xarray.core.resample.DataArrayResample

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

DataArrayGroupBy object specialized to time resampling operations.

The resampling is done over a specified dimension.

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

Create a GroupBy object.

Parameters:

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

• groupers (tuple of ResolvedGrouper) – The resolved groupers to group by.

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

Methods

__init__(*args[, dim, resample_dim])	Create a GroupBy object.
all([dim, keep_attrs])	Reduce this DataArray's data by applying all along some dimension(s).
any([dim, keep_attrs])	Reduce this DataArray'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.
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 DataArray's data by applying count along some dimension(s).
cumprod([dim, skipna, keep_attrs])	Reduce this DataArray's data by applying cumprod (i.e., cumulative product) along some dimension(s).
cumsum([dim, skipna, keep_attrs])	Reduce this DataArray's data by applying cumsum (i.e., cumulative sum) 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 to each array in the group and concatenate the results.
max([dim, skipna, keep_attrs])	Reduce this DataArray's data by applying max (i.e., maximum) along some dimension(s).
mean([dim, skipna, keep_attrs])	Reduce this DataArray's data by applying mean along some dimension(s).
median([dim, skipna, keep_attrs])	Reduce this DataArray's data by applying median along some dimension(s).
min([dim, skipna, keep_attrs])	Reduce this DataArray's data by applying min (i.e., minimum) along some dimension(s).
nearest([tolerance])	Take new values from nearest original coordinate to up-sampled coordinates.
pad([tolerance])	Forward fill new values at up-sampled frequency.
prod([dim, skipna, min_count, keep_attrs])	Reduce this DataArray's data by applying prod (i.e., product) along some dimension(s).
quantile(q[, dim, method, keep_attrs, skipna])	Compute the qth quantile over each array in the groups.
reduce(func[, dim, axis, keep_attrs, shortcut])	Reduce the items in this group by applying func along the resampling dim.
shuffle_to_chunks([chunks])	Sort or "shuffle" the underlying object.
std([dim, skipna, ddof, keep_attrs])	Reduce this DataArray's data by applying std (i.e., standard deviation) along some dimension(s).
sum([dim, skipna, min_count, keep_attrs])	Reduce this DataArray's data by applying sum along some dimension(s).
var([dim, skipna, ddof, keep_attrs])	Reduce this DataArray's data by applying var (i.e., variance) along some dimension(s).
where(cond[, other])	Return elements from self or other depending on cond.

Attributes

dims
encoded
group1d
groupers
groups	Mapping from group labels to indices.
sizes	Ordered mapping from dimension names to lengths.