BUG: can't resample with non-nano dateindex, out-of-nanosecond-bounds

pandas/core/resample.py

@@ -1731,6 +1731,7 @@ def _get_time_bins(self, ax: DatetimeIndex):
             ax.min(),
             ax.max(),
             self.freq,
+            unit=ax.unit,
             closed=self.closed,
             origin=self.origin,
             offset=self.offset,
@@ -1750,7 +1751,8 @@ def _get_time_bins(self, ax: DatetimeIndex):
             name=ax.name,
             ambiguous=True,
             nonexistent="shift_forward",
-        ).as_unit(ax.unit)
+            unit=ax.unit,
+        )
 
         ax_values = ax.asi8
         binner, bin_edges = self._adjust_bin_edges(binner, ax_values)
@@ -1960,6 +1962,7 @@ def _get_timestamp_range_edges(
     first: Timestamp,
     last: Timestamp,
     freq: BaseOffset,
+    unit: str,
     closed: Literal["right", "left"] = "left",
     origin: TimeGrouperOrigin = "start_day",
     offset: Timedelta | None = None,
@@ -2015,7 +2018,7 @@ def _get_timestamp_range_edges(
                 origin = origin.tz_localize(None)
 
         first, last = _adjust_dates_anchored(
-            first, last, freq, closed=closed, origin=origin, offset=offset
+            first, last, freq, closed=closed, origin=origin, offset=offset, unit=unit
         )
         if isinstance(freq, Day):
             first = first.tz_localize(index_tz)
@@ -2082,7 +2085,7 @@ def _get_period_range_edges(
     adjust_last = freq.is_on_offset(last_ts)
 
     first_ts, last_ts = _get_timestamp_range_edges(
-        first_ts, last_ts, freq, closed=closed, origin=origin, offset=offset
+        first_ts, last_ts, freq, unit="ns", closed=closed, origin=origin, offset=offset
     )
 
     first = (first_ts + int(adjust_first) * freq).to_period(freq)
@@ -2115,32 +2118,35 @@ def _adjust_dates_anchored(
     closed: Literal["right", "left"] = "right",
     origin: TimeGrouperOrigin = "start_day",
     offset: Timedelta | None = None,
+    unit: str = "ns",
 ) -> tuple[Timestamp, Timestamp]:
     # First and last offsets should be calculated from the start day to fix an
     # error cause by resampling across multiple days when a one day period is
     # not a multiple of the frequency. See GH 8683
     # To handle frequencies that are not multiple or divisible by a day we let
     # the possibility to define a fixed origin timestamp. See GH 31809
-    first = first.as_unit("ns")
-    last = last.as_unit("ns")
+    first = first.as_unit(unit)
+    last = last.as_unit(unit)
     if offset is not None:
-        offset = offset.as_unit("ns")
+        offset = offset.as_unit(unit)
+
+    freq_value = Timedelta(freq).as_unit(unit)._value
 
-    origin_nanos = 0  # origin == "epoch"
+    origin_timestamp = 0  # origin == "epoch"
     if origin == "start_day":
-        origin_nanos = first.normalize()._value
+        origin_timestamp = first.normalize()._value
     elif origin == "start":
-        origin_nanos = first._value
+        origin_timestamp = first._value
     elif isinstance(origin, Timestamp):
-        origin_nanos = origin.as_unit("ns")._value
+        origin_timestamp = origin.as_unit(unit)._value
     elif origin in ["end", "end_day"]:
         origin_last = last if origin == "end" else last.ceil("D")
-        sub_freq_times = (origin_last._value - first._value) // freq.nanos
+        sub_freq_times = (origin_last._value - first._value) // freq_value
         if closed == "left":
             sub_freq_times += 1
         first = origin_last - sub_freq_times * freq
-        origin_nanos = first._value
-    origin_nanos += offset._value if offset else 0
+        origin_timestamp = first._value
+    origin_timestamp += offset._value if offset else 0
 
     # GH 10117 & GH 19375. If first and last contain timezone information,
     # Perform the calculation in UTC in order to avoid localizing on an
@@ -2152,19 +2158,19 @@ def _adjust_dates_anchored(
     if last_tzinfo is not None:
         last = last.tz_convert("UTC")
 
-    foffset = (first._value - origin_nanos) % freq.nanos
-    loffset = (last._value - origin_nanos) % freq.nanos
+    foffset = (first._value - origin_timestamp) % freq_value
+    loffset = (last._value - origin_timestamp) % freq_value
 
     if closed == "right":
         if foffset > 0:
             # roll back
             fresult_int = first._value - foffset
         else:
-            fresult_int = first._value - freq.nanos
+            fresult_int = first._value - freq_value
 
         if loffset > 0:
             # roll forward
-            lresult_int = last._value + (freq.nanos - loffset)
+            lresult_int = last._value + (freq_value - loffset)
         else:
             # already the end of the road
             lresult_int = last._value
@@ -2177,11 +2183,11 @@ def _adjust_dates_anchored(
 
         if loffset > 0:
             # roll forward
-            lresult_int = last._value + (freq.nanos - loffset)
+            lresult_int = last._value + (freq_value - loffset)
         else:
-            lresult_int = last._value + freq.nanos
-    fresult = Timestamp(fresult_int)
-    lresult = Timestamp(lresult_int)
+            lresult_int = last._value + freq_value
+    fresult = Timestamp(fresult_int, unit=unit)
+    lresult = Timestamp(lresult_int, unit=unit)
     if first_tzinfo is not None:
         fresult = fresult.tz_localize("UTC").tz_convert(first_tzinfo)
     if last_tzinfo is not None:

pandas/tests/resample/test_datetime_index.py

@@ -1838,7 +1838,7 @@ def test_get_timestamp_range_edges(first, last, freq, exp_first, exp_last, unit)
     exp_last = Timestamp(exp_last)
 
     freq = pd.tseries.frequencies.to_offset(freq)
-    result = _get_timestamp_range_edges(first, last, freq)
+    result = _get_timestamp_range_edges(first, last, freq, unit="ns")
     expected = (exp_first, exp_last)
     assert result == expected
 
@@ -1949,3 +1949,28 @@ def test_resample_unsigned_int(any_unsigned_int_numpy_dtype, unit):
         ),
     )
     tm.assert_frame_equal(result, expected)
+
+
+def test_long_rule_non_nano():
+    # https://github.com/pandas-dev/pandas/issues/51024
+    idx = date_range("0300-01-01", "2000-01-01", unit="s", freq="100Y")
+    ser = Series([1, 4, 2, 8, 5, 7, 1, 4, 2, 8, 5, 7, 1, 4, 2, 8, 5], index=idx)
+    result = ser.resample("200Y").mean()
+    expected_idx = DatetimeIndex(
+        np.array(
+            [
+                "0300-12-31",
+                "0500-12-31",
+                "0700-12-31",
+                "0900-12-31",
+                "1100-12-31",
+                "1300-12-31",
+                "1500-12-31",
+                "1700-12-31",
+                "1900-12-31",
+            ]
+        ).astype("datetime64[s]"),
+        freq="200A-DEC",
+    )
+    expected = Series([1.0, 3.0, 6.5, 4.0, 3.0, 6.5, 4.0, 3.0, 6.5], index=expected_idx)
+    tm.assert_series_equal(result, expected)