BUG: incorrect OutOfBoundsDatetime with non-nano dtype (#55756)

* BUG: incorrect OutOfBoundsDatetime with non-nano dtype

* GH ref

Author: jbrockmendel

Diff for: doc/source/whatsnew/v2.2.0.rst

@@ -330,6 +330,7 @@ Datetimelike
 - Bug in addition or subtraction of :class:`BusinessDay` offset with ``offset`` attribute to non-nanosecond :class:`Index`, :class:`Series`, or :class:`DataFrame` column giving incorrect results (:issue:`55608`)
 - Bug in addition or subtraction of :class:`DateOffset` objects with microsecond components to ``datetime64`` :class:`Index`, :class:`Series`, or :class:`DataFrame` columns with non-nanosecond resolution (:issue:`55595`)
 - Bug in addition or subtraction of very large :class:`Tick` objects with :class:`Timestamp` or :class:`Timedelta` objects raising ``OverflowError`` instead of ``OutOfBoundsTimedelta`` (:issue:`55503`)
+- Bug in creating a :class:`Index`, :class:`Series`, or :class:`DataFrame` with a non-nanosecond ``datetime64`` dtype and inputs that would be out of bounds for a ``datetime64[ns]`` incorrectly raising ``OutOfBoundsDatetime`` (:issue:`55756`)
 -
 
 Timedelta

Diff for: pandas/_libs/tslib.pyi

@@ -23,6 +23,7 @@ def array_to_datetime(
     dayfirst: bool = ...,
     yearfirst: bool = ...,
     utc: bool = ...,
+    creso: int = ...,
 ) -> tuple[np.ndarray, tzinfo | None]: ...
 
 # returned ndarray may be object dtype or datetime64[ns]

Diff for: pandas/_libs/tslib.pyx

@@ -64,6 +64,7 @@ from pandas._libs.tslibs.conversion cimport (
     get_datetime64_nanos,
     parse_pydatetime,
 )
+from pandas._libs.tslibs.dtypes cimport npy_unit_to_abbrev
 from pandas._libs.tslibs.nattype cimport (
     NPY_NAT,
     c_NaT as NaT,
@@ -277,6 +278,7 @@ def array_with_unit_to_datetime(
         result, tz = array_to_datetime(
             values.astype(object, copy=False),
             errors=errors,
+            creso=NPY_FR_ns,
         )
         return result, tz
 
@@ -408,6 +410,7 @@ cpdef array_to_datetime(
     bint dayfirst=False,
     bint yearfirst=False,
     bint utc=False,
+    NPY_DATETIMEUNIT creso=NPY_FR_ns,
 ):
     """
     Converts a 1D array of date-like values to a numpy array of either:
@@ -434,6 +437,7 @@ cpdef array_to_datetime(
          yearfirst parsing behavior when encountering datetime strings
     utc : bool, default False
          indicator whether the dates should be UTC
+    creso : NPY_DATETIMEUNIT, default NPY_FR_ns
 
     Returns
     -------
@@ -457,13 +461,14 @@ cpdef array_to_datetime(
         set out_tzoffset_vals = set()
         tzinfo tz_out = None
         cnp.flatiter it = cnp.PyArray_IterNew(values)
-        NPY_DATETIMEUNIT creso = NPY_FR_ns
         DatetimeParseState state = DatetimeParseState()
+        str reso_str
 
     # specify error conditions
     assert is_raise or is_ignore or is_coerce
 
-    result = np.empty((<object>values).shape, dtype="M8[ns]")
+    reso_str = npy_unit_to_abbrev(creso)
+    result = np.empty((<object>values).shape, dtype=f"M8[{reso_str}]")
     iresult = result.view("i8").ravel()
 
     for i in range(n):
@@ -480,11 +485,11 @@ cpdef array_to_datetime(
                 iresult[i] = parse_pydatetime(val, &dts, creso=creso)
 
             elif PyDate_Check(val):
-                iresult[i] = pydate_to_dt64(val, &dts)
-                check_dts_bounds(&dts)
+                iresult[i] = pydate_to_dt64(val, &dts, reso=creso)
+                check_dts_bounds(&dts, creso)
 
             elif is_datetime64_object(val):
-                iresult[i] = get_datetime64_nanos(val, NPY_FR_ns)
+                iresult[i] = get_datetime64_nanos(val, creso)
 
             elif is_integer_object(val) or is_float_object(val):
                 # these must be ns unit by-definition
@@ -493,23 +498,23 @@ cpdef array_to_datetime(
                     iresult[i] = NPY_NAT
                 else:
                     # we now need to parse this as if unit='ns'
-                    iresult[i] = cast_from_unit(val, "ns")
+                    iresult[i] = cast_from_unit(val, "ns", out_reso=creso)
 
             elif isinstance(val, str):
                 # string
                 if type(val) is not str:
                     # GH#32264 np.str_ object
                     val = str(val)
 
-                if parse_today_now(val, &iresult[i], utc):
+                if parse_today_now(val, &iresult[i], utc, creso):
                     # We can't _quite_ dispatch this to convert_str_to_tsobject
                     #  bc there isn't a nice way to pass "utc"
                     continue
 
                 _ts = convert_str_to_tsobject(
                     val, None, unit="ns", dayfirst=dayfirst, yearfirst=yearfirst
                 )
-                _ts.ensure_reso(NPY_FR_ns, val)
+                _ts.ensure_reso(creso, val)
 
                 iresult[i] = _ts.value
 

Diff for: pandas/_libs/tslibs/conversion.pxd

@@ -43,7 +43,9 @@ cdef int64_t get_datetime64_nanos(object val, NPY_DATETIMEUNIT reso) except? -1
 
 cpdef datetime localize_pydatetime(datetime dt, tzinfo tz)
 cdef int64_t cast_from_unit(object ts, str unit, NPY_DATETIMEUNIT out_reso=*) except? -1
-cpdef (int64_t, int) precision_from_unit(str unit, NPY_DATETIMEUNIT out_reso=*)
+cpdef (int64_t, int) precision_from_unit(
+    NPY_DATETIMEUNIT in_reso, NPY_DATETIMEUNIT out_reso=*
+)
 
 cdef maybe_localize_tso(_TSObject obj, tzinfo tz, NPY_DATETIMEUNIT reso)
 

Diff for: pandas/_libs/tslibs/conversion.pyi

@@ -9,6 +9,6 @@ DT64NS_DTYPE: np.dtype
 TD64NS_DTYPE: np.dtype
 
 def precision_from_unit(
-    unit: str,
+    in_reso: int,  # NPY_DATETIMEUNIT
 ) -> tuple[int, int]: ...  # (int64_t, _)
 def localize_pydatetime(dt: datetime, tz: tzinfo | None) -> datetime: ...

Diff for: pandas/_libs/tslibs/conversion.pyx

@@ -106,6 +106,7 @@ cdef int64_t cast_from_unit(
     cdef:
         int64_t m
         int p
+        NPY_DATETIMEUNIT in_reso
 
     if unit in ["Y", "M"]:
         if is_float_object(ts) and not ts.is_integer():
@@ -123,7 +124,14 @@ cdef int64_t cast_from_unit(
         dt64obj = np.datetime64(ts, unit)
         return get_datetime64_nanos(dt64obj, out_reso)
 
-    m, p = precision_from_unit(unit, out_reso)
+    in_reso = abbrev_to_npy_unit(unit)
+    if out_reso < in_reso and in_reso != NPY_DATETIMEUNIT.NPY_FR_GENERIC:
+        # We will end up rounding (always *down*), so don't need the fractional
+        #  part of `ts`.
+        m, _ = precision_from_unit(out_reso, in_reso)
+        return (<int64_t>ts) // m
+
+    m, p = precision_from_unit(in_reso, out_reso)
 
     # cast the unit, multiply base/frac separately
     # to avoid precision issues from float -> int
@@ -146,8 +154,8 @@ cdef int64_t cast_from_unit(
         ) from err
 
 
-cpdef inline (int64_t, int) precision_from_unit(
-    str unit,
+cpdef (int64_t, int) precision_from_unit(
+    NPY_DATETIMEUNIT in_reso,
     NPY_DATETIMEUNIT out_reso=NPY_DATETIMEUNIT.NPY_FR_ns,
 ):
     """
@@ -163,25 +171,24 @@ cpdef inline (int64_t, int) precision_from_unit(
         int64_t m
         int64_t multiplier
         int p
-        NPY_DATETIMEUNIT reso = abbrev_to_npy_unit(unit)
 
-    if reso == NPY_DATETIMEUNIT.NPY_FR_GENERIC:
-        reso = NPY_DATETIMEUNIT.NPY_FR_ns
-    if reso == NPY_DATETIMEUNIT.NPY_FR_Y:
+    if in_reso == NPY_DATETIMEUNIT.NPY_FR_GENERIC:
+        in_reso = NPY_DATETIMEUNIT.NPY_FR_ns
+    if in_reso == NPY_DATETIMEUNIT.NPY_FR_Y:
         # each 400 years we have 97 leap years, for an average of 97/400=.2425
         #  extra days each year. We get 31556952 by writing
         #  3600*24*365.2425=31556952
         multiplier = periods_per_second(out_reso)
         m = multiplier * 31556952
-    elif reso == NPY_DATETIMEUNIT.NPY_FR_M:
+    elif in_reso == NPY_DATETIMEUNIT.NPY_FR_M:
         # 2629746 comes from dividing the "Y" case by 12.
         multiplier = periods_per_second(out_reso)
         m = multiplier * 2629746
     else:
         # Careful: if get_conversion_factor raises, the exception does
         #  not propagate, instead we get a warning about an ignored exception.
         #  https://github.com/pandas-dev/pandas/pull/51483#discussion_r1115198951
-        m = get_conversion_factor(reso, out_reso)
+        m = get_conversion_factor(in_reso, out_reso)
 
     p = <int>log10(m)  # number of digits in 'm' minus 1
     return m, p

Diff for: pandas/_libs/tslibs/strptime.pxd

@@ -4,8 +4,10 @@ from cpython.datetime cimport (
 )
 from numpy cimport int64_t
 
+from pandas._libs.tslibs.np_datetime cimport NPY_DATETIMEUNIT
 
-cdef bint parse_today_now(str val, int64_t* iresult, bint utc)
+
+cdef bint parse_today_now(str val, int64_t* iresult, bint utc, NPY_DATETIMEUNIT creso)
 
 
 cdef class DatetimeParseState:

Diff for: pandas/_libs/tslibs/strptime.pyx

@@ -111,22 +111,27 @@ def _test_format_is_iso(f: str) -> bool:
     return format_is_iso(f)
 
 
-cdef bint parse_today_now(str val, int64_t* iresult, bint utc):
+cdef bint parse_today_now(
+    str val, int64_t* iresult, bint utc, NPY_DATETIMEUNIT creso
+):
     # We delay this check for as long as possible
     # because it catches relatively rare cases
+    cdef:
+        _Timestamp ts
 
-    # Multiply by 1000 to convert to nanos, since these methods naturally have
-    #  microsecond resolution
     if val == "now":
         if utc:
-            iresult[0] = Timestamp.utcnow()._value * 1000
+            ts = <_Timestamp>Timestamp.utcnow()
+            iresult[0] = ts._as_creso(creso)._value
         else:
             # GH#18705 make sure to_datetime("now") matches Timestamp("now")
             # Note using Timestamp.now() is faster than Timestamp("now")
-            iresult[0] = Timestamp.now()._value * 1000
+            ts = <_Timestamp>Timestamp.now()
+            iresult[0] = ts._as_creso(creso)._value
         return True
     elif val == "today":
-        iresult[0] = Timestamp.today()._value * 1000
+        ts = <_Timestamp>Timestamp.today()
+        iresult[0] = ts._as_creso(creso)._value
         return True
     return False
 
@@ -363,7 +368,7 @@ def array_strptime(
                 check_dts_bounds(&dts)
                 continue
 
-            if parse_today_now(val, &iresult[i], utc):
+            if parse_today_now(val, &iresult[i], utc, NPY_FR_ns):
                 continue
 
             # Some ISO formats can't be parsed by string_to_dts

Diff for: pandas/_libs/tslibs/timedeltas.pyx

@@ -303,18 +303,16 @@ cdef object ensure_td64ns(object ts):
     cdef:
         NPY_DATETIMEUNIT td64_unit
         int64_t td64_value, mult
-        str unitstr
 
     td64_unit = get_datetime64_unit(ts)
     if (
         td64_unit != NPY_DATETIMEUNIT.NPY_FR_ns
         and td64_unit != NPY_DATETIMEUNIT.NPY_FR_GENERIC
     ):
-        unitstr = npy_unit_to_abbrev(td64_unit)
 
         td64_value = cnp.get_timedelta64_value(ts)
 
-        mult = precision_from_unit(unitstr)[0]
+        mult = precision_from_unit(td64_unit)[0]
         try:
             # NB: cython#1381 this cannot be *=
             td64_value = td64_value * mult

Diff for: pandas/core/arrays/datetimes.py

@@ -2251,18 +2251,19 @@ def _sequence_to_dt64ns(
                 dayfirst=dayfirst,
                 yearfirst=yearfirst,
                 allow_object=False,
+                out_unit=out_unit or "ns",
             )
             copy = False
             if tz and inferred_tz:
                 #  two timezones: convert to intended from base UTC repr
                 assert converted.dtype == "i8"
                 # GH#42505
                 # by convention, these are _already_ UTC, e.g
-                result = converted.view(DT64NS_DTYPE)
+                result = converted.view(out_dtype)
 
             elif inferred_tz:
                 tz = inferred_tz
-                result = converted.view(DT64NS_DTYPE)
+                result = converted.view(out_dtype)
 
             else:
                 result, _ = _construct_from_dt64_naive(
@@ -2360,6 +2361,7 @@ def objects_to_datetime64ns(
     utc: bool = False,
     errors: DateTimeErrorChoices = "raise",
     allow_object: bool = False,
+    out_unit: str = "ns",
 ):
     """
     Convert data to array of timestamps.
@@ -2375,6 +2377,7 @@ def objects_to_datetime64ns(
     allow_object : bool
         Whether to return an object-dtype ndarray instead of raising if the
         data contains more than one timezone.
+    out_unit : str, default "ns"
 
     Returns
     -------
@@ -2399,6 +2402,7 @@ def objects_to_datetime64ns(
         utc=utc,
         dayfirst=dayfirst,
         yearfirst=yearfirst,
+        creso=abbrev_to_npy_unit(out_unit),
     )
 
     if tz_parsed is not None:

Diff for: pandas/core/arrays/timedeltas.py

@@ -29,6 +29,7 @@
     to_offset,
 )
 from pandas._libs.tslibs.conversion import precision_from_unit
+from pandas._libs.tslibs.dtypes import abbrev_to_npy_unit
 from pandas._libs.tslibs.fields import (
     get_timedelta_days,
     get_timedelta_field,
@@ -1078,7 +1079,7 @@ def sequence_to_td64ns(
         else:
             mask = np.isnan(data)
         # The next few lines are effectively a vectorized 'cast_from_unit'
-        m, p = precision_from_unit(unit or "ns")
+        m, p = precision_from_unit(abbrev_to_npy_unit(unit or "ns"))
         with warnings.catch_warnings():
             # Suppress RuntimeWarning about All-NaN slice
             warnings.filterwarnings(

Diff for: pandas/core/dtypes/astype.py

@@ -105,11 +105,10 @@ def _astype_nansafe(
         # then coerce to datetime64[ns] and use DatetimeArray.astype
 
         if lib.is_np_dtype(dtype, "M"):
-            from pandas import to_datetime
+            from pandas.core.arrays import DatetimeArray
 
-            dti = to_datetime(arr.ravel())
-            dta = dti._data.reshape(arr.shape)
-            return dta.astype(dtype, copy=False)._ndarray
+            dta = DatetimeArray._from_sequence(arr, dtype=dtype)
+            return dta._ndarray
 
         elif lib.is_np_dtype(dtype, "m"):
             from pandas.core.construction import ensure_wrapped_if_datetimelike

Diff for: pandas/core/tools/datetimes.py

@@ -31,6 +31,7 @@
     timezones as libtimezones,
 )
 from pandas._libs.tslibs.conversion import precision_from_unit
+from pandas._libs.tslibs.dtypes import abbrev_to_npy_unit
 from pandas._libs.tslibs.parsing import (
     DateParseError,
     guess_datetime_format,
@@ -550,7 +551,7 @@ def _to_datetime_with_unit(arg, unit, name, utc: bool, errors: str) -> Index:
             tz_parsed = None
 
         elif arg.dtype.kind == "f":
-            mult, _ = precision_from_unit(unit)
+            mult, _ = precision_from_unit(abbrev_to_npy_unit(unit))
 
             mask = np.isnan(arg) | (arg == iNaT)
             fvalues = (arg * mult).astype("f8", copy=False)

Diff for: pandas/tests/frame/methods/test_astype.py

@@ -382,7 +382,12 @@ def test_astype_from_object_to_datetime_unit(self, unit):
             ["2017-01-01", "2017-01-02", "2017-02-03"],
         ]
         df = DataFrame(vals, dtype=object)
-        with pytest.raises(TypeError, match="Cannot cast"):
+        msg = (
+            rf"Unexpected value for 'dtype': 'datetime64\[{unit}\]'. "
+            r"Must be 'datetime64\[s\]', 'datetime64\[ms\]', 'datetime64\[us\]', "
+            r"'datetime64\[ns\]' or DatetimeTZDtype"
+        )
+        with pytest.raises(ValueError, match=msg):
             df.astype(f"M8[{unit}]")
 
     @pytest.mark.parametrize("unit", ["Y", "M", "W", "D", "h", "m"])