@@ -2872,18 +2872,21 @@ def interpolate(self, method='linear', axis=0, limit=None, inplace=False,
28722872 'polynomial', 'spline' 'piecewise_polynomial', 'pchip'}
28732873
28742874 * 'linear': ignore the index and treat the values as equally
2875- spaced. default
2875+ spaced. This is the only method supported on MultiIndexes.
2876+ default
28762877 * 'time': interpolation works on daily and higher resolution
28772878 data to interpolate given length of interval
28782879 * 'index', 'values': use the actual numerical values of the index
28792880 * 'nearest', 'zero', 'slinear', 'quadratic', 'cubic',
28802881 'barycentric', 'polynomial' is passed to
2881- `scipy.interpolate.interp1d` with the order given both
2882+ `scipy.interpolate.interp1d` with the order given. Both
28822883 'polynomial' and 'spline' requre that you also specify and order
2883- (int) e.g. df.interpolate(method='polynomial', order=4)
2884+ (int) e.g. df.interpolate(method='polynomial', order=4). These
2885+ use the actual numerical values of the index
28842886 * 'krogh', 'piecewise_polynomial', 'spline', and 'pchip' are all
28852887 wrappers around the scipy interpolation methods of similar
2886- names. See the scipy documentation for more on their behavior:
2888+ names. These use the actual numerical values of the index. See
2889+ the scipy documentation for more on their behavior:
28872890 http://docs.scipy.org/doc/scipy/reference/interpolate.html#univariate-interpolation
28882891 http://docs.scipy.org/doc/scipy/reference/tutorial/interpolate.html
28892892
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