numpy.ndarray.tolist will do it:

a.tolist()

If your data is a pandas series you can call their tolist wrapper with the same result.

Use the astype method.

>>> x = np.array([[1.0, 2.3], [1.3, 2.9]])
>>> x
array([[ 1. ,  2.3],
       [ 1.3,  2.9]])
>>> x.astype(int)
array([[1, 2],
       [1, 2]])

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.

Make a sample array

In [206]: x=np.array([1e9, 2e10, 1e6])
In [207]: x
Out[207]: array([  1.00000000e+09,   2.00000000e+10,   1.00000000e+06])

We can convert to ints - except notice that the largest one is too large the default int32

In [208]: x.astype(int)
Out[208]: array([ 1000000000, -2147483648,     1000000])

In [212]: x.astype(np.int64)
Out[212]: array([ 1000000000, 20000000000,     1000000], dtype=int64)

Writing a csv with the default format (float) (this is the default format regardless of the array dtype):

In [213]: np.savetxt('text.txt',x)
In [214]: cat text.txt
1.000000000000000000e+09
2.000000000000000000e+10
1.000000000000000000e+06

We can specify a format:

In [215]: np.savetxt('text.txt',x, fmt='%d')
In [216]: cat text.txt
1000000000
20000000000
1000000

Potentially there are 3 issues:

• integer v float in the array itself, it's dtype
• display or print of the array
• writing the array to a csv file

You don't need the math module if you are already using numpy

import numpy as np

length = 7
angle = 30 
x = [1.21660254e+02, 1.71660254e+02, 2.38660254e+02, 2.05660254e+02]

xP2x = np.round(np.array(x) + length * np.cos(np.radians(-angle))).astype(int)
print(xP2x)

Out:

[128 178 245 212]

Use .astype.

>>> a = numpy.array([1, 2, 3, 4], dtype=numpy.float64)
>>> a
array([ 1.,  2.,  3.,  4.])
>>> a.astype(numpy.int64)
array([1, 2, 3, 4])

See the documentation for more options.

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.