downcast : optional, 'infer' or None, defaults to None
            Downcast dtypes if possible.

            .. deprecated::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
        --------

        >>> import datetime as dt
        >>> timesteps = [
        ...    dt.datetime(2023, 3, 1, 7, 0, 0),
        ...    dt.datetime(2023, 3, 1, 7, 0, 1),
        ...    dt.datetime(2023, 3, 1, 7, 0, 2),
        ...    dt.datetime(2023, 3, 1, 7, 0, 3),
        ...    dt.datetime(2023, 3, 1, 7, 0, 4)]
        >>> 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
        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: 500L, dtype: float64

        Internal reindexing with ``as_freq()`` 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: 400L, dtype: float64

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