More refinements

Author: rhshadrach

pandas/tests/arrays/integer/test_arithmetic.py

@@ -287,7 +287,7 @@ def test_reduce_to_float(op):
 
     # op
     result = getattr(df.C, op)()
-    assert isinstance(result, float)
+    assert type(result) == float
 
     # groupby
     result = getattr(df.groupby("A"), op)()

pandas/tests/series/methods/test_item.py

@@ -24,7 +24,7 @@ def test_item(self):
 
         ser = Series([0.5], index=[3])
         result = ser.item()
-        assert isinstance(result, float)
+        assert type(result) == float
         assert result == 0.5
 
         ser = Series([1, 2])

pandas/tests/test_nanops.py

@@ -1283,7 +1283,7 @@ def test_nanmean_overflow(disable_bottleneck, val, using_python_scalars):
     result = ser.mean()
     assert result == val
     if using_python_scalars:
-        assert isinstance(result, float)
+        assert type(result) == float
     else:
         np_result = ser.values.mean()
         assert result == np_result
@@ -1312,7 +1312,7 @@ def test_returned_dtype(disable_bottleneck, dtype, method, using_python_scalars)
         if is_integer_dtype(dtype) and method in ["min", "max"]:
             assert isinstance(result, int)
         else:
-            assert isinstance(result, float)
+            assert type(result) == float
     elif is_integer_dtype(dtype) and method not in ["min", "max"]:
         assert result.dtype == np.float64
     else:

pandas/tests/window/moments/test_moments_consistency_rolling.py

@@ -1,24 +1,16 @@
 import numpy as np
 import pytest
 
-from pandas._config import using_python_scalars
-
 from pandas import Series
 import pandas._testing as tm
 
 
 def no_nans(x):
-    if using_python_scalars() and isinstance(x, Series):
-        return x.notna().all()
-    else:
-        return x.notna().all().all()
+    return x.notna().all(axis=None)
 
 
 def all_na(x):
-    if using_python_scalars() and isinstance(x, Series):
-        return x.isnull().all()
-    else:
-        return x.isnull().all().all()
+    return x.isnull().all(axis=None)
 
 
 @pytest.fixture(params=[(1, 0), (5, 1)])