groupby multiple coords along a single dimension in xarray

user_6224363 0
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I have an xarray with multiple coordinates along a single dimension. In the example below, coords a and b are defined along dimension dim1. How would I groupby using two coordinates that are defined along the same dimension(s)? Unlike this question, I am not trying to group along different dimensions, but a single one. 

import xarray as xr

d = xr.DataArray([[1, 2, 3], [4, 5, 6], [7, 8, 9], [10, 11, 12]],
    coords={
        'a': ('dim1',['A', 'A', 'B', 'B']),
        'b': ('dim1',['1', '2', '1', '2']),
        'c': ('dim2',['x', 'y', 'z'])
    },
    dims=['dim1', 'dim2'])
d.groupby(['a','b']) # this gives: TypeError: `group` must be an xarray.DataArray or the name of an xarray variable or dimension
python-xarray pandas-groupby python-3.x python
2 Answers

This is my current workaround:

import numpy as np
import xarray as xr

def groupby_multicoords(da, fields):
    common_dim = da.coords[fields[0]].dims[0]
    tups_arr = np.empty(len(da[common_dim]), dtype=object)
    tups_arr[:] = list(zip(*(da[f].values for f in fields)))
    return da.assign_coords(grouping_zip=xr.DataArray(tups_arr, dims=common_dim)).groupby('grouping_zip')

and then, groupby_multicoords(da=d, fields=['a', 'b'])

However, after grouping I am still left with the 'grouping_zip' coord. I would be grateful to replace it with d.groupby(['a','b']) ..

user_83890700

You can stack them into a single MultiIndex with .stack(new=[“dim1”,”dim2”), and then groupby that dimension.

user_30647360
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