xarray.core.resample.DataArrayResample

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

DataArrayGroupBy object specialized to time resampling operations over a specified dimension

__init__(*args, dim=None, resample_dim=None, **kwargs)

Create a GroupBy object

Parameters

objDataset or DataArray

Object to group.

groupDataArray

Array with the group values.

squeezeboolean, 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.

grouperpd.Grouper, optional

Used for grouping values along the group array.

binsarray-like, optional

If bins is specified, the groups will be discretized into the specified bins by pandas.cut.

restore_coord_dimsbool, optional

If True, also restore the dimension order of multi-dimensional coordinates.

cut_kwargsdict, optional

Extra keyword arguments to pass to pandas.cut

Methods

__init__(*args[, dim, resample_dim])	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, args])	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([tolerance])	Backward fill new values at up-sampled frequency.
bfill([tolerance])	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([tolerance])	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([tolerance])	Take new values from nearest original coordinate to up-sampled frequency coordinates.
pad([tolerance])	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).
quantile(q[, dim, interpolation, keep_attrs])	Compute the qth quantile over each array in the groups and concatenate them together into a new array.
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