Yes, given an array, array, and a value, item to search for, you can use np.where as:

itemindex = numpy.where(array == item)

The result is a tuple with first all the row indices, then all the column indices.

For example, if an array is two dimensions and it contained your item at two locations then

array[itemindex[0][0]][itemindex[1][0]]

would be equal to your item and so would be:

array[itemindex[0][1]][itemindex[1][1]]

Use np.where to get the indices where a given condition is True.

Examples:

For a 2D np.ndarray called a:

i, j = np.where(a == value) # when comparing arrays of integers

i, j = np.where(np.isclose(a, value)) # when comparing floating-point arrays

For a 1D array:

i, = np.where(a == value) # integers

i, = np.where(np.isclose(a, value)) # floating-point

Note that this also works for conditions like >=, <=, != and so forth...

You can also create a subclass of np.ndarray with an index() method:

class myarray(np.ndarray):
    def __new__(cls, *args, **kwargs):
        return np.array(*args, **kwargs).view(myarray)
    def index(self, value):
        return np.where(self == value)

Testing:

a = myarray([1,2,3,4,4,4,5,6,4,4,4])
a.index(4)
#(array([ 3,  4,  5,  8,  9, 10]),)

Would np.searchsorted work for you ?

>>> master = np.array([1,2,3,4,5])
>>> search = np.array([4,2,2,3])
>>> np.searchsorted(master, search)
array([3, 1, 1, 2])