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# pandas.Series.interpolate

`Series.``interpolate`(method='linear', axis=0, limit=None, inplace=False, downcast=None, **kwargs)

  Interpolate values according to different methods.

  Parameters : | 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’, ‘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 ‘polynomial’ and ‘spline’ requre that you also specify and order (int) e.g. df.interpolate(method=’polynomial’, order=4) ‘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: http://docs.scipy.org/doc/scipy/reference/interpolate.html#univariate-interpolation http://docs.scipy.org/doc/scipy/reference/tutorial/interpolate.html axis : {0, 1}, default 0 0: fill column-by-column 1: fill row-by-row limit : int, default None. Maximum number of consecutive NaNs to fill. inplace : bool, default False Update the NDFrame in place if possible. downcast : optional, ‘infer’ or None, defaults to None Downcast dtypes if possible.
  Returns : | Series or DataFrame of same shape interpolated at the NaNs

  See also

  `reindex`, `replace`, `fillna`

  Examples

  # Filling in NaNs: >>> s = pd.Series([0, 1, np.nan, 3]) >>> s.interpolate() 0 0 1 1 2 2 3 3 dtype: float64

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