计算一个给定的NumPy数组的平均数、标准差和方差

在NumPy中，我们可以通过两种方法计算给定数组沿第二轴的平均数、标准差和方差，第一种是使用内置函数，第二种是通过平均数、标准差和方差的公式。

方法1：使用 numpy.mean() , numpy.std() **, **numpy.var()

import numpy as np
  
  
# Original array
array = np.arange(10)
print(array)
  
r1 = np.mean(array)
print("\nMean: ", r1)
  
r2 = np.std(array)
print("\nstd: ", r2)
  
r3 = np.var(array)
print("\nvariance: ", r3)

输出:

[0 1 2 3 4 5 6 7 8 9]

Mean:  4.5

std:  2.8722813232690143

variance:  8.25

方法2：

import numpy as np
  
# Original array
array = np.arange(10)
print(array)
  
r1 = np.average(array)
print("\nMean: ", r1)
  
r2 = np.sqrt(np.mean((array - np.mean(array)) ** 2))
print("\nstd: ", r2)
  
r3 = np.mean((array - np.mean(array)) ** 2)
print("\nvariance: ", r3)

输出:

[0 1 2 3 4 5 6 7 8 9]

Mean:  4.5

std:  2.8722813232690143

variance:  8.25

例子：比较内置的方法和公式。

import numpy as np
  
# Original array
x = np.arange(5)
print(x)
  
r11 = np.mean(x)
r12 = np.average(x)
print("\nMean: ", r11, r12)
  
r21 = np.std(x)
r22 = np.sqrt(np.mean((x - np.mean(x)) ** 2))
print("\nstd: ", r21, r22)
  
r31 = np.var(x)
r32 = np.mean((x - np.mean(x)) ** 2)
print("\nvariance: ", r31, r32)

输出:

[0 1 2 3 4]

Mean:  2.0 2.0

std:  1.4142135623730951 1.4142135623730951

variance:  2.0 2.0