What might be the cause of 'invalid value encountered in less_equal' in numpy

Question

I experienced a RuntimeWarning

 RuntimeWarning: invalid value encountered in less_equal

Generated by this line of code of mine:

Answer 1

Numpy dtypes are so strict. So it doesnt produce an array like np.array([False, True, np.nan]), it returns array([ 0., 1., nan]) which a float array.

If you try to change a bool array like:

x= np.array([False, False, False])
x[0] = 5

will retrun array([ True, False, False]) ... wow

But I think 5>np.nan cannot be False, it should be nan, False would mean that a data comparison has been made and it returned the result like 3>5, which I think it's a disaster. Numpy produces data that we actually don't have. If it could have returned nan then we could handle it with ease.

So I tried to modify the behavior with a function.

def ngrater(x, y):
    with np.errstate(invalid='ignore'):
        c=x>y
        c=c.astype(np.object)
        c[np.isnan(x)] = np.nan
        c[np.isnan(y)] = np.nan
        return c
a = np.array([np.nan,1,2,3,4,5, np.nan, np.nan, np.nan]) #9 elements
b = np.array([0,1,-2,-3,-4,-5, -5, -5, -5]) #9 elements

ngrater(a,b)

returns: array([nan, False, True, True, True, True, nan, nan, nan], dtype=object)

But I think whole memory structure is changed in that way. Instead of getting a memory-block with uniform unites, it will produce a block of pointers, where the real data is somewhere else. So function may perform slower and probably that's why Numpy doesn't do that. We need a superBool dtype which will contain also np.nan, or we just have to use float arrays +1:True, -1:False, nan:nan