wip: clean-up

Revert some changes made in pydata#5102 + additional (temporary) fixes.

xarray/core/alignment.py

@@ -18,7 +18,7 @@
 import pandas as pd
 
 from . import dtypes
-from .indexes import Index, PandasIndex, get_indexer_nd, wrap_pandas_index
+from .indexes import Index, PandasIndex, get_indexer_nd
 from .utils import is_dict_like, is_full_slice, maybe_coerce_to_str, safe_cast_to_index
 from .variable import IndexVariable, Variable
 
@@ -53,7 +53,10 @@ def _get_joiner(join, index_cls):
 def _override_indexes(objects, all_indexes, exclude):
     for dim, dim_indexes in all_indexes.items():
         if dim not in exclude:
-            lengths = {index.size for index in dim_indexes}
+            lengths = {
+                getattr(index, "size", index.to_pandas_index().size)
+                for index in dim_indexes
+            }
             if len(lengths) != 1:
                 raise ValueError(
                     f"Indexes along dimension {dim!r} don't have the same length."
@@ -300,11 +303,12 @@ def align(
     joined_indexes = {}
     for dim, matching_indexes in all_indexes.items():
         if dim in indexes:
-            # TODO: benbovy - flexible indexes. maybe move this logic in util func
             if isinstance(indexes[dim], Index):
                 index = indexes[dim]
             else:
-                index = PandasIndex(safe_cast_to_index(indexes[dim]))
+                index, _ = PandasIndex.from_pandas_index(
+                    safe_cast_to_index(indexes[dim]), dim
+                )
             if (
                 any(not index.equals(other) for other in matching_indexes)
                 or dim in unlabeled_dim_sizes
@@ -323,17 +327,18 @@ def align(
                 joiner = _get_joiner(join, type(matching_indexes[0]))
                 index = joiner(matching_indexes)
                 # make sure str coords are not cast to object
-                index = maybe_coerce_to_str(index, all_coords[dim])
+                index = maybe_coerce_to_str(index.to_pandas_index(), all_coords[dim])
                 joined_indexes[dim] = index
             else:
                 index = all_coords[dim][0]
 
         if dim in unlabeled_dim_sizes:
             unlabeled_sizes = unlabeled_dim_sizes[dim]
-            # TODO: benbovy - flexible indexes: expose a size property for xarray.Index?
-            # Some indexes may not have a defined size (e.g., built from multiple coords of
-            # different sizes)
-            labeled_size = index.size
+            # TODO: benbovy - flexible indexes: https://github.com/pydata/xarray/issues/5647
+            if isinstance(index, PandasIndex):
+                labeled_size = index.to_pandas_index().size
+            else:
+                labeled_size = index.size
             if len(unlabeled_sizes | {labeled_size}) > 1:
                 raise ValueError(
                     f"arguments without labels along dimension {dim!r} cannot be "
@@ -350,7 +355,14 @@ def align(
 
     result = []
     for obj in objects:
-        valid_indexers = {k: v for k, v in joined_indexes.items() if k in obj.dims}
+        # TODO: benbovy - flexible indexes: https://github.com/pydata/xarray/issues/5647
+        valid_indexers = {}
+        for k, index in joined_indexes.items():
+            if k in obj.dims:
+                if isinstance(index, Index):
+                    valid_indexers[k] = index.to_pandas_index()
+                else:
+                    valid_indexers[k] = index
         if not valid_indexers:
             # fast path for no reindexing necessary
             new_obj = obj.copy(deep=copy)
@@ -471,7 +483,11 @@ def reindex_like_indexers(
     ValueError
         If any dimensions without labels have different sizes.
     """
-    indexers = {k: v for k, v in other.xindexes.items() if k in target.dims}
+    # TODO: benbovy - flexible indexes: https://github.com/pydata/xarray/issues/5647
+    # this doesn't support yet indexes other than pd.Index
+    indexers = {
+        k: v.to_pandas_index() for k, v in other.xindexes.items() if k in target.dims
+    }
 
     for dim in other.dims:
         if dim not in indexers and dim in target.dims:
@@ -560,7 +576,8 @@ def reindex_variables(
                 "from that to be indexed along {:s}".format(str(indexer.dims), dim)
             )
 
-        target = new_indexes[dim] = wrap_pandas_index(safe_cast_to_index(indexers[dim]))
+        target = safe_cast_to_index(indexers[dim])
+        new_indexes[dim] = PandasIndex(target, dim)
 
         if dim in indexes:
             # TODO (benbovy - flexible indexes): support other indexes than pd.Index?

xarray/core/combine.py

@@ -77,9 +77,8 @@ def _infer_concat_order_from_coords(datasets):
                     "inferring concatenation order"
                 )
 
-            # TODO (benbovy, flexible indexes): all indexes should be Pandas.Index
-            # get pd.Index objects from Index objects
-            indexes = [index.array for index in indexes]
+            # TODO (benbovy, flexible indexes): support flexible indexes?
+            indexes = [index.to_pandas_index() for index in indexes]
 
             # If dimension coordinate values are same on every dataset then
             # should be leaving this dimension alone (it's just a "bystander")

xarray/core/dataarray.py

@@ -51,13 +51,7 @@
 )
 from .dataset import Dataset, split_indexes
 from .formatting import format_item
-from .indexes import (
-    Index,
-    Indexes,
-    default_indexes,
-    propagate_indexes,
-    wrap_pandas_index,
-)
+from .indexes import Index, Indexes, default_indexes, propagate_indexes
 from .indexing import is_fancy_indexer
 from .merge import PANDAS_TYPES, MergeError, _extract_indexes_from_coords
 from .options import OPTIONS, _get_keep_attrs
@@ -473,15 +467,14 @@ def _overwrite_indexes(self, indexes: Mapping[Hashable, Any]) -> "DataArray":
             return self
         coords = self._coords.copy()
         for name, idx in indexes.items():
-            coords[name] = IndexVariable(name, idx)
+            coords[name] = IndexVariable(name, idx.to_pandas_index())
         obj = self._replace(coords=coords)
 
         # switch from dimension to level names, if necessary
         dim_names: Dict[Any, str] = {}
         for dim, idx in indexes.items():
-            # TODO: benbovy - flexible indexes: update when MultiIndex has its own class
-            pd_idx = idx.array
-            if not isinstance(pd_idx, pd.MultiIndex) and pd_idx.name != dim:
+            pd_idx = idx.to_pandas_index()
+            if not isinstance(idx, pd.MultiIndex) and pd_idx.name != dim:
                 dim_names[dim] = idx.name
         if dim_names:
             obj = obj.rename(dim_names)
@@ -1046,12 +1039,7 @@ def copy(self: T_DataArray, deep: bool = True, data: Any = None) -> T_DataArray:
         if self._indexes is None:
             indexes = self._indexes
         else:
-            # TODO: benbovy: flexible indexes: support all xarray indexes (not just pandas.Index)
-            # xarray Index needs a copy method.
-            indexes = {
-                k: wrap_pandas_index(v.to_pandas_index().copy(deep=deep))
-                for k, v in self._indexes.items()
-            }
+            indexes = {k: v.copy(deep=deep) for k, v in self._indexes.items()}
         return self._replace(variable, coords, indexes=indexes)
 
     def __copy__(self) -> "DataArray":

xarray/core/dataset.py

@@ -71,7 +71,6 @@
     propagate_indexes,
     remove_unused_levels_categories,
     roll_index,
-    wrap_pandas_index,
 )
 from .indexing import is_fancy_indexer
 from .merge import (
@@ -1184,7 +1183,7 @@ def _overwrite_indexes(self, indexes: Mapping[Any, Index]) -> "Dataset":
         variables = self._variables.copy()
         new_indexes = dict(self.xindexes)
         for name, idx in indexes.items():
-            variables[name] = IndexVariable(name, idx)
+            variables[name] = IndexVariable(name, idx.to_pandas_index())
             new_indexes[name] = idx
         obj = self._replace(variables, indexes=new_indexes)
 
@@ -2474,6 +2473,10 @@ def sel(
         pos_indexers, new_indexes = remap_label_indexers(
             self, indexers=indexers, method=method, tolerance=tolerance
         )
+        # TODO: benbovy - flexible indexes: also use variables returned by Index.query
+        # (temporary dirty fix).
+        new_indexes = {k: v[0] for k, v in new_indexes.items()}
+
         result = self.isel(indexers=pos_indexers, drop=drop)
         return result._overwrite_indexes(new_indexes)
 
@@ -3297,20 +3300,21 @@ def _rename_dims(self, name_dict):
         return {name_dict.get(k, k): v for k, v in self.dims.items()}
 
     def _rename_indexes(self, name_dict, dims_set):
+        # TODO: benbovy - flexible indexes: https://github.com/pydata/xarray/issues/5645
         if self._indexes is None:
             return None
         indexes = {}
-        for k, v in self.xindexes.items():
-            # TODO: benbovy - flexible indexes: make it compatible with any xarray Index
-            index = v.to_pandas_index()
+        for k, v in self.indexes.items():
             new_name = name_dict.get(k, k)
             if new_name not in dims_set:
                 continue
-            if isinstance(index, pd.MultiIndex):
-                new_names = [name_dict.get(k, k) for k in index.names]
-                indexes[new_name] = PandasMultiIndex(index.rename(names=new_names))
+            if isinstance(v, pd.MultiIndex):
+                new_names = [name_dict.get(k, k) for k in v.names]
+                indexes[new_name] = PandasMultiIndex(
+                    v.rename(names=new_names), new_name
+                )
             else:
-                indexes[new_name] = PandasIndex(index.rename(new_name))
+                indexes[new_name] = PandasIndex(v.rename(new_name), new_name)
         return indexes
 
     def _rename_all(self, name_dict, dims_dict):
@@ -3539,7 +3543,10 @@ def swap_dims(
                     if new_index.nlevels == 1:
                         # make sure index name matches dimension name
                         new_index = new_index.rename(k)
-                    indexes[k] = wrap_pandas_index(new_index)
+                    if isinstance(new_index, pd.MultiIndex):
+                        indexes[k] = PandasMultiIndex(new_index, k)
+                    else:
+                        indexes[k] = PandasIndex(new_index, k)
             else:
                 var = v.to_base_variable()
             var.dims = dims
@@ -3812,7 +3819,7 @@ def reorder_levels(
                 raise ValueError(f"coordinate {dim} has no MultiIndex")
             new_index = index.reorder_levels(order)
             variables[dim] = IndexVariable(coord.dims, new_index)
-            indexes[dim] = PandasMultiIndex(new_index)
+            indexes[dim] = PandasMultiIndex(new_index, dim)
 
         return self._replace(variables, indexes=indexes)
 
@@ -3840,7 +3847,7 @@ def _stack_once(self, dims, new_dim):
         coord_names = set(self._coord_names) - set(dims) | {new_dim}
 
         indexes = {k: v for k, v in self.xindexes.items() if k not in dims}
-        indexes[new_dim] = wrap_pandas_index(idx)
+        indexes[new_dim] = PandasMultiIndex(idx, new_dim)
 
         return self._replace_with_new_dims(
             variables, coord_names=coord_names, indexes=indexes
@@ -4029,8 +4036,9 @@ def _unstack_once(self, dim: Hashable, fill_value) -> "Dataset":
                     variables[name] = var
 
         for name, lev in zip(index.names, index.levels):
-            variables[name] = IndexVariable(name, lev)
-            indexes[name] = PandasIndex(lev)
+            idx, idx_vars = PandasIndex.from_pandas_index(lev, name)
+            variables[name] = idx_vars[name]
+            indexes[name] = idx
 
         coord_names = set(self._coord_names) - {dim} | set(index.names)
 
@@ -4068,8 +4076,9 @@ def _unstack_full_reindex(
                     variables[name] = var
 
         for name, lev in zip(new_dim_names, index.levels):
-            variables[name] = IndexVariable(name, lev)
-            indexes[name] = PandasIndex(lev)
+            idx, idx_vars = PandasIndex.from_pandas_index(lev, name)
+            variables[name] = idx_vars[name]
+            indexes[name] = idx
 
         coord_names = set(self._coord_names) - {dim} | set(new_dim_names)
 
@@ -5839,10 +5848,13 @@ def diff(self, dim, n=1, label="upper"):
 
         indexes = dict(self.xindexes)
         if dim in indexes:
-            # TODO: benbovy - flexible indexes: check slicing of xarray indexes?
-            # or only allow this for pandas indexes?
-            index = indexes[dim].to_pandas_index()
-            indexes[dim] = PandasIndex(index[kwargs_new[dim]])
+            if isinstance(indexes[dim], PandasIndex):
+                # maybe optimize? (pandas index already indexed above with var.isel)
+                new_index = indexes[dim].index[kwargs_new[dim]]
+                if isinstance(new_index, pd.MultiIndex):
+                    indexes[dim] = PandasMultiIndex(new_index, dim)
+                else:
+                    indexes[dim] = PandasIndex(new_index, dim)
 
         difference = self._replace_with_new_dims(variables, indexes=indexes)
 

xarray/core/indexes.py

@@ -180,6 +180,8 @@ def from_variables(cls, variables: Mapping[Hashable, "Variable"]):
 
     @classmethod
     def from_pandas_index(cls, index: pd.Index, dim: Hashable):
+        from .variable import IndexVariable
+
         if index.name is None:
             name = dim
         else:
@@ -247,15 +249,15 @@ def union(self, other):
 
         new_index = self.index.union(other)
 
-        return type(self).from_pandas_index(new_index, self.dim)
+        return type(self)(new_index, self.dim)
 
     def intersection(self, other):
         if isinstance(other, PandasIndex):
             other = other.index
 
         new_index = self.index.intersection(other)
 
-        return type(self).from_pandas_index(new_index, self.dim)
+        return type(self)(new_index, self.dim)
 
     def copy(self, deep=True):
         return type(self)(self.index.copy(deep=deep), self.dim)
@@ -421,13 +423,6 @@ def query(self, labels, method=None, tolerance=None):
             return indexer, None
 
 
-def wrap_pandas_index(index):
-    if isinstance(index, pd.MultiIndex):
-        return PandasMultiIndex(index)
-    else:
-        return PandasIndex(index)
-
-
 def remove_unused_levels_categories(index: pd.Index) -> pd.Index:
     """
     Remove unused levels from MultiIndex and unused categories from CategoricalIndex
@@ -512,7 +507,13 @@ def isel_variable_and_index(
     index: Index,
     indexers: Mapping[Hashable, Union[int, slice, np.ndarray, "Variable"]],
 ) -> Tuple["Variable", Optional[Index]]:
-    """Index a Variable and pandas.Index together."""
+    """Index a Variable and an Index together.
+
+    If the index cannot be indexed, return None (it will be dropped).
+
+    (note: not compatible yet with xarray flexible indexes).
+
+    """
     from .variable import Variable
 
     if not indexers:
@@ -535,8 +536,11 @@ def isel_variable_and_index(
     indexer = indexers[dim]
     if isinstance(indexer, Variable):
         indexer = indexer.data
-    pd_index = index.to_pandas_index()
-    new_index = wrap_pandas_index(pd_index[indexer])
+    try:
+        new_index = index[indexer]
+    except NotImplementedError:
+        new_index = None
+
     return new_variable, new_index
 
 
@@ -548,7 +552,7 @@ def roll_index(index: PandasIndex, count: int, axis: int = 0) -> PandasIndex:
         new_idx = pd_index[-count:].append(pd_index[:-count])
     else:
         new_idx = pd_index[:]
-    return PandasIndex(new_idx)
+    return PandasIndex(new_idx, index.dim)
 
 
 def propagate_indexes(

xarray/core/indexing.py

@@ -572,9 +572,7 @@ def as_indexable(array):
     if isinstance(array, np.ndarray):
         return NumpyIndexingAdapter(array)
     if isinstance(array, pd.Index):
-        from .indexes import PandasIndex
-
-        return PandasIndex(array)
+        return PandasIndexingAdapter(array)
     if isinstance(array, dask_array_type):
         return DaskIndexingAdapter(array)
     if hasattr(array, "__array_function__"):
@@ -1270,7 +1268,7 @@ class PandasIndexingAdapter(ExplicitlyIndexedNDArrayMixin):
     __slots__ = ("array", "_dtype")
 
     def __init__(self, array: pd.Index, dtype: DTypeLike = None):
-        self.array = array
+        self.array = utils.safe_cast_to_index(array)
 
         if dtype is None:
             if isinstance(array, pd.PeriodIndex):