from __future__ import annotations import copy import functools import itertools import warnings from collections.abc import Callable, Hashable, Iterator, Mapping, Sequence from dataclasses import dataclass, field from typing import TYPE_CHECKING, Any, Generic, Literal, Union, cast import numpy as np import pandas as pd from packaging.version import Version from xarray.computation import ops from xarray.computation.arithmetic import ( DataArrayGroupbyArithmetic, DatasetGroupbyArithmetic, ) from xarray.core import dtypes, duck_array_ops, nputils from xarray.core._aggregations import ( DataArrayGroupByAggregations, DatasetGroupByAggregations, ) from xarray.core.common import ImplementsArrayReduce, ImplementsDatasetReduce from xarray.core.coordinates import Coordinates, coordinates_from_variable from xarray.core.duck_array_ops import where from xarray.core.formatting import format_array_flat from xarray.core.indexes import ( PandasMultiIndex, filter_indexes_from_coords, ) from xarray.core.options import OPTIONS, _get_keep_attrs from xarray.core.types import ( Dims, QuantileMethods, T_DataArray, T_DataWithCoords, T_Xarray, ) from xarray.core.utils import ( FrozenMappingWarningOnValuesAccess, contains_only_chunked_or_numpy, either_dict_or_kwargs, emit_user_level_warning, hashable, is_scalar, maybe_wrap_array, module_available, peek_at, ) from xarray.core.variable import IndexVariable, Variable from xarray.namedarray.pycompat import is_chunked_array from xarray.structure.alignment import align, broadcast from xarray.structure.concat import concat from xarray.structure.merge import merge_coords if TYPE_CHECKING: from numpy.typing import ArrayLike from xarray.core.dataarray import DataArray from xarray.core.dataset import Dataset from xarray.core.types import ( GroupIndex, GroupIndices, GroupInput, GroupKey, T_Chunks, ) from xarray.core.utils import Frozen from xarray.groupers import EncodedGroups, Grouper def check_reduce_dims(reduce_dims, dimensions): if reduce_dims is not ...: if is_scalar(reduce_dims): reduce_dims = [reduce_dims] if any(dim not in dimensions for dim in reduce_dims): raise ValueError( f"cannot reduce over dimensions {reduce_dims!r}. expected either '...' " f"to reduce over all dimensions or one or more of {dimensions!r}. " f"Alternatively, install the `flox` package. " ) def _codes_to_group_indices(codes: np.ndarray, N: int) -> GroupIndices: """Converts integer codes for groups to group indices.""" assert codes.ndim == 1 groups: GroupIndices = tuple([] for _ in range(N)) for n, g in enumerate(codes): if g >= 0: groups[g].append(n) return groups def _dummy_copy(xarray_obj): from xarray.core.dataarray import DataArray from xarray.core.dataset import Dataset if isinstance(xarray_obj, Dataset): res = Dataset( { k: dtypes.get_fill_value(v.dtype) for k, v in xarray_obj.data_vars.items() }, { k: dtypes.get_fill_value(v.dtype) for k, v in xarray_obj.coords.items() if k not in xarray_obj.dims }, xarray_obj.attrs, ) elif isinstance(xarray_obj, DataArray): res = DataArray( dtypes.get_fill_value(xarray_obj.dtype), { k: dtypes.get_fill_value(v.dtype) for k, v in xarray_obj.coords.items() if k not in xarray_obj.dims }, dims=[], name=xarray_obj.name, attrs=xarray_obj.attrs, ) else: # pragma: no cover raise AssertionError return res def _is_one_or_none(obj) -> bool: return obj == 1 or obj is None def _consolidate_slices(slices: list[slice]) -> list[slice]: """Consolidate adjacent slices in a list of slices.""" result: list[slice] = [] last_slice = slice(None) for slice_ in slices: if not isinstance(slice_, slice): raise ValueError(f"list element is not a slice: {slice_!r}") if ( result and last_slice.stop == slice_.start and _is_one_or_none(last_slice.step) and _is_one_or_none(slice_.step) ): last_slice = slice(last_slice.start, slice_.stop, slice_.step) result[-1] = last_slice else: result.append(slice_) last_slice = slice_ return result def _inverse_permutation_indices(positions, N: int | None = None) -> np.ndarray | None: """Like inverse_permutation, but also handles slices. Parameters ---------- positions : list of ndarray or slice If slice objects, all are assumed to be slices. Returns ------- np.ndarray of indices or None, if no permutation is necessary. """ if not positions: return None if isinstance(positions[0], slice): positions = _consolidate_slices(positions) if positions == slice(None): return None positions = [np.arange(sl.start, sl.stop, sl.step) for sl in positions] newpositions = nputils.inverse_permutation(np.concatenate(positions), N) return newpositions[newpositions != -1] class _DummyGroup(Generic[T_Xarray]): """Class for keeping track of grouped dimensions without coordinates. Should not be user visible. """ __slots__ = ("coords", "dataarray", "name", "size") def __init__(self, obj: T_Xarray, name: Hashable, coords) -> None: self.name = name self.coords = coords self.size = obj.sizes[name] @property def dims(self) -> tuple[Hashable]: return (self.name,) @property def ndim(self) -> Literal[1]: return 1 @property def values(self) -> range: return range(self.size) @property def data(self) -> np.ndarray: return np.arange(self.size, dtype=int) def __array__( self, dtype: np.typing.DTypeLike = None, /, *, copy: bool | None = None ) -> np.ndarray: if copy is False: raise NotImplementedError(f"An array copy is necessary, got {copy = }.") return np.arange(self.size) @property def shape(self) -> tuple[int, ...]: return (self.size,) @property def attrs(self) -> dict: return {} def __getitem__(self, key): if isinstance(key, tuple): (key,) = key return self.values[key] def to_index(self) -> pd.Index: # could be pd.RangeIndex? return pd.Index(np.arange(self.size)) def copy(self, deep: bool = True, data: Any = None): raise NotImplementedError def to_dataarray(self) -> DataArray: from xarray.core.dataarray import DataArray return DataArray( data=self.data, dims=(self.name,), coords=self.coords, name=self.name ) def to_array(self) -> DataArray: """Deprecated version of to_dataarray.""" return self.to_dataarray() T_Group = Union["T_DataArray", _DummyGroup] def _ensure_1d( group: T_Group, obj: T_DataWithCoords ) -> tuple[ T_Group, T_DataWithCoords, Hashable | None, list[Hashable], ]: # 1D cases: do nothing if isinstance(group, _DummyGroup) or group.ndim == 1: return group, obj, None, [] from xarray.core.dataarray import DataArray if isinstance(group, DataArray): for dim in set(group.dims) - set(obj.dims): obj = obj.expand_dims(dim) # try to stack the dims of the group into a single dim orig_dims = group.dims stacked_dim = "stacked_" + "_".join(map(str, orig_dims)) # these dimensions get created by the stack operation inserted_dims = [dim for dim in group.dims if dim not in group.coords] # `newgroup` construction is optimized so we don't create an index unnecessarily, # or stack any non-dim coords unnecessarily newgroup = DataArray(group.variable.stack({stacked_dim: orig_dims})) newobj = obj.stack({stacked_dim: orig_dims}) return newgroup, newobj, stacked_dim, inserted_dims raise TypeError(f"group must be DataArray or _DummyGroup, got {type(group)!r}.") @dataclass class ResolvedGrouper(Generic[T_DataWithCoords]): """ Wrapper around a Grouper object. The Grouper object represents an abstract instruction to group an object. The ResolvedGrouper object is a concrete version that contains all the common logic necessary for a GroupBy problem including the intermediates necessary for executing a GroupBy calculation. Specialization to the grouping problem at hand, is accomplished by calling the `factorize` method on the encapsulated Grouper object. This class is private API, while Groupers are public. """ grouper: Grouper group: T_Group obj: T_DataWithCoords eagerly_compute_group: Literal[False] | None = field(repr=False, default=None) # returned by factorize: encoded: EncodedGroups = field(init=False, repr=False) @property def full_index(self) -> pd.Index: return self.encoded.full_index @property def codes(self) -> DataArray: return self.encoded.codes @property def unique_coord(self) -> Variable | _DummyGroup: return self.encoded.unique_coord def __post_init__(self) -> None: # This copy allows the BinGrouper.factorize() method # to update BinGrouper.bins when provided as int, using the output # of pd.cut # We do not want to modify the original object, since the same grouper # might be used multiple times. from xarray.groupers import BinGrouper, UniqueGrouper self.grouper = copy.deepcopy(self.grouper) self.group = _resolve_group(self.obj, self.group) if self.eagerly_compute_group: raise ValueError( f""""Eagerly computing the DataArray you're grouping by ({self.group.name!r}) " has been removed. Please load this array's data manually using `.compute` or `.load`. To intentionally avoid eager loading, either (1) specify `.groupby({self.group.name}=UniqueGrouper(labels=...))` or (2) pass explicit bin edges using ``bins`` or `.groupby({self.group.name}=BinGrouper(bins=...))`; as appropriate.""" ) if self.eagerly_compute_group is not None: emit_user_level_warning( "Passing `eagerly_compute_group` is now deprecated. It has no effect.", DeprecationWarning, ) if not isinstance(self.group, _DummyGroup) and is_chunked_array( self.group.variable._data ): # This requires a pass to discover the groups present if isinstance(self.grouper, UniqueGrouper) and self.grouper.labels is None: raise ValueError( "Please pass `labels` to UniqueGrouper when grouping by a chunked array." ) # this requires a pass to compute the bin edges if isinstance(self.grouper, BinGrouper) and isinstance( self.grouper.bins, int ): raise ValueError( "Please pass explicit bin edges to BinGrouper using the ``bins`` kwarg" "when grouping by a chunked array." ) self.encoded = self.grouper.factorize(self.group) @property def name(self) -> Hashable: """Name for the grouped coordinate after reduction.""" # the name has to come from unique_coord because we need `_bins` suffix for BinGrouper (name,) = self.encoded.unique_coord.dims return name @property def size(self) -> int: """Number of groups.""" return len(self) def __len__(self) -> int: """Number of groups.""" return len(self.encoded.full_index) def _parse_group_and_groupers( obj: T_Xarray, group: GroupInput, groupers: dict[str, Grouper], *, eagerly_compute_group: Literal[False] | None, ) -> tuple[ResolvedGrouper, ...]: from xarray.core.dataarray import DataArray from xarray.core.variable import Variable from xarray.groupers import Grouper, UniqueGrouper if group is not None and groupers: raise ValueError( "Providing a combination of `group` and **groupers is not supported." ) if group is None and not groupers: raise ValueError("Either `group` or `**groupers` must be provided.") if isinstance(group, np.ndarray | pd.Index): raise TypeError( f"`group` must be a DataArray. Received {type(group).__name__!r} instead" ) if isinstance(group, Grouper): raise TypeError( "Cannot group by a Grouper object. " f"Instead use `.groupby(var_name={type(group).__name__}(...))`. " "You may need to assign the variable you're grouping by as a coordinate using `assign_coords`." ) if isinstance(group, Mapping): grouper_mapping = either_dict_or_kwargs(group, groupers, "groupby") group = None rgroupers: tuple[ResolvedGrouper, ...] if isinstance(group, DataArray | Variable): rgroupers = ( ResolvedGrouper( UniqueGrouper(), group, obj, eagerly_compute_group=eagerly_compute_group ), ) else: if group is not None: if TYPE_CHECKING: assert isinstance(group, str | Sequence) group_iter: Sequence[Hashable] = ( (group,) if isinstance(group, str) else group ) grouper_mapping = {g: UniqueGrouper() for g in group_iter} elif groupers: grouper_mapping = cast("Mapping[Hashable, Grouper]", groupers) rgroupers = tuple( ResolvedGrouper( grouper, group, obj, eagerly_compute_group=eagerly_compute_group ) for group, grouper in grouper_mapping.items() ) return rgroupers def _validate_groupby_squeeze(squeeze: Literal[False]) -> None: # While we don't generally check the type of every arg, passing # multiple dimensions as multiple arguments is common enough, and the # consequences hidden enough (strings evaluate as true) to warrant # checking here. # A future version could make squeeze kwarg only, but would face # backward-compat issues. if squeeze is not False: raise TypeError(f"`squeeze` must be False, but {squeeze!r} was supplied.") def _resolve_group( obj: T_DataWithCoords, group: T_Group | Hashable | IndexVariable ) -> T_Group: from xarray.core.dataarray import DataArray error_msg = ( "the group variable's length does not " "match the length of this variable along its " "dimensions" ) newgroup: T_Group if isinstance(group, DataArray): try: align(obj, group, join="exact", copy=False) except ValueError as err: raise ValueError(error_msg) from err newgroup = group.copy(deep=False) newgroup.name = group.name or "group" elif isinstance(group, IndexVariable): # This assumption is built in to _ensure_1d. if group.ndim != 1: raise ValueError( "Grouping by multi-dimensional IndexVariables is not allowed." "Convert to and pass a DataArray instead." ) (group_dim,) = group.dims if len(group) != obj.sizes[group_dim]: raise ValueError(error_msg) newgroup = DataArray(group) else: if not hashable(group): raise TypeError( "`group` must be an xarray.DataArray or the " "name of an xarray variable or dimension. " f"Received {group!r} instead." ) group_da: DataArray = obj[group] if group_da.name not in obj._indexes and group_da.name in obj.dims: # DummyGroups should not appear on groupby results newgroup = _DummyGroup(obj, group_da.name, group_da.coords) else: newgroup = group_da if newgroup.size == 0: raise ValueError(f"{newgroup.name} must not be empty") return newgroup @dataclass class ComposedGrouper: """ Helper class for multi-variable GroupBy. This satisfies the Grouper interface, but is awkward to wrap in ResolvedGrouper. For one, it simply re-infers a new EncodedGroups using known information in existing ResolvedGroupers. So passing in a `group` (hard to define), and `obj` (pointless) is not useful. """ groupers: tuple[ResolvedGrouper, ...] def factorize(self) -> EncodedGroups: from xarray.groupers import EncodedGroups groupers = self.groupers # At this point all arrays have been factorized. codes = tuple(grouper.codes for grouper in groupers) shape = tuple(grouper.size for grouper in groupers) masks = tuple((code == -1) for code in codes) # We broadcast the codes against each other broadcasted_codes = broadcast(*codes) # This fully broadcasted DataArray is used as a template later first_codes = broadcasted_codes[0] # Now we convert to a single variable GroupBy problem _flatcodes = np.ravel_multi_index( tuple(codes.data for codes in broadcasted_codes), shape, mode="wrap" ) # NaNs; as well as values outside the bins are coded by -1 # Restore these after the raveling broadcasted_masks = broadcast(*masks) mask = functools.reduce(np.logical_or, broadcasted_masks) # type: ignore[arg-type] _flatcodes = where(mask.data, -1, _flatcodes) full_index = pd.MultiIndex.from_product( [grouper.full_index.values for grouper in groupers], names=tuple(grouper.name for grouper in groupers), ) if not full_index.is_unique: raise ValueError( "The output index for the GroupBy is non-unique. " "This is a bug in the Grouper provided." ) # This will be unused when grouping by dask arrays, so skip.. if not is_chunked_array(_flatcodes): # Constructing an index from the product is wrong when there are missing groups # (e.g. binning, resampling). Account for that now. midx = full_index[np.sort(pd.unique(_flatcodes[~mask]))] group_indices = _codes_to_group_indices(_flatcodes.ravel(), len(full_index)) else: midx = full_index group_indices = None dim_name = "stacked_" + "_".join(str(grouper.name) for grouper in groupers) coords = Coordinates.from_pandas_multiindex(midx, dim=dim_name) for grouper in groupers: coords.variables[grouper.name].attrs = grouper.group.attrs return EncodedGroups( codes=first_codes.copy(data=_flatcodes), full_index=full_index, group_indices=group_indices, unique_coord=Variable(dims=(dim_name,), data=midx.values), coords=coords, ) class GroupBy(Generic[T_Xarray]): """A object that implements the split-apply-combine pattern. Modeled after `pandas.GroupBy`. The `GroupBy` object can be iterated over (unique_value, grouped_array) pairs, but the main way to interact with a groupby object are with the `apply` or `reduce` methods. You can also directly call numpy methods like `mean` or `std`. You should create a GroupBy object by using the `DataArray.groupby` or `Dataset.groupby` methods. See Also -------- Dataset.groupby DataArray.groupby """ __slots__ = ( "_by_chunked", "_codes", "_dims", "_group_dim", # cached properties "_groups", "_inserted_dims", "_len", "_obj", # Save unstacked object for flox "_original_obj", "_restore_coord_dims", "_sizes", "_stacked_dim", "encoded", # stack nD vars "group1d", "groupers", ) _obj: T_Xarray groupers: tuple[ResolvedGrouper, ...] _restore_coord_dims: bool _original_obj: T_Xarray _group_indices: GroupIndices _codes: tuple[DataArray, ...] _group_dim: Hashable _by_chunked: bool _groups: dict[GroupKey, GroupIndex] | None _dims: tuple[Hashable, ...] | Frozen[Hashable, int] | None _sizes: Mapping[Hashable, int] | None _len: int # _ensure_1d: group1d: T_Group _stacked_dim: Hashable | None _inserted_dims: list[Hashable] encoded: EncodedGroups def __init__( self, obj: T_Xarray, groupers: tuple[ResolvedGrouper, ...], restore_coord_dims: bool = True, ) -> None: """Create a GroupBy object Parameters ---------- obj : Dataset or DataArray Object to group. grouper : Grouper Grouper object restore_coord_dims : bool, default: True If True, also restore the dimension order of multi-dimensional coordinates. """ self._original_obj = obj self._restore_coord_dims = restore_coord_dims self.groupers = groupers if len(groupers) == 1: (grouper,) = groupers self.encoded = grouper.encoded else: if any( isinstance(obj._indexes.get(grouper.name, None), PandasMultiIndex) for grouper in groupers ): raise NotImplementedError( "Grouping by multiple variables, one of which " "wraps a Pandas MultiIndex, is not supported yet." ) self.encoded = ComposedGrouper(groupers).factorize() # specification for the groupby operation # TODO: handle obj having variables that are not present on any of the groupers # simple broadcasting fails for ExtensionArrays. codes = self.encoded.codes self._by_chunked = is_chunked_array(codes._variable._data) if not self._by_chunked: (self.group1d, self._obj, self._stacked_dim, self._inserted_dims) = ( _ensure_1d(group=codes, obj=obj) ) (self._group_dim,) = self.group1d.dims else: self.group1d = None # This transpose preserves dim order behaviour self._obj = obj.transpose(..., *codes.dims) self._stacked_dim = None self._inserted_dims = [] self._group_dim = None # cached attributes self._groups = None self._dims = None self._sizes = None self._len = len(self.encoded.full_index) @property def sizes(self) -> Mapping[Hashable, int]: """Ordered mapping from dimension names to lengths. Immutable. See Also -------- DataArray.sizes Dataset.sizes """ if self._sizes is None: index = self.encoded.group_indices[0] self._sizes = self._obj.isel({self._group_dim: index}).sizes return self._sizes def shuffle_to_chunks(self, chunks: T_Chunks = None) -> T_Xarray: """ Sort or "shuffle" the underlying object. "Shuffle" means the object is sorted so that all group members occur sequentially, in the same chunk. Multiple groups may occur in the same chunk. This method is particularly useful for chunked arrays (e.g. dask, cubed). particularly when you need to map a function that requires all members of a group to be present in a single chunk. For chunked array types, the order of appearance is not guaranteed, but will depend on the input chunking. Parameters ---------- chunks : int, tuple of int, "auto" or mapping of hashable to int or tuple of int, optional How to adjust chunks along dimensions not present in the array being grouped by. Returns ------- DataArrayGroupBy or DatasetGroupBy Examples -------- >>> import dask.array >>> da = xr.DataArray( ... dims="x", ... data=dask.array.arange(10, chunks=3), ... coords={"x": [1, 2, 3, 1, 2, 3, 1, 2, 3, 0]}, ... name="a", ... ) >>> shuffled = da.groupby("x").shuffle_to_chunks() >>> shuffled Size: 80B dask.array Coordinates: * x (x) int64 80B 0 1 1 1 2 2 2 3 3 3 >>> shuffled.groupby("x").quantile(q=0.5).compute() Size: 32B array([9., 3., 4., 5.]) Coordinates: quantile float64 8B 0.5 * x (x) int64 32B 0 1 2 3 See Also -------- dask.dataframe.DataFrame.shuffle dask.array.shuffle """ self._raise_if_by_is_chunked() return self._shuffle_obj(chunks) def _shuffle_obj(self, chunks: T_Chunks) -> T_Xarray: from xarray.core.dataarray import DataArray was_array = isinstance(self._obj, DataArray) as_dataset = self._obj._to_temp_dataset() if was_array else self._obj for grouper in self.groupers: if grouper.name not in as_dataset._variables: as_dataset.coords[grouper.name] = grouper.group shuffled = as_dataset._shuffle( dim=self._group_dim, indices=self.encoded.group_indices, chunks=chunks ) unstacked: Dataset = self._maybe_unstack(shuffled) if was_array: return self._obj._from_temp_dataset(unstacked) else: return unstacked # type: ignore[return-value] def map( self, func: Callable, args: tuple[Any, ...] = (), shortcut: bool | None = None, **kwargs: Any, ) -> T_Xarray: raise NotImplementedError() def reduce( self, func: Callable[..., Any], dim: Dims = None, *, axis: int | Sequence[int] | None = None, keep_attrs: bool | None = None, keepdims: bool = False, shortcut: bool = True, **kwargs: Any, ) -> T_Xarray: raise NotImplementedError() def _raise_if_by_is_chunked(self): if self._by_chunked: raise ValueError( "This method is not supported when lazily grouping by a chunked array. " "Either load the array in to memory prior to grouping using .load or .compute, " " or explore another way of applying your function, " "potentially using the `flox` package." ) def _raise_if_not_single_group(self): if len(self.groupers) != 1: raise NotImplementedError( "This method is not supported for grouping by multiple variables yet." ) @property def groups(self) -> dict[GroupKey, GroupIndex]: """ Mapping from group labels to indices. The indices can be used to index the underlying object. """ # provided to mimic pandas.groupby if self._groups is None: self._groups = dict( zip( self.encoded.unique_coord.data, self.encoded.group_indices, strict=True, ) ) return self._groups def __getitem__(self, key: GroupKey) -> T_Xarray: """ Get DataArray or Dataset corresponding to a particular group label. """ self._raise_if_by_is_chunked() return self._obj.isel({self._group_dim: self.groups[key]}) def __len__(self) -> int: return self._len def __iter__(self) -> Iterator[tuple[GroupKey, T_Xarray]]: return zip(self.encoded.unique_coord.data, self._iter_grouped(), strict=True) def __repr__(self) -> str: text = ( f"<{self.__class__.__name__}, " f"grouped over {len(self.groupers)} grouper(s)," f" {self._len} groups in total:" ) for grouper in self.groupers: coord = grouper.unique_coord labels = ", ".join(format_array_flat(coord, 30).split()) text += ( f"\n {grouper.name!r}: {type(grouper.grouper).__name__}({grouper.group.name!r}), " f"{coord.size}/{grouper.full_index.size} groups with labels {labels}" ) return text + ">" def _iter_grouped(self) -> Iterator[T_Xarray]: """Iterate over each element in this group""" self._raise_if_by_is_chunked() for indices in self.encoded.group_indices: if indices: yield self._obj.isel({self._group_dim: indices}) def _infer_concat_args(self, applied_example): if self._group_dim in applied_example.dims: coord = self.group1d positions = self.encoded.group_indices else: coord = self.encoded.unique_coord positions = None (dim,) = coord.dims return dim, positions def _binary_op(self, other, f, reflexive=False): from xarray.core.dataarray import DataArray from xarray.core.dataset import Dataset g = f if not reflexive else lambda x, y: f(y, x) self._raise_if_not_single_group() (grouper,) = self.groupers obj = self._original_obj name = grouper.name group = grouper.group codes = self.encoded.codes dims = group.dims if isinstance(group, _DummyGroup): group = coord = group.to_dataarray() else: coord = grouper.unique_coord if isinstance(coord, Variable): assert coord.ndim == 1 (coord_dim,) = coord.dims # TODO: explicitly create Index here coord = DataArray(coord, coords={coord_dim: coord.data}) if not isinstance(other, Dataset | DataArray): raise TypeError( "GroupBy objects only support binary ops " "when the other argument is a Dataset or " "DataArray" ) if name not in other.dims: raise ValueError( "incompatible dimensions for a grouped " f"binary operation: the group variable {name!r} " "is not a dimension on the other argument " f"with dimensions {other.dims!r}" ) # Broadcast out scalars for backwards compatibility # TODO: get rid of this when fixing GH2145 for var in other.coords: if other[var].ndim == 0: other[var] = ( other[var].drop_vars(var).expand_dims({name: other.sizes[name]}) ) # need to handle NaNs in group or elements that don't belong to any bins mask = codes == -1 if mask.any(): obj = obj.where(~mask, drop=True) group = group.where(~mask, drop=True) codes = codes.where(~mask, drop=True).astype(int) # if other is dask-backed, that's a hint that the # "expanded" dataset is too big to hold in memory. # this can be the case when `other` was read from disk # and contains our lazy indexing classes # We need to check for dask-backed Datasets # so utils.is_duck_dask_array does not work for this check if obj.chunks and not other.chunks: # TODO: What about datasets with some dask vars, and others not? # This handles dims other than `name`` chunks = {k: v for k, v in obj.chunksizes.items() if k in other.dims} # a chunk size of 1 seems reasonable since we expect individual elements of # other to be repeated multiple times across the reduced dimension(s) chunks[name] = 1 other = other.chunk(chunks) # codes are defined for coord, so we align `other` with `coord` # before indexing other, _ = align(other, coord, join="right", copy=False) expanded = other.isel({name: codes}) result = g(obj, expanded) if group.ndim > 1: # backcompat: # TODO: get rid of this when fixing GH2145 for var in set(obj.coords) - set(obj.xindexes): if set(obj[var].dims) < set(group.dims): result[var] = obj[var].reset_coords(drop=True).broadcast_like(group) if isinstance(result, Dataset) and isinstance(obj, Dataset): for var in set(result): for d in dims: if d not in obj[var].dims: result[var] = result[var].transpose(d, ...) return result def _restore_dim_order(self, stacked): raise NotImplementedError def _maybe_reindex(self, combined): """Reindexing is needed in two cases: 1. Our index contained empty groups (e.g., from a resampling or binning). If we reduced on that dimension, we want to restore the full index. 2. We use a MultiIndex for multi-variable GroupBy. The MultiIndex stores each level's labels in sorted order which are then assigned on unstacking. So we need to restore the correct order here. """ has_missing_groups = ( self.encoded.unique_coord.size != self.encoded.full_index.size ) indexers = {} for grouper in self.groupers: index = combined._indexes.get(grouper.name, None) if (has_missing_groups and index is not None) or ( len(self.groupers) > 1 and not isinstance(grouper.full_index, pd.RangeIndex) and not index.index.equals(grouper.full_index) ): indexers[grouper.name] = grouper.full_index if indexers: combined = combined.reindex(**indexers) return combined def _maybe_unstack(self, obj): """This gets called if we are applying on an array with a multidimensional group.""" from xarray.groupers import UniqueGrouper stacked_dim = self._stacked_dim if stacked_dim is not None and stacked_dim in obj.dims: inserted_dims = self._inserted_dims obj = obj.unstack(stacked_dim) for dim in inserted_dims: if dim in obj.coords: del obj.coords[dim] obj._indexes = filter_indexes_from_coords(obj._indexes, set(obj.coords)) elif len(self.groupers) > 1: # TODO: we could clean this up by setting the appropriate `stacked_dim` # and `inserted_dims` # if multiple groupers all share the same single dimension, then # we don't stack/unstack. Do that manually now. dims_to_unstack = self.encoded.unique_coord.dims if all(dim in obj.dims for dim in dims_to_unstack): obj = obj.unstack(*dims_to_unstack) to_drop = [ grouper.name for grouper in self.groupers if isinstance(grouper.group, _DummyGroup) and isinstance(grouper.grouper, UniqueGrouper) ] obj = obj.drop_vars(to_drop) return obj def _flox_reduce( self, dim: Dims, keep_attrs: bool | None = None, **kwargs: Any, ) -> T_Xarray: """Adaptor function that translates our groupby API to that of flox.""" import flox from flox.xarray import xarray_reduce from xarray.core.dataset import Dataset obj = self._original_obj variables = ( {k: v.variable for k, v in obj.data_vars.items()} if isinstance(obj, Dataset) # type: ignore[redundant-expr] # seems to be a mypy bug else obj._coords ) if keep_attrs is None: keep_attrs = _get_keep_attrs(default=True) if Version(flox.__version__) < Version("0.9") and not self._by_chunked: # preserve current strategy (approximately) for dask groupby # on older flox versions to prevent surprises. # flox >=0.9 will choose this on its own. kwargs.setdefault("method", "cohorts") midx_grouping_vars: tuple[Hashable, ...] = () for grouper in self.groupers: name = grouper.name maybe_midx = obj._indexes.get(name, None) if isinstance(maybe_midx, PandasMultiIndex): midx_grouping_vars += tuple(maybe_midx.index.names) + (name,) # For datasets, running a numeric-only reduction on non-numeric # variable will just drop it. non_numeric: dict[Hashable, Variable] if kwargs.pop("numeric_only", None): non_numeric = { name: var for name, var in variables.items() if ( not (np.issubdtype(var.dtype, np.number) or (var.dtype == np.bool_)) # this avoids dropping any levels of a MultiIndex, which raises # a warning and name not in midx_grouping_vars and name not in obj.dims ) } else: non_numeric = {} if "min_count" in kwargs: if kwargs["func"] not in ["sum", "prod"]: raise TypeError("Received an unexpected keyword argument 'min_count'") elif kwargs["min_count"] is None: # set explicitly to avoid unnecessarily accumulating count kwargs["min_count"] = 0 parsed_dim: tuple[Hashable, ...] if isinstance(dim, str): parsed_dim = (dim,) elif dim is None: parsed_dim_list = list() # preserve order for dim_ in itertools.chain( *(grouper.codes.dims for grouper in self.groupers) ): if dim_ not in parsed_dim_list: parsed_dim_list.append(dim_) parsed_dim = tuple(parsed_dim_list) elif dim is ...: parsed_dim = tuple(obj.dims) else: parsed_dim = tuple(dim) # Do this so we raise the same error message whether flox is present or not. # Better to control it here than in flox. for grouper in self.groupers: if any( d not in grouper.codes.dims and d not in obj.dims for d in parsed_dim ): raise ValueError(f"cannot reduce over dimensions {dim}.") has_missing_groups = ( self.encoded.unique_coord.size != self.encoded.full_index.size ) if self._by_chunked or has_missing_groups or kwargs.get("min_count", 0) > 0: # Xarray *always* returns np.nan when there are no observations in a group, # We can fake that here by forcing min_count=1 when it is not set. # This handles boolean reductions, and count # See GH8090, GH9398 # Note that `has_missing_groups=False` when `self._by_chunked is True`. # We *choose* to always do the masking, so that behaviour is predictable # in some way. The real solution is to expose fill_value as a kwarg, # and set appropriate defaults :/. kwargs.setdefault("fill_value", np.nan) kwargs.setdefault("min_count", 1) # pass RangeIndex as a hint to flox that `by` is already factorized expected_groups = tuple( pd.RangeIndex(len(grouper)) for grouper in self.groupers ) codes = tuple(g.codes for g in self.groupers) result = xarray_reduce( obj.drop_vars(non_numeric.keys()), *codes, dim=parsed_dim, expected_groups=expected_groups, isbin=False, keep_attrs=keep_attrs, **kwargs, ) # we did end up reducing over dimension(s) that are # in the grouped variable group_dims = set(grouper.group.dims) new_coords = [] to_drop = [] if group_dims & set(parsed_dim): for grouper in self.groupers: output_index = grouper.full_index if isinstance(output_index, pd.RangeIndex): # flox always assigns an index so we must drop it here if we don't need it. to_drop.append(grouper.name) continue # TODO: We can't simply use `self.encoded.coords` here because it corresponds to `unique_coord`, # NOT `full_index`. We would need to construct a new Coordinates object, that corresponds to `full_index`. new_coords.append( # Using IndexVariable here ensures we reconstruct PandasMultiIndex with # all associated levels properly. coordinates_from_variable( IndexVariable( dims=grouper.name, data=output_index, attrs=grouper.codes.attrs, ) ) ) result = result.assign_coords( Coordinates._construct_direct(*merge_coords(new_coords)) ).drop_vars(to_drop) # broadcast any non-dim coord variables that don't # share all dimensions with the grouper result_variables = ( result._variables if isinstance(result, Dataset) else result._coords ) to_broadcast: dict[Hashable, Variable] = {} for name, var in variables.items(): dims_set = set(var.dims) if ( dims_set <= set(parsed_dim) and (dims_set & set(result.dims)) and name not in result_variables ): to_broadcast[name] = var for name, var in to_broadcast.items(): if new_dims := tuple(d for d in parsed_dim if d not in var.dims): new_sizes = tuple( result.sizes.get(dim, obj.sizes.get(dim)) for dim in new_dims ) result[name] = var.set_dims( new_dims + var.dims, new_sizes + var.shape ).transpose(..., *result.dims) if not isinstance(result, Dataset): # only restore dimension order for arrays result = self._restore_dim_order(result) return result def fillna(self, value: Any) -> T_Xarray: """Fill missing values in this object by group. This operation follows the normal broadcasting and alignment rules that xarray uses for binary arithmetic, except the result is aligned to this object (``join='left'``) instead of aligned to the intersection of index coordinates (``join='inner'``). Parameters ---------- value Used to fill all matching missing values by group. Needs to be of a valid type for the wrapped object's fillna method. Returns ------- same type as the grouped object See Also -------- Dataset.fillna DataArray.fillna """ return ops.fillna(self, value) def quantile( self, q: ArrayLike, dim: Dims = None, *, method: QuantileMethods = "linear", keep_attrs: bool | None = None, skipna: bool | None = None, interpolation: QuantileMethods | None = None, ) -> T_Xarray: """Compute the qth quantile over each array in the groups and concatenate them together into a new array. Parameters ---------- q : float or sequence of float Quantile to compute, which must be between 0 and 1 inclusive. dim : str or Iterable of Hashable, optional Dimension(s) over which to apply quantile. Defaults to the grouped dimension. method : str, default: "linear" This optional parameter specifies the interpolation method to use when the desired quantile lies between two data points. The options sorted by their R type as summarized in the H&F paper [1]_ are: 1. "inverted_cdf" 2. "averaged_inverted_cdf" 3. "closest_observation" 4. "interpolated_inverted_cdf" 5. "hazen" 6. "weibull" 7. "linear" (default) 8. "median_unbiased" 9. "normal_unbiased" The first three methods are discontiuous. The following discontinuous variations of the default "linear" (7.) option are also available: * "lower" * "higher" * "midpoint" * "nearest" See :py:func:`numpy.quantile` or [1]_ for details. The "method" argument was previously called "interpolation", renamed in accordance with numpy version 1.22.0. keep_attrs : bool or None, default: None If True, the dataarray's attributes (`attrs`) will be copied from the original object to the new one. If False, the new object will be returned without attributes. skipna : bool or None, default: None If True, skip missing values (as marked by NaN). By default, only skips missing values for float dtypes; other dtypes either do not have a sentinel missing value (int) or skipna=True has not been implemented (object, datetime64 or timedelta64). Returns ------- quantiles : Variable If `q` is a single quantile, then the result is a scalar. If multiple percentiles are given, first axis of the result corresponds to the quantile. In either case a quantile dimension is added to the return array. The other dimensions are the dimensions that remain after the reduction of the array. See Also -------- numpy.nanquantile, numpy.quantile, pandas.Series.quantile, Dataset.quantile DataArray.quantile Examples -------- >>> da = xr.DataArray( ... [[1.3, 8.4, 0.7, 6.9], [0.7, 4.2, 9.4, 1.5], [6.5, 7.3, 2.6, 1.9]], ... coords={"x": [0, 0, 1], "y": [1, 1, 2, 2]}, ... dims=("x", "y"), ... ) >>> ds = xr.Dataset({"a": da}) >>> da.groupby("x").quantile(0) Size: 64B array([[0.7, 4.2, 0.7, 1.5], [6.5, 7.3, 2.6, 1.9]]) Coordinates: * y (y) int64 32B 1 1 2 2 quantile float64 8B 0.0 * x (x) int64 16B 0 1 >>> ds.groupby("y").quantile(0, dim=...) Size: 40B Dimensions: (y: 2) Coordinates: quantile float64 8B 0.0 * y (y) int64 16B 1 2 Data variables: a (y) float64 16B 0.7 0.7 >>> da.groupby("x").quantile([0, 0.5, 1]) Size: 192B array([[[0.7 , 1. , 1.3 ], [4.2 , 6.3 , 8.4 ], [0.7 , 5.05, 9.4 ], [1.5 , 4.2 , 6.9 ]], [[6.5 , 6.5 , 6.5 ], [7.3 , 7.3 , 7.3 ], [2.6 , 2.6 , 2.6 ], [1.9 , 1.9 , 1.9 ]]]) Coordinates: * y (y) int64 32B 1 1 2 2 * quantile (quantile) float64 24B 0.0 0.5 1.0 * x (x) int64 16B 0 1 >>> ds.groupby("y").quantile([0, 0.5, 1], dim=...) Size: 88B Dimensions: (y: 2, quantile: 3) Coordinates: * quantile (quantile) float64 24B 0.0 0.5 1.0 * y (y) int64 16B 1 2 Data variables: a (y, quantile) float64 48B 0.7 5.35 8.4 0.7 2.25 9.4 References ---------- .. [1] R. J. Hyndman and Y. Fan, "Sample quantiles in statistical packages," The American Statistician, 50(4), pp. 361-365, 1996 """ # Dataset.quantile does this, do it for flox to ensure same output. q = np.asarray(q, dtype=np.float64) if ( method == "linear" and OPTIONS["use_flox"] and contains_only_chunked_or_numpy(self._obj) and module_available("flox", minversion="0.9.4") ): result = self._flox_reduce( func="quantile", q=q, dim=dim, keep_attrs=keep_attrs, skipna=skipna ) return result else: if dim is None: dim = (self._group_dim,) return self.map( self._obj.__class__.quantile, shortcut=False, q=q, dim=dim or self._group_dim, method=method, keep_attrs=keep_attrs, skipna=skipna, interpolation=interpolation, ) def where(self, cond, other=dtypes.NA) -> T_Xarray: """Return elements from `self` or `other` depending on `cond`. Parameters ---------- cond : DataArray or Dataset Locations at which to preserve this objects values. dtypes have to be `bool` other : scalar, DataArray or Dataset, optional Value to use for locations in this object where ``cond`` is False. By default, inserts missing values. Returns ------- same type as the grouped object See Also -------- Dataset.where """ return ops.where_method(self, cond, other) def _first_or_last( self, op: Literal["first" | "last"], skipna: bool | None, keep_attrs: bool | None, ): if all( isinstance(maybe_slice, slice) and (maybe_slice.stop == maybe_slice.start + 1) for maybe_slice in self.encoded.group_indices ): # NB. this is currently only used for reductions along an existing # dimension return self._obj if keep_attrs is None: keep_attrs = _get_keep_attrs(default=True) if ( module_available("flox", minversion="0.10.0") and OPTIONS["use_flox"] and contains_only_chunked_or_numpy(self._obj) ): import flox.xrdtypes result = self._flox_reduce( dim=None, func=op, skipna=skipna, keep_attrs=keep_attrs, fill_value=flox.xrdtypes.NA, ) else: result = self.reduce( getattr(duck_array_ops, op), dim=[self._group_dim], skipna=skipna, keep_attrs=keep_attrs, ) return result def first( self, skipna: bool | None = None, keep_attrs: bool | None = None ) -> T_Xarray: """ Return the first element of each group along the group dimension Parameters ---------- skipna : bool or None, optional If True, skip missing values (as marked by NaN). By default, only skips missing values for float dtypes; other dtypes either do not have a sentinel missing value (int) or ``skipna=True`` has not been implemented (object, datetime64 or timedelta64). keep_attrs : bool or None, optional If True, ``attrs`` will be copied from the original object to the new one. If False, the new object will be returned without attributes. """ return self._first_or_last("first", skipna, keep_attrs) def last( self, skipna: bool | None = None, keep_attrs: bool | None = None ) -> T_Xarray: """ Return the last element of each group along the group dimension Parameters ---------- skipna : bool or None, optional If True, skip missing values (as marked by NaN). By default, only skips missing values for float dtypes; other dtypes either do not have a sentinel missing value (int) or ``skipna=True`` has not been implemented (object, datetime64 or timedelta64). keep_attrs : bool or None, optional If True, ``attrs`` will be copied from the original object to the new one. If False, the new object will be returned without attributes. """ return self._first_or_last("last", skipna, keep_attrs) def assign_coords(self, coords=None, **coords_kwargs): """Assign coordinates by group. See Also -------- Dataset.assign_coords Dataset.swap_dims """ coords_kwargs = either_dict_or_kwargs(coords, coords_kwargs, "assign_coords") return self.map(lambda ds: ds.assign_coords(**coords_kwargs)) def _maybe_reorder(xarray_obj, dim, positions, N: int | None): order = _inverse_permutation_indices(positions, N) if order is None or len(order) != xarray_obj.sizes[dim]: return xarray_obj else: return xarray_obj[{dim: order}] class DataArrayGroupByBase(GroupBy["DataArray"], DataArrayGroupbyArithmetic): """GroupBy object specialized to grouping DataArray objects""" __slots__ = () _dims: tuple[Hashable, ...] | None @property def dims(self) -> tuple[Hashable, ...]: self._raise_if_by_is_chunked() if self._dims is None: index = self.encoded.group_indices[0] self._dims = self._obj.isel({self._group_dim: index}).dims return self._dims def _iter_grouped_shortcut(self): """Fast version of `_iter_grouped` that yields Variables without metadata """ self._raise_if_by_is_chunked() var = self._obj.variable for _idx, indices in enumerate(self.encoded.group_indices): if indices: yield var[{self._group_dim: indices}] def _concat_shortcut(self, applied, dim, positions=None): # nb. don't worry too much about maintaining this method -- it does # speed things up, but it's not very interpretable and there are much # faster alternatives (e.g., doing the grouped aggregation in a # compiled language) # TODO: benbovy - explicit indexes: this fast implementation doesn't # create an explicit index for the stacked dim coordinate stacked = Variable.concat(applied, dim, shortcut=True) reordered = _maybe_reorder(stacked, dim, positions, N=self.group1d.size) return self._obj._replace_maybe_drop_dims(reordered) def _restore_dim_order(self, stacked: DataArray) -> DataArray: def lookup_order(dimension): for grouper in self.groupers: if dimension == grouper.name and grouper.group.ndim == 1: (dimension,) = grouper.group.dims if dimension in self._obj.dims: axis = self._obj.get_axis_num(dimension) else: axis = 1e6 # some arbitrarily high value return axis new_order = sorted(stacked.dims, key=lookup_order) stacked = stacked.transpose( *new_order, transpose_coords=self._restore_coord_dims ) return stacked def map( self, func: Callable[..., DataArray], args: tuple[Any, ...] = (), shortcut: bool | None = None, **kwargs: Any, ) -> DataArray: """Apply a function to each array in the group and concatenate them together into a new array. `func` is called like `func(ar, *args, **kwargs)` for each array `ar` in this group. Apply uses heuristics (like `pandas.GroupBy.apply`) to figure out how to stack together the array. The rule is: 1. If the dimension along which the group coordinate is defined is still in the first grouped array after applying `func`, then stack over this dimension. 2. Otherwise, stack over the new dimension given by name of this grouping (the argument to the `groupby` function). Parameters ---------- func : callable Callable to apply to each array. shortcut : bool, optional Whether or not to shortcut evaluation under the assumptions that: (1) The action of `func` does not depend on any of the array metadata (attributes or coordinates) but only on the data and dimensions. (2) The action of `func` creates arrays with homogeneous metadata, that is, with the same dimensions and attributes. If these conditions are satisfied `shortcut` provides significant speedup. This should be the case for many common groupby operations (e.g., applying numpy ufuncs). *args : tuple, optional Positional arguments passed to `func`. **kwargs Used to call `func(ar, **kwargs)` for each array `ar`. Returns ------- applied : DataArray The result of splitting, applying and combining this array. """ grouped = self._iter_grouped_shortcut() if shortcut else self._iter_grouped() applied = (maybe_wrap_array(arr, func(arr, *args, **kwargs)) for arr in grouped) return self._combine(applied, shortcut=shortcut) def apply(self, func, shortcut=False, args=(), **kwargs): """ Backward compatible implementation of ``map`` See Also -------- DataArrayGroupBy.map """ warnings.warn( "GroupBy.apply may be deprecated in the future. Using GroupBy.map is encouraged", PendingDeprecationWarning, stacklevel=2, ) return self.map(func, shortcut=shortcut, args=args, **kwargs) def _combine(self, applied, shortcut=False): """Recombine the applied objects like the original.""" applied_example, applied = peek_at(applied) dim, positions = self._infer_concat_args(applied_example) if shortcut: combined = self._concat_shortcut(applied, dim, positions) else: combined = concat( applied, dim, data_vars="all", coords="different", compat="equals", join="outer", ) combined = _maybe_reorder(combined, dim, positions, N=self.group1d.size) if isinstance(combined, type(self._obj)): # only restore dimension order for arrays combined = self._restore_dim_order(combined) # assign coord and index when the applied function does not return that coord if dim not in applied_example.dims: combined = combined.assign_coords(self.encoded.coords) combined = self._maybe_unstack(combined) combined = self._maybe_reindex(combined) return combined def reduce( self, func: Callable[..., Any], dim: Dims = None, *, axis: int | Sequence[int] | None = None, keep_attrs: bool | None = None, keepdims: bool = False, shortcut: bool = True, **kwargs: Any, ) -> DataArray: """Reduce the items in this group by applying `func` along some dimension(s). Parameters ---------- func : callable Function which can be called in the form `func(x, axis=axis, **kwargs)` to return the result of collapsing an np.ndarray over an integer valued axis. dim : "...", str, Iterable of Hashable or None, optional Dimension(s) over which to apply `func`. If None, apply over the groupby dimension, if "..." apply over all dimensions. axis : int or sequence of int, optional Axis(es) over which to apply `func`. Only one of the 'dimension' and 'axis' arguments can be supplied. If neither are supplied, then `func` is calculated over all dimension for each group item. keep_attrs : bool, optional If True, the datasets's attributes (`attrs`) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes. **kwargs : dict Additional keyword arguments passed on to `func`. Returns ------- reduced : Array Array with summarized data and the indicated dimension(s) removed. """ if self._by_chunked: raise ValueError( "This method is not supported when lazily grouping by a chunked array. " "Try installing the `flox` package if you are using one of the standard " "reductions (e.g. `mean`). " ) if dim is None: dim = [self._group_dim] if keep_attrs is None: keep_attrs = _get_keep_attrs(default=True) def reduce_array(ar: DataArray) -> DataArray: return ar.reduce( func=func, dim=dim, axis=axis, keep_attrs=keep_attrs, keepdims=keepdims, **kwargs, ) check_reduce_dims(dim, self.dims) return self.map(reduce_array, shortcut=shortcut) class DataArrayGroupBy( DataArrayGroupByBase, DataArrayGroupByAggregations, ImplementsArrayReduce, ): __slots__ = () class DatasetGroupByBase(GroupBy["Dataset"], DatasetGroupbyArithmetic): __slots__ = () _dims: Frozen[Hashable, int] | None @property def dims(self) -> Frozen[Hashable, int]: self._raise_if_by_is_chunked() if self._dims is None: index = self.encoded.group_indices[0] self._dims = self._obj.isel({self._group_dim: index}).dims return FrozenMappingWarningOnValuesAccess(self._dims) def map( self, func: Callable[..., Dataset], args: tuple[Any, ...] = (), shortcut: bool | None = None, **kwargs: Any, ) -> Dataset: """Apply a function to each Dataset in the group and concatenate them together into a new Dataset. `func` is called like `func(ds, *args, **kwargs)` for each dataset `ds` in this group. Apply uses heuristics (like `pandas.GroupBy.apply`) to figure out how to stack together the datasets. The rule is: 1. If the dimension along which the group coordinate is defined is still in the first grouped item after applying `func`, then stack over this dimension. 2. Otherwise, stack over the new dimension given by name of this grouping (the argument to the `groupby` function). Parameters ---------- func : callable Callable to apply to each sub-dataset. args : tuple, optional Positional arguments to pass to `func`. **kwargs Used to call `func(ds, **kwargs)` for each sub-dataset `ar`. Returns ------- applied : Dataset The result of splitting, applying and combining this dataset. """ # ignore shortcut if set (for now) applied = (func(ds, *args, **kwargs) for ds in self._iter_grouped()) return self._combine(applied) def apply(self, func, args=(), shortcut=None, **kwargs): """ Backward compatible implementation of ``map`` See Also -------- DatasetGroupBy.map """ warnings.warn( "GroupBy.apply may be deprecated in the future. Using GroupBy.map is encouraged", PendingDeprecationWarning, stacklevel=2, ) return self.map(func, shortcut=shortcut, args=args, **kwargs) def _combine(self, applied): """Recombine the applied objects like the original.""" applied_example, applied = peek_at(applied) dim, positions = self._infer_concat_args(applied_example) combined = concat( applied, dim, data_vars="all", coords="different", compat="equals", join="outer", ) combined = _maybe_reorder(combined, dim, positions, N=self.group1d.size) # assign coord when the applied function does not return that coord if dim not in applied_example.dims: combined = combined.assign_coords(self.encoded.coords) combined = self._maybe_unstack(combined) combined = self._maybe_reindex(combined) return combined def reduce( self, func: Callable[..., Any], dim: Dims = None, *, axis: int | Sequence[int] | None = None, keep_attrs: bool | None = None, keepdims: bool = False, shortcut: bool = True, **kwargs: Any, ) -> Dataset: """Reduce the items in this group by applying `func` along some dimension(s). Parameters ---------- func : callable Function which can be called in the form `func(x, axis=axis, **kwargs)` to return the result of collapsing an np.ndarray over an integer valued axis. dim : ..., str, Iterable of Hashable or None, optional Dimension(s) over which to apply `func`. By default apply over the groupby dimension, with "..." apply over all dimensions. axis : int or sequence of int, optional Axis(es) over which to apply `func`. Only one of the 'dimension' and 'axis' arguments can be supplied. If neither are supplied, then `func` is calculated over all dimension for each group item. keep_attrs : bool, optional If True, the datasets's attributes (`attrs`) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes. **kwargs : dict Additional keyword arguments passed on to `func`. Returns ------- reduced : Dataset Array with summarized data and the indicated dimension(s) removed. """ if self._by_chunked: raise ValueError( "This method is not supported when lazily grouping by a chunked array. " "Try installing the `flox` package if you are using one of the standard " "reductions (e.g. `mean`). " ) if dim is None: dim = [self._group_dim] if keep_attrs is None: keep_attrs = _get_keep_attrs(default=True) def reduce_dataset(ds: Dataset) -> Dataset: return ds.reduce( func=func, dim=dim, axis=axis, keep_attrs=keep_attrs, keepdims=keepdims, **kwargs, ) check_reduce_dims(dim, self.dims) return self.map(reduce_dataset) def assign(self, **kwargs: Any) -> Dataset: """Assign data variables by group. See Also -------- Dataset.assign """ return self.map(lambda ds: ds.assign(**kwargs)) class DatasetGroupBy( DatasetGroupByBase, DatasetGroupByAggregations, ImplementsDatasetReduce, ): __slots__ = ()