# xarray.core.resample.DatasetResample¶

class xarray.core.resample.DatasetResample(*args, dim=None, resample_dim=None, **kwargs)

DatasetGroupBy object specialized to resampling a specified dimension

__init__(self, *args, dim=None, resample_dim=None, **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.

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

• cut_kwargs (dict, optional) – Extra keyword arguments to pass to pandas.cut

Methods

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

Attributes

 groups