@@ -116,7 +116,13 @@ dimensions *other than* the provided one:
116116
117117.. ipython :: python
118118
119- ds.groupby(' x' ).std(xr.ALL_DIMS )
119+ ds.groupby(' x' ).std(... )
120+
121+ .. note ::
122+
123+ We use an ellipsis (`... `) here to indicate we want to reduce over all
124+ other dimensions
125+
120126
121127First and last
122128~~~~~~~~~~~~~~
@@ -127,7 +133,7 @@ values for group along the grouped dimension:
127133
128134.. ipython :: python
129135
130- ds.groupby(' letters' ).first(xr. ALL_DIMS )
136+ ds.groupby(' letters' ).first(... )
131137
132138 By default, they skip missing values (control this with ``skipna ``).
133139
@@ -142,7 +148,7 @@ coordinates. For example:
142148
143149.. ipython :: python
144150
145- alt = arr.groupby(' letters' ).mean(xr. ALL_DIMS )
151+ alt = arr.groupby(' letters' ).mean(... )
146152 alt
147153 ds.groupby(' letters' ) - alt
148154
@@ -195,7 +201,7 @@ __ http://cfconventions.org/cf-conventions/v1.6.0/cf-conventions.html#_two_dimen
195201 ' lat' : ([' ny' ,' nx' ], [[10 ,10 ],[20 ,20 ]] ),},
196202 dims = [' ny' ,' nx' ])
197203 da
198- da.groupby(' lon' ).sum(xr. ALL_DIMS )
204+ da.groupby(' lon' ).sum(... )
199205 da.groupby(' lon' ).apply(lambda x : x - x.mean(), shortcut = False )
200206
201207 Because multidimensional groups have the ability to generate a very large
@@ -213,4 +219,4 @@ applying your function, and then unstacking the result:
213219.. ipython :: python
214220
215221 stacked = da.stack(gridcell = [' ny' , ' nx' ])
216- stacked.groupby(' gridcell' ).sum(xr. ALL_DIMS ).unstack(' gridcell' )
222+ stacked.groupby(' gridcell' ).sum(... ).unstack(' gridcell' )
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