使用重复索引增加Numpy数组 [英] Increment Numpy array with repeated indices

问题描述

我有一个Numpy数组和一个索引列表，其值我想增加一个。此列表可能包含重复的索引，我希望增量与每个索引的重复次数成比例。没有重复，命令很简单：

I have a Numpy array and a list of indices whose values I would like to increment by one. This list may contain repeated indices, and I would like the increment to scale with the number of repeats of each index. Without repeats, the command is simple:

a=np.zeros(6).astype('int')
b=[3,2,5]
a[b]+=1

重复，我想出了以下方法。

With repeats, I've come up with the following method.

b=[3,2,5,2]                     # indices to increment by one each replicate
bbins=np.bincount(b)
b.sort()                        # sort b because bincount is sorted
incr=bbins[np.nonzero(bbins)]   # create increment array
bu=np.unique(b)                 # sorted, unique indices (len(bu)=len(incr))
a[bu]+=incr

这是最好的方法吗？假设 np.bincount 和 np.unique 操作会导致相同的排序顺序，是否存在风险？我错过了一些简单的Numpy操作来解决这个问题吗？

Is this the best way? Is there are risk involved with assuming that the np.bincount and np.unique operations would result in the same sorted order? Am I missing some simple Numpy operation to solve this?

推荐答案

执行后

bbins=np.bincount(b)

为什么不这样做：

a[:len(bbins)] += bbins

（编辑进一步简化。）

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