How to use numpy with 'None' value in Python?

Question

I'd like to calculate the mean of an array in Python in this form:

Matrice = [1, 2, None]

I'd just like to have my None value ignored by the numpy.mean calculation but I can't figure out how to do it.

Answer 1

You are looking for masked arrays. Here's an example.

import MA
a = MA.array([1, 2, None], mask = [0, 0, 1])
print "average =", MA.average(a)

Unfortunately, masked arrays aren't thoroughly supported in numpy, so you've got to look around to see what can and can't be done with them.

Answer 2

You can use scipy for that:

import scipy.stats.stats as st
m=st.nanmean(vec)

Answer 3

haven't used numpy, but in standard python you can filter out None using list comprehensions or the filter function

>>> [i for i in [1, 2, None] if i != None]
[1, 2]
>>> filter(lambda x: x != None, [1, 2, None])
[1, 2]

and then average the result to ignore the None

Answer 4

You might also be able to kludge with values like NaN or Inf.

In [1]: array([1, 2, None])
Out[1]: array([1, 2, None], dtype=object)

In [2]: array([1, 2, NaN])
Out[2]: array([  1.,   2.,  NaN])

Actually, it might not even be a kludge. Wikipedia says:

NaNs may be used to represent missing values in computations.

Actually, this doesn't work for the mean() function, though, so nevermind. :)

In [20]: mean([1, 2, NaN])
Out[20]: nan

Answer 5

You can also use filter, pass None to it, it will filter non True objects, also 0, :D So, use it when you dont need 0 too.

>>> filter(None,[1, 2, None])
[1, 2]

Answer 6

You can 'upcast' the array to numpy's float64 dtype and then use numpy's nanmean method as in the following example:

import numpy as np

arr = [1,2,3, None]
arr2 = np.array(arr, dtype=np.float64)
print(arr2) # [ 1.  2.  3. nan]
print(np.nanmean(arr2)) # 2.0

Answer 7

np.mean(Matrice[Matrice != None])