# scipy stats.kurtosis() function | Python

`scipy.stats.kurtosis(array, axis=0, fisher=True, bias=True)` function calculates the kurtosis (Fisher or Pearson) of a data set. It is the the fourth central moment divided by the square of the variance. It is a measure of the “tailedness” i.e. descriptor of shape of probability distribution of a real-valued random variable. In simple terms, one can say it is a measure of how heavy tail is compared to a normal distribution.

Its formula – 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 kurtosis ` ` `  `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``( ``'\nKurtosis for normal distribution :'``, kurtosis(y1)) ` ` `  `print``( ``'\nKurtosis for normal distribution :'``,  ` `      ``kurtosis(y1, fisher ``=` `False``)) ` ` `  `print``( ``'\nKurtosis 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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