xarray.core.groupby.DataArrayGroupBy
xarray.core.groupby.DataArrayGroupBy¶
-
class
xarray.core.groupby.
DataArrayGroupBy
(obj, group, squeeze=False, grouper=None, bins=None, restore_coord_dims=True, cut_kwargs=None)[source]¶ -
__init__
(obj, group, squeeze=False, grouper=None, bins=None, restore_coord_dims=True, cut_kwargs=None)[source]¶ Create a GroupBy object
- Parameters
group (
DataArray
) – Array with the group values.squeeze (
bool
, 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 (
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
, optional) – Extra keyword arguments to pass to pandas.cut
Methods
__init__
(obj, group[, squeeze, grouper, …])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[, shortcut, args])Backward compatible implementation of
map
assign_coords
([coords])Assign coordinates by group.
count
([dim, keep_attrs])Reduce this DataArray’s data by applying
count
along some dimension(s).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
last
([skipna, keep_attrs])Return the last element of each group along the group dimension
map
(func[, shortcut, args])Apply a function to each array in the group and concatenate them together into a new array.
max
([dim, skipna, keep_attrs])Reduce this DataArray’s data by applying
max
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
along some dimension(s).prod
([dim, skipna, min_count, keep_attrs])Reduce this DataArray’s data by applying
prod
along some dimension(s).quantile
(q[, dim, interpolation, …])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, skipna, keep_attrs])Reduce this DataArray’s data by applying
std
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, keep_attrs])Reduce this DataArray’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.
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