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
ofHashable
tostr
,datetime.timedelta
,pd.Timedelta
,pd.DateOffset
, orResampler
, 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
, orcftime.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
, orstr
, defaultis 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
, orResampler
) – The keyword arguments form ofindexer
. 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
References