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 (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__(*args[, dim, resample_dim]) Create a GroupBy object
all([dim]) Reduce this DatasetResample’s data by applying all along some dimension(s).
any([dim]) Reduce this DatasetResample’s data by applying any along some dimension(s).
apply(func[, args, shortcut]) Backward compatible implementation of map
argmax([dim, skipna]) Reduce this DatasetResample’s data by applying argmax along some dimension(s).
argmin([dim, 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([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]) Reduce this DatasetResample’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
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]) Reduce this DatasetResample’s data by applying max along some dimension(s).
mean([dim, skipna]) Reduce this DatasetResample’s data by applying mean along some dimension(s).
median([dim, skipna]) Reduce this DatasetResample’s data by applying median along some dimension(s).
min([dim, skipna]) Reduce this DatasetResample’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]) Reduce this DatasetResample’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, keep_attrs]) Reduce the items in this group by applying func along the pre-defined resampling dimension.
std([dim, skipna]) Reduce this DatasetResample’s data by applying std along some dimension(s).
sum([dim, skipna]) Reduce this DatasetResample’s data by applying sum along some dimension(s).
var([dim, 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

dims
groups