From a209fe487fbf4b798ad7a23b3fb3d038203346df Mon Sep 17 00:00:00 2001 From: Andy Li Date: Tue, 23 Jun 2015 13:25:54 -0700 Subject: [PATCH] improve documentation for pandas.Series.interpolate --- pandas/core/generic.py | 11 +++++++---- 1 file changed, 7 insertions(+), 4 deletions(-) diff --git a/pandas/core/generic.py b/pandas/core/generic.py index b9e007a1e4d58..13c38789d03db 100644 --- a/pandas/core/generic.py +++ b/pandas/core/generic.py @@ -2872,18 +2872,21 @@ def interpolate(self, method='linear', axis=0, limit=None, inplace=False, 'polynomial', 'spline' 'piecewise_polynomial', 'pchip'} * 'linear': ignore the index and treat the values as equally - spaced. default + spaced. This is the only method supported on MultiIndexes. + default * 'time': interpolation works on daily and higher resolution data to interpolate given length of interval * '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 + `scipy.interpolate.interp1d` with the order given. Both 'polynomial' and 'spline' requre that you also specify and order - (int) e.g. df.interpolate(method='polynomial', order=4) + (int) e.g. df.interpolate(method='polynomial', order=4). These + use the actual numerical values of the index * 'krogh', 'piecewise_polynomial', 'spline', and 'pchip' are all wrappers around the scipy interpolation methods of similar - names. See the scipy documentation for more on their behavior: + names. These use the actual numerical values of the index. See + the scipy documentation for more on their behavior: http://docs.scipy.org/doc/scipy/reference/interpolate.html#univariate-interpolation http://docs.scipy.org/doc/scipy/reference/tutorial/interpolate.html