DOC: fix docstring for DataFrame.interpolate

Author: John David Reaver

pandas/core/common.py

@@ -1392,7 +1392,7 @@ def backfill_2d(values, limit=None, mask=None, dtype=None):
 
 
 def _clean_interp_method(method, order=None, **kwargs):
-    valid = ['linear', 'time', 'values', 'nearest', 'zero', 'slinear',
+    valid = ['linear', 'time', 'index', 'values', 'nearest', 'zero', 'slinear',
              'quadratic', 'cubic', 'barycentric', 'polynomial',
              'krogh', 'piecewise_polynomial',
              'pchip', 'spline']
@@ -1457,7 +1457,7 @@ def _interp_limit(invalid, limit):
         result.fill(np.nan)
         return result
 
-    if method in ['linear', 'time', 'values']:
+    if method in ['linear', 'time', 'index', 'values']:
         if method in ('values', 'index'):
             inds = np.asarray(xvalues)
             # hack for DatetimeIndex, #1646

pandas/core/generic.py

@@ -2532,15 +2532,15 @@ def interpolate(self, method='linear', axis=0, limit=None, inplace=False,
 
         Parameters
         ----------
-        method : {'linear', 'time', 'values', 'index' 'nearest', 'zero',
+        method : {'linear', 'time', 'index', 'values', 'nearest', 'zero',
                   'slinear', 'quadratic', 'cubic', 'barycentric', 'krogh',
                   'polynomial', 'spline' 'piecewise_polynomial', 'pchip'}
 
             * 'linear': ignore the index and treat the values as equally
               spaced. default
             * 'time': interpolation works on daily and higher resolution
               data to interpolate given length of interval
-            * 'index': use the actual numerical values of the index
+            * 'index', 'values': use the actual numerical values of the index
             * 'nearest', 'zero', 'slinear', 'quadratic', 'cubic',
               'barycentric', 'polynomial' is passed to
               `scipy.interpolate.interp1d` with the order given both

pandas/tests/test_generic.py

@@ -512,7 +512,7 @@ def test_interpolate_index_values(self):
 
         vals = s.index.values.astype(float)
 
-        result = s.interpolate(method='values')
+        result = s.interpolate(method='index')
 
         expected = s.copy()
         bad = isnull(expected.values)
@@ -522,6 +522,12 @@ def test_interpolate_index_values(self):
 
         assert_series_equal(result[bad], expected)
 
+        # 'values' is synonymous with 'index' for the method kwarg
+        other_result = s.interpolate(method='values')
+
+        assert_series_equal(other_result, result)
+        assert_series_equal(other_result[bad], expected)
+
     def test_interpolate_non_ts(self):
         s = Series([1, 3, np.nan, np.nan, np.nan, 11])
         with tm.assertRaises(ValueError):