避免由numpy.vsplit添加额外的尺寸 [英] Avoid extra dimension added by numpy.vsplit

问题描述

我们可以使用vstack(或hstack)连接多个一维数组，例如D = np.vstack([a,b,c]).
反向操作为[a2,b2,c2] = np.vsplit(D, 3). 但是维度在往返过程中会发生变化:

We can join several 1d arrays with vstack (or hstack), e.g. D = np.vstack([a,b,c]).
The reverse operation is [a2,b2,c2] = np.vsplit(D, 3). But the dimensionality changes in the round-trip:

import numpy as np
a = np.random.rand(10,)
b = np.random.rand(10,)
c = np.random.rand(10,)
D = np.vstack([a,b,c])
[a2,b2,c2] = np.vsplit(D, 3)

>>> a.shape
(10,)

>>> a2.shape
(1, 10)

我知道挤压以移除尺寸:

I know about squeeze to remove a dimension:

>>> a2.squeeze().shape
(10,)

但这很麻烦，尤其是在拆分多个数组时.

But this is cumbersome, especially when splitting more than a couple of arrays.

有什么方法可以自动"执行挤压操作，或者以其他方式控制vsplit的输出以避免尺寸不匹配?

Is there any way to 'automatically' perform a squeeze, or otherwise control the output of vsplit to avoid the mismatch in dimensions?

(拆分文档就我所知，没有提及任何控制输出尺寸的方法)

(the split docs do not mention any way to control the output dimensions as far as I can tell)

推荐答案

In [98]: D = np.arange(12).reshape(4,3)
In [99]: np.vsplit(D, 4)
Out[99]: 
[array([[0, 1, 2]]),
 array([[3, 4, 5]]),
 array([[6, 7, 8]]),
 array([[ 9, 10, 11]])]

split使用切片来选择行，从而保留了该维度

split is using a slice to select rows, thus preserving that dimension

[D[i:i+1,:] for i in range(4)]

这是一种常规行为，它可以返回其他尺寸分割.

That's a general behavior that lets it return other size splits.

但是看来您想一次返回一行.有很多方法可以做到这一点:

But it appears you want to return one row at a time. There are many ways of doing this:

很容易迭代地应用squeeze(而且成本也不高，因为split已经在迭代):

It's easy to apply squeeze iteratively (and not much more expensive, since split is already iterating):

In [100]: [np.squeeze(x) for x in np.vsplit(D, 4)]
Out[100]: [array([0, 1, 2]), array([3, 4, 5]), array([6, 7, 8]), array([ 9, 10, 11])]

或者您可以使用纯列表理解:

Or you can use a plain list comprehension:

In [101]: [x for x in D]
Out[101]: [array([0, 1, 2]), array([3, 4, 5]), array([6, 7, 8]), array([ 9, 10, 11])]

或将数组转换为列表(与D.tolist()不同:

Or convert the array to a list (this is different from D.tolist():

In [102]: list(D)
Out[102]: [array([0, 1, 2]), array([3, 4, 5]), array([6, 7, 8]), array([ 9, 10, 11])]

或按索引迭代.类似于split，但是使用标量索引而不是切片.很高兴理解D[i,:]和D[i:i+1, :]之间的区别.

Or iteration by index. This is like split, but uses a scalar index rather than the slice. It's good to understand the difference between D[i,:] and D[i:i+1, :].

In [103]: [D[i] for i in range(4)]
Out[103]: [array([0, 1, 2]), array([3, 4, 5]), array([6, 7, 8]), array([ 9, 10, 11])]

由于使用的是开箱包装，因此不需要任何包装.打开包装将为您完成迭代"行:

Since you are using unpacking, you don't need any of this. The unpacking will do the row 'iteration' for you:

In [106]: a,b,c,d = D
In [107]: a,b,c,d
Out[107]: (array([0, 1, 2]), array([3, 4, 5]), array([6, 7, 8]), array([ 9, 10, 11]))

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