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)[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 ofNaN
values viaDataFrame.interpolate()
happens.- Parameters:
- 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.
- 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).
- downcastoptional, ‘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
and07:00:02
.