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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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