ENH: TDA.total_seconds support non-nano

pandas/_libs/tslibs/__init__.py

@@ -29,13 +29,15 @@
     "astype_overflowsafe",
     "get_unit_from_dtype",
     "periods_per_day",
+    "periods_per_second",
 ]
 
 from pandas._libs.tslibs import dtypes
 from pandas._libs.tslibs.conversion import localize_pydatetime
 from pandas._libs.tslibs.dtypes import (
     Resolution,
     periods_per_day,
+    periods_per_second,
 )
 from pandas._libs.tslibs.nattype import (
     NaT,

pandas/_libs/tslibs/dtypes.pxd

@@ -7,7 +7,7 @@ cdef str npy_unit_to_abbrev(NPY_DATETIMEUNIT unit)
 cdef NPY_DATETIMEUNIT abbrev_to_npy_unit(str abbrev)
 cdef NPY_DATETIMEUNIT freq_group_code_to_npy_unit(int freq) nogil
 cpdef int64_t periods_per_day(NPY_DATETIMEUNIT reso=*) except? -1
-cdef int64_t periods_per_second(NPY_DATETIMEUNIT reso) except? -1
+cpdef int64_t periods_per_second(NPY_DATETIMEUNIT reso) except? -1
 cdef int64_t get_conversion_factor(NPY_DATETIMEUNIT from_unit, NPY_DATETIMEUNIT to_unit) except? -1
 
 cdef dict attrname_to_abbrevs

pandas/_libs/tslibs/dtypes.pyi

@@ -6,6 +6,7 @@ _attrname_to_abbrevs: dict[str, str]
 _period_code_map: dict[str, int]
 
 def periods_per_day(reso: int) -> int: ...
+def periods_per_second(reso: int) -> int: ...
 
 class PeriodDtypeBase:
     _dtype_code: int  # PeriodDtypeCode

pandas/_libs/tslibs/dtypes.pyx

@@ -403,7 +403,7 @@ cpdef int64_t periods_per_day(NPY_DATETIMEUNIT reso=NPY_DATETIMEUNIT.NPY_FR_ns)
     return day_units
 
 
-cdef int64_t periods_per_second(NPY_DATETIMEUNIT reso) except? -1:
+cpdef int64_t periods_per_second(NPY_DATETIMEUNIT reso) except? -1:
     if reso == NPY_DATETIMEUNIT.NPY_FR_ns:
         return 1_000_000_000
     elif reso == NPY_DATETIMEUNIT.NPY_FR_us:

pandas/core/arrays/timedeltas.py

@@ -21,6 +21,7 @@
     Timedelta,
     astype_overflowsafe,
     iNaT,
+    periods_per_second,
     to_offset,
 )
 from pandas._libs.tslibs.conversion import precision_from_unit
@@ -818,10 +819,11 @@ def total_seconds(self) -> npt.NDArray[np.float64]:
                        dtype='timedelta64[ns]', freq=None)
 
         >>> idx.total_seconds()
-        Float64Index([0.0, 86400.0, 172800.0, 259200.00000000003, 345600.0],
+        Float64Index([0.0, 86400.0, 172800.0, 259200.0, 345600.0],
                      dtype='float64')
         """
-        return self._maybe_mask_results(1e-9 * self.asi8, fill_value=None)
+        pps = periods_per_second(self._reso)
+        return self._maybe_mask_results(self.asi8 / pps, fill_value=None)
 
     def to_pytimedelta(self) -> npt.NDArray[np.object_]:
         """
@@ -832,7 +834,7 @@ def to_pytimedelta(self) -> npt.NDArray[np.object_]:
         -------
         timedeltas : ndarray[object]
         """
-        return tslibs.ints_to_pytimedelta(self._ndarray)
+        return ints_to_pytimedelta(self._ndarray)
 
     days = _field_accessor("days", "days", "Number of days for each element.")
     seconds = _field_accessor(

pandas/tests/arrays/test_timedeltas.py

@@ -33,7 +33,7 @@ def test_non_nano(self, unit, reso):
         assert tda[0]._reso == reso
 
     @pytest.mark.parametrize("field", TimedeltaArray._field_ops)
-    def test_fields(self, unit, reso, field):
+    def test_fields(self, unit, field):
         arr = np.arange(5, dtype=np.int64).view(f"m8[{unit}]")
         tda = TimedeltaArray._simple_new(arr, dtype=arr.dtype)
 
@@ -44,6 +44,28 @@ def test_fields(self, unit, reso, field):
         expected = getattr(tda_nano, field)
         tm.assert_numpy_array_equal(result, expected)
 
+    def test_to_pytimedelta(self, unit):
+        arr = np.arange(5, dtype=np.int64).view(f"m8[{unit}]")
+        tda = TimedeltaArray._simple_new(arr, dtype=arr.dtype)
+
+        as_nano = arr.astype("m8[ns]")
+        tda_nano = TimedeltaArray._simple_new(as_nano, dtype=as_nano.dtype)
+
+        result = tda.to_pytimedelta()
+        expected = tda_nano.to_pytimedelta()
+        tm.assert_numpy_array_equal(result, expected)
+
+    def test_total_seconds(self, unit):
+        arr = np.arange(5, dtype=np.int64).view(f"m8[{unit}]")
+        tda = TimedeltaArray._simple_new(arr, dtype=arr.dtype)
+
+        as_nano = arr.astype("m8[ns]")
+        tda_nano = TimedeltaArray._simple_new(as_nano, dtype=as_nano.dtype)
+
+        result = tda.total_seconds()
+        expected = tda_nano.total_seconds()
+        tm.assert_numpy_array_equal(result, expected)
+
 
 class TestTimedeltaArray:
     @pytest.mark.parametrize("dtype", [int, np.int32, np.int64, "uint32", "uint64"])

pandas/tests/tslibs/test_api.py

@@ -54,6 +54,7 @@ def test_namespace():
         "astype_overflowsafe",
         "get_unit_from_dtype",
         "periods_per_day",
+        "periods_per_second",
     ]
 
     expected = set(submodules + api)