How to get rid of "RuntimeWarning: invalid value encountered in greater"

There is a better way - you don't want to suppress the warning forever, because it could help you find other mistakes later on.

Following the suggestions found in this question: RuntimeWarning: invalid value encountered in divide

The Right Way:

If the result is the one you want, you can just write:

with np.errstate(invalid='ignore'):
    result = (array > 0.5)

# ... use result, and your warnings are not suppressed.

A different Wrong Way:

Otherwise, you could meet your restrictions by copying the array:

to_compare = array.copy()
to_compare[np.isnan(to_compare)] = 0.5  # you don't need -np.inf, anything <= 0.5 is OK
result = (to_compare > 0.5)

And you don't need to "recover" the NaNs in your array.

You would have that warning whenever an array containing at least one NaN is compared. The solution would be to use masking to compare only the non-NaN elements and we would try to have a generic solution to cover all types of comparisons with the help of comparison based NumPy ufuncs, as shown below -

def compare_nan_array(func, a, thresh):
    out = ~np.isnan(a)
    out[out] = func(a[out] , thresh)
    return out

The idea being :

• Get the mask of non-NaNs.

• Use that to get the non-NaN values from input array. Then perform the required comparison (greater than, greater than equal to, etc.) to get another mask, which represents the compared mask output for the masked places.

• Use this to refine the mask of non-NaNs and this is the final output.

Sample run -

In [41]: np.random.seed(0)

In [42]: a = np.random.randint(0,9,(4,5)).astype(float)

In [43]: a.ravel()[np.random.choice(a.size, 16, replace=0)] = np.nan

In [44]: a
Out[44]: 
array([[ nan,  nan,  nan,  nan,  nan],
       [ nan,  nan,  nan,   4.,   7.],
       [ nan,  nan,  nan,   1.,  nan],
       [ nan,   7.,  nan,  nan,  nan]])

In [45]: a > 5  # Shows warning with the usual comparison
__main__:1: RuntimeWarning: invalid value encountered in greater
Out[45]: 
array([[False, False, False, False, False],
       [False, False, False, False,  True],
       [False, False, False, False, False],
       [False,  True, False, False, False]], dtype=bool)

# With suggested masking based method
In [46]: compare_nan_array(np.greater, a, 5)
Out[46]: 
array([[False, False, False, False, False],
       [False, False, False, False,  True],
       [False, False, False, False, False],
       [False,  True, False, False, False]], dtype=bool)

Let's test out the generic behavior by testing for lesser than 5 -

In [47]: a < 5
__main__:1: RuntimeWarning: invalid value encountered in less
Out[47]: 
array([[False, False, False, False, False],
       [False, False, False,  True, False],
       [False, False, False,  True, False],
       [False, False, False, False, False]], dtype=bool)

In [48]: compare_nan_array(np.less, a, 5)
Out[48]: 
array([[False, False, False, False, False],
       [False, False, False,  True, False],
       [False, False, False,  True, False],
       [False, False, False, False, False]], dtype=bool)