@J.F. Sebastian's answer:

a.astype(numpy.int32)

Some numpy functions for how to control the rounding: rint, floor,trunc, ceil. depending how u wish to round the floats, up, down, or to the nearest int.

>>> x = np.array([[1.0,2.3],[1.3,2.9]])
>>> x
array([[ 1. ,  2.3],
       [ 1.3,  2.9]])
>>> y = np.trunc(x)
>>> y
array([[ 1.,  2.],
       [ 1.,  2.]])
>>> z = np.ceil(x)
>>> z
array([[ 1.,  3.],
       [ 2.,  3.]])
>>> t = np.floor(x)
>>> t
array([[ 1.,  2.],
       [ 1.,  2.]])
>>> a = np.rint(x)
>>> a
array([[ 1.,  2.],
       [ 1.,  3.]])

To make one of this in to int, or one of the other types in numpy, astype (as answered by BrenBern):

a.astype(int)
array([[1, 2],
       [1, 3]])

>>> y.astype(int)
array([[1, 2],
       [1, 2]])

It is a printing option, see the documentation: printing options. Briefly stated: you need to use the suppress option when printing:

np.set_printoptions(suppress=True)  # for small floating point.

np.set_printoptions(suppress=True, formatter={'all':lambda x: str(x)})

You could use the following code:

X[X=='y'] = 1
X[X=='n'] = 0

This replaces the indexes of 'y' with 1 and of 'n' with 0. Generally the X=='y' returns a Boolean array which contains True where the 'y' and False everywhere else and so on.

That is a pure python solution and it produces a list .

With simple python operations, you can map inner list with float. That will convert all string elements to float and assign it as the zero indexed item of your list.

a = [['-0.99' , '0.56' , '0.56' , '0.56', '-2.02' , '-0.96']]

a[0] = map(float, a[0])

print a
[[-0.99, 0.56, 0.56, 0.56, -2.02, -0.96]]

Update: Try the following

a = [['-0.99' , '0.56' , '0.56' , '0.56', '-2.02' , '-0.96', '', 'nan']]
for _position, _value in enumerate(a[0]):
    try:
        _new_value = float(_value)
    except ValueError:
        _new_value = 0.0
    a[0][_position] = _new_value

[[-0.99, 0.56, 0.56, 0.56, -2.02, -0.96, 0.0, nan]]

It enumerates the objects in the list and try to parse them to float, if it fails, then replace it with 0.0

While astype is probably the "best" option there are several other ways to convert it to an integer array. I'm using this arr in the following examples:

>>> import numpy as np
>>> arr = np.array([1,2,3,4], dtype=float)
>>> arr
array([ 1.,  2.,  3.,  4.])

The int* functions from NumPy

>>> np.int64(arr)
array([1, 2, 3, 4])

>>> np.int_(arr)
array([1, 2, 3, 4])

The NumPy *array functions themselves:

>>> np.array(arr, dtype=int)
array([1, 2, 3, 4])

>>> np.asarray(arr, dtype=int)
array([1, 2, 3, 4])

>>> np.asanyarray(arr, dtype=int)
array([1, 2, 3, 4])

The astype method (that was already mentioned but for completeness sake):

>>> arr.astype(int)
array([1, 2, 3, 4])

Note that passing int as dtype to astype or array will default to a default integer type that depends on your platform. For example on Windows it will be int32, on 64bit Linux with 64bit Python it's int64. If you need a specific integer type and want to avoid the platform "ambiguity" you should use the corresponding NumPy types like np.int32 or np.int64.