scipy stats.kurtosis()函数| Python

scipy.stats.kurtosis(array, axis=0, fisher=True, bias=True)函数计算数据集的峰度（Fisher 或 Pearson）。它是第四个中心矩除以方差的平方。它是“尾性”的度量，即实值随机变量的概率分布形状的描述符。简单来说，可以说它是衡量尾部与正态分布相比的重度。

它的公式——

Parameters :
array : Input array or object having the elements.
axis : Axis along which the kurtosis value is to be measured. By default axis = 0.
fisher : Bool; Fisher’s definition is used (normal 0.0) if True; else Pearson’s definition is used (normal 3.0) if set to False.
bias : Bool; calculations are corrected for statistical bias, if set to False.

Returns : Kurtosis value of the normal distribution for the data set.

代码#1：

# Graph using numpy.linspace() 
# finding kurtosis
  
from scipy.stats import kurtosis
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( '\nKurtosis for normal distribution :', kurtosis(y1))
  
print( '\nKurtosis for normal distribution :', 
      kurtosis(y1, fisher = False))
  
print( '\nKurtosis for normal distribution :', 
      kurtosis(y1, fisher = True))

输出 ：

正态分布峰度：-0.3073930877422071 正态分布峰度：2.692606912257793 正态分布峰度：-0.3073930877422071