final Resampler.interpolate(method='linear', *, axis=0, limit=None, inplace=False, limit_direction='forward', limit_area=None, downcast=<no_default>, **kwargs)[source]#

Interpolate values between target timestamps according to different methods.

The original index is first reindexed to target timestamps (see core.resample.Resampler.asfreq()), then the interpolation of NaN values via DataFrame.interpolate() happens.

methodstr, 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’, ‘barycentric’, ‘polynomial’: Passed to scipy.interpolate.interp1d, whereas ‘spline’ is passed to scipy.interpolate.UnivariateSpline. 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). Note that, slinear method in Pandas refers to the Scipy first order spline instead of Pandas first order spline.

  • ‘krogh’, ‘piecewise_polynomial’, ‘spline’, ‘pchip’, ‘akima’, ‘cubicspline’: Wrappers around the SciPy interpolation methods of similar names. See Notes.

  • ‘from_derivatives’: Refers to scipy.interpolate.BPoly.from_derivatives.

axis{{0 or ‘index’, 1 or ‘columns’, None}}, default None

Axis to interpolate along. For Series this parameter is unused and defaults to 0.

limitint, optional

Maximum number of consecutive NaNs to fill. Must be greater than 0.

inplacebool, default False

Update the data in place if possible.

limit_direction{{‘forward’, ‘backward’, ‘both’}}, Optional

Consecutive NaNs will be filled in this direction.

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).

downcastoptional, ‘infer’ or None, defaults to None

Downcast dtypes if possible.

Deprecated since version 2.1.0.


Keyword arguments to pass on to the interpolating function.

DataFrame or Series

Interpolated values at the specified freq.

See also


Return the values at the new freq, essentially a reindex.


Fill NaN values using an interpolation method.


Backward fill NaN values in the resampled data.


Forward fill NaN values.


For high-frequent or non-equidistant time-series with timestamps the reindexing followed by interpolation may lead to information loss as shown in the last example.


>>> start = "2023-03-01T07:00:00"
>>> timesteps = pd.date_range(start, periods=5, freq="s")
>>> series = pd.Series(data=[1, -1, 2, 1, 3], index=timesteps)
>>> series
2023-03-01 07:00:00    1
2023-03-01 07:00:01   -1
2023-03-01 07:00:02    2
2023-03-01 07:00:03    1
2023-03-01 07:00:04    3
Freq: s, dtype: int64

Downsample the dataframe to 0.5Hz by providing the period time of 2s.

>>> series.resample("2s").interpolate("linear")
2023-03-01 07:00:00    1
2023-03-01 07:00:02    2
2023-03-01 07:00:04    3
Freq: 2s, dtype: int64

Upsample the dataframe to 2Hz by providing the period time of 500ms.

>>> series.resample("500ms").interpolate("linear")
2023-03-01 07:00:00.000    1.0
2023-03-01 07:00:00.500    0.0
2023-03-01 07:00:01.000   -1.0
2023-03-01 07:00:01.500    0.5
2023-03-01 07:00:02.000    2.0
2023-03-01 07:00:02.500    1.5
2023-03-01 07:00:03.000    1.0
2023-03-01 07:00:03.500    2.0
2023-03-01 07:00:04.000    3.0
Freq: 500ms, dtype: float64

Internal reindexing with asfreq() prior to interpolation leads to an interpolated timeseries on the basis of the reindexed timestamps (anchors). It is assured that all available datapoints from original series become anchors, so it also works for resampling-cases that lead to non-aligned timestamps, as in the following example:

>>> series.resample("400ms").interpolate("linear")
2023-03-01 07:00:00.000    1.0
2023-03-01 07:00:00.400    0.2
2023-03-01 07:00:00.800   -0.6
2023-03-01 07:00:01.200   -0.4
2023-03-01 07:00:01.600    0.8
2023-03-01 07:00:02.000    2.0
2023-03-01 07:00:02.400    1.6
2023-03-01 07:00:02.800    1.2
2023-03-01 07:00:03.200    1.4
2023-03-01 07:00:03.600    2.2
2023-03-01 07:00:04.000    3.0
Freq: 400ms, dtype: float64

Note that the series correctly decreases between two anchors 07:00:00 and 07:00:02.