我怎么能压扁的二维数组numpy的，它在第二轴的长度不同？ [英] how can I flatten an 2d numpy array, which has different length in the second axis?

问题描述

我有一个numpy的阵列，它看起来像：

I have a numpy array which looks like:

myArray = np.array([[1,2],[3]])

但我不能将其压平，

But I can not flatten it,

In: myArray.flatten()
Out: array([[1, 2], [3]], dtype=object)

如果我改变数组中的第二个轴的长度相同，那么我可以将其压平。

If I change the array to the same length in the second axis, then I can flatten it.

In: myArray2 = np.array([[1,2],[3,4]])
In: myArray2.flatten()
Out: array([1, 2, 3, 4])

我的问题是：

我可以使用像 myArray.flatten（）不管阵列的尺寸和元素的长度，得到输出一些事情：阵列（[1,2,3]）？

Can I use some thing like myArray.flatten() regardless the dimension of the array and the length of its elements, and get the output: array([1,2,3])?

推荐答案

myArray的是对象的一维数组的。您的列表对象将只保持与相同的顺序压扁（）或拉威尔（）。您可以使用 hstack 以水平堆叠顺序数组：

myArray is a 1-dimensional array of objects. Your list objects will simply remain in the same order with flatten() or ravel(). You can use hstack to stack the arrays in sequence horizontally:

>>> np.hstack(myArray)
array([1, 2, 3])

请注意，这基本上等同于使用 CONCATENATE 为1的轴（这应该是有意义直观的）：

Note that this is basically equivalent to using concatenate with an axis of 1 (this should make sense intuitively):

>>> np.concatenate(myArray, axis=1)
array([1, 2, 3])

如果您的不的但是有这个问题，的可以的合并项目，它总是preferable使用压扁（） 或拉威尔（）性能：

If you don't have this issue however and can merge the items, it is always preferable to use flatten() or ravel() for performance:

In [1]: u = timeit.Timer('np.hstack(np.array([[1,2],[3,4]]))'\
   ....: , setup = 'import numpy as np')
In [2]: print u.timeit()
11.0124390125

In [3]: u = timeit.Timer('np.array([[1,2],[3,4]]).flatten()'\
   ....: , setup = 'import numpy as np')
In [4]: print u.timeit()
3.05757689476

Iluengo的回答也有你覆盖了更多的信息，为什么你不能使用压扁（ ）或拉威尔（）给您的数组类型。

Iluengo's answer also has you covered for further information as to why you cannot use flatten() or ravel() given your array type.

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