  # scipy stats.kurtosis () function | python

NumPy | Python Methods and Functions

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