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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
463841931 MDU6SXNzdWU0NjM4NDE5MzE= 3077 Question: Guaranteed zero-copy round-trip from numpy? amueller 449558 closed 0     2 2019-07-03T16:17:33Z 2019-07-05T14:58:15Z 2019-07-05T14:58:15Z NONE      

This is a question about casting from and to numpy. I asked a similar question for pandas here: https://github.com/pandas-dev/pandas/issues/27211

The question is whether we can rely on having zero-copy wrapping and unwrapping of numpy arrays into DataArray, i.e. is it future proof to assume something like

```python import xarray as xr import numpy as np

X = np.random.uniform(size=(10000, 10)) X_xr = xr.DataArray(X) X_again = np.asarray(X_xr) print(X.array_interface['data'][0] == X_again.array_interface['data'][0]) True ``` will always be true and no copy is happening?

Context: We want to attach some meta-data to our numpy arrays, in particular I'm interested in column names. Pandas is an obvious candidate for doing that, as we only have 2d array most of the time. However, pandas might change their internal structure so that we can't do zero copy wrapping and unwrapping any more.

Xarray is another candidate, even though it's a bit unnatural given that our data is usually 2d. This is a design decision that's very hard to undo, so I want to make sure that it's reasonably future-proof if we want to consider using DataArray as a possible output format.

 
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   completed xarray 13221727 issue

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