mean, nanmean and warning: Mean of empty slice
PythonNumpyWarningsPython Problem Overview
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 np.mean
returns nan
for both a
and b
:
>>> np.mean(a)
nan
>>> np.mean(b)
nan
Since numpy 1.8 (released 20 April 2016), we've been blessed with https://docs.scipy.org/doc/numpy/reference/generated/numpy.nanmean.html">nanmean</a>;, which ignores nan
values:
>>> np.nanmean(b)
3.0
However, when the array has nothing but nan
values, it raises a warning:
>>> np.nanmean(a)
nan
C:\python-3.4.3\lib\site-packages\numpy\lib\nanfunctions.py:598: RuntimeWarning: Mean of empty slice
warnings.warn("Mean of empty slice", RuntimeWarning)
I don't like suppressing warnings; is there a better function I can use to get the behaviour of nanmean
without that warning?
Python Solutions
Solution 1 - Python
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.
Solution 2 - Python
A 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:
>>> 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 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
Solution 3 - Python
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.