html_url,issue_url,id,node_id,user,created_at,updated_at,author_association,body,reactions,performed_via_github_app,issue
https://github.com/pydata/xarray/issues/4473#issuecomment-711458608,https://api.github.com/repos/pydata/xarray/issues/4473,711458608,MDEyOklzc3VlQ29tbWVudDcxMTQ1ODYwOA==,5635139,2020-10-19T01:19:08Z,2020-10-19T01:19:08Z,MEMBER,"Here's a very quick POC:

```python
from numpy_groupies.aggregate_numba import aggregate

def npg_groupby(da: xr.DataArray, dim, func='sum'):
    group_idx, labels = pd.factorize(da.indexes[dim])
    axis = da.get_axis_num(dim)
    array = npg.aggregate(group_idx=group_idx, a=da, func=func, axis=axis)
    return array
```

Run on this array:
```python
size_factor = 1000

da = xr.DataArray(
    np.arange(1440 * size_factor).reshape(45 * size_factor, 8, 4),
    dims=(""x"", ""y"", ""z""),
    coords=dict(x=list(range(45)) * size_factor, y=[1, 2, 3, 4] * 2, z=[1, 2] * 2),
)
```

It's about 2x as fast, though only generates the numpy array:

```python
%%timeit 
npg_groupby(da, 'x')
# 15 ms ± 130 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
```

```python
%%timeit
da.groupby('x').sum()
# 37.6 ms ± 244 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)
```

Any thoughts on any of:
- What's the best way of reconstituting the coords etc, after npg produces the array? 
- Presumably we're going to have a fairly different design for this than the existing groupby operations — that design is very nested — wrapping functions and eventually calling `.map` to loop over each group in python.
- Presumably we're going to need to keep the existing logic around for dask — is it reasonable for an initial version to defer to the existing logic for all dask arrays? (+ @shoyer 's thoughts above on this)","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,711626733
https://github.com/pydata/xarray/issues/4473#issuecomment-702339435,https://api.github.com/repos/pydata/xarray/issues/4473,702339435,MDEyOklzc3VlQ29tbWVudDcwMjMzOTQzNQ==,5635139,2020-10-01T19:07:39Z,2020-10-01T19:07:39Z,MEMBER,"> I'm not entirely sure, but I suspect something like the approach in #4184 might be more directly relevant for speeding up `unstack` (at least with NumPy arrays).

Great. I need to think through how to do that — the approach of using MultiIndex `codes` to index the array directly is very elegant — I'll try applying it to stack / unstack as a project.","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,711626733
https://github.com/pydata/xarray/issues/4473#issuecomment-701653835,https://api.github.com/repos/pydata/xarray/issues/4473,701653835,MDEyOklzc3VlQ29tbWVudDcwMTY1MzgzNQ==,5635139,2020-09-30T21:22:40Z,2020-10-01T05:56:13Z,MEMBER,"This looks amazing! Thanks for finding it.

Highly speculative, but would this also be a faster approach to stacking & unstacking? ""Form ~5~ 4"" in the [readme](https://github.com/ml31415/numpy-groupies). ","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,711626733