issue_comments

Mh. Actually it looks like ndarray.flat is the fastest way to iterate over numpy. Still considerably slower than a CPython iterator though

```python import numpy

N = 1000000 a = numpy.arange(N)

def exhaust(it): for _ in it: pass

%timeit exhaust(a) 24.8 ms ± 723 µs per loop (mean ± std. dev. of 7 runs, 10 loops each) %timeit exhaust(a.flat) 20.4 ms ± 701 µs per loop (mean ± std. dev. of 7 runs, 10 loops each) %timeit exhaust(a.tolist()) 27.2 ms ± 1.16 ms per loop (mean ± std. dev. of 7 runs, 10 loops each) %timeit exhaust(range(N)) 10.5 ms ± 234 µs per loop (mean ± std. dev. of 7 runs, 100 loops each) ```

@yohai Iterating point by point in pure python over numpy data is horribly slow. numpy.ndarray.flat is there mostly to be used within cython/numba code. In a DataArray it's much worse than in a plain ndarray, because every time you invoke the slice operator to fetch a single element it's being applied to all coordinates too.

If you just need to iterate over the values of a DataArray, then DataArray.values.ravel().tolist() is the fastest option that I know of (ndarray.tolist() is much faster than list(ndarray)!). If you need the coords as well, then I suspect you may be doing it wrong - could you show a simple example of your use case?

Indeed this is extremely inefficient. I'm afraid it's a -1 from me.

You can get the same with a much faster one-liner: a.stack(__flat=a.dims).reset_index('__flat') (although admittedly it's more RAM-intensive). On related notes, - stack() could use a set_index=True optional parameter that avoids you from going through a MultiIndex if you don't need one - stack() should accept non-string hashables; this would allow avoiding potential collisions (e.g. flat = object()) - there is an issue with the stack -> reset_index round-trip where it converts unicode variables to object #907