ENH: add option to use Series.values to interpolate, close pandas-dev…

…#1206

RELEASE.rst

@@ -55,7 +55,8 @@ pandas 0.8.0
     DataFrame (#929, #1241)
   - Add 'kde' plot kind for Series/DataFrame.plot (#1059)
   - More flexible multiple function aggregation with GroupBy
-  - Add pct_chagne function
+  - Add pct_change function to Series/DataFrame
+  - Add option to interpolate by Index values in Series.interpolate (#1206)
 
 **Improvements to existing features**
 

pandas/core/series.py

@@ -2527,10 +2527,11 @@ def interpolate(self, method='linear'):
 
         Parameters
         ----------
-        method : {'linear', 'time'}
+        method : {'linear', 'time', 'values'}
             Interpolation method.
-            Time interpolation works on daily and higher resolution
+            'time' interpolation works on daily and higher resolution
             data to interpolate given length of interval
+            'values' using the actual index numeric values
 
         Returns
         -------
@@ -2541,6 +2542,10 @@ def interpolate(self, method='linear'):
                 raise Exception('time-weighted interpolation only works'
                                 'on TimeSeries')
             inds = np.array([d.toordinal() for d in self.index])
+        elif method == 'values':
+            inds = self.index.values
+            if inds.dtype == np.object_:
+                inds = lib.maybe_convert_objects(inds)
         else:
             inds = np.arange(len(self))
 

pandas/tests/test_series.py

@@ -2685,6 +2685,21 @@ def test_interpolate(self):
         # try time interpolation on a non-TimeSeries
         self.assertRaises(Exception, self.series.interpolate, method='time')
 
+    def test_interpolate_index_values(self):
+        s = Series(np.nan, index=np.sort(np.random.rand(30)))
+        s[::3] = np.random.randn(10)
+
+        vals = s.index.values.astype(float)
+
+        result = s.interpolate(method='values')
+
+        expected = s.copy()
+        bad = isnull(expected.values)
+        good = -bad
+        expected[bad] = np.interp(vals[bad], vals[good], s.values[good])
+
+        assert_series_equal(result, expected)
+
     def test_weekday(self):
         # Just run the function
         weekdays = self.ts.weekday

pandas/tseries/tests/test_offsets.py

@@ -1319,7 +1319,6 @@ def test_alias_equality(self):
         for k, v in _offset_map.iteritems():
             if v is None:
                 continue
-            foo
             self.assertEqual(k, v.copy())
 
     def test_rule_code(self):

pandas/tseries/tests/test_timezones.py

@@ -282,7 +282,6 @@ def test_tz_convert(self):
         expected = DataFrame({'a': 1}, rng.tz_convert('UTC'))
         self.assert_(result.index.tz.zone == 'UTC')
         assert_frame_equal(result, expected)
-        foo
 
         df = df.T
         result = df.tz_convert('utc', axis=1)