BUG: Fixed timedelta numeric operations

doc/source/whatsnew/v0.22.0.txt

@@ -338,7 +338,8 @@ Reshaping
 Numeric
 ^^^^^^^
 
--
+- Fixed ``std`` and ``var`` computations for timedelta arrays not returning results in timedelta units (:issue:`18880`)
+- Fixed ``skipna`` handling for some operations like ``sum`` on timedelta arrays (:issue:`18880`)
 -
 -
 

pandas/core/nanops.py

@@ -239,6 +239,9 @@ def _get_values(values, skipna, fill_value=None, fill_value_typ=None,
     elif copy:
         values = values.copy()
 
+    if is_timedelta64_dtype(values) and not skipna:
+        values = values.astype('float64')
+        values[mask] = np.nan
     values = _view_if_needed(values)
 
     # return a platform independent precision dtype
@@ -406,7 +409,10 @@ def _get_counts_nanvar(mask, axis, ddof, dtype=float):
 @disallow('M8')
 @bottleneck_switch(ddof=1)
 def nanstd(values, axis=None, skipna=True, ddof=1):
-    result = np.sqrt(nanvar(values, axis=axis, skipna=skipna, ddof=ddof))
+    var_ = nanvar(values, axis=axis, skipna=skipna, ddof=ddof)
+    if is_timedelta64_dtype(values):
+        var_ = var_.value
+    result = np.sqrt(var_)
     return _wrap_results(result, values.dtype)
 
 
@@ -448,7 +454,7 @@ def nanvar(values, axis=None, skipna=True, ddof=1):
     # precision as the original values array.
     if is_float_dtype(dtype):
         result = result.astype(dtype)
-    return _wrap_results(result, values.dtype)
+    return _wrap_results(result, dtype)
 
 
 @disallow('M8', 'm8')

pandas/tests/series/test_analytics.py

@@ -1215,6 +1215,39 @@ def test_timedelta64_analytics(self):
         expected = Timedelta('1 days')
         assert result == expected
 
+    def test_timedelta64_sum(self):
+        # https://github.com/pandas-dev/pandas/issues/18880
+        s = pd.Series(pd.timedelta_range(0, periods=12, freq='S'))
+        s[0] = np.nan
+
+        result = s.sum(skipna=False)
+        assert result is pd.NaT
+
+        result = s.sum()
+        assert result == pd.Timedelta(minutes=1, seconds=6)
+
+    @pytest.mark.parametrize('method', [
+        'sum', 'mean', 'min', 'max', 'median',
+        'std', 'var',
+    ])
+    def test_timedelta64_many(self, method):
+        s_float = pd.Series(np.arange(12) * 1e3)
+        s_timed = pd.Series(pd.timedelta_range(0, periods=12, freq='us'))
+
+        expected = pd.Timedelta(getattr(s_float, method)())
+        result = getattr(s_timed, method)()
+        if pd.isna(expected):
+            assert pd.isna(result)
+        else:
+            assert result == expected
+
+        s_float[0] = np.nan
+        s_timed[0] = np.nan
+        result = getattr(s_timed, method)(skipna=False)
+        expected = getattr(s_float, method)(skipna=False)
+        assert pd.isna(result)
+        assert pd.isna(expected)
+
     def test_idxmin(self):
         # test idxmin
         # _check_stat_op approach can not be used here because of isna check.

pandas/tests/test_nanops.py

@@ -888,6 +888,19 @@ def test_nanstd_roundoff(self):
             result = data.std(ddof=ddof)
             assert result == 0.0
 
+    def test_nanvar_timedelta(self):
+        result = pd.Series(dtype='m8[ns]').var()
+        assert result is pd.NaT
+
+        result = pd.Series([1, 1], dtype='m8[ns]').var()
+        assert result == pd.Timedelta(0)
+
+        result = pd.Series([10, 20], dtype='m8[ns]').var()
+        assert result == pd.Timedelta(50)
+
+        result = pd.Series([np.nan, 10, 20, np.nan], dtype='m8[ns]').var()
+        assert result == pd.Timedelta(50)
+
     @property
     def prng(self):
         return np.random.RandomState(1234)