Python 按列解压 NumPy 数组
声明：本页面是StackOverFlow热门问题的中英对照翻译，遵循CC BY-SA 4.0协议，如果您需要使用它，必须同样遵循CC BY-SA许可，注明原文地址和作者信息，同时你必须将它归于原作者（不是我）：StackOverFlow
原文地址: http://stackoverflow.com/questions/27046533/
Warning: these are provided under cc-by-sa 4.0 license. You are free to use/share it, But you must attribute it to the original authors (not me):
StackOverFlow
Unpack NumPy array by column
提问by jeff_new
If I have a NumPy array, for example 5x3, is there a way to unpack it column by column all at once to pass to a function rather than like this: my_func(arr[:, 0], arr[:, 1], arr[:, 2])?
如果我有一个 NumPy 数组，例如 5x3，有没有办法一次一列一列地解压它以传递给一个函数，而不是像这样：my_func(arr[:, 0], arr[:, 1], arr[:, 2])？
Kind of like *argsfor list unpacking but by column.
有点像*args列表解包，但按列。
采纳答案by Alex Riley
You can unpack the transpose of the array in order to use the columns for your function arguments:
您可以解压缩数组的转置，以便将列用于函数参数：
my_func(*arr.T)
Here's a simple example:
这是一个简单的例子：
>>> x = np.arange(15).reshape(5, 3)
array([[ 0, 5, 10],
[ 1, 6, 11],
[ 2, 7, 12],
[ 3, 8, 13],
[ 4, 9, 14]])
Let's write a function to add the columns together (normally done with x.sum(axis=1)in NumPy):
让我们编写一个函数来将列相加（通常x.sum(axis=1)在 NumPy 中完成）：
def add_cols(a, b, c):
return a+b+c
Then we have:
然后我们有：
>>> add_cols(*x.T)
array([15, 18, 21, 24, 27])
NumPy arrays will be unpacked along the first dimension, hence the need to transpose the array.
NumPy 数组将沿第一维解包，因此需要转置数组。
回答by Stephanie
numpy.splitsplits an array into multiple sub-arrays. In your case, indices_or_sectionsis 3 since you have 3 columns, and axis = 1since we're splitting by column.
numpy.split将数组拆分为多个子数组。在您的情况下，indices_or_sections是 3，因为您有 3 列，而且axis = 1我们按列拆分。
my_func(numpy.split(array, 3, 1))
回答by CookieMaster
I guess numpy.splitwill not suffice in the future. Instead, it should be
我想numpy.split将来是不够的。相反，它应该是
my_func(tuple(numpy.split(array, 3, 1)))
Currently, python prints the following warning:
目前，python 打印以下警告：
FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use
arr[tuple(seq)]instead ofarr[seq]. In the future this will be interpreted as an array index,arr[np.array(seq)], which will result either in an error or a different result.
FutureWarning：不推荐使用非元组序列进行多维索引；使用
arr[tuple(seq)]代替arr[seq]. 将来，这将被解释为数组索引，arr[np.array(seq)]，这将导致错误或不同的结果。
