scipy stats.kurtosis () function | python

Its formula is —

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.

Code # 1:

# Graph using numpy.linspace ()
# finding kurtosa

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   (  `Kurtosis for normal distribution:`  , kurtosis (y1)) 
   
   print   (  `Kurtosis for normal distribution:`  , 
     kurtosis (y1, fisher   =   False  )) 
    
   print   (  `Kurtosis for normal distribution : ` , 
     kurtosis (y1 , fisher   =   True  ))

Output:

    
 Kurtosis for normal distribution: -0.3073930877422071 Kurtosis for normal distribution: 2.692606912257793 Kurtosis for normal distribution: -0.3073930877422071

scipy stats.kurtosis () function | python

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