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

Create a GroupBy object

Parameters

• obj (Dataset or DataArray) – Object to group.

• group (Hashable, DataArray or Index) – Array with the group values or name of the variable.

• squeeze (bool, default: False) – 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 (pandas.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, default: True) – If True, also restore the dimension order of multi-dimensional coordinates.

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

Methods

__init__(*args[, dim, resample_dim])	Create a GroupBy object
all([dim, keep_attrs])	Reduce this Dataset's data by applying all along some dimension(s).
any([dim, keep_attrs])	Reduce this Dataset's data by applying any along some dimension(s).
apply(func[, args, shortcut])	Backward compatible implementation of map
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([coords])	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, keep_attrs])	Reduce this Dataset's data by applying count along some dimension(s).
cumprod([dim, skipna, keep_attrs])	Reduce this Dataset's data by applying cumprod along some dimension(s).
cumsum([dim, skipna, keep_attrs])	Reduce this Dataset's data by applying cumsum 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
map(func[, args, shortcut])	Apply a function over each Dataset in the groups generated for resampling and concatenate them together into a new Dataset.
max([dim, skipna, keep_attrs])	Reduce this Dataset's data by applying max along some dimension(s).
mean([dim, skipna, keep_attrs])	Reduce this Dataset's data by applying mean along some dimension(s).
median([dim, skipna, keep_attrs])	Reduce this Dataset's data by applying median along some dimension(s).
min([dim, skipna, keep_attrs])	Reduce this Dataset'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, skipna, min_count, keep_attrs])	Reduce this Dataset's data by applying prod along some dimension(s).
quantile(q[, dim, method, 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, ...])	Reduce the items in this group by applying func along the pre-defined resampling dimension.
std([dim, skipna, ddof, keep_attrs])	Reduce this Dataset's data by applying std along some dimension(s).
sum([dim, skipna, min_count, keep_attrs])	Reduce this Dataset's data by applying sum along some dimension(s).
var([dim, skipna, ddof, keep_attrs])	Reduce this Dataset's data by applying var along some dimension(s).
where(cond[, other])	Return elements from self or other depending on cond.

Attributes

dims
groups	Mapping from group labels to indices.
sizes	Ordered mapping from dimension names to lengths.