API: properly box numeric timedelta ops on Series (GH4984)

doc/source/release.rst

@@ -93,6 +93,7 @@ Improvements to existing features
     is frequency conversion.
   - Timedelta64 support ``fillna/ffill/bfill`` with an integer interpreted as seconds,
     or a ``timedelta`` (:issue:`3371`)
+  - Box numeric ops on ``timedelta`` Series (:issue:`4984`)
   - Datetime64 support ``ffill/bfill``
   - Performance improvements with ``__getitem__`` on ``DataFrames`` with
     when the key is a column

doc/source/timeseries.rst

@@ -1204,6 +1204,25 @@ pass a timedelta to get a particular value.
    y.fillna(10)
    y.fillna(timedelta(days=-1,seconds=5))
 
+.. _timeseries.timedeltas_reductions:
+
+Time Deltas & Reductions
+------------------------
+
+.. warning::
+
+   A numeric reduction operation for ``timedelta64[ns]`` will return a single-element ``Series`` of
+   dtype ``timedelta64[ns]``.
+
+You can do numeric reduction operations on timedeltas.
+
+.. ipython:: python
+
+   y2 = y.fillna(timedelta(days=-1,seconds=5))
+   y2
+   y2.mean()
+   y2.quantile(.1)
+
 .. _timeseries.timedeltas_convert:
 
 Time Deltas & Conversions

doc/source/v0.13.0.txt

@@ -292,6 +292,14 @@ Enhancements
          td.fillna(0)
          td.fillna(timedelta(days=1,seconds=5))
 
+    - You can do numeric reduction operations on timedeltas. Note that these will return
+      a single-element Series.
+
+      .. ipython:: python
+
+         td.mean()
+         td.quantile(.1)
+
   - ``plot(kind='kde')`` now accepts the optional parameters ``bw_method`` and
     ``ind``, passed to scipy.stats.gaussian_kde() (for scipy >= 0.11.0) to set
     the bandwidth, and to gkde.evaluate() to specify the indicies at which it

pandas/core/nanops.py

@@ -5,7 +5,7 @@
 
 import numpy as np
 
-from pandas.core.common import isnull, notnull, _values_from_object
+from pandas.core.common import isnull, notnull, _values_from_object, is_float
 import pandas.core.common as com
 import pandas.lib as lib
 import pandas.algos as algos
@@ -188,6 +188,10 @@ def _wrap_results(result,dtype):
             # as series will do the right thing in py3 (and deal with numpy 1.6.2
             # bug in that it results dtype of timedelta64[us]
             from pandas import Series
+
+            # coerce float to results
+            if is_float(result):
+                result = int(result)
             result = Series([result],dtype='timedelta64[ns]')
         else:
             result = result.view(dtype)
@@ -224,11 +228,15 @@ def nanmean(values, axis=None, skipna=True):
             the_mean[ct_mask] = np.nan
     else:
         the_mean = the_sum / count if count > 0 else np.nan
-    return the_mean
+
+    return  _wrap_results(the_mean,dtype)
 
 @disallow('M8')
 @bottleneck_switch()
 def nanmedian(values, axis=None, skipna=True):
+
+    values, mask, dtype = _get_values(values, skipna)
+
     def get_median(x):
         mask = notnull(x)
         if not skipna and not mask.all():
@@ -257,7 +265,7 @@ def get_median(x):
         return ret
 
     # otherwise return a scalar value
-    return get_median(values) if notempty else np.nan
+    return _wrap_results(get_median(values),dtype) if notempty else np.nan
 
 
 @disallow('M8')

pandas/core/series.py

@@ -1981,7 +1981,12 @@ def quantile(self, q=0.5):
         valid_values = self.dropna().values
         if len(valid_values) == 0:
             return pa.NA
-        return _quantile(valid_values, q * 100)
+        result = _quantile(valid_values, q * 100)
+        if result.dtype == _TD_DTYPE:
+            from pandas.tseries.timedeltas import to_timedelta
+            return to_timedelta(result)
+
+        return result
 
     def ptp(self, axis=None, out=None):
         return _values_from_object(self).ptp(axis, out)

pandas/tseries/tests/test_timedeltas.py

@@ -7,7 +7,7 @@
 import numpy as np
 import pandas as pd
 
-from pandas import (Index, Series, DataFrame, isnull, notnull,
+from pandas import (Index, Series, DataFrame, Timestamp, isnull, notnull,
                     bdate_range, date_range, _np_version_under1p7)
 import pandas.core.common as com
 from pandas.compat import StringIO, lrange, range, zip, u, OrderedDict, long
@@ -123,8 +123,8 @@ def conv(v):
     def test_nat_converters(self):
         _skip_if_numpy_not_friendly()
 
-        self.assert_(to_timedelta('nat') == tslib.iNaT)
-        self.assert_(to_timedelta('nan') == tslib.iNaT)
+        self.assert_(to_timedelta('nat',box=False) == tslib.iNaT)
+        self.assert_(to_timedelta('nan',box=False) == tslib.iNaT)
 
     def test_to_timedelta(self):
         _skip_if_numpy_not_friendly()
@@ -133,11 +133,11 @@ def conv(v):
             return v.astype('m8[ns]')
         d1 = np.timedelta64(1,'D')
 
-        self.assert_(to_timedelta('1 days 06:05:01.00003') == conv(d1+np.timedelta64(6*3600+5*60+1,'s')+np.timedelta64(30,'us')))
-        self.assert_(to_timedelta('15.5us') == conv(np.timedelta64(15500,'ns')))
+        self.assert_(to_timedelta('1 days 06:05:01.00003',box=False) == conv(d1+np.timedelta64(6*3600+5*60+1,'s')+np.timedelta64(30,'us')))
+        self.assert_(to_timedelta('15.5us',box=False) == conv(np.timedelta64(15500,'ns')))
 
         # empty string
-        result = to_timedelta('')
+        result = to_timedelta('',box=False)
         self.assert_(result == tslib.iNaT)
 
         result = to_timedelta(['', ''])
@@ -150,7 +150,7 @@ def conv(v):
 
         # ints
         result = np.timedelta64(0,'ns')
-        expected = to_timedelta(0)
+        expected = to_timedelta(0,box=False)
         self.assert_(result == expected)
 
         # Series
@@ -163,6 +163,35 @@ def conv(v):
         expected = to_timedelta([0,10],unit='s')
         tm.assert_series_equal(result, expected)
 
+        # single element conversion
+        v = timedelta(seconds=1)
+        result = to_timedelta(v,box=False)
+        expected = to_timedelta([v])
+
+        v = np.timedelta64(timedelta(seconds=1))
+        result = to_timedelta(v,box=False)
+        expected = to_timedelta([v])
+
+    def test_timedelta_ops(self):
+        _skip_if_numpy_not_friendly()
+
+        # GH4984
+        # make sure ops return timedeltas
+        s = Series([Timestamp('20130101') + timedelta(seconds=i*i) for i in range(10) ])
+        td = s.diff()
+
+        result = td.mean()
+        expected = to_timedelta(timedelta(seconds=9))
+        tm.assert_series_equal(result, expected)
+
+        result = td.quantile(.1)
+        expected = to_timedelta('00:00:02.6')
+        tm.assert_series_equal(result, expected)
+
+        result = td.median()
+        expected = to_timedelta('00:00:08')
+        tm.assert_series_equal(result, expected)
+
 if __name__ == '__main__':
     nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'],
                    exit=False)

pandas/tseries/timedeltas.py

@@ -58,7 +58,7 @@ def _convert_listlike(arg, box):
     elif is_list_like(arg):
         return _convert_listlike(arg, box=box)
 
-    return _convert_listlike([ arg ], box=False)[0]
+    return _convert_listlike([ arg ], box=box)
 
 _short_search = re.compile(
     "^\s*(?P<neg>-?)\s*(?P<value>\d*\.?\d*)\s*(?P<unit>d|s|ms|us|ns)?\s*$",re.IGNORECASE)