BUG: nanoseconds and reso in dateutil paths (pandas-dev#56051)

* BUG: nanoseconds and reso in dateutil paths

* GH ref

doc/source/whatsnew/v2.2.0.rst

@@ -364,6 +364,8 @@ Datetimelike
 - Bug in creating a :class:`Index`, :class:`Series`, or :class:`DataFrame` with a non-nanosecond :class:`DatetimeTZDtype` and inputs that would be out of bounds with nanosecond resolution incorrectly raising ``OutOfBoundsDatetime`` (:issue:`54620`)
 - Bug in creating a :class:`Index`, :class:`Series`, or :class:`DataFrame` with a non-nanosecond ``datetime64`` (or :class:`DatetimeTZDtype`) from mixed-numeric inputs treating those as nanoseconds instead of as multiples of the dtype's unit (which would happen with non-mixed numeric inputs) (:issue:`56004`)
 - 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`)
+- Bug in parsing datetime strings with nanosecond resolution with non-ISO8601 formats incorrectly truncating sub-microsecond components (:issue:`56051`)
+- Bug in parsing datetime strings with sub-second resolution and trailing zeros incorrectly inferring second or millisecond resolution (:issue:`55737`)
 -
 
 Timedelta

pandas/_libs/tslibs/conversion.pyx

@@ -508,7 +508,7 @@ cdef _TSObject convert_str_to_tsobject(str ts, tzinfo tz,
         npy_datetimestruct dts
         int out_local = 0, out_tzoffset = 0, string_to_dts_failed
         datetime dt
-        int64_t ival
+        int64_t ival, nanos = 0
         NPY_DATETIMEUNIT out_bestunit, reso
         _TSObject obj
 
@@ -560,10 +560,14 @@ cdef _TSObject convert_str_to_tsobject(str ts, tzinfo tz,
                 return obj
 
         dt = parse_datetime_string(
-            ts, dayfirst=dayfirst, yearfirst=yearfirst, out_bestunit=&out_bestunit
+            ts,
+            dayfirst=dayfirst,
+            yearfirst=yearfirst,
+            out_bestunit=&out_bestunit,
+            nanos=&nanos,
         )
         reso = get_supported_reso(out_bestunit)
-        return convert_datetime_to_tsobject(dt, tz, nanos=0, reso=reso)
+        return convert_datetime_to_tsobject(dt, tz, nanos=nanos, reso=reso)
 
     return convert_datetime_to_tsobject(dt, tz)
 

pandas/_libs/tslibs/parsing.pxd

@@ -1,4 +1,5 @@
 from cpython.datetime cimport datetime
+from numpy cimport int64_t
 
 from pandas._libs.tslibs.np_datetime cimport NPY_DATETIMEUNIT
 
@@ -10,5 +11,6 @@ cdef datetime parse_datetime_string(
     str date_string,
     bint dayfirst,
     bint yearfirst,
-    NPY_DATETIMEUNIT* out_bestunit
+    NPY_DATETIMEUNIT* out_bestunit,
+    int64_t* nanos,
 )

pandas/_libs/tslibs/parsing.pyx

@@ -34,6 +34,7 @@ from numpy cimport (
     PyArray_IterNew,
     flatiter,
     float64_t,
+    int64_t,
 )
 
 cnp.import_array()
@@ -272,8 +273,11 @@ def py_parse_datetime_string(
     #  parse_datetime_string cpdef bc it has a pointer argument)
     cdef:
         NPY_DATETIMEUNIT out_bestunit
+        int64_t nanos
 
-    return parse_datetime_string(date_string, dayfirst, yearfirst, &out_bestunit)
+    return parse_datetime_string(
+        date_string, dayfirst, yearfirst, &out_bestunit, &nanos
+    )
 
 
 cdef datetime parse_datetime_string(
@@ -283,7 +287,8 @@ cdef datetime parse_datetime_string(
     str date_string,
     bint dayfirst,
     bint yearfirst,
-    NPY_DATETIMEUNIT* out_bestunit
+    NPY_DATETIMEUNIT* out_bestunit,
+    int64_t* nanos,
 ):
     """
     Parse datetime string, only returns datetime.
@@ -311,7 +316,7 @@ cdef datetime parse_datetime_string(
         default = datetime.now().replace(hour=0, minute=0, second=0, microsecond=0)
         dt = dateutil_parse(date_string, default=default,
                             dayfirst=dayfirst, yearfirst=yearfirst,
-                            ignoretz=False, out_bestunit=out_bestunit)
+                            ignoretz=False, out_bestunit=out_bestunit, nanos=nanos)
         return dt
 
     dt = _parse_delimited_date(date_string, dayfirst, out_bestunit)
@@ -330,7 +335,7 @@ cdef datetime parse_datetime_string(
 
     dt = dateutil_parse(date_string, default=_DEFAULT_DATETIME,
                         dayfirst=dayfirst, yearfirst=yearfirst,
-                        ignoretz=False, out_bestunit=out_bestunit)
+                        ignoretz=False, out_bestunit=out_bestunit, nanos=nanos)
     return dt
 
 
@@ -436,7 +441,7 @@ def parse_datetime_string_with_reso(
 
     parsed = dateutil_parse(date_string, _DEFAULT_DATETIME,
                             dayfirst=dayfirst, yearfirst=yearfirst,
-                            ignoretz=False, out_bestunit=&out_bestunit)
+                            ignoretz=False, out_bestunit=&out_bestunit, nanos=NULL)
     reso = npy_unit_to_attrname[out_bestunit]
     return parsed, reso
 
@@ -639,7 +644,8 @@ cdef datetime dateutil_parse(
     bint ignoretz,
     bint dayfirst,
     bint yearfirst,
-    NPY_DATETIMEUNIT* out_bestunit
+    NPY_DATETIMEUNIT* out_bestunit,
+    int64_t* nanos,
 ):
     """ lifted from dateutil to get resolution"""
 
@@ -671,11 +677,8 @@ cdef datetime dateutil_parse(
     if reso is None:
         raise DateParseError(f"Unable to parse datetime string: {timestr}")
 
-    if reso == "microsecond":
-        if repl["microsecond"] == 0:
-            reso = "second"
-        elif repl["microsecond"] % 1000 == 0:
-            reso = "millisecond"
+    if reso == "microsecond" and repl["microsecond"] % 1000 == 0:
+        reso = _find_subsecond_reso(timestr, nanos=nanos)
 
     try:
         ret = default.replace(**repl)
@@ -745,6 +748,38 @@ cdef datetime dateutil_parse(
     return ret
 
 
+cdef object _reso_pattern = re.compile(r"\d:\d{2}:\d{2}\.(?P<frac>\d+)")
+
+cdef _find_subsecond_reso(str timestr, int64_t* nanos):
+    # GH#55737
+    # Check for trailing zeros in a H:M:S.f pattern
+    match = _reso_pattern.search(timestr)
+    if not match:
+        reso = "second"
+    else:
+        frac = match.groupdict()["frac"]
+        if len(frac) <= 3:
+            reso = "millisecond"
+        elif len(frac) > 6:
+            if frac[6:] == "0" * len(frac[6:]):
+                # corner case where we haven't lost any data
+                reso = "nanosecond"
+            elif len(frac) <= 9:
+                reso = "nanosecond"
+                if nanos is not NULL:
+                    if len(frac) < 9:
+                        frac = frac + "0" * (9 - len(frac))
+                    nanos[0] = int(frac[6:])
+            else:
+                # TODO: should we warn/raise in higher-than-nano cases?
+                reso = "nanosecond"
+                if nanos is not NULL:
+                    nanos[0] = int(frac[6:9])
+        else:
+            reso = "microsecond"
+    return reso
+
+
 # ----------------------------------------------------------------------
 # Parsing for type-inference
 
@@ -916,6 +951,7 @@ def guess_datetime_format(dt_str: str, bint dayfirst=False) -> str | None:
             yearfirst=False,
             ignoretz=False,
             out_bestunit=&out_bestunit,
+            nanos=NULL,
         )
     except (ValueError, OverflowError, InvalidOperation):
         # In case the datetime can't be parsed, its format cannot be guessed

pandas/tests/scalar/timestamp/test_constructors.py

@@ -445,6 +445,18 @@ def test_constructor_str_infer_reso(self):
         ts = Timestamp("300 June 1:30:01.300")
         assert ts.unit == "ms"
 
+        # dateutil path -> don't drop trailing zeros
+        ts = Timestamp("01-01-2013T00:00:00.000000000+0000")
+        assert ts.unit == "ns"
+
+        ts = Timestamp("2016/01/02 03:04:05.001000 UTC")
+        assert ts.unit == "us"
+
+        # higher-than-nanosecond -> we drop the trailing bits
+        ts = Timestamp("01-01-2013T00:00:00.000000002100+0000")
+        assert ts == Timestamp("01-01-2013T00:00:00.000000002+0000")
+        assert ts.unit == "ns"
+
 
 class TestTimestampConstructors:
     def test_weekday_but_no_day_raises(self):