numpy.astype returns a copy of the array. It does not manipulate your array in-place. So you have to assign the value to a variable:

Y = Y.astype(int)

Solution 1 (faster)

integer = int("".join([str(x) for x in arr]))

Solution 2 (slower)

integer = np.sum(np.array([(10**exp) * elem for (elem, exp) in zip(arr, reversed(range(len(arr))))]))

Background (Digging Deeper)

I just measured, the solution with string casting and joining and int casting is about 5 times faster on random numbers with 30 digits.

The rest of this background info is just for fun. :-)

Why is int() so much faster...

Using int() is very fast because the function in cpython sourcecode corresponds to Modules/main.c's pymain_str_to_int() which uses the C-Function strtol under the hood.

But I still want to be faster...

So basically you could become faster if you would build a module in C for python similar to this:

int fast_arraytoint( const char * arr, int * result )
{
    long val = 0;
    while( *arr ) {
        val = val*10 + (*arr++);
    }
    if (val > INT_MAX) {
        return -1;
    }

    *result = (int)val;
    return 0;
}

To dig even deeper one must figure out how python handles big integers, the ones that have a value higher than INT_MAX and integrate the C-Module so it could be handled for all types of python's ints.

It is quite trivial

(X=='y').astype(int)

Should do the trick. It simply converts your array to True or False according to your requirements and then astype will impose the required datatype. By default int will give you 1 for True and 0 for False.

If you are not satisfied with lists (because they can contain anything and take up too much memory) you can use efficient array of integers:

import array
array.array('i')

See here

If you need to initialize it,

a = array.array('i',(0 for i in range(0,10)))