BUG: -1 to the power of pd.NA was returning -1 (#30960)

doc/source/user_guide/missing_data.rst

@@ -831,7 +831,6 @@ Operation        Result
 ================ ======
 ``pd.NA ** 0``   0
 ``1 ** pd.NA``   1
-``-1 ** pd.NA``  -1
 ================ ======
 
 In equality and comparison operations, ``pd.NA`` also propagates. This deviates

pandas/_libs/missing.pyx

@@ -417,12 +417,12 @@ class NAType(C_NAType):
         if other is C_NA:
             return NA
         elif isinstance(other, (numbers.Number, np.bool_)):
-            if other == 1 or other == -1:
+            if other == 1:
                 return other
             else:
                 return NA
         elif isinstance(other, np.ndarray):
-            return np.where((other == 1) | (other == -1), other, NA)
+            return np.where(other == 1, other, NA)
 
         return NotImplemented
 

pandas/core/frame.py

@@ -102,7 +102,6 @@
 from pandas.core.arrays.datetimelike import DatetimeLikeArrayMixin as DatetimeLikeArray
 from pandas.core.arrays.sparse import SparseFrameAccessor
 from pandas.core.generic import NDFrame, _shared_docs
-from pandas.core.groupby import generic as groupby_generic
 from pandas.core.indexes import base as ibase
 from pandas.core.indexes.api import Index, ensure_index, ensure_index_from_sequences
 from pandas.core.indexes.datetimes import DatetimeIndex
@@ -129,6 +128,7 @@
 import pandas.plotting
 
 if TYPE_CHECKING:
+    from pandas.core.groupby.generic import DataFrameGroupBy
     from pandas.io.formats.style import Styler
 
 # ---------------------------------------------------------------------
@@ -5777,13 +5777,14 @@ def groupby(
         group_keys: bool = True,
         squeeze: bool = False,
         observed: bool = False,
-    ) -> "groupby_generic.DataFrameGroupBy":
+    ) -> "DataFrameGroupBy":
+        from pandas.core.groupby.generic import DataFrameGroupBy
 
         if level is None and by is None:
             raise TypeError("You have to supply one of 'by' and 'level'")
         axis = self._get_axis_number(axis)
 
-        return groupby_generic.DataFrameGroupBy(
+        return DataFrameGroupBy(
             obj=self,
             keys=by,
             axis=axis,

pandas/core/generic.py

@@ -8,6 +8,7 @@
 import re
 from textwrap import dedent
 from typing import (
+    TYPE_CHECKING,
     Any,
     Callable,
     Dict,
@@ -101,6 +102,9 @@
 from pandas.io.formats.printing import pprint_thing
 from pandas.tseries.frequencies import to_offset
 
+if TYPE_CHECKING:
+    from pandas.core.resample import Resampler
+
 # goal is to be able to define the docs close to function, while still being
 # able to share
 _shared_docs: Dict[str, str] = dict()
@@ -7685,7 +7689,7 @@ def resample(
         base: int = 0,
         on=None,
         level=None,
-    ):
+    ) -> "Resampler":
         """
         Resample time-series data.
 
@@ -7950,10 +7954,10 @@ def resample(
         2000-01-04     36      90
         """
 
-        from pandas.core.resample import resample
+        from pandas.core.resample import get_resampler
 
         axis = self._get_axis_number(axis)
-        return resample(
+        return get_resampler(
             self,
             freq=rule,
             label=label,

pandas/core/resample.py

@@ -1262,15 +1262,15 @@ def _constructor(self):
         return TimedeltaIndexResampler
 
 
-def resample(obj, kind=None, **kwds):
+def get_resampler(obj, kind=None, **kwds):
     """
     Create a TimeGrouper and return our resampler.
     """
     tg = TimeGrouper(**kwds)
     return tg._get_resampler(obj, kind=kind)
 
 
-resample.__doc__ = Resampler.__doc__
+get_resampler.__doc__ = Resampler.__doc__
 
 
 def get_resampler_for_grouping(

pandas/core/series.py

@@ -70,7 +70,6 @@
     is_empty_data,
     sanitize_array,
 )
-from pandas.core.groupby import generic as groupby_generic
 from pandas.core.indexers import maybe_convert_indices
 from pandas.core.indexes.accessors import CombinedDatetimelikeProperties
 from pandas.core.indexes.api import (
@@ -94,6 +93,7 @@
 
 if TYPE_CHECKING:
     from pandas.core.frame import DataFrame
+    from pandas.core.groupby.generic import SeriesGroupBy
 
 __all__ = ["Series"]
 
@@ -1634,13 +1634,14 @@ def groupby(
         group_keys: bool = True,
         squeeze: bool = False,
         observed: bool = False,
-    ) -> "groupby_generic.SeriesGroupBy":
+    ) -> "SeriesGroupBy":
+        from pandas.core.groupby.generic import SeriesGroupBy
 
         if level is None and by is None:
             raise TypeError("You have to supply one of 'by' and 'level'")
         axis = self._get_axis_number(axis)
 
-        return groupby_generic.SeriesGroupBy(
+        return SeriesGroupBy(
             obj=self,
             keys=by,
             axis=axis,

pandas/tests/arrays/test_integer.py

@@ -363,24 +363,26 @@ def test_divide_by_zero(self, zero, negative):
         tm.assert_numpy_array_equal(result, expected)
 
     def test_pow_scalar(self):
-        a = pd.array([0, 1, None, 2], dtype="Int64")
+        a = pd.array([-1, 0, 1, None, 2], dtype="Int64")
         result = a ** 0
-        expected = pd.array([1, 1, 1, 1], dtype="Int64")
+        expected = pd.array([1, 1, 1, 1, 1], dtype="Int64")
         tm.assert_extension_array_equal(result, expected)
 
         result = a ** 1
-        expected = pd.array([0, 1, None, 2], dtype="Int64")
+        expected = pd.array([-1, 0, 1, None, 2], dtype="Int64")
         tm.assert_extension_array_equal(result, expected)
 
         result = a ** pd.NA
-        expected = pd.array([None, 1, None, None], dtype="Int64")
+        expected = pd.array([None, None, 1, None, None], dtype="Int64")
         tm.assert_extension_array_equal(result, expected)
 
         result = a ** np.nan
-        expected = np.array([np.nan, 1, np.nan, np.nan], dtype="float64")
+        expected = np.array([np.nan, np.nan, 1, np.nan, np.nan], dtype="float64")
         tm.assert_numpy_array_equal(result, expected)
 
         # reversed
+        a = a[1:]  # Can't raise integers to negative powers.
+
         result = 0 ** a
         expected = pd.array([1, 0, None, 0], dtype="Int64")
         tm.assert_extension_array_equal(result, expected)

pandas/tests/scalar/test_na_scalar.py

@@ -96,19 +96,7 @@ def test_pow_special(value, asarray):
 
 
 @pytest.mark.parametrize(
-    "value",
-    [
-        1,
-        1.0,
-        -1,
-        -1.0,
-        True,
-        np.bool_(True),
-        np.int_(1),
-        np.float_(1),
-        np.int_(-1),
-        np.float_(-1),
-    ],
+    "value", [1, 1.0, True, np.bool_(True), np.int_(1), np.float_(1)],
 )
 @pytest.mark.parametrize("asarray", [True, False])
 def test_rpow_special(value, asarray):
@@ -125,6 +113,21 @@ def test_rpow_special(value, asarray):
     assert result == value
 
 
+@pytest.mark.parametrize(
+    "value", [-1, -1.0, np.int_(-1), np.float_(-1)],
+)
+@pytest.mark.parametrize("asarray", [True, False])
+def test_rpow_minus_one(value, asarray):
+    if asarray:
+        value = np.array([value])
+    result = value ** pd.NA
+
+    if asarray:
+        result = result[0]
+
+    assert pd.isna(result)
+
+
 def test_unary_ops():
     assert +NA is NA
     assert -NA is NA