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# pandas.core.resample.Resampler.interpolate

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

  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.

  Parameters:
    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’, ‘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.

    limit int, optional

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

    inplace bool, default False

      Update the data in place if possible.

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

      Consecutive NaNs will be filled in this direction.

      If limit is specified:

        - If ‘method’ is ‘pad’ or ‘ffill’, ‘limit_direction’ must be ‘forward’.

        - If ‘method’ is ‘backfill’ or ‘bfill’, ‘limit_direction’ must be ‘backwards’.

      If ‘limit’ is not specified:

        - If ‘method’ is ‘backfill’ or ‘bfill’, the default is ‘backward’

        - else the default is ‘forward’

        raises ValueError if limit_direction is ‘forward’ or ‘both’ and

          method is ‘backfill’ or ‘bfill’.

        raises ValueError if limit_direction is ‘backward’ or ‘both’ and

          method is ‘pad’ or ‘ffill’.

    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).

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

      Downcast dtypes if possible.

      Deprecated since version 2.1.0.

    ``**kwargs`` optional

      Keyword arguments to pass on to the interpolating function.

  Returns:
    DataFrame or Series

      Interpolated values at the specified freq.

  See also

  `core.resample.Resampler.asfreq`

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

  `DataFrame.interpolate`

    Fill NaN values using an interpolation method.

  Notes

  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.

  Examples

      >>> 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

  Upsample 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

  Downsample 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 the reindexed timestamps (anchors). Since not all datapoints from original series become anchors, it can lead to misleading interpolation results 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    1.2
      2023-03-01 07:00:00.800    1.4
      2023-03-01 07:00:01.200    1.6
      2023-03-01 07:00:01.600    1.8
      2023-03-01 07:00:02.000    2.0
      2023-03-01 07:00:02.400    2.2
      2023-03-01 07:00:02.800    2.4
      2023-03-01 07:00:03.200    2.6
      2023-03-01 07:00:03.600    2.8
      2023-03-01 07:00:04.000    3.0
      Freq: 400ms, dtype: float64

  Note that the series erroneously increases between two anchors `07:00:00` and `07:00:02`.