mean, nanmean and warning: Mean of empty slice

I really can't see any good reason not to just suppress the warning.

The safest way would be to use the warnings.catch_warnings context manager to suppress the warning only where you anticipate it occurring - that way you won't miss any additional RuntimeWarnings that might be unexpectedly raised in some other part of your code:

import numpy as npimport warningsx = np.ones((1000, 1000)) * np.nan# I expect to see RuntimeWarnings in this blockwith warnings.catch_warnings():    warnings.simplefilter("ignore", category=RuntimeWarning)    foo = np.nanmean(x, axis=1)

@dawg's solution would also work, but ultimately any additional steps that you have to take in order to avoid computing np.nanmean on an array of all NaNs are going to incur some extra overhead that you could avoid by just suppressing the warning. Also your intent will be much more clearly reflected in the code.

A NaN value is defined to not be equal to itself:

>>> float('nan') == float('nan')False>>> np.NaN == np.NaNFalse

You can use a Python conditional and the property of a nan never being equal to itself to get this behavior:

>>> a = np.array([np.NaN, np.NaN])>>> b = np.array([np.NaN, np.NaN, 3])>>> np.NaN if np.all(a!=a) else np.nanmean(a)nan>>> np.NaN if np.all(b!=b) else np.nanmean(b)3.0

You can also do:

import warningsimport numpy as npa = np.array([np.NaN, np.NaN])b = np.array([np.NaN, np.NaN, 3])with warnings.catch_warnings():    warnings.filterwarnings('error')    try:        x=np.nanmean(a)    except RuntimeWarning:        x=np.NaN    print x    

I have got this runtime warning when I perform np.nanmean over a 3-D array, e.g. (time, lon, lat). Maybe not a direct answer to your question, but I would like to add this warning message in my case can be related to point cell value with all NaN value series.