TYPING: add skeleton stub for Timestamp

pandas/_libs/tslibs/timestamps.pyi

@@ -0,0 +1,109 @@
+# flake8: noqa
+
+from typing import Any
+
+class RoundTo:
+    @property
+    def MINUS_INFTY(self) -> int: ...
+    @property
+    def PLUS_INFTY(self) -> int: ...
+    @property
+    def NEAREST_HALF_EVEN(self) -> int: ...
+    @property
+    def NEAREST_HALF_PLUS_INFTY(self) -> int: ...
+    @property
+    def NEAREST_HALF_MINUS_INFTY(self) -> int: ...
+
+def round_nsint64(values, mode, freq): ...
+
+class Timestamp:
+    value: Any = ...
+    freq: Any = ...
+    tzinfo: Any = ...
+    min: Any = ...
+    max: Any = ...
+    weekofyear: Any = ...
+    daysinmonth: Any = ...
+    astimezone: Any = ...
+    def fromordinal(cls, ordinal, freq=..., tz=...): ...
+    def now(cls, tz=...): ...
+    def today(cls, tz=...): ...
+    def utcnow(cls): ...
+    def utcfromtimestamp(cls, ts): ...
+    def fromtimestamp(cls, ts): ...
+    def strptime(cls, date_string, format): ...
+    def combine(cls, date, time): ...
+    def __new__(
+        cls,
+        ts_input=...,
+        freq=...,
+        tz=...,
+        unit=...,
+        year=...,
+        month=...,
+        day=...,
+        hour=...,
+        minute=...,
+        second=...,
+        microsecond=...,
+        nanosecond=...,
+        tzinfo=...,
+    ): ...
+    def round(self, freq, ambiguous=..., nonexistent=...): ...
+    def floor(self, freq, ambiguous=..., nonexistent=...): ...
+    def ceil(self, freq, ambiguous=..., nonexistent=...): ...
+    @property
+    def tz(self): ...
+    @tz.setter
+    def tz(self, value): ...
+    def to_period(self, freq=...): ...
+    @property
+    def dayofweek(self): ...
+    def day_name(self, locale=...): ...
+    def month_name(self, locale=...): ...
+    @property
+    def weekday_name(self): ...
+    @property
+    def dayofyear(self): ...
+    @property
+    def week(self): ...
+    @property
+    def quarter(self): ...
+    @property
+    def days_in_month(self): ...
+    @property
+    def freqstr(self): ...
+    @property
+    def is_month_start(self): ...
+    @property
+    def is_month_end(self): ...
+    @property
+    def is_quarter_start(self): ...
+    @property
+    def is_quarter_end(self): ...
+    @property
+    def is_year_start(self): ...
+    @property
+    def is_year_end(self): ...
+    @property
+    def is_leap_year(self): ...
+    @property
+    def resolution(self): ...
+    def tz_localize(self, tz, ambiguous=..., nonexistent=..., errors=...): ...
+    def tz_convert(self, tz): ...
+    def replace(
+        self,
+        year=...,
+        month=...,
+        day=...,
+        hour=...,
+        minute=...,
+        second=...,
+        microsecond=...,
+        nanosecond=...,
+        tzinfo=...,
+        fold=...,
+    ): ...
+    def isoformat(self, sep=...): ...
+    def to_julian_date(self): ...
+    def normalize(self): ...

pandas/core/arrays/datetimelike.py

@@ -42,7 +42,6 @@
 from pandas.core.dtypes.inference import is_array_like
 from pandas.core.dtypes.missing import is_valid_nat_for_dtype, isna
 
-from pandas._typing import DatetimeLikeScalar
 from pandas.core import missing, nanops
 from pandas.core.algorithms import checked_add_with_arr, take, unique1d, value_counts
 import pandas.core.common as com
@@ -62,7 +61,7 @@ def _simple_new(cls, values, **kwargs):
         raise AbstractMethodError(cls)
 
     @property
-    def _scalar_type(self) -> Type[DatetimeLikeScalar]:
+    def _scalar_type(self) -> Type[Union[Period, Timestamp, Timedelta]]:
         """The scalar associated with this datelike
 
         * PeriodArray : Period