scipy stats.skew() | Python

scipy.stats.skew(array, axis=0, bias=True)函数计算数据集的偏度。

skewness = 0 : normally distributed.
skewness > 0 : more weight in the left tail of the distribution.
skewness < 0 : more weight in the right tail of the distribution. 

它的公式——

Parameters :
array : Input array or object having the elements.
axis : Axis along which the skewness value is to be measured. By default axis = 0.
bias : Bool; calculations are corrected for statistical bias, if set to False.

Returns : Skewness value of the data set, along the axis.

代码#1：

# Graph using numpy.linspace() 
# finding Skewness
  
from scipy.stats import skew
import numpy as np 
import pylab as p 
  
x1 = np.linspace( -5, 5, 1000 )
y1 = 1./(np.sqrt(2.*np.pi)) * np.exp( -.5*(x1)**2  )
  
p.plot(x1, y1, '*')
  
print( '\nSkewness for data : ', skew(y1))

输出 ：

数据偏度：1.1108237139164436

代码#2：

# Graph using numpy.linspace() 
# finding Skewness
  
  
from scipy.stats import skew
import numpy as np 
import pylab as p 
  
x1 = np.linspace( -5, 12, 1000 )
y1 = 1./(np.sqrt(2.*np.pi)) * np.exp( -.5*(x1)**2  )
  
p.plot(x1, y1, '.')
  
print( '\nSkewness for data : ', skew(y1))

输出 ：

数据偏度：1.917677776148478

代码#3：随机数据

# finding Skewness
  
from scipy.stats import skew
import numpy as np 
  
# random values based on a normal distribution
x = np.random.normal(0, 2, 10000)
  
print ("X : \n", x)
  
print('\nSkewness for data : ', skew(x))

输出 ：

X : 
 [ 0.03255323 -6.18574775 -0.58430139 ...  3.22112446  1.16543279
  0.84083317]

Skewness for data :  0.03248837584866293