xarray.DataArray.resample

xarray.DataArray.resample#

DataArray.resample(indexer=None, *, skipna=None, closed=None, label=None, offset=None, origin='start_day', restore_coord_dims=None, **indexer_kwargs)[source]#

Returns a Resample object for performing resampling operations.

Handles both downsampling and upsampling. The resampled dimension must be a datetime-like coordinate. If any intervals contain no values from the original object, they will be given the value NaN.

Parameters:

  • indexer (Mapping of Hashable to str, datetime.timedelta, pd.Timedelta, pd.DateOffset, or Resampler, optional) – Mapping from the dimension name to resample frequency [1]. The dimension must be datetime-like.

  • skipna (bool, optional) – Whether to skip missing values when aggregating in downsampling.

  • closed ({"left", "right"}, optional) – Side of each interval to treat as closed.

  • label ({"left", "right"}, optional) – Side of each interval to use for labeling.

  • origin ({'epoch', 'start', 'start_day', 'end', 'end_day'}, pd.Timestamp, datetime.datetime, np.datetime64, or cftime.datetime, default 'start_day') – The datetime on which to adjust the grouping. The timezone of origin must match the timezone of the index.

    If a datetime is not used, these values are also supported: - ‘epoch’: origin is 1970-01-01 - ‘start’: origin is the first value of the timeseries - ‘start_day’: origin is the first day at midnight of the timeseries - ‘end’: origin is the last value of the timeseries - ‘end_day’: origin is the ceiling midnight of the last day

  • offset (pd.Timedelta, datetime.timedelta, or str, default is None) – An offset timedelta added to the origin.

  • restore_coord_dims (bool, optional) – If True, also restore the dimension order of multi-dimensional coordinates.

  • **indexer_kwargs (str, datetime.timedelta, pd.Timedelta, pd.DateOffset, or Resampler) – The keyword arguments form of indexer. One of indexer or indexer_kwargs must be provided.

Returns:

resampled (core.resample.DataArrayResample) – This object resampled.

Examples

Downsample monthly time-series data to seasonal data:

>>> da = xr.DataArray(
...     np.linspace(0, 11, num=12),
...     coords=[
...         pd.date_range(
...             "1999-12-15",
...             periods=12,
...             freq=pd.DateOffset(months=1),
...         )
...     ],
...     dims="time",
... )
>>> da
<xarray.DataArray (time: 12)> Size: 96B
array([ 0.,  1.,  2.,  3.,  4.,  5.,  6.,  7.,  8.,  9., 10., 11.])
Coordinates:
  * time     (time) datetime64[ns] 96B 1999-12-15 2000-01-15 ... 2000-11-15
>>> da.resample(time="QS-DEC").mean()
<xarray.DataArray (time: 4)> Size: 32B
array([ 1.,  4.,  7., 10.])
Coordinates:
  * time     (time) datetime64[ns] 32B 1999-12-01 2000-03-01 ... 2000-09-01

Upsample monthly time-series data to daily data:

>>> da.resample(time="1D").interpolate("linear")  # +doctest: ELLIPSIS
<xarray.DataArray (time: 337)> Size: 3kB
array([ 0.        ,  0.03225806,  0.06451613,  0.09677419,  0.12903226,
        0.16129032,  0.19354839,  0.22580645,  0.25806452,  0.29032258,
        0.32258065,  0.35483871,  0.38709677,  0.41935484,  0.4516129 ,
        0.48387097,  0.51612903,  0.5483871 ,  0.58064516,  0.61290323,
        0.64516129,  0.67741935,  0.70967742,  0.74193548,  0.77419355,
        0.80645161,  0.83870968,  0.87096774,  0.90322581,  0.93548387,
        0.96774194,  1.        ,  ...,
        9.        ,  9.03333333,  9.06666667,  9.1       ,  9.13333333,
        9.16666667,  9.2       ,  9.23333333,  9.26666667,  9.3       ,
        9.33333333,  9.36666667,  9.4       ,  9.43333333,  9.46666667,
        9.5       ,  9.53333333,  9.56666667,  9.6       ,  9.63333333,
        9.66666667,  9.7       ,  9.73333333,  9.76666667,  9.8       ,
        9.83333333,  9.86666667,  9.9       ,  9.93333333,  9.96666667,
       10.        , 10.03225806, 10.06451613, 10.09677419, 10.12903226,
       10.16129032, 10.19354839, 10.22580645, 10.25806452, 10.29032258,
       10.32258065, 10.35483871, 10.38709677, 10.41935484, 10.4516129 ,
       10.48387097, 10.51612903, 10.5483871 , 10.58064516, 10.61290323,
       10.64516129, 10.67741935, 10.70967742, 10.74193548, 10.77419355,
       10.80645161, 10.83870968, 10.87096774, 10.90322581, 10.93548387,
       10.96774194, 11.        ])
Coordinates:
  * time     (time) datetime64[ns] 3kB 1999-12-15 1999-12-16 ... 2000-11-15

Limit scope of upsampling method

>>> da.resample(time="1D").nearest(tolerance="1D")
<xarray.DataArray (time: 337)> Size: 3kB
array([ 0.,  0., nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
       nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
       nan, nan, nan, nan,  1.,  1.,  1., nan, nan, nan, nan, nan, nan,
       nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
       nan, nan, nan, nan, nan, nan, nan, nan, nan,  2.,  2.,  2., nan,
       nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
       nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,  3.,
        3.,  3., nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
       nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
       nan, nan, nan, nan,  4.,  4.,  4., nan, nan, nan, nan, nan, ...,
       nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
       nan, nan, nan, nan, nan, 10., 10., 10., nan, nan, nan, nan, nan,
       nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
       nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 11., 11.])
Coordinates:
  * time     (time) datetime64[ns] 3kB 1999-12-15 1999-12-16 ... 2000-11-15

See also

Dataset.resample, pandas.Series.resample, pandas.DataFrame.resample

References