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# Duck Array Support
## Sparse Arrays
`sparse.COO` arrays from the `pydata/sparse` project are supported using algorithms that work on the underlying dense data.
See `aggregate_sparse.py` for details.
At the moment the following reductions are supported: `sum`, `nansum`, `min`, `nanmin`, `max`, `nanmax`, `count`.
## Other array types
Aggregating over other array types will work if the array types supports the following methods, [ufunc.reduceat](https://numpy.org/doc/stable/reference/generated/numpy.ufunc.reduceat.html) or [ufunc.at](https://numpy.org/doc/stable/reference/generated/numpy.ufunc.at.html)
| Reduction | `method="numpy"` | `method="flox"` |
| ------------------------------ | ---------------- | ----------------- |
| sum, nansum | bincount | add.reduceat |
| mean, nanmean | bincount | add.reduceat |
| var, nanvar | bincount | add.reduceat |
| std, nanstd | bincount | add.reduceat |
| count | bincount | add.reduceat |
| prod | multiply.at | multiply.reduceat |
| max, nanmax, argmax, nanargmax | maximum.at | maximum.reduceat |
| min, nanmin, argmin, nanargmin | minimum.at | minimum.reduceat |
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