# pandas.Series.interpolate¶

Interpolate values according to different methods.

Please note that only `method='linear'`

is supported for
DataFrame/Series with a MultiIndex.

- param method
- str, default ‘linear’
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.

New in version 0.18.1.

Added support for the ‘akima’ method. Added interpolate method ‘from_derivatives’ which replaces ‘piecewise_polynomial’ in SciPy 0.18; backwards-compatible with SciPy < 0.18

- param axis
- {0 or ‘index’, 1 or ‘columns’, None}, default None
Axis to interpolate along.

- param limit
- int, optional
Maximum number of consecutive NaNs to fill. Must be greater than 0.

- param inplace
- bool, default False
Update the data in place if possible.

- param limit_direction
- {‘forward’, ‘backward’, ‘both’}, default ‘forward’
If limit is specified, consecutive NaNs will be filled in this direction.

- param limit_area
- {None, ‘inside’, ‘outside’}, default None
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).

New in version 0.23.0.

- param downcast
- optional, ‘infer’ or None, defaults to None
Downcast dtypes if possible. **kwargs Keyword arguments to pass on to the interpolating function.

- return
Series or DataFrame Returns the same object type as the caller, interpolated at some or all

`NaN`

values.

Warning

This feature is currently unsupported by Intel Scalable Dataframe Compiler