Pandas DataFrame: interpolate() function

Last update on August 19 2022 21:50:33 (UTC/GMT +8 hours)

DataFrame-interpolate() function

The interpolate() function is used to interpolate values according to different methods.

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

Syntax:

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

Parameters:

Name	Description	Type/Default Value	Required / Optional
method	Interpolation technique to use. One of: ‘linear’: Ignore the index and treat the values as equally spaced. This is the only method supported on MultiIndexes. ‘time’: Works on daily and higher resolution data to interpolate given length of interval. ‘index’, ‘values’: use the actual numerical values of the index. ‘pad’: Fill in NaNs using existing values. ‘nearest’, ‘zero’, ‘slinear’, ‘quadratic’, ‘cubic’, ‘spline’, ‘barycentric’, ‘polynomial’: Passed to scipy.interpolate.interp1d. These methods use the numerical values of the index. Both ‘polynomial’ and ‘spline’ require that you also specify an order (int), e.g. df.interpolate(method='polynomial', order=5). ‘krogh’, ‘piecewise_polynomial’, ‘spline’, ‘pchip’, ‘akima’: Wrappers around the SciPy interpolation methods of similar names. See Notes. ‘from_derivatives’: Refers to scipy.interpolate.BPoly.from_derivatives which replaces ‘piecewise_polynomial’ interpolation method in scipy 0.18.	str Default Value: ‘linear’	Required
axis	Axis to interpolate along.	{0 or ‘index’, 1 or ‘columns’, None} Default Value: None	Required
limit	Maximum number of consecutive NaNs to fill. Must be greater than 0.	int	Optional
inplace	Update the data in place if possible.	bool Default Value: False	Required
limit_direction	If limit is specified, consecutive NaNs will be filled in this direction.	{‘forward’, ‘backward’, ‘both’} Default Value: ‘forward’	Required
limit_area	If limit is specified, consecutive NaNs will be filled with this restriction. None: No fill restriction. ‘inside’: Only fill NaNs surrounded by valid values (interpolate). ‘outside’: Only fill NaNs outside valid values (extrapolate).	{None, ‘inside’, ‘outside’} Default Value: None	Required
downcast	Downcast dtypes if possible.	‘infer’ or None Default Value: None	Optional
**kwargs	Keyword arguments to pass on to the interpolating function		Required

Returns: Series or DataFrame
Returns the same object type as the caller, interpolated at some or all NaN values.

Example:

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