REF: remove unused allow_object kwarg from sequence_to_dt64ns

pandas/core/arrays/datetimes.py

@@ -9,7 +9,6 @@
 from typing import (
     TYPE_CHECKING,
     Literal,
-    overload,
 )
 import warnings
 
@@ -356,7 +355,7 @@ def _from_sequence_not_strict(
 
         freq, freq_infer = dtl.maybe_infer_freq(freq)
 
-        subarr, tz, inferred_freq = sequence_to_dt64ns(
+        subarr, tz, inferred_freq = _sequence_to_dt64ns(
             data,
             dtype=dtype,
             copy=copy,
@@ -1972,41 +1971,22 @@ def std(
 # Constructor Helpers
 
 
-@overload
-def sequence_to_datetimes(
-    data, allow_object: Literal[False] = ..., require_iso8601: bool = ...
-) -> DatetimeArray:
-    ...
-
-
-@overload
-def sequence_to_datetimes(
-    data, allow_object: Literal[True] = ..., require_iso8601: bool = ...
-) -> np.ndarray | DatetimeArray:
-    ...
-
-
-def sequence_to_datetimes(
-    data, allow_object: bool = False, require_iso8601: bool = False
-) -> np.ndarray | DatetimeArray:
+def sequence_to_datetimes(data, require_iso8601: bool = False) -> DatetimeArray:
     """
     Parse/convert the passed data to either DatetimeArray or np.ndarray[object].
     """
-    result, tz, freq = sequence_to_dt64ns(
+    result, tz, freq = _sequence_to_dt64ns(
         data,
-        allow_object=allow_object,
         allow_mixed=True,
         require_iso8601=require_iso8601,
     )
-    if result.dtype == object:
-        return result
 
     dtype = tz_to_dtype(tz)
     dta = DatetimeArray._simple_new(result, freq=freq, dtype=dtype)
     return dta
 
 
-def sequence_to_dt64ns(
+def _sequence_to_dt64ns(
     data,
     dtype=None,
     copy=False,
@@ -2015,7 +1995,6 @@ def sequence_to_dt64ns(
     yearfirst=False,
     ambiguous="raise",
     *,
-    allow_object: bool = False,
     allow_mixed: bool = False,
     require_iso8601: bool = False,
 ):
@@ -2030,9 +2009,6 @@ def sequence_to_dt64ns(
     yearfirst : bool, default False
     ambiguous : str, bool, or arraylike, default 'raise'
         See pandas._libs.tslibs.tzconversion.tz_localize_to_utc.
-    allow_object : bool, default False
-        Whether to return an object-dtype ndarray instead of raising if the
-        data contains more than one timezone.
     allow_mixed : bool, default False
         Interpret integers as timestamps when datetime objects are also present.
     require_iso8601 : bool, default False
@@ -2102,7 +2078,7 @@ def sequence_to_dt64ns(
                 data,
                 dayfirst=dayfirst,
                 yearfirst=yearfirst,
-                allow_object=allow_object,
+                allow_object=False,
                 allow_mixed=allow_mixed,
                 require_iso8601=require_iso8601,
             )
@@ -2112,9 +2088,6 @@ def sequence_to_dt64ns(
                 data = data.view(DT64NS_DTYPE)
             elif inferred_tz:
                 tz = inferred_tz
-            elif allow_object and data.dtype == object:
-                # We encountered mixed-timezones.
-                return data, None, None
 
         data_dtype = data.dtype
 

pandas/core/dtypes/cast.py

@@ -1523,7 +1523,7 @@ def try_datetime(v: np.ndarray) -> ArrayLike:
         try:
             # GH#19671 we pass require_iso8601 to be relatively strict
             #  when parsing strings.
-            dta = sequence_to_datetimes(v, require_iso8601=True, allow_object=False)
+            dta = sequence_to_datetimes(v, require_iso8601=True)
         except (ValueError, TypeError):
             # e.g. <class 'numpy.timedelta64'> is not convertible to datetime
             return v.reshape(shape)
@@ -1635,7 +1635,7 @@ def maybe_cast_to_datetime(
 
                 try:
                     if is_datetime64:
-                        dta = sequence_to_datetimes(value, allow_object=False)
+                        dta = sequence_to_datetimes(value)
                         # GH 25843: Remove tz information since the dtype
                         # didn't specify one
 
@@ -1663,7 +1663,7 @@ def maybe_cast_to_datetime(
                         # datetime64tz is assumed to be naive which should
                         # be localized to the timezone.
                         is_dt_string = is_string_dtype(value.dtype)
-                        dta = sequence_to_datetimes(value, allow_object=False)
+                        dta = sequence_to_datetimes(value)
                         if dta.tz is not None:
                             value = dta.astype(dtype, copy=False)
                         elif is_dt_string:

pandas/tests/arrays/datetimes/test_constructors.py

@@ -6,7 +6,7 @@
 import pandas as pd
 import pandas._testing as tm
 from pandas.core.arrays import DatetimeArray
-from pandas.core.arrays.datetimes import sequence_to_dt64ns
+from pandas.core.arrays.datetimes import _sequence_to_dt64ns
 
 
 class TestDatetimeArrayConstructor:
@@ -42,7 +42,7 @@ def test_freq_validation(self):
         "meth",
         [
             DatetimeArray._from_sequence,
-            sequence_to_dt64ns,
+            _sequence_to_dt64ns,
             pd.to_datetime,
             pd.DatetimeIndex,
         ],
@@ -97,7 +97,7 @@ def test_bool_dtype_raises(self):
             DatetimeArray._from_sequence(arr)
 
         with pytest.raises(TypeError, match=msg):
-            sequence_to_dt64ns(arr)
+            _sequence_to_dt64ns(arr)
 
         with pytest.raises(TypeError, match=msg):
             pd.DatetimeIndex(arr)
@@ -128,13 +128,13 @@ def test_tz_dtype_mismatch_raises(self):
             ["2000"], dtype=DatetimeTZDtype(tz="US/Central")
         )
         with pytest.raises(TypeError, match="data is already tz-aware"):
-            sequence_to_dt64ns(arr, dtype=DatetimeTZDtype(tz="UTC"))
+            _sequence_to_dt64ns(arr, dtype=DatetimeTZDtype(tz="UTC"))
 
     def test_tz_dtype_matches(self):
         arr = DatetimeArray._from_sequence(
             ["2000"], dtype=DatetimeTZDtype(tz="US/Central")
         )
-        result, _, _ = sequence_to_dt64ns(arr, dtype=DatetimeTZDtype(tz="US/Central"))
+        result, _, _ = _sequence_to_dt64ns(arr, dtype=DatetimeTZDtype(tz="US/Central"))
         tm.assert_numpy_array_equal(arr._data, result)
 
     @pytest.mark.parametrize("order", ["F", "C"])
@@ -144,8 +144,8 @@ def test_2d(self, order):
         if order == "F":
             arr = arr.T
 
-        res = sequence_to_dt64ns(arr)
-        expected = sequence_to_dt64ns(arr.ravel())
+        res = _sequence_to_dt64ns(arr)
+        expected = _sequence_to_dt64ns(arr.ravel())
 
         tm.assert_numpy_array_equal(res[0].ravel(), expected[0])
         assert res[1] == expected[1]

pandas/tests/arrays/test_datetimelike.py

@@ -26,7 +26,7 @@
     PeriodArray,
     TimedeltaArray,
 )
-from pandas.core.arrays.datetimes import sequence_to_dt64ns
+from pandas.core.arrays.datetimes import _sequence_to_dt64ns
 from pandas.core.arrays.timedeltas import sequence_to_td64ns
 
 
@@ -1361,7 +1361,7 @@ def test_from_pandas_array(dtype):
     expected = cls._from_sequence(data)
     tm.assert_extension_array_equal(result, expected)
 
-    func = {"M8[ns]": sequence_to_dt64ns, "m8[ns]": sequence_to_td64ns}[dtype]
+    func = {"M8[ns]": _sequence_to_dt64ns, "m8[ns]": sequence_to_td64ns}[dtype]
     result = func(arr)[0]
     expected = func(data)[0]
     tm.assert_equal(result, expected)
@@ -1424,7 +1424,7 @@ def test_from_obscure_array(dtype, array_likes):
     result = cls._from_sequence(data)
     tm.assert_extension_array_equal(result, expected)
 
-    func = {"M8[ns]": sequence_to_dt64ns, "m8[ns]": sequence_to_td64ns}[dtype]
+    func = {"M8[ns]": _sequence_to_dt64ns, "m8[ns]": sequence_to_td64ns}[dtype]
     result = func(arr)[0]
     expected = func(data)[0]
     tm.assert_equal(result, expected)