xarray.Dataset.resample#

Dataset.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.