issue_comments

Looks good. Merge?

I was thinking about only allowing it to work only if the array has exactly matching coordinates. Which would be the case in (4) a[(x > 0) & (y > 0)]. But then it would be difficult to stay consistent in the 1d case- mask or select?

My sense is that we'll probably be happier if we have entirely distinct APIs for masking (.where) and selection ([] and .loc[]).

That's a concrete and easy to understand distinction. I'm convinced.

Right. Consider following pandas rather than numpy here:

``` In [9]: a = pd.DataFrame(np.random.randn(3, 4))

In [10]: a Out[10]: 0 1 2 3 0 -1.188669 0.055286 -0.476962 0.144261 1 1.779646 2.332629 0.326515 -0.179862 2 -0.016739 1.221892 -0.032720 -0.779563

In [11]: a[a < 0] Out[11]: 0 1 2 3 0 -1.188669 NaN -0.476962 NaN 1 NaN NaN NaN -0.179862 2 -0.016739 NaN -0.032720 -0.779563 ```

Agreed- if a[a < 0] = 0 works then a[a < 0] should work also.

Both R and pandas allow the user to do a[a < 0] and a[a < 0] = 0. So what I'm wondering is why not extend xray's indexing to also work on arrays that are the same shape and have the same labels as the original array?

Here's something related that one can do in Numpy- replace all negative entries with 0.

``` In [15]: a = np.arange(-5, 5).reshape(2, 5)

In [16]: a Out[16]: array([[-5, -4, -3, -2, -1], [ 0, 1, 2, 3, 4]])

In [17]: a[a < 0] = 0

In [18]: a Out[18]: array([[0, 0, 0, 0, 0], [0, 1, 2, 3, 4]]) ```

Would it be possible to modify __getitem__ for this common use case?

Checking if I understand- this exists in line 79 of ops.py

where = _dask_or_eager_func('where')

so this PR is to expose it in the users API?

Plot makes for a compelling example.