how to use np.max for empty numpy array without ValueError: zero-size array to reduction operation maximum which has no identity

Question

I get a case that when I tried to use np.max() in an empty numpy array it will report such error messages.

# values is an empty numpy array here
max_val = np.max(values)

ValueError: zero-size array to reduction operation maximum which has no identity

So the way I think to fix it is that I try to deal with the empty numpy array first before calling the np.max() like follows:

# add some values as missing values on purposes.
def deal_empty_np_array(a:np.array):
    if a.size == 0:
        a = np.append(a, [-999999, -999999])

    return a

values = deal_empty_np_array(values)
max_val = np.max(values);

OR use the try catch way like this link.

So I am wondering if there is a better solution for this awkward case.
Thanks in advance.

PS: Sorry for not giving a clean description before.

Answer 1

In [3]: np.max([])                                                                               
---------------------------------------------------------------------------
...
ValueError: zero-size array to reduction operation maximum which has no identity

But check the docs. In newer numpy ufunc like max take an initial parameter that lets you work with an empty array:

In [4]: np.max([],initial=10)                                                                    
Out[4]: 10.0

Answer 2

I think you can simply check it, and eventually re-assign it, before calling np.max:

import numpy as np

values = -999 if values.size==0 else values
max_val = np.max(values)