Pandas DataFrame: interpolate() function

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.


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


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.
Default Value: ‘linear’
axis    Axis to interpolate along. {0 or ‘index’, 1 or ‘columns’, None}
Default Value: None
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
limit_direction    If limit is specified, consecutive NaNs will be filled in this direction.  {‘forward’, ‘backward’, ‘both’}
Default Value: ‘forward’
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
downcast  Downcast dtypes if possible. ‘infer’ or None
Default Value: None
**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.


Download the Pandas DataFrame Notebooks from here.

Previous: DataFrame-replace() function
Next: DataFrame - droplevel() function

Follow us on Facebook and Twitter for latest update.