BUG: fix Series[timedelta64] arithmetic with Timedelta scalars

doc/source/timedeltas.rst

@@ -267,6 +267,14 @@ yields another ``timedelta64[ns]`` dtypes Series.
    td * -1
    td * pd.Series([1, 2, 3, 4])
 
+Rounded division (floor-division) of a ``timedelta64[ns]`` Series by a scalar
+``Timedelta`` gives a series of integers.
+
+.. ipython:: python
+
+   td // pd.Timedelta(days=3, hours=4)
+   pd.Timedelta(days=3, hours=4) // td
+
 Attributes
 ----------
 

doc/source/whatsnew/v0.22.0.txt

@@ -277,6 +277,7 @@ Conversion
 - Fixed a bug where ``FY5253`` date offsets could incorrectly raise an ``AssertionError`` in arithmetic operatons (:issue:`14774`)
 - Bug in :meth:`Index.astype` with a categorical dtype where the resultant index is not converted to a :class:`CategoricalIndex` for all types of index (:issue:`18630`)
 - Bug in :meth:`Series.astype` and ``Categorical.astype()`` where an existing categorical data does not get updated (:issue:`10696`, :issue:`18593`)
+- Bug in :func:`Series.__floordiv__`  and :func:`Series.__rfloordiv__` where operating on a scalar ``timedelta`` raises an exception (:issue:`18824`)
 
 
 Indexing

pandas/core/ops.py

@@ -409,7 +409,7 @@ def _validate(self, lvalues, rvalues, name):
 
             if name not in ('__div__', '__rdiv__', '__truediv__',
                             '__rtruediv__', '__add__', '__radd__', '__sub__',
-                            '__rsub__'):
+                            '__rsub__', '__floordiv__', '__rfloordiv__'):
                 raise TypeError("can only operate on a timedeltas for addition"
                                 ", subtraction, and division, but the operator"
                                 " [{name}] was passed".format(name=name))
@@ -595,7 +595,9 @@ def _offset(lvalues, rvalues):
             # integer gets converted to timedelta in np < 1.6
             if ((self.is_timedelta_lhs and self.is_timedelta_rhs) and
                     not self.is_integer_rhs and not self.is_integer_lhs and
-                    self.name in ('__div__', '__truediv__')):
+                    self.name in ('__div__', '__rdiv__',
+                                  '__truediv__', '__rtruediv__',
+                                  '__floordiv__', '__rfloordiv__')):
                 self.dtype = 'float64'
                 self.fill_value = np.nan
                 lvalues = lvalues.astype(np.float64)

pandas/tests/series/test_operators.py

@@ -962,6 +962,14 @@ def test_timedelta64_ops_nat(self):
 
 
 class TestDatetimeSeriesArithmetic(object):
+    @pytest.mark.xfail(reason='GH#18824 bug in Timedelta.__floordiv__')
+    def test_timedelta_rfloordiv(self):
+        td1 = Series([timedelta(minutes=5, seconds=3)] * 3)
+        td1.iloc[2] = np.nan
+        tdscalar = Timedelta(minutes=5, seconds=4)
+        tm.assert_series_equal(tdscalar // td1,
+                               Series([1, 1, np.nan]))
+
     def test_operators_datetimelike(self):
         def run_ops(ops, get_ser, test_ser):
 
@@ -976,16 +984,24 @@ def run_ops(ops, get_ser, test_ser):
         # ## timedelta64 ###
         td1 = Series([timedelta(minutes=5, seconds=3)] * 3)
         td1.iloc[2] = np.nan
-        td2 = timedelta(minutes=5, seconds=4)
-        ops = ['__mul__', '__floordiv__', '__pow__', '__rmul__',
-               '__rfloordiv__', '__rpow__']
-        run_ops(ops, td1, td2)
-        td1 + td2
-        td2 + td1
-        td1 - td2
-        td2 - td1
-        td1 / td2
-        td2 / td1
+        tdscalar = Timedelta(minutes=5, seconds=4)
+        ops = ['__mul__', '__pow__', '__rmul__', '__rpow__']
+        run_ops(ops, td1, tdscalar)
+        td1 + tdscalar
+        tdscalar + td1
+        td1 - tdscalar
+        tdscalar - td1
+        td1 / tdscalar
+        tdscalar / td1
+        tm.assert_series_equal(td1 // tdscalar, Series([0, 0, np.nan]))
+        tm.assert_series_equal(td1 // tdscalar.to_pytimedelta(),
+                               Series([0, 0, np.nan]))
+        tm.assert_series_equal(td1 // tdscalar.to_timedelta64(),
+                               Series([0, 0, np.nan]))
+        tm.assert_series_equal(tdscalar.to_pytimedelta() // td1,
+                               Series([1, 1, np.nan]))
+        tm.assert_series_equal(tdscalar.to_timedelta64() // td1,
+                               Series([1, 1, np.nan]))
 
         # ## datetime64 ###
         dt1 = Series([Timestamp('20111230'), Timestamp('20120101'),