issue_comments

Thanks @joonro, you are very kind!

I'm going to close this issue since I think we resolved the original question.

@joonro Yes, this does get messy. We'll eventually support indexing like X[X > 0] directly, which will help significantly.

In the meantime, you can still break things up onto multiple lines by saving temporary variables:

condition = X.loc[..., 'variable'].values > 0 X.loc[..., 'variable'].values[condition] = Y.loc[..., 'variable'].values[condition]

Using abbreviations like ... for :, :, : (assuming 'variable' is along the last axis) can also help.

Can you give an example of how you need to use .values in xarray operations? Within xarray, we should be able to remove the need to use that. On Sat, Aug 27, 2016 at 1:06 PM Joon Ro notifications@github.com wrote:

Thanks a lot for the discussions. I agree it is very important to be consistent and explicit. Another thing was that sometimes .values makes a line of code really long - especially when I want to index a DataArray with another DataArray with some conditions, as I often have to use .values for each of them.

Currently I do not have a good idea about how to improve this - I will report back if one occurs to me. Thanks again!

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@darothen Let's discuss this over in #988.

I wonder if it is reasonable to return a scalar when there is neither coords nor attrs associated with the return value, or it would be too much ad-hoc thing. For example, in the original example the return value was <xarray.DataArray ()>, which does not have any useful information.

This is a bad path to go down :). Now your code might suddenly break when you add a metadata field!

In principle, we could pick some subset of operations for which to always do this and others for which to never do this (e.g., aggregating out all dimensions, but not indexing out all dimensions), but I think this inconsistency would be even more surprising. It's pretty easy to see how this could lead to bugs, too. At least now you know you always need to type .values or .item()!

I agree that this can be annoying. The downside in making this switch is that we would lose xarray specific fields like coords and attrs that are currently preserved, e.g.,

```

array = xr.DataArray([1, 2, 3], coords=[('x', ['a', 'b', 'c'])]) array <xarray.DataArray (x: 3)> array([1, 2, 3]) Coordinates: * x (x) |S1 'a' 'b' 'c' array[0] <xarray.DataArray ()> array(1) Coordinates: x |S1 'a' array[0].coords['x'].item() 'a' ```

Also, strictly from a simplicity point of view for xarray, it's nice for every function to return fixed types.

NumPy solved this problem by creating it's own scalar types (e.g., np.float64) that define fields like shape and dtype while also subclassing Python's builtin numeric types. We could do the same, but this could lead to a different set of subtle cross-compatibility issues.