# scipy stats.kurtosistest() function | Python

`scipy.stats.kurtosistest(array, axis=0)` function test whether the given data set has normal kurtosis (Fisher or Pearson) or not.

What is Kurtosis ?
It is 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 kurtosistest is to be computed. By default axis = 0.

Returns : Z-score (Statistics value) and P-value for the normally distributed data set.

Code #1:

 `# Graph using numpy.linspace()  ` `# finding kurtosis ` ` `  `from` `scipy.stats ``import` `kurtosistest ` `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 :\n'``, kurtosistest(y1)) `

Output :

``` Kurtosis for normal distribution :
KurtosistestResult(statistic=-2.2557936070461615, pvalue=0.024083559905734513)
```
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