# 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 `

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