How does python numpy.where() work?

Question

I am playing with numpy and digging through documentation and I have come across some magic. Namely I am talking about numpy.where():

Answer 1

Old Answer it is kind of confusing. It gives you the LOCATIONS (all of them) of where your statment is true.

so:

>>> a = np.arange(100)
>>> np.where(a > 30)
(array([31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47,
       48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64,
       65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81,
       82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98,
       99]),)
>>> np.where(a == 90)
(array([90]),)

a = a*40
>>> np.where(a > 1000)
(array([26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42,
       43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59,
       60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76,
       77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93,
       94, 95, 96, 97, 98, 99]),)
>>> a[25]
1000
>>> a[26]
1040

I use it as an alternative to list.index(), but it has many other uses as well. I have never used it with 2D arrays.

http://docs.scipy.org/doc/numpy/reference/generated/numpy.where.html

New Answer It seems that the person was asking something more fundamental.

The question was how could YOU implement something that allows a function (such as where) to know what was requested.

First note that calling any of the comparison operators do an interesting thing.

a > 1000
array([False, False, False, False, False, False, False, False, False,
       False, False, False, False, False, False, False, False, False,
       False, False, False, False, False, False, False, False,  True,
        True,  True,  True,  True,  True,  True,  True,  True,  True,
        True,  True,  True,  True,  True,  True,  True,  True,  True,
        True,  True,  True,  True,  True,  True,  True,  True,  True,
        True,  True,  True,  True,  True,  True,  True,  True,  True,
        True,  True,  True,  True,  True,  True,  True,  True,  True,
        True,  True,  True,  True,  True,  True,  True,  True,  True,
        True,  True,  True,  True,  True,  True,  True,  True,  True,
        True`,  True,  True,  True,  True,  True,  True,  True,  True,  True], dtype=bool)`

This is done by overloading the "__gt__" method. For instance:

>>> class demo(object):
    def __gt__(self, item):
        print item


>>> a = demo()
>>> a > 4
4

As you can see, "a > 4" was valid code.

You can get a full list and documentation of all overloaded functions here: http://docs.python.org/reference/datamodel.html

Something that is incredible is how simple it is to do this. ALL operations in python are done in such a way. Saying a > b is equivalent to a.gt(b)!