_indexer_mixed_dtypes(self, target):
             ([date(9999, 1, 1), date(9999, 1, 1)], [-1, -1]),
         ],
     )
+    @pytest.mark.filterwarnings("ignore:Comparison of Timestamp.*:FutureWarning")
     def test_get_indexer_out_of_bounds_date(self, target, positions):
         values = DatetimeIndex([Timestamp("2020-01-01"), Timestamp("2020-01-02")])
 

pandas/tests/indexing/test_coercion.py

@@ -88,13 +88,9 @@ def _assert_setitem_series_conversion(
         # check dtype explicitly for sure
         assert temp.dtype == expected_dtype
 
-        # AFAICT the problem is in Series.__setitem__ where with integer dtype
-        #  ser[1] = 2.2 casts 2.2 to 2 instead of casting the ser to floating
-        # FIXME: dont leave commented-out
-        # .loc works different rule, temporary disable
-        # temp = original_series.copy()
-        # temp.loc[1] = loc_value
-        # tm.assert_series_equal(temp, expected_series)
+        temp = original_series.copy()
+        temp.loc[1] = loc_value
+        tm.assert_series_equal(temp, expected_series)
 
     @pytest.mark.parametrize(
         "val,exp_dtype", [(1, object), (1.1, object), (1 + 1j, object), (True, object)]

pandas/tests/io/formats/test_printing.py

@@ -1,4 +1,5 @@
 import numpy as np
+import pytest
 
 import pandas._config.config as cf
 
@@ -119,6 +120,9 @@ def test_ambiguous_width(self):
 
 
 class TestTableSchemaRepr:
+    @pytest.mark.filterwarnings(
+        "ignore:.*signature may therefore change.*:FutureWarning"
+    )
     def test_publishes(self, ip):
         ipython = ip.instance(config=ip.config)
         df = pd.DataFrame({"A": [1, 2]})