update tests

Author: jbrockmendel

pandas/core/dtypes/missing.py

@@ -686,7 +686,8 @@ def na_value_for_dtype(dtype: DtypeObj, compat: bool = True):
     if isinstance(dtype, ExtensionDtype):
         return dtype.na_value
     elif dtype.kind in "mM":
-        return dtype.type("NaT", "ns")
+        unit = np.datetime_data(dtype)[0]
+        return dtype.type("NaT", unit)
     elif dtype.kind == "f":
         return np.nan
     elif dtype.kind in "iu":

pandas/tests/arithmetic/test_timedelta64.py

@@ -750,7 +750,7 @@ def test_timedelta_ops_with_missing_values(self):
         with pytest.raises(TypeError, match=msg):
             # Passing datetime64-dtype data to TimedeltaIndex is no longer
             #  supported GH#29794
-            pd.to_timedelta(Series([NaT]))  # TODO: belongs elsewhere?
+            pd.to_timedelta(Series([NaT], dtype="M8[ns]"))  # TODO: belongs elsewhere?
 
         sn = pd.to_timedelta(Series([NaT], dtype="m8[ns]"))
 
@@ -759,7 +759,9 @@ def test_timedelta_ops_with_missing_values(self):
         with pytest.raises(TypeError, match=msg):
             # Passing datetime64-dtype data to TimedeltaIndex is no longer
             #  supported GH#29794
-            DataFrame([NaT]).apply(pd.to_timedelta)  # TODO: belongs elsewhere?
+            DataFrame([NaT], dtype="M8[ns]").apply(
+                pd.to_timedelta
+            )  # TODO: belongs elsewhere?
 
         dfn = DataFrame([NaT._value]).apply(pd.to_timedelta)
 

pandas/tests/arrays/categorical/test_missing.py

@@ -204,7 +204,7 @@ def test_compare_categorical_with_missing(self, a1, a2, categories):
     @pytest.mark.parametrize(
         "na_value, dtype",
         [
-            (pd.NaT, "datetime64[ns]"),
+            (pd.NaT, "datetime64[s]"),
             (None, "float64"),
             (np.nan, "float64"),
             (pd.NA, "float64"),

pandas/tests/frame/methods/test_combine_first.py

@@ -191,9 +191,6 @@ def test_combine_first_convert_datatime_correctly(
         df1, df2 = DataFrame({"a": data1}), DataFrame({"a": data2})
         result = df1.combine_first(df2)
         expected = DataFrame({"a": data_expected})
-        if df1.isna().all(axis=None):
-            expected = expected.astype("M8[ns]")
-        # equiv: expected = expected.astype(df1["a"].dtype)
         tm.assert_frame_equal(result, expected)
 
     def test_combine_first_align_nan(self):
@@ -449,7 +446,6 @@ def test_combine_first_timestamp_bug_NaT():
     expected = DataFrame(
         [[pd.NaT, datetime(2020, 1, 1), datetime(2020, 1, 2)]], columns=["a", "b", "c"]
     )
-    expected["b"] = expected["b"].astype("M8[ns]")
 
     tm.assert_frame_equal(result, expected)
 

pandas/tests/frame/test_constructors.py

@@ -1995,6 +1995,9 @@ def test_constructor_datetimes_with_nulls(self, arr):
         if isinstance(arr, np.ndarray):
             # inferred from a pydatetime object
             unit = "us"
+        elif not any(isinstance(x, np.datetime64) for y in arr for x in y):
+            # TODO: this condition is not clear about why we have different behavior
+            unit = "s"
         expected = Series([np.dtype(f"datetime64[{unit}]")])
         tm.assert_series_equal(result, expected)
 

pandas/tests/groupby/test_groupby.py

@@ -1548,7 +1548,7 @@ def test_groupby_nat_exclude():
         {"nan": [np.nan, np.nan, np.nan], "nat": [pd.NaT, pd.NaT, pd.NaT]}
     )
     assert nan_df["nan"].dtype == "float64"
-    assert nan_df["nat"].dtype == "datetime64[ns]"
+    assert nan_df["nat"].dtype == "datetime64[s]"
 
     for key in ["nan", "nat"]:
         grouped = nan_df.groupby(key)

pandas/tests/indexes/test_index_new.py

@@ -60,16 +60,16 @@ def test_infer_nat(self, val):
         values = [NaT, val]
 
         idx = Index(values)
-        assert idx.dtype == "datetime64[ns]" and idx.isna().all()
+        assert idx.dtype == "datetime64[s]" and idx.isna().all()
 
         idx = Index(values[::-1])
-        assert idx.dtype == "datetime64[ns]" and idx.isna().all()
+        assert idx.dtype == "datetime64[s]" and idx.isna().all()
 
         idx = Index(np.array(values, dtype=object))
-        assert idx.dtype == "datetime64[ns]" and idx.isna().all()
+        assert idx.dtype == "datetime64[s]" and idx.isna().all()
 
         idx = Index(np.array(values, dtype=object)[::-1])
-        assert idx.dtype == "datetime64[ns]" and idx.isna().all()
+        assert idx.dtype == "datetime64[s]" and idx.isna().all()
 
     @pytest.mark.parametrize("na_value", [None, np.nan])
     @pytest.mark.parametrize("vtype", [list, tuple, iter])

pandas/tests/series/test_constructors.py

@@ -763,7 +763,7 @@ def test_constructor_pass_nan_nat(self):
         tm.assert_series_equal(Series(np.array([np.nan, np.nan])), exp)
 
         exp = Series([NaT, NaT])
-        assert exp.dtype == "datetime64[ns]"
+        assert exp.dtype == "datetime64[s]"
         tm.assert_series_equal(Series([NaT, NaT]), exp)
         tm.assert_series_equal(Series(np.array([NaT, NaT])), exp)
 
@@ -1220,7 +1220,7 @@ def test_construction_to_datetimelike_unit(self, arr_dtype, kind, unit):
     def test_constructor_with_naive_string_and_datetimetz_dtype(self, arg):
         # GH 17415: With naive string
         result = Series([arg], dtype="datetime64[ns, CET]")
-        expected = Series(Timestamp(arg).as_unit("ns")).dt.tz_localize("CET")
+        expected = Series([Timestamp(arg)], dtype="M8[ns]").dt.tz_localize("CET")
         tm.assert_series_equal(result, expected)
 
     def test_constructor_datetime64_bigendian(self):
@@ -1583,7 +1583,7 @@ def test_NaT_scalar(self):
     def test_NaT_cast(self):
         # GH10747
         result = Series([np.nan]).astype("M8[ns]")
-        expected = Series([NaT])
+        expected = Series([NaT], dtype="M8[ns]")
         tm.assert_series_equal(result, expected)
 
     def test_constructor_name_hashable(self):