numpy阵列多重面膜 [英] Numpy array multiple mask

问题描述

尝试基于整数掩码数组多次对numpy数组进行切片和平均:

Trying to slice and average a numpy array multiple times, based on an integer mask array:

即

import numpy as np

data = np.arange(11)
mask = np.array([0, 1, 1, 1, 0, 2, 2, 3, 3, 3, 3])

results = list()
for maskid in range(1,4):
    result = np.average(data[mask==maskid])
    results.append(result)
output = np.array(result)

有没有一种方法可以更快地执行此操作，而又没有"for"循环?

Is there a way to do this faster, aka without the "for" loop?

推荐答案

使用另一种具有 np.unique 用于一般情况，其中mask中的元素不一定是从0-

Another one with np.unique for a generic case when the elements in mask aren't necessarily sequential starting from 0 -

_,ids, count = np.unique(mask, return_inverse=1, return_counts=1)
out = np.bincount(ids, data)/count

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