xarray.core.resample.DatasetResample

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

DatasetGroupBy object specialized to resampling 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, keep_attrs]) Reduce this DatasetResample’s data by applying all along some dimension(s).
any([dim, keep_attrs]) Reduce this DatasetResample’s data by applying any along some dimension(s).
apply(func, **kwargs) Apply a function over each Dataset in the groups generated for resampling and concatenate them together into a new Dataset.
argmax([dim, keep_attrs, skipna]) Reduce this DatasetResample’s data by applying argmax along some dimension(s).
argmin([dim, keep_attrs, skipna]) Reduce this DatasetResample’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(**kwargs) Assign data variables by group.
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, keep_attrs]) Reduce this DatasetResample’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, keep_attrs, skipna]) Reduce this DatasetResample’s data by applying max along some dimension(s).
mean([dim, keep_attrs, skipna]) Reduce this DatasetResample’s data by applying mean along some dimension(s).
median([dim, keep_attrs, skipna]) Reduce this DatasetResample’s data by applying median along some dimension(s).
min([dim, keep_attrs, skipna]) Reduce this DatasetResample’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, keep_attrs, skipna]) Reduce this DatasetResample’s data by applying prod along some dimension(s).
reduce(func[, dim, keep_attrs]) Reduce the items in this group by applying func along the pre-defined resampling dimension.
std([dim, keep_attrs, skipna]) Reduce this DatasetResample’s data by applying std along some dimension(s).
sum([dim, keep_attrs, skipna]) Reduce this DatasetResample’s data by applying sum along some dimension(s).
var([dim, keep_attrs, skipna]) Reduce this DatasetResample’s data by applying var along some dimension(s).
where(cond[, other]) Return elements from self or other depending on cond.

Attributes

groups