我使用 numpy 并有两个数组，用 genfromtxt 读取.
它们的形状是 <10000,>根据 np.shape() .
我希望这两个向量位于形状为 <10000,2> 的数组中.现在我使用:
x = zip(x1,x2)
但我不确定是否有 numpy 函数可以做得更好/更有效。我不认为concatenate做我想做的事(或者我做错了)。
请您参考如下方法:
有numpy.column_stack:
>>> a = numpy.arange(10)
>>> b = numpy.arange(1, 11)
>>> a
array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
>>> b
array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10])
>>> numpy.column_stack((a, b))
array([[ 0, 1],
[ 1, 2],
[ 2, 3],
[ 3, 4],
[ 4, 5],
[ 5, 6],
[ 6, 7],
[ 7, 8],
[ 8, 9],
[ 9, 10]])
>>> numpy.column_stack((a, b)).shape
(10, 2)
我不保证这在内存使用等方面比zip更好，但在这一切之下，它似乎依赖于在 numpy.concatenate(在 C 中实现)上，所以这至少是令人鼓舞的:
>>> import inspect
>>> print inspect.getsource(numpy.column_stack)
def column_stack(tup):
"""
Stack 1-D arrays as columns into a 2-D array.
Take a sequence of 1-D arrays and stack them as columns
to make a single 2-D array. 2-D arrays are stacked as-is,
just like with `hstack`. 1-D arrays are turned into 2-D columns
first.
Parameters
----------
tup : sequence of 1-D or 2-D arrays.
Arrays to stack. All of them must have the same first dimension.
Returns
-------
stacked : 2-D array
The array formed by stacking the given arrays.
See Also
--------
hstack, vstack, concatenate
Notes
-----
This function is equivalent to ``np.vstack(tup).T``.
Examples
--------
>>> a = np.array((1,2,3))
>>> b = np.array((2,3,4))
>>> np.column_stack((a,b))
array([[1, 2],
[2, 3],
[3, 4]])
"""
arrays = []
for v in tup:
arr = array(v, copy=False, subok=True)
if arr.ndim < 2:
arr = array(arr, copy=False, subok=True, ndmin=2).T
arrays.append(arr)
return _nx.concatenate(arrays, 1)
