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statsmodels.robust_kurtosis() in Python

• Last Updated : 10 May, 2020

With the help of `statsmodels.robust_kurtosis()` method, we can calculate the four kurtosis value by using `statsmodels.robust_kurtosis()` method.

Syntax : `statsmodels.robust_kurtosis(numpy_array)`

Return : Return four value of kurtosis i.e kr1, kr2, kr3 and kr4.

Example #1 :
In this example we can see that by using `statsmodels.robust_kurtosis()` method, we are able to get the four kurtosis value of a numpy array by using this method.

 `# import numpy and statsmodels``import` `numpy as np``from` `statsmodels.stats.stattools ``import` `robust_kurtosis``   ` `g ``=` `np.array([``1``, ``2``, ``3``, ``4``, ``7``, ``8``])``# Using statsmodels.robust_kurtosis() method``gfg ``=` `robust_kurtosis(g)``   ` `print``(gfg)`

Output :

(-1.3893422240232831, -0.17059511548521722, -0.9698425074861872, -1.218346951670164)

Example #2 :

 `# import numpy and statsmodels``import` `numpy as np``from` `statsmodels.stats.stattools ``import` `robust_kurtosis``   ` `g ``=` `np.array([``1``, ``2``, ``8``, ``9``, ``10``])``# Using statsmodels.robust_kurtosis() method``gfg ``=` `robust_kurtosis(g)``   ` `print``(gfg)`

Output :

(-1.7408163265306122, -0.5902379726280743, -1.4602271228708026, -1.6487040945273066)

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