How can I prevent NAN issues?

Question

I\'m getting Mean of empty slice runtime warnings. When I print out what my variables are (numpy arrays), several of them contain nan values. The Runti

Answer 1

I assume the warning comes up in

np.mean(dataPoints[t2 == [x]], axis=0)

If t2 == [x] is all False (no match between t2 and x, then dataPoints[...] will be an empty array, resulting in the mean warning.

I think you need to be more careful with that test. Maybe even skip the mean if the masked array is empty.

== tests with floating values are unpredictable. You need to use something like np.isclose or np.allclose to test equivalence with a tolerance.

The second warning comes from later in the mean calc, presumably when trying to divide by 0, the number of elements.

The full mean code can be found in numpy.core._methods.py.

In sum, don't try to take the mean of an empty array.