issues: 484097190

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id node_id number title user state locked assignee milestone comments created_at updated_at closed_at author_association active_lock_reason draft pull_request body reactions performed_via_github_app state_reason repo type
484097190 MDU6SXNzdWU0ODQwOTcxOTA= 3241 aggregation functions treat duck arrays differently depending on dtype 14808389 closed 0     6 2019-08-22T16:30:56Z 2019-09-02T22:52:47Z 2019-09-02T22:52:47Z MEMBER      

While working on #3238, I tried replacing np.arange with np.linspace to create test arrays: ```python

ureg = pint.UnitRegistry()

with int values

array = np.arange(10).astype(int) * ureg.m np.max(array) <Quantity(9, 'meter')> np.max(xr.DataArray(data=array)) # works as expected <xarray.DataArray ()> <Quantity(9, 'meter')>

now with floats

array = np.arange(10).astype(float) * ureg.m np.max(array) <Quantity(9.0, 'meter')> np.max(xr.DataArray(data=array)) # unit information is lost <xarray.DataArray ()> array(9.) `` Judging by the build logs of #3238, this seems to be the case for all aggregation functions except fromnp.median` and of course those that return booleans or indices.

 
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