Python NumPy 中的 np.mean() 与 np.average() 对比? [英] np.mean() vs np.average() in Python NumPy?

问题描述

我注意到

In [30]: np.mean([1, 2, 3])
Out[30]: 2.0

In [31]: np.average([1, 2, 3])
Out[31]: 2.0

不过应该​​还是有区别的，毕竟是两个不同的功能.

However, there should be some differences, since after all they are two different functions.

它们之间有什么区别?

推荐答案

np.average 采用可选的权重参数.如果未提供，则它们是等效的.看一下源码:Mean, 平均值

np.average takes an optional weight parameter. If it is not supplied they are equivalent. Take a look at the source code: Mean, Average

np.mean:

try:
    mean = a.mean
except AttributeError:
    return _wrapit(a, 'mean', axis, dtype, out)
return mean(axis, dtype, out)

np.average:

np.average:

...
if weights is None :
    avg = a.mean(axis)
    scl = avg.dtype.type(a.size/avg.size)
else:
    #code that does weighted mean here

if returned: #returned is another optional argument
    scl = np.multiply(avg, 0) + scl
    return avg, scl
else:
    return avg
...

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