Catching numpy runtimewarning as exception and suppressing them

I am aware this have been partly answered here.

Anyway I am not sure I'm achieving what I want. I'll briefly explain what I am doing:

• Reading through a huge list of json files which have a particularly nested structure.
• Extracting the lowest level value from them and averaging when these values are lists.
• Collecting these values into numpy arrays.
• Dumping my numpy arrays into pickled files.

Everything goes quite smooth, but I receive some numpy runtime warning:

• RuntimeWarning: Mean of empty slice.
• RuntimeWarning: invalid value encountered in double_scalars

Function which gives me trouble is the one who actually extract the values which performs this operation: v = np.mean(v)

I know the error can be caused by a list of zeros or by some NaN/Inf or whatever is into the value.

I would like to get rid of them just by throwing away the current .json sample from my data set.

So I've set: np.seterr(all='warn')

And I made this awkward code to try to catch it:

def ExtracValues(d):
    for v in d.values():
        if isinstance(v, dict):
            yield from ExtracValues(v)
        else:
            if isinstance(v,list):
                # v = np.mean(v)            #just averaging vectorial values of features.   #it may be here that raises the empty slice warning of numpy.
                try:
                    v = np.mean(v)
                except Warning:
                    return #trying to trash samples which are no behaving good                  
            yield v

My problem is that I don't know if it's effectively working, because the warning are still printed on stdout. I suppose the code should have stopped after setting all="Warning", but how could I easily check if I am right?

Also, is there a more pythonic way to shorten that function. I really don't like the try/except nested in that way.