Say I construct two numpy arrays:
a = np.array([np.NaN, np.NaN]) b = np.array([np.NaN, np.NaN, 3])
Now I find that
nan for both
np.mean(a) nan np.mean(b) nan
Since numpy 1.8 (released 20 April 2016), we’ve been blessed with nanmean, which ignores
However, when the array has nothing but
nan values, it raises a warning:
3.4.3libsite-packagesnumpylibnanfunctions.py:598: RuntimeWarning: Mean of empty slice warnings.warn("Mean of empty slice", RuntimeWarning)np.nanmean(a) nan C:python-
I don’t like suppressing warnings; is there a better function I can use to get the behaviour of
nanmean without that warning?
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 np import warnings x = np.ones((1000, 1000)) * np.nan # I expect to see RuntimeWarnings in this block with 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.
NaN value is defined to not be equal to itself:
float('nan') == float('nan') False np.NaN == np.NaN False
You can use a Python conditional and the property of a nan never being equal to itself to get this behavior:
3]) np.NaN if np.all(a!=a) else np.nanmean(a) nan np.NaN if np.all(b!=b) else np.nanmean(b) 3.0a = np.array([np.NaN, np.NaN]) b = np.array([np.NaN, np.NaN,
You can also do:
import warnings import numpy as np a = 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