REGR: to_timedelta precision issues with floating data

Author: jorisvandenbossche

doc/source/whatsnew/v0.24.2.rst

@@ -31,6 +31,7 @@ Fixed Regressions
 - Fixed regression in ``IntervalDtype`` construction where passing an incorrect string with 'Interval' as a prefix could result in a ``RecursionError``. (:issue:`25338`)
 - Fixed regression in creating a period-dtype array from a read-only NumPy array of period objects. (:issue:`25403`)
 - Fixed regression in :class:`Categorical`, where constructing it from a categorical ``Series`` and an explicit ``categories=`` that differed from that in the ``Series`` created an invalid object which could trigger segfaults. (:issue:`25318`)
+- Fixed regression in :func:`to_timedelta` loosing precision when converting floating data to ``Timedelta`` data (:issue:`25077`).
 - Fixed pip installing from source into an environment without NumPy (:issue:`25193`)
 
 .. _whatsnew_0242.enhancements:

pandas/core/arrays/timedeltas.py

@@ -918,13 +918,9 @@ def sequence_to_td64ns(data, copy=False, unit="ns", errors="raise"):
         copy = copy and not copy_made
 
     elif is_float_dtype(data.dtype):
-        # treat as multiples of the given unit.  If after converting to nanos,
-        #  there are fractional components left, these are truncated
-        #  (i.e. NOT rounded)
-        mask = np.isnan(data)
-        coeff = np.timedelta64(1, unit) / np.timedelta64(1, 'ns')
-        data = (coeff * data).astype(np.int64).view('timedelta64[ns]')
-        data[mask] = iNaT
+        # object_to_td64ns has custom logic for float -> int conversion
+        # to avoid precision issues
+        data = objects_to_td64ns(data, unit=unit, errors=errors)
         copy = False
 
     elif is_timedelta64_dtype(data.dtype):

pandas/tests/indexes/timedeltas/test_tools.py

@@ -181,3 +181,10 @@ def test_to_timedelta_on_missing_values(self):
 
         actual = pd.to_timedelta(pd.NaT)
         assert actual.value == timedelta_NaT.astype('int64')
+
+    def test_to_timedelta_float(self):
+        # https://github.com/pandas-dev/pandas/issues/25077
+        arr = np.arange(0, 1, 1e-6)[-10:]
+        result = pd.to_timedelta(arr, unit='s')
+        expected_asi8 = np.arange(999990000, int(1e9), 1000, dtype='int64')
+        tm.assert_numpy_array_equal(result.asi8, expected_asi8)