Get mean value avoiding nan using numpy in python [duplicate]

Question

How to calculate mean value of an array (A) avoiding nan?

import numpy as np  A = [5    nan    nan    nan    nan  10] M = np.mean(A[A!=nan]) does not work Any idea? 

Answer 1

An other possibility is the following:

import numpy from scipy.stats import nanmean # nanmedian exists too, if you need it A = numpy.array([5, numpy.nan, numpy.nan, numpy.nan, numpy.nan, 10]) print nanmean(A) # gives 7.5 as expected 

i guess this looks more elegant (and readable) than the other solution already given

edit: apparently (@Jaime) reports that this functionality already exists directly in the latest numpy (1.8) as well, so no need to import scipy.stats anymore if you have that version of numpy:

import numpy A = numpy.array([5, numpy.nan, numpy.nan, numpy.nan, numpy.nan, 10]) print numpy.nanmean(A)  

the first solution works also for people who dont have the latest version of numpy (like me)

Answer 2

Use numpy.isnan:

>>> import numpy as np  >>> A = np.array([5, np.nan, np.nan, np.nan, np.nan, 10]) >>> np.isnan(A) array([False,  True,  True,  True,  True, False], dtype=bool) >>> ~np.isnan(A) array([ True, False, False, False, False,  True], dtype=bool) >>> A[~np.isnan(A)] array([  5.,  10.]) >>> A[~np.isnan(A)].mean() 7.5 

because you cannot compare nan with nan:

>>> np.nan == np.nan False >>> np.nan != np.nan True >>> np.isnan(np.nan) True