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. / (sqrt-in-python/">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