Backport PR #52391 on branch 2.0.x (BUG: dt.days for timedelta non-nano overflows int32) (#52673)

* Backport PR #52391: BUG: dt.days for timedelta non-nano overflows int32

* fix test_map

---------

Co-authored-by: Matthew Roeschke <10647082+mroeschke@users.noreply.github.com>

Author: meeseeksmachine

asv_bench/benchmarks/tslibs/fields.py

@@ -12,7 +12,7 @@
 class TimeGetTimedeltaField:
     params = [
         _sizes,
-        ["days", "seconds", "microseconds", "nanoseconds"],
+        ["seconds", "microseconds", "nanoseconds"],
     ]
     param_names = ["size", "field"]
 

doc/source/whatsnew/v2.0.1.rst

@@ -24,6 +24,7 @@ Fixed regressions
 
 Bug fixes
 ~~~~~~~~~
+- Bug in :attr:`Series.dt.days` that would overflow ``int32`` number of days (:issue:`52391`)
 - Bug in :class:`arrays.DatetimeArray` constructor returning an incorrect unit when passed a non-nanosecond numpy datetime array (:issue:`52555`)
 - Bug in :func:`pandas.testing.assert_series_equal` where ``check_dtype=False`` would still raise for datetime or timedelta types with different resolutions (:issue:`52449`)
 - Bug in :func:`read_csv` casting PyArrow datetimes to NumPy when ``dtype_backend="pyarrow"`` and ``parse_dates`` is set causing a performance bottleneck in the process (:issue:`52546`)

pandas/_libs/tslibs/fields.pyi

@@ -30,6 +30,10 @@ def get_timedelta_field(
     field: str,
     reso: int = ...,  # NPY_DATETIMEUNIT
 ) -> npt.NDArray[np.int32]: ...
+def get_timedelta_days(
+    tdindex: npt.NDArray[np.int64],  # const int64_t[:]
+    reso: int = ...,  # NPY_DATETIMEUNIT
+) -> npt.NDArray[np.int64]: ...
 def isleapyear_arr(
     years: np.ndarray,
 ) -> npt.NDArray[np.bool_]: ...

pandas/_libs/tslibs/fields.pyx

@@ -508,18 +508,7 @@ def get_timedelta_field(
 
     out = np.empty(count, dtype="i4")
 
-    if field == "days":
-        with nogil:
-            for i in range(count):
-                if tdindex[i] == NPY_NAT:
-                    out[i] = -1
-                    continue
-
-                pandas_timedelta_to_timedeltastruct(tdindex[i], reso, &tds)
-                out[i] = tds.days
-        return out
-
-    elif field == "seconds":
+    if field == "seconds":
         with nogil:
             for i in range(count):
                 if tdindex[i] == NPY_NAT:
@@ -555,6 +544,34 @@ def get_timedelta_field(
     raise ValueError(f"Field {field} not supported")
 
 
+@cython.wraparound(False)
+@cython.boundscheck(False)
+def get_timedelta_days(
+    const int64_t[:] tdindex,
+    NPY_DATETIMEUNIT reso=NPY_FR_ns,
+):
+    """
+    Given a int64-based timedelta index, extract the days,
+    field and return an array of these values.
+    """
+    cdef:
+        Py_ssize_t i, count = len(tdindex)
+        ndarray[int64_t] out
+        pandas_timedeltastruct tds
+
+    out = np.empty(count, dtype="i8")
+
+    with nogil:
+        for i in range(count):
+            if tdindex[i] == NPY_NAT:
+                out[i] = -1
+                continue
+
+            pandas_timedelta_to_timedeltastruct(tdindex[i], reso, &tds)
+            out[i] = tds.days
+    return out
+
+
 cpdef isleapyear_arr(ndarray years):
     """vectorized version of isleapyear; NaT evaluates as False"""
     cdef:

pandas/core/arrays/timedeltas.py

@@ -31,7 +31,10 @@
     to_offset,
 )
 from pandas._libs.tslibs.conversion import precision_from_unit
-from pandas._libs.tslibs.fields import get_timedelta_field
+from pandas._libs.tslibs.fields import (
+    get_timedelta_days,
+    get_timedelta_field,
+)
 from pandas._libs.tslibs.timedeltas import (
     array_to_timedelta64,
     floordiv_object_array,
@@ -78,7 +81,13 @@
 def _field_accessor(name: str, alias: str, docstring: str):
     def f(self) -> np.ndarray:
         values = self.asi8
-        result = get_timedelta_field(values, alias, reso=self._creso)
+        if alias == "days":
+            result = get_timedelta_days(values, reso=self._creso)
+        else:
+            # error: Incompatible types in assignment (
+            # expression has type "ndarray[Any, dtype[signedinteger[_32Bit]]]",
+            # variable has type "ndarray[Any, dtype[signedinteger[_64Bit]]]
+            result = get_timedelta_field(values, alias, reso=self._creso)  # type: ignore[assignment]  # noqa: E501
         if self._hasna:
             result = self._maybe_mask_results(
                 result, fill_value=None, convert="float64"

pandas/tests/indexes/timedeltas/test_timedelta.py

@@ -54,7 +54,7 @@ def test_map(self):
 
         f = lambda x: x.days
         result = rng.map(f)
-        exp = Index([f(x) for x in rng], dtype=np.int32)
+        exp = Index([f(x) for x in rng], dtype=np.int64)
         tm.assert_index_equal(result, exp)
 
     def test_pass_TimedeltaIndex_to_index(self):
@@ -67,7 +67,7 @@ def test_pass_TimedeltaIndex_to_index(self):
 
     def test_fields(self):
         rng = timedelta_range("1 days, 10:11:12.100123456", periods=2, freq="s")
-        tm.assert_index_equal(rng.days, Index([1, 1], dtype=np.int32))
+        tm.assert_index_equal(rng.days, Index([1, 1], dtype=np.int64))
         tm.assert_index_equal(
             rng.seconds,
             Index([10 * 3600 + 11 * 60 + 12, 10 * 3600 + 11 * 60 + 13], dtype=np.int32),

pandas/tests/series/accessors/test_dt_accessor.py

@@ -792,3 +792,23 @@ def test_normalize_pre_epoch_dates():
     result = ser.dt.normalize()
     expected = pd.to_datetime(Series(["1969-01-01", "2016-01-01"]))
     tm.assert_series_equal(result, expected)
+
+
+def test_day_attribute_non_nano_beyond_int32():
+    # GH 52386
+    data = np.array(
+        [
+            136457654736252,
+            134736784364431,
+            245345345545332,
+            223432411,
+            2343241,
+            3634548734,
+            23234,
+        ],
+        dtype="timedelta64[s]",
+    )
+    ser = Series(data)
+    result = ser.dt.days
+    expected = Series([1579371003, 1559453522, 2839645203, 2586, 27, 42066, 0])
+    tm.assert_series_equal(result, expected)

pandas/tests/tslibs/test_fields.py

@@ -35,6 +35,6 @@ def test_get_start_end_field_readonly(dtindex):
 
 def test_get_timedelta_field_readonly(dtindex):
     # treat dtindex as timedeltas for this next one
-    result = fields.get_timedelta_field(dtindex, "days")
-    expected = np.arange(5, dtype=np.int32) * 32
+    result = fields.get_timedelta_field(dtindex, "seconds")
+    expected = np.array([0] * 5, dtype=np.int32)
     tm.assert_numpy_array_equal(result, expected)