如何将2D float numpy数组转换为2D int numpy数组？

How to convert real numpy array to int numpy array? Tried using map directly to array but it did not work.

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]])

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]])

you can use np.int_:

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

If you’re not sure your input is going to be a Numpy array, you can use asarray with dtype=int instead of astype:

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

If the input array already has the correct dtype, asarray avoids the array copy while astype does not (unless you specify copy=False):

>>> a = np.array([1,2,3,4])
>>> a is np.asarray(a)  # no copy :)
True
>>> a is a.astype(int)  # copy :(
False
>>> a is a.astype(int, copy=False)  # no copy :)
True