EA: Tighten signature on DatetimeArray._from_sequence

pandas/core/arrays/datetimes.py

@@ -296,7 +296,11 @@ def _simple_new(cls, values, freq=None, dtype=DT64NS_DTYPE):
         return result
 
     @classmethod
-    def _from_sequence(
+    def _from_sequence(cls, scalars, dtype=None, copy: bool = False):
+        return cls._from_sequence_not_strict(scalars, dtype=dtype, copy=copy)
+
+    @classmethod
+    def _from_sequence_not_strict(
         cls,
         data,
         dtype=None,

pandas/core/indexes/datetimes.py

@@ -267,7 +267,7 @@ def __new__(
 
         name = maybe_extract_name(name, data, cls)
 
-        dtarr = DatetimeArray._from_sequence(
+        dtarr = DatetimeArray._from_sequence_not_strict(
             data,
             dtype=dtype,
             copy=copy,

pandas/core/nanops.py

@@ -1616,7 +1616,9 @@ def na_accum_func(values: ArrayLike, accum_func, skipna: bool) -> ArrayLike:
             result = result.view(orig_dtype)
         else:
             # DatetimeArray
-            result = type(values)._from_sequence(result, dtype=orig_dtype)
+            result = type(values)._simple_new(  # type: ignore[attr-defined]
+                result, dtype=orig_dtype
+            )
 
     elif skipna and not issubclass(values.dtype.type, (np.integer, np.bool_)):
         vals = values.copy()

pandas/tests/arrays/test_array.py

@@ -204,7 +204,9 @@ def test_array_copy():
                 datetime.datetime(2000, 1, 1, tzinfo=cet),
                 datetime.datetime(2001, 1, 1, tzinfo=cet),
             ],
-            DatetimeArray._from_sequence(["2000", "2001"], tz=cet),
+            DatetimeArray._from_sequence(
+                ["2000", "2001"], dtype=pd.DatetimeTZDtype(tz=cet)
+            ),
         ),
         # timedelta
         (

pandas/tests/arrays/test_datetimes.py

@@ -71,7 +71,7 @@ def test_mixing_naive_tzaware_raises(self, meth):
     def test_from_pandas_array(self):
         arr = pd.array(np.arange(5, dtype=np.int64)) * 3600 * 10 ** 9
 
-        result = DatetimeArray._from_sequence(arr, freq="infer")
+        result = DatetimeArray._from_sequence(arr)._with_freq("infer")
 
         expected = pd.date_range("1970-01-01", periods=5, freq="H")._data
         tm.assert_datetime_array_equal(result, expected)
@@ -162,7 +162,9 @@ def test_cmp_dt64_arraylike_tznaive(self, all_compare_operators):
 
 class TestDatetimeArray:
     def test_astype_to_same(self):
-        arr = DatetimeArray._from_sequence(["2000"], tz="US/Central")
+        arr = DatetimeArray._from_sequence(
+            ["2000"], dtype=DatetimeTZDtype(tz="US/Central")
+        )
         result = arr.astype(DatetimeTZDtype(tz="US/Central"), copy=False)
         assert result is arr
 
@@ -193,7 +195,9 @@ def test_astype_int(self, dtype):
         tm.assert_numpy_array_equal(result, expected)
 
     def test_tz_setter_raises(self):
-        arr = DatetimeArray._from_sequence(["2000"], tz="US/Central")
+        arr = DatetimeArray._from_sequence(
+            ["2000"], dtype=DatetimeTZDtype(tz="US/Central")
+        )
         with pytest.raises(AttributeError, match="tz_localize"):
             arr.tz = "UTC"
 
@@ -282,7 +286,8 @@ def test_fillna_preserves_tz(self, method):
 
         fill_val = dti[1] if method == "pad" else dti[3]
         expected = DatetimeArray._from_sequence(
-            [dti[0], dti[1], fill_val, dti[3], dti[4]], freq=None, tz="US/Central"
+            [dti[0], dti[1], fill_val, dti[3], dti[4]],
+            dtype=DatetimeTZDtype(tz="US/Central"),
         )
 
         result = arr.fillna(method=method)
@@ -434,19 +439,24 @@ def test_shift_value_tzawareness_mismatch(self):
 
 class TestSequenceToDT64NS:
     def test_tz_dtype_mismatch_raises(self):
-        arr = DatetimeArray._from_sequence(["2000"], tz="US/Central")
+        arr = DatetimeArray._from_sequence(
+            ["2000"], dtype=DatetimeTZDtype(tz="US/Central")
+        )
         with pytest.raises(TypeError, match="data is already tz-aware"):
             sequence_to_dt64ns(arr, dtype=DatetimeTZDtype(tz="UTC"))
 
     def test_tz_dtype_matches(self):
-        arr = DatetimeArray._from_sequence(["2000"], tz="US/Central")
+        arr = DatetimeArray._from_sequence(
+            ["2000"], dtype=DatetimeTZDtype(tz="US/Central")
+        )
         result, _, _ = sequence_to_dt64ns(arr, dtype=DatetimeTZDtype(tz="US/Central"))
         tm.assert_numpy_array_equal(arr._data, result)
 
 
 class TestReductions:
     @pytest.mark.parametrize("tz", [None, "US/Central"])
     def test_min_max(self, tz):
+        dtype = DatetimeTZDtype(tz=tz) if tz is not None else np.dtype("M8[ns]")
         arr = DatetimeArray._from_sequence(
             [
                 "2000-01-03",
@@ -456,7 +466,7 @@ def test_min_max(self, tz):
                 "2000-01-05",
                 "2000-01-04",
             ],
-            tz=tz,
+            dtype=dtype,
         )
 
         result = arr.min()
@@ -476,7 +486,8 @@ def test_min_max(self, tz):
     @pytest.mark.parametrize("tz", [None, "US/Central"])
     @pytest.mark.parametrize("skipna", [True, False])
     def test_min_max_empty(self, skipna, tz):
-        arr = DatetimeArray._from_sequence([], tz=tz)
+        dtype = DatetimeTZDtype(tz=tz) if tz is not None else np.dtype("M8[ns]")
+        arr = DatetimeArray._from_sequence([], dtype=dtype)
         result = arr.min(skipna=skipna)
         assert result is pd.NaT
 

pandas/tests/extension/test_datetime.py

@@ -181,8 +181,10 @@ def test_concat_mixed_dtypes(self, data):
     @pytest.mark.parametrize("obj", ["series", "frame"])
     def test_unstack(self, obj):
         # GH-13287: can't use base test, since building the expected fails.
+        dtype = DatetimeTZDtype(tz="US/Central")
         data = DatetimeArray._from_sequence(
-            ["2000", "2001", "2002", "2003"], tz="US/Central"
+            ["2000", "2001", "2002", "2003"],
+            dtype=dtype,
         )
         index = pd.MultiIndex.from_product(([["A", "B"], ["a", "b"]]), names=["a", "b"])
 

pandas/tests/indexes/datetimes/test_constructors.py

@@ -16,7 +16,9 @@
 
 
 class TestDatetimeIndex:
-    @pytest.mark.parametrize("dt_cls", [DatetimeIndex, DatetimeArray._from_sequence])
+    @pytest.mark.parametrize(
+        "dt_cls", [DatetimeIndex, DatetimeArray._from_sequence_not_strict]
+    )
     def test_freq_validation_with_nat(self, dt_cls):
         # GH#11587 make sure we get a useful error message when generate_range
         #  raises

pandas/tests/scalar/test_nat.py

@@ -12,6 +12,7 @@
 
 from pandas import (
     DatetimeIndex,
+    DatetimeTZDtype,
     Index,
     NaT,
     Period,
@@ -440,7 +441,9 @@ def test_nat_rfloordiv_timedelta(val, expected):
         DatetimeIndex(["2011-01-01", "2011-01-02"], name="x"),
         DatetimeIndex(["2011-01-01", "2011-01-02"], tz="US/Eastern", name="x"),
         DatetimeArray._from_sequence(["2011-01-01", "2011-01-02"]),
-        DatetimeArray._from_sequence(["2011-01-01", "2011-01-02"], tz="US/Pacific"),
+        DatetimeArray._from_sequence(
+            ["2011-01-01", "2011-01-02"], dtype=DatetimeTZDtype(tz="US/Pacific")
+        ),
         TimedeltaIndex(["1 day", "2 day"], name="x"),
     ],
 )