在python中重塑一个numpy数组

问题描述

我有一个 48x365 元素的 numpy 数组，其中每个元素都是一个包含 3 个整数的列表.我希望能够将它变成一个 1x17520 的数组，所有列表都完好无损地作为元素.使用

np.reshape(-1)

似乎将元素分解为三个单独的整数并制作一个 1x52560 数组.所以我要么需要一种重新排列原始数组的新方法，要么需要一种将新 np.reshape 数组中的元素(仍按顺序排列)重新分组为 3 的列表的方法.

感谢您的帮助.

解决方案

您是否有不能明确执行的原因?如:

>>>a = numpy.arange(17520 * 3).reshape(48, 365, 3)>>>a.reshape((17520,3))数组([[ 0, 1, 2],[ 3, 4, 5],[ 6, 7, 8],...,[52551, 52552, 52553],[52554, 52555, 52556],[52557、52558、52559]])

您也可以使用 -1 来实现，它只需要与另一个适当大小的 arg 配对即可.

>>>a.reshape((17520,-1))数组([[ 0, 1, 2],[ 3, 4, 5],[ 6, 7, 8],...,[52551, 52552, 52553],[52554, 52555, 52556],[52557、52558、52559]])

或

>>>a.reshape((-1,3))数组([[ 0, 1, 2],[ 3, 4, 5],[ 6, 7, 8],...,[52551, 52552, 52553],[52554, 52555, 52556],[52557、52558、52559]])

稍后我突然想到您还可以创建一个记录数组——这在某些情况下可能是合适的:

a = numpy.recarray((17520,), dtype=[('x', int), ('y', int), ('z', int)])

这可以按照您尝试的原始方式进行重塑，即 reshape(-1).尽管如此，正如 larsmans 的评论所说，将您的数据视为 3d 数组是最简单的.

I have a 48x365 element numpy array where each element is a list containing 3 integers. I want to be able to turn it into a 1x17520 array with all the lists intact as elements. Using

np.reshape(-1)

seems to break the elements into three separate integers and makes a 1x52560 array. So I either need a new way of rearranging the original array or a way of grouping the elements in the new np.reshape array (which are still in order) back into lists of 3.

Thanks for your help.

解决方案

Is there a reason you can't do it explicitly? As in:

>>> a = numpy.arange(17520 * 3).reshape(48, 365, 3)
>>> a.reshape((17520,3))
array([[    0,     1,     2],
       [    3,     4,     5],
       [    6,     7,     8],
       ...,
       [52551, 52552, 52553],
       [52554, 52555, 52556],
       [52557, 52558, 52559]])

You could also do it with -1, it just has to be paired with another arg of the appropriate size.

>>> a.reshape((17520,-1))
array([[    0,     1,     2],
       [    3,     4,     5],
       [    6,     7,     8],
       ...,
       [52551, 52552, 52553],
       [52554, 52555, 52556],
       [52557, 52558, 52559]])

or

>>> a.reshape((-1,3))
array([[    0,     1,     2],
       [    3,     4,     5],
       [    6,     7,     8],
       ...,
       [52551, 52552, 52553],
       [52554, 52555, 52556],
       [52557, 52558, 52559]])

---

It occurred to me a bit later that you could also create a record array -- this might be appropriate in some situations:

a = numpy.recarray((17520,), dtype=[('x', int), ('y', int), ('z', int)])

This can be reshaped in the original way you tried, i.e. reshape(-1). Still, as larsmans' comment says, just treating your data as a 3d array is easiest.

这篇关于在python中重塑一个numpy数组的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持！