scipy stats.skew() | Python

Last Updated : 11 Feb, 2019

scipy.stats.skew(array, axis=0, bias=True) function calculates the skewness of the data set.

skewness = 0 : normally distributed.
skewness > 0 : more weight in the left tail of the distribution.
skewness < 0 : more weight in the right tail of the distribution.

Its formula –

Parameters :
array : Input array or object having the elements.
axis : Axis along which the skewness value is to be measured. By default axis = 0.
bias : Bool; calculations are corrected for statistical bias, if set to False.

Returns : Skewness value of the data set, along the axis.

Code #1:

# Graph using numpy.linspace()  
# finding Skewness 
  
from scipy.stats import skew 
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( '\nSkewness for data : ', skew(y1)) 

Output :



Skewness for data : 1.1108237139164436

Code #2:

# Graph using numpy.linspace()  
# finding Skewness 
  
  
from scipy.stats import skew 
import numpy as np  
import pylab as p  
  
x1 = np.linspace( -5, 12, 1000 ) 
y1 = 1./(np.sqrt(2.*np.pi)) * np.exp( -.5*(x1)**2  ) 
  
p.plot(x1, y1, '.') 
  
print( '\nSkewness for data : ', skew(y1)) 

Output :



Skewness for data : 1.917677776148478

Code #3: On Random data

# finding Skewness 
  
from scipy.stats import skew 
import numpy as np  
  
# random values based on a normal distribution 
x = np.random.normal(0, 2, 10000) 
  
print ("X : \n", x) 
  
print('\nSkewness for data : ', skew(x)) 

Output :

X : 
 [ 0.03255323 -6.18574775 -0.58430139 ...  3.22112446  1.16543279
  0.84083317]

Skewness for data :  0.03248837584866293

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