Counting the number of non-NaN elements in a numpy ndarray in Python

Question

I need to calculate the number of non-NaN elements in a numpy ndarray matrix. How would one efficiently do this in Python? Here is my simple code for achieving this:

import numpy as np  def numberOfNonNans(data):     count = 0     for i in data:         if not np.isnan(i):             count += 1     return count  

Is there a built-in function for this in numpy? Efficiency is important because I'm doing Big Data analysis.

Thnx for any help!

Answer 1

np.count_nonzero(~np.isnan(data)) 

~ inverts the boolean matrix returned from np.isnan.

np.count_nonzero counts values that is not 0\false. .sum should give the same result. But maybe more clearly to use count_nonzero

Testing speed:

In [23]: data = np.random.random((10000,10000))  In [24]: data[[np.random.random_integers(0,10000, 100)],:][:, [np.random.random_integers(0,99, 100)]] = np.nan  In [25]: %timeit data.size - np.count_nonzero(np.isnan(data)) 1 loops, best of 3: 309 ms per loop  In [26]: %timeit np.count_nonzero(~np.isnan(data)) 1 loops, best of 3: 345 ms per loop  In [27]: %timeit data.size - np.isnan(data).sum() 1 loops, best of 3: 339 ms per loop 

data.size - np.count_nonzero(np.isnan(data)) seems to barely be the fastest here. other data might give different relative speed results.