Refinements

Author: rhshadrach

pandas/core/dtypes/cast.py

@@ -1443,6 +1443,12 @@ def maybe_unbox_numpy_scalar(value):
     if using_python_scalars() and isinstance(value, np.generic):
         if isinstance(result, np.longdouble):
             result = float(result)
+        elif isinstance(result, np.complex256):
+            result = complex(result)
+        elif isinstance(result, np.datetime64):
+            result = Timestamp(result)
+        elif isinstance(result, np.timedelta64):
+            result = Timedelta(result)
         else:
             result = value.item()
     return result

pandas/tests/arrays/floating/test_function.py

@@ -183,7 +183,7 @@ def test_preserve_dtypes(op, using_python_scalars):
     # op
     result = getattr(df.C, op)()
     if using_python_scalars:
-        assert isinstance(result, float)
+        assert type(result) == float
     else:
         assert isinstance(result, np.float64)
 

pandas/tests/dtypes/cast/test_maybe_box_native.py

@@ -1,9 +1,15 @@
-from datetime import datetime
+from datetime import (
+    datetime,
+    timedelta,
+)
 
 import numpy as np
 import pytest
 
-from pandas.core.dtypes.cast import maybe_box_native
+from pandas.core.dtypes.cast import (
+    maybe_box_native,
+    maybe_unbox_numpy_scalar,
+)
 
 from pandas import (
     Interval,
@@ -38,3 +44,61 @@ def test_maybe_box_native(obj, expected_dtype):
     boxed_obj = maybe_box_native(obj)
     result_dtype = type(boxed_obj)
     assert result_dtype is expected_dtype
+
+
+@pytest.mark.parametrize("typecode", np.typecodes["All"])
+def test_maybe_unbox_numpy_scalar(typecode, using_python_scalars):
+    # https://github.com/pandas-dev/pandas/pull/63016
+    if typecode == "?":
+        scalar = False
+        expected = bool
+    elif typecode in "bhilqnpBHILQNP":
+        scalar = 0
+        expected = int
+    elif typecode in "efdg":
+        scalar = 0.0
+        expected = float
+    elif typecode in "FDG":
+        scalar = 0.0 + 0.0j
+        expected = complex
+    elif typecode in "SV":
+        scalar = b""
+        expected = bytes
+    elif typecode == "U":
+        scalar = ""
+        expected = str
+    elif typecode == "O":
+        scalar = 0
+        expected = int
+    elif typecode == "M":
+        scalar = datetime(2025, 1, 1)
+        expected = Timestamp
+    elif typecode == "m":
+        scalar = timedelta(seconds=3)
+        expected = Timedelta
+    else:
+        raise ValueError(f"typecode {typecode} not recognized")
+    value = np.array([scalar], dtype=typecode)[0]
+    result = maybe_unbox_numpy_scalar(value)
+    if using_python_scalars:
+        assert type(result) == expected
+    else:
+        assert result is value
+
+
+def test_maybe_unbox_numpy_scalar_timestamp(unit, using_python_scalars):
+    # https://github.com/pandas-dev/pandas/pull/63016
+    value = np.datetime64(1, unit)
+    expected = Timestamp(1, unit=unit) if using_python_scalars else value
+    result = maybe_unbox_numpy_scalar(value)
+    assert result == expected
+    assert type(result) == type(expected)
+
+
+def test_maybe_unbox_numpy_scalar_datetime(unit, using_python_scalars):
+    # https://github.com/pandas-dev/pandas/pull/63016
+    value = np.timedelta64(1, unit)
+    expected = Timedelta(1, unit=unit) if using_python_scalars else value
+    result = maybe_unbox_numpy_scalar(value)
+    assert result == expected
+    assert type(result) == type(expected)

pandas/tests/reductions/test_reductions.py

@@ -1276,7 +1276,7 @@ def test_signedness_preserved_after_sum(self, using_python_scalars):
         if using_python_scalars:
             assert isinstance(result, int)
         else:
-            assert result.dtype == "uint64"
+            assert isinstance(result, np.uint64)
 
 
 class TestDatetime64SeriesReductions:

pandas/tests/reductions/test_stat_reductions.py

@@ -248,7 +248,7 @@ def test_skew(self, using_python_scalars):
             else:
                 assert 0 == s.skew()
                 if using_python_scalars:
-                    assert isinstance(s.skew(), float)
+                    assert type(s.skew()) == float
                 else:
                     assert isinstance(s.skew(), np.float64)  # GH53482
                 assert (df.skew() == 0).all()

pandas/tests/series/test_ufunc.py

@@ -335,7 +335,7 @@ def test_max(self, values_for_np_reduce, box_with_array, using_python_scalars):
         values = values_for_np_reduce
 
         same_type = True
-        if box is pd.Index and values.dtype.kind in ["i", "f"]:
+        if box is pd.Index and values.dtype.kind in "if":
             # ATM Index casts to object, so we get python ints/floats
             same_type = False
 
@@ -349,7 +349,7 @@ def test_max(self, values_for_np_reduce, box_with_array, using_python_scalars):
             tm.assert_series_equal(result, expected)
         else:
             expected = values[1]
-            if using_python_scalars and values.dtype.kind in ["i", "f"]:
+            if using_python_scalars and values.dtype.kind in "if":
                 expected = expected.item()
             assert result == expected
             if same_type:
@@ -361,7 +361,7 @@ def test_min(self, values_for_np_reduce, box_with_array, using_python_scalars):
         values = values_for_np_reduce
 
         same_type = True
-        if box is pd.Index and values.dtype.kind in ["i", "f"]:
+        if box is pd.Index and values.dtype.kind in "if":
             # ATM Index casts to object, so we get python ints/floats
             same_type = False
 

pandas/tests/window/moments/test_moments_consistency_ewm.py

@@ -133,10 +133,7 @@ def test_moments_consistency_var(all_data, adjust, ignore_na, min_periods, bias)
     var_x = all_data.ewm(
         com=com, min_periods=min_periods, adjust=adjust, ignore_na=ignore_na
     ).var(bias=bias)
-    if isinstance(all_data, Series):
-        assert not (var_x < 0).any()
-    else:
-        assert not (var_x < 0).any().any()
+    assert not (var_x < 0).any(axis=None)
 
     if bias:
         # check that biased var(x) == mean(x^2) - mean(x)^2
@@ -159,10 +156,7 @@ def test_moments_consistency_var_constant(
     ).var(bias=bias)
 
     # check that variance of constant series is identically 0
-    if isinstance(consistent_data, Series):
-        assert not (var_x > 0).any()
-    else:
-        assert not (var_x > 0).any().any()
+    assert not (var_x > 0).any(axis=None)
     expected = consistent_data * np.nan
     expected[count_x >= max(min_periods, 1)] = 0.0
     if not bias:
@@ -176,29 +170,20 @@ def test_ewm_consistency_std(all_data, adjust, ignore_na, min_periods, bias):
     var_x = all_data.ewm(
         com=com, min_periods=min_periods, adjust=adjust, ignore_na=ignore_na
     ).var(bias=bias)
-    if isinstance(all_data, Series):
-        assert not (var_x < 0).any()
-    else:
-        assert not (var_x < 0).any().any()
+    assert not (var_x < 0).any(axis=None)
 
     std_x = all_data.ewm(
         com=com, min_periods=min_periods, adjust=adjust, ignore_na=ignore_na
     ).std(bias=bias)
-    if isinstance(all_data, Series):
-        assert not (std_x < 0).any()
-    else:
-        assert not (std_x < 0).any().any()
+    assert not (std_x < 0).any(axis=None)
 
     # check that var(x) == std(x)^2
     tm.assert_equal(var_x, std_x * std_x)
 
     cov_x_x = all_data.ewm(
         com=com, min_periods=min_periods, adjust=adjust, ignore_na=ignore_na
     ).cov(all_data, bias=bias)
-    if isinstance(all_data, Series):
-        assert not (cov_x_x < 0).any()
-    else:
-        assert not (cov_x_x < 0).any().any()
+    assert not (cov_x_x < 0).any(axis=None)
 
     # check that var(x) == cov(x, x)
     tm.assert_equal(var_x, cov_x_x)

pandas/tests/window/moments/test_moments_consistency_expanding.py

@@ -38,10 +38,7 @@ def test_expanding_apply_consistency_sum_nans(request, all_data, min_periods, f)
 @pytest.mark.parametrize("ddof", [0, 1])
 def test_moments_consistency_var(all_data, min_periods, ddof):
     var_x = all_data.expanding(min_periods=min_periods).var(ddof=ddof)
-    if isinstance(all_data, Series):
-        assert not (var_x < 0).any()
-    else:
-        assert not (var_x < 0).any().any()
+    assert not (var_x < 0).any(axis=None)
 
     if ddof == 0:
         # check that biased var(x) == mean(x^2) - mean(x)^2
@@ -56,10 +53,7 @@ def test_moments_consistency_var_constant(consistent_data, min_periods, ddof):
     var_x = consistent_data.expanding(min_periods=min_periods).var(ddof=ddof)
 
     # check that variance of constant series is identically 0
-    if isinstance(consistent_data, Series):
-        assert not (var_x > 0).any()
-    else:
-        assert not (var_x > 0).any().any()
+    assert not (var_x > 0).any(axis=None)
     expected = consistent_data * np.nan
     expected[count_x >= max(min_periods, 1)] = 0.0
     if ddof == 1:
@@ -70,25 +64,16 @@ def test_moments_consistency_var_constant(consistent_data, min_periods, ddof):
 @pytest.mark.parametrize("ddof", [0, 1])
 def test_expanding_consistency_var_std_cov(all_data, min_periods, ddof):
     var_x = all_data.expanding(min_periods=min_periods).var(ddof=ddof)
-    if isinstance(all_data, Series):
-        assert not (var_x < 0).any()
-    else:
-        assert not (var_x < 0).any().any()
+    assert not (var_x < 0).any(axis=None)
 
     std_x = all_data.expanding(min_periods=min_periods).std(ddof=ddof)
-    if isinstance(all_data, Series):
-        assert not (std_x < 0).any()
-    else:
-        assert not (std_x < 0).any().any()
+    assert not (std_x < 0).any(axis=None)
 
     # check that var(x) == std(x)^2
     tm.assert_equal(var_x, std_x * std_x)
 
     cov_x_x = all_data.expanding(min_periods=min_periods).cov(all_data, ddof=ddof)
-    if isinstance(all_data, Series):
-        assert not (cov_x_x < 0).any()
-    else:
-        assert not (cov_x_x < 0).any().any()
+    assert not (cov_x_x < 0).any(axis=None)
 
     # check that var(x) == cov(x, x)
     tm.assert_equal(var_x, cov_x_x)

pandas/tests/window/moments/test_moments_consistency_rolling.py

@@ -56,10 +56,7 @@ def test_moments_consistency_var(all_data, rolling_consistency_cases, center, dd
     var_x = all_data.rolling(window=window, min_periods=min_periods, center=center).var(
         ddof=ddof
     )
-    if isinstance(all_data, Series):
-        assert not (var_x < 0).any()
-    else:
-        assert not (var_x < 0).any().any()
+    assert not (var_x < 0).any(axis=None)
 
     if ddof == 0:
         # check that biased var(x) == mean(x^2) - mean(x)^2
@@ -88,10 +85,7 @@ def test_moments_consistency_var_constant(
     ).var(ddof=ddof)
 
     # check that variance of constant series is identically 0
-    if isinstance(consistent_data, Series):
-        assert not (var_x > 0).any()
-    else:
-        assert not (var_x > 0).any().any()
+    assert not (var_x > 0).any(axis=None)
     expected = consistent_data * np.nan
     expected[count_x >= max(min_periods, 1)] = 0.0
     if ddof == 1:
@@ -108,29 +102,20 @@ def test_rolling_consistency_var_std_cov(
     var_x = all_data.rolling(window=window, min_periods=min_periods, center=center).var(
         ddof=ddof
     )
-    if isinstance(all_data, Series):
-        assert not (var_x < 0).any()
-    else:
-        assert not (var_x < 0).any().any()
+    assert not (var_x < 0).any(axis=None)
 
     std_x = all_data.rolling(window=window, min_periods=min_periods, center=center).std(
         ddof=ddof
     )
-    if isinstance(all_data, Series):
-        assert not (std_x < 0).any()
-    else:
-        assert not (std_x < 0).any().any()
+    assert not (std_x < 0).any(axis=None)
 
     # check that var(x) == std(x)^2
     tm.assert_equal(var_x, std_x * std_x)
 
     cov_x_x = all_data.rolling(
         window=window, min_periods=min_periods, center=center
     ).cov(all_data, ddof=ddof)
-    if isinstance(all_data, Series):
-        assert not (cov_x_x < 0).any()
-    else:
-        assert not (cov_x_x < 0).any().any()
+    assert not (cov_x_x < 0).any(axis=None)
 
     # check that var(x) == cov(x, x)
     tm.assert_equal(var_x, cov_x_x)