python np array 减去一个数_从多维Numpy数组中减去均值

I'm currently learning about broadcasting in Numpy and in the book I'm reading (Python for Data Analysis by Wes McKinney the author has mentioned the following example to "demean" a two-dimensional array:

import numpy as np

arr = np.random.randn(4, 3)

print(arr.mean(0))

demeaned = arr - arr.mean(0)

print(demeaned)

print(demeand.mean(0))

Which effectively causes the array demeaned to have a mean of 0.

我的想法是将其应用于类似图像的三维数组：

import numpy as np

arr = np.random.randint(0, 256, (400,400,3))

demeaned = arr - arr.mean(2)

当然哪个失败了，因为根据广播规则，尾随尺寸必须匹配，在这里不是这种情况：

print(arr.shape) # (400, 400, 3)

print(arr.mean(2).shape) # (400, 400)

现在，通过从数组第三维中的每个索引中减去均值，我已经使其大部分工作了：

demeaned = np.ones(arr.shape)

for i in range(3):

demeaned[...,i] = arr[...,i] - means

print(demeaned.mean(0))

此时，返回值非常接近零，我认为这是一个精度误差。我真的对这个想法正确，还是我想念了另一个警告？

Also, this doesn't seam to be the cleanest, most 'numpy'-way to achieve what i wanted to achieve. Is there a function or a principle that i can make use of to improve the code?