# xarray.core.resample.DataArrayResample¶

class xarray.core.resample.DataArrayResample(*args, **kwargs)

DataArrayGroupBy object specialized to time resampling operations over a specified dimension

__init__(*args, **kwargs)

Create a GroupBy object

Parameters: obj : Dataset or DataArray Object to group. group : DataArray Array with the group values. squeeze : boolean, optional If “group” is a coordinate of object, squeeze controls whether the subarrays have a dimension of length 1 along that coordinate or if the dimension is squeezed out. grouper : pd.Grouper, optional Used for grouping values along the group array. bins : array-like, optional If bins is specified, the groups will be discretized into the specified bins by pandas.cut. cut_kwargs : dict, optional Extra keyword arguments to pass to pandas.cut

Methods

 __init__(*args, **kwargs) Create a GroupBy object all([dim, axis, keep_attrs]) Reduce this DataArrayResample’s data by applying all along some dimension(s). any([dim, axis, keep_attrs]) Reduce this DataArrayResample’s data by applying any along some dimension(s). apply(func[, shortcut]) Apply a function over each array in the group and concatenate them together into a new array. argmax([dim, axis, skipna, keep_attrs]) Reduce this DataArrayResample’s data by applying argmax along some dimension(s). argmin([dim, axis, skipna, keep_attrs]) Reduce this DataArrayResample’s data by applying argmin along some dimension(s). asfreq() Return values of original object at the new up-sampling frequency; essentially a re-index with new times set to NaN. assign_coords(**kwargs) Assign coordinates by group. backfill() Backward fill new values at up-sampled frequency. bfill() Backward fill new values at up-sampled frequency. count([dim, axis, keep_attrs]) Reduce this DataArrayResample’s data by applying count along some dimension(s). ffill() Forward fill new values at up-sampled frequency. fillna(value) Fill missing values in this object by group. first([skipna, keep_attrs]) Return the first element of each group along the group dimension interpolate([kind]) Interpolate up-sampled data using the original data as knots. last([skipna, keep_attrs]) Return the last element of each group along the group dimension max([dim, axis, skipna, keep_attrs]) Reduce this DataArrayResample’s data by applying max along some dimension(s). mean([dim, axis, skipna, keep_attrs]) Reduce this DataArrayResample’s data by applying mean along some dimension(s). median([dim, axis, skipna, keep_attrs]) Reduce this DataArrayResample’s data by applying median along some dimension(s). min([dim, axis, skipna, keep_attrs]) Reduce this DataArrayResample’s data by applying min along some dimension(s). nearest() Take new values from nearest original coordinate to up-sampled frequency coordinates. pad() Forward fill new values at up-sampled frequency. prod([dim, axis, skipna, keep_attrs]) Reduce this DataArrayResample’s data by applying prod along some dimension(s). reduce(func[, dim, axis, keep_attrs, shortcut]) Reduce the items in this group by applying func along some dimension(s). std([dim, axis, skipna, keep_attrs]) Reduce this DataArrayResample’s data by applying std along some dimension(s). sum([dim, axis, skipna, keep_attrs]) Reduce this DataArrayResample’s data by applying sum along some dimension(s). var([dim, axis, skipna, keep_attrs]) Reduce this DataArrayResample’s data by applying var along some dimension(s). where(cond[, other]) Return elements from self or other depending on cond.

Attributes

 groups