API: integer values and non-nano dtype (#51092)

* API: integer values and non-nano dtype

* troubleshoot circleci

* Update doc/source/whatsnew/v2.0.0.rst

---------

Co-authored-by: Matthew Roeschke <[email protected]>

Author: jbrockmendel

doc/source/whatsnew/v2.0.0.rst

@@ -706,6 +706,7 @@ Other API changes
 - Passing ``dtype`` of "timedelta64[s]", "timedelta64[ms]", or "timedelta64[us]" to :class:`TimedeltaIndex`, :class:`Series`, or :class:`DataFrame` constructors will now retain that dtype instead of casting to "timedelta64[ns]"; passing a dtype with lower resolution for :class:`Series` or :class:`DataFrame` will be cast to the lowest supported resolution "timedelta64[s]" (:issue:`49014`)
 - Passing a ``np.datetime64`` object with non-nanosecond resolution to :class:`Timestamp` will retain the input resolution if it is "s", "ms", "us", or "ns"; otherwise it will be cast to the closest supported resolution (:issue:`49008`)
 - Passing ``datetime64`` values with resolution other than nanosecond to :func:`to_datetime` will retain the input resolution if it is "s", "ms", "us", or "ns"; otherwise it will be cast to the closest supported resolution (:issue:`50369`)
+- Passing integer values and a non-nanosecond datetime64 dtype (e.g. "datetime64[s]") :class:`DataFrame`, :class:`Series`, or :class:`Index` will treat the values as multiples of the dtype's unit, matching the behavior of e.g. ``Series(np.array(values, dtype="M8[s]"))`` (:issue:`51092`)
 - Passing a string in ISO-8601 format to :class:`Timestamp` will retain the resolution of the parsed input if it is "s", "ms", "us", or "ns"; otherwise it will be cast to the closest supported resolution (:issue:`49737`)
 - The ``other`` argument in :meth:`DataFrame.mask` and :meth:`Series.mask` now defaults to ``no_default`` instead of ``np.nan`` consistent with :meth:`DataFrame.where` and :meth:`Series.where`. Entries will be filled with the corresponding NULL value (``np.nan`` for numpy dtypes, ``pd.NA`` for extension dtypes). (:issue:`49111`)
 - Changed behavior of :meth:`Series.quantile` and :meth:`DataFrame.quantile` with :class:`SparseDtype` to retain sparse dtype (:issue:`49583`)

pandas/core/arrays/datetimes.py

@@ -335,6 +335,7 @@ def _from_sequence_not_strict(
             dayfirst=dayfirst,
             yearfirst=yearfirst,
             ambiguous=ambiguous,
+            out_unit=unit,
         )
         # We have to call this again after possibly inferring a tz above
         _validate_tz_from_dtype(dtype, tz, explicit_tz_none)
@@ -1966,6 +1967,7 @@ def _sequence_to_dt64ns(
     dayfirst: bool = False,
     yearfirst: bool = False,
     ambiguous: TimeAmbiguous = "raise",
+    out_unit: str | None = None,
 ):
     """
     Parameters
@@ -1977,6 +1979,8 @@ def _sequence_to_dt64ns(
     yearfirst : bool, default False
     ambiguous : str, bool, or arraylike, default 'raise'
         See pandas._libs.tslibs.tzconversion.tz_localize_to_utc.
+    out_unit : str or None, default None
+        Desired output resolution.
 
     Returns
     -------
@@ -2004,6 +2008,10 @@ def _sequence_to_dt64ns(
     data, copy = maybe_convert_dtype(data, copy, tz=tz)
     data_dtype = getattr(data, "dtype", None)
 
+    out_dtype = DT64NS_DTYPE
+    if out_unit is not None:
+        out_dtype = np.dtype(f"M8[{out_unit}]")
+
     if (
         is_object_dtype(data_dtype)
         or is_string_dtype(data_dtype)
@@ -2090,7 +2098,7 @@ def _sequence_to_dt64ns(
         # assume this data are epoch timestamps
         if data.dtype != INT64_DTYPE:
             data = data.astype(np.int64, copy=False)
-        result = data.view(DT64NS_DTYPE)
+        result = data.view(out_dtype)
 
     if copy:
         result = result.copy()
@@ -2203,7 +2211,7 @@ def maybe_convert_dtype(data, copy: bool, tz: tzinfo | None = None):
         # GH#23675, GH#45573 deprecated to treat symmetrically with integer dtypes.
         # Note: data.astype(np.int64) fails ARM tests, see
         # https://github.com/pandas-dev/pandas/issues/49468.
-        data = data.astype("M8[ns]").view("i8")
+        data = data.astype(DT64NS_DTYPE).view("i8")
         copy = False
 
     elif is_timedelta64_dtype(data.dtype) or is_bool_dtype(data.dtype):

pandas/tests/series/test_constructors.py

@@ -52,6 +52,16 @@
 
 
 class TestSeriesConstructors:
+    def test_from_ints_with_non_nano_dt64_dtype(self, index_or_series):
+        values = np.arange(10)
+
+        res = index_or_series(values, dtype="M8[s]")
+        expected = index_or_series(values.astype("M8[s]"))
+        tm.assert_equal(res, expected)
+
+        res = index_or_series(list(values), dtype="M8[s]")
+        tm.assert_equal(res, expected)
+
     def test_from_na_value_and_interval_of_datetime_dtype(self):
         # GH#41805
         ser = Series([None], dtype="interval[datetime64[ns]]")