英文:
Given two numpy arrays, how to split one into an array of lists based on the second
问题
我有两个NumPy数组：一个包含任意值，另一个包含大于1的整数。这些整数的总和等于第一个数组的长度。示例：
values = np.array(["a", "b", "c", "d", "e", "f", "g", "h"])
lengths = np.array([1, 3, 2, 2])
len(values) == sum(lengths) # True
我想根据第二个数组的长度来分割第一个数组，并最终得到类似于以下的结果：
output = np.array([["a"], ["b", "c", "d"], ["e", "f"], ["g", "h"]], dtype=object)
使用Python循环遍历数组很容易，但当两个列表都非常大（包含数亿个元素）时，速度很慢。是否有一种方法可以使用原生的NumPy操作来执行此操作，这应该更快？
英文:
I have two numpy arrays: one containing arbitrary values, and one containing integers larger than 1. The sum of the integers is equal to the length of the first array. Sample:
values = np.array(["a", "b", "c", "d", "e", "f", "g", "h"])
lengths = np.array([1, 3, 2, 2])
len(values) == sum(lengths) # True
I would like to split the first array according to the lengths of the second array, and end up with something like:
output = np.array([["a"], ["b", "c", "d"], ["e", "f"], ["g", "h"]], dtype=object)
It's easy to iterate over the array with a Python loop, but it's also slow when both lists are very large (hundreds of millions of elements). Is there a way to do this operation using native numpy operations, which presumably should be must faster?
答案1
得分: 3
你可以使用numpy中的split方法：
output = np.split(values, np.cumsum(lengths))[:-1]
英文:
You can use the split method from numpy:
output = np.split(values, np.cumsum(lengths))[:-1]
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