Python numpy vstack 与 column_stack
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numpy vstack vs. column_stack
提问by ZenBalance
What exactly is the difference between numpy vstackand column_stack. Reading through the documentation, it looks as if column_stackis an implementation of vstackfor 1D arrays. Is it a more efficient implementation? Otherwise, I cannot find a reason for just having vstack.
numpyvstack和column_stack. 通读文档，它看起来好像column_stack是一vstack维数组的实现。它是更有效的实现吗？否则，我找不到只拥有vstack.
采纳答案by mgilson
I think the following code illustrates the difference nicely:
我认为以下代码很好地说明了差异：
>>> np.vstack(([1,2,3],[4,5,6]))
array([[1, 2, 3],
[4, 5, 6]])
>>> np.column_stack(([1,2,3],[4,5,6]))
array([[1, 4],
[2, 5],
[3, 6]])
>>> np.hstack(([1,2,3],[4,5,6]))
array([1, 2, 3, 4, 5, 6])
I've included hstackfor comparison as well. Notice how column_stackstacks along the second dimension whereas vstackstacks along the first dimension. The equivalent to column_stackis the following hstackcommand:
我也包括在内hstack进行比较。请注意如何column_stack沿第二维vstack堆叠，而如何沿第一维堆叠。等效于column_stack以下hstack命令：
>>> np.hstack(([[1],[2],[3]],[[4],[5],[6]]))
array([[1, 4],
[2, 5],
[3, 6]])
I hope we can agree that column_stackis more convenient.
我希望我们能同意这样column_stack更方便。
回答by SethMMorton
In the Notes section to column_stack, it points out this:
在column_stack的注释部分，它指出了这一点：
This function is equivalent to
np.vstack(tup).T.
该函数等效于
np.vstack(tup).T。
There are many functions in numpythat are convenient wrappers of other functions. For example, the Notes section of vstacksays:
有许多函数numpy可以方便地封装其他函数。例如，vstack的注释部分说：
Equivalent to
np.concatenate(tup, axis=0)if tup contains arrays that are at least 2-dimensional.
相当于
np.concatenate(tup, axis=0)如果 tup 包含至少是二维的数组。
It looks like column_stackis just a convenience function for vstack.
看起来column_stack只是vstack.
