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  2. scipy stats.kurtosis () function | python

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