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

Question

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

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

From the numpy docs linked above, "The numpy.ma module provides a nearly work-alike replacement for numpy that supports data arrays with masks."

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

You can use scipy for that:

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

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