resample
- UnitsAwareDataArray.resample(indexer: Mapping[Any, str] | None = None, skipna: bool | None = None, closed: SideOptions | None = None, label: SideOptions | None = None, base: int | None = None, offset: pd.Timedelta | datetime.timedelta | str | None = None, origin: str | DatetimeLike = 'start_day', loffset: datetime.timedelta | str | None = None, restore_coord_dims: bool | None = None, **indexer_kwargs: str) DataArrayResample
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, 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.
base (int, optional) – For frequencies that evenly subdivide 1 day, the “origin” of the aggregated intervals. For example, for “24H” frequency, base could range from 0 through 23.
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.
loffset (timedelta or str, optional) –
Offset used to adjust the resampled time labels. Some pandas date offset strings are supported.
Deprecated since version 2023.03.0: Following pandas, the
loffsetparameter is deprecated in favor of using time offset arithmetic, and will be removed in a future version of xarray.restore_coord_dims (bool, optional) – If True, also restore the dimension order of multi-dimensional coordinates.
**indexer_kwargs (str) – The keyword arguments form of
indexer. One of indexer or indexer_kwargs must be provided.
- Returns:
resampled – This object resampled.
- Return type:
core.resample.DataArrayResample
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. , 1.03225806, 1.06451613, 1.09677419, 1.12903226, 1.16129032, 1.19354839, 1.22580645, 1.25806452, 1.29032258, 1.32258065, 1.35483871, 1.38709677, 1.41935484, 1.4516129 , 1.48387097, 1.51612903, 1.5483871 , 1.58064516, 1.61290323, 1.64516129, 1.67741935, 1.70967742, 1.74193548, 1.77419355, 1.80645161, 1.83870968, 1.87096774, 1.90322581, 1.93548387, 1.96774194, 2. , 2.03448276, 2.06896552, 2.10344828, 2.13793103, 2.17241379, 2.20689655, 2.24137931, 2.27586207, 2.31034483, 2.34482759, 2.37931034, 2.4137931 , 2.44827586, 2.48275862, 2.51724138, 2.55172414, 2.5862069 , 2.62068966, 2.65517241, 2.68965517, 2.72413793, 2.75862069, 2.79310345, 2.82758621, 2.86206897, 2.89655172, 2.93103448, 2.96551724, 3. , 3.03225806, 3.06451613, 3.09677419, 3.12903226, 3.16129032, 3.19354839, 3.22580645, 3.25806452, ... 7.87096774, 7.90322581, 7.93548387, 7.96774194, 8. , 8.03225806, 8.06451613, 8.09677419, 8.12903226, 8.16129032, 8.19354839, 8.22580645, 8.25806452, 8.29032258, 8.32258065, 8.35483871, 8.38709677, 8.41935484, 8.4516129 , 8.48387097, 8.51612903, 8.5483871 , 8.58064516, 8.61290323, 8.64516129, 8.67741935, 8.70967742, 8.74193548, 8.77419355, 8.80645161, 8.83870968, 8.87096774, 8.90322581, 8.93548387, 8.96774194, 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, nan, nan, nan, nan, 5., 5., 5., 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, 6., 6., 6., 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, 7., 7., 7., 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, 8., 8., 8., 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, 9., 9., 9., 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, 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.resampleReferences