[Tutor] np array.any() question

Andre' Walker-Loud walksloud at gmail.com
Fri Sep 21 18:07:59 CEST 2012


> To summarize,
> with a numpy array called a, the OP is getting an error doing:
> 
> (3 < a < 5).any()
> 
>> ...
> 
> I think not.  The last expression you give will even return true if one
> of the values is > 3 and a DIFFERENT value is < 5.   And i suspect the
> OP only wants a TRUE if at least one item in the array is between 3 and 5.
> 
> I know nothing about numpy, but does it have a way to do an element-wise
> AND of two bool arrays of the same size?  Maybe it's a function call and
> not 'and'  Or maybe it's the & operator or something.

Yes, numpy does have a function like this:

numpy.all()  [ "and" function ]
numpy.any() [ "or" function ]



> I find the operator overloading i've seen in numpy to be very confusing,
> so I haven't tried to download it and try it out.  Maybe if I read the
> docs directly, instead of just seeing examples and problems here, I'd
> think differently.

here you go :)

http://docs.scipy.org/doc/numpy/reference/generated/numpy.all.html#numpy.all
http://docs.scipy.org/doc/numpy/reference/generated/numpy.any.html#numpy.any

you can ask if any satisfy the conditions and return true if 1+ are true:

>>> a = numpy.array([  7.,   1.,   1.,   1.,   2.,   4.,   0.,   7.,   6.,  10.])
>>> numpy.any([ 3 < a, a < 5])
True
>>> numpy.all([ 3 < a, a < 5])
False

or you can ask for an array back telling you which are true and which false:

>>> numpy.any([ 3 < a, a < 5], axis=0)
array([ True,  True,  True,  True,  True,  True,  True,  True,  True,  True], dtype=bool)
>>> numpy.any([ 3 < a, a < 5], axis=0)
array([False, False, False, False, False,  True, False, False, False, False], dtype=bool)


Note you are not limited to just comparing two arrays, but can compare as many as you like.

To the OP: in this case, google (insert whatever search engine you like) is your friend.  A good first bet is if there is some operation you like in python, other people like it also, and someone has taken the time to make a numpy version which acts element-wise on equal sized arrays.


Andre






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