Python NaN Values – How to Check for NaN Values

The easiest way to get this behaviour is to use numpy.float64 instead of Python default float type:

>>> import numpy
>>> numpy.float64(1.0) / 0.0
inf

Of course this requires NumPy. You can use numpy.seterr() to fine-tune the error handling.

I think this makes sense because of your pulling numpy into scope indirectly via the star import.

>>> import numpy as np
>>> [0.0,0.0]/0
Traceback (most recent call last):
  File "<ipython-input-3-aae9e30b3430>", line 1, in <module>
    [0.0,0.0]/0
TypeError: unsupported operand type(s) for /: 'list' and 'int'

>>> [0.0,0.0]/np.float64(0)
array([ nan,  nan])

When you did

from matplotlib.pylab import *

it pulled in numpy.sum:

>>> from matplotlib.pylab import *
>>> sum is np.sum
True
>>> [0.0,0.0]/sum([0.0, 0.0])
array([ nan,  nan])

You can test that this nan object (nan isn't unique in general) is in a list via identity, but if you try it in an array it seems to test via equality, and nan != nan:

>>> nan == nan
False
>>> nan == nan, nan is nan
(False, True)
>>> nan in [nan]
True
>>> nan in np.array([nan])
False

You could use np.isnan:

>>> np.isnan([nan, nan])
array([ True,  True], dtype=bool)
>>> np.isnan([nan, nan]).any()
True