pandas.Panel.interpolate

Panel.interpolate(method='linear', axis=0, limit=None, inplace=False, limit_direction='forward', downcast=None, **kwargs)

Interpolate values according to different methods.

Please note that only method='linear' is supported for DataFrames/Series with a MultiIndex.

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. 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. Both ‘polynomial’ and ‘spline’ require that you also specify an order (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. These use the actual numerical values of the index. See the scipy documentation for more on their behavior here # noqa and here # noqa

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.

limit_direction : {‘forward’, ‘backward’, ‘both’}, defaults to ‘forward’

If limit is specified, consecutive NaNs will be filled in this direction.

New in version 0.17.0.

inplace : bool, default False

Update the NDFrame in place if possible.

downcast : optional, ‘infer’ or None, defaults to None

Downcast dtypes if possible.

kwargs : keyword arguments to pass on to the interpolating function.

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