xarray.core.resample.DatasetResample
xarray.core.resample.DatasetResample¶
-
class
xarray.core.resample.
DatasetResample
(*args, dim=None, resample_dim=None, **kwargs)[source]¶ DatasetGroupBy object specialized to resampling a specified dimension
-
__init__
(*args, dim=None, resample_dim=None, **kwargs)[source]¶ Initialize self. See help(type(self)) for accurate signature.
Methods
__init__
(*args[, dim, resample_dim])Initialize self.
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).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, keep_attrs])Reduce the items in this group by applying func along the pre-defined resampling dimension.
std
([dim, skipna, 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, 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
Mapping from group labels to indices.
-