scipy stats.kurtosis() function | Python

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

scipy stats.kurtosis() function | Python

V

vishal3096

Improve

Article Tags :

Practice Tags :

python

Similar Reads

scipy stats.skewtest() function | Python

scipy.stats.skewtest(array, axis=0) function test whether the skew is different from the normal distribution. This function tests the null hypothesis that the skewness of the population that the sample was drawn from is the same as that of a corresponding normal distribution. Its formula - Parameter

sciPy stats.sem() function | Python

scipy.stats.sem(arr, axis=0, ddof=0) function is used to compute the standard error of the mean of the input data. Parameters : arr : [array_like]Input array or object having the elements to calculate the standard error. axis : Axis along which the mean is to be computed. By default axis = 0. ddof :

scipy stats.beta() | Python

The scipy.stats.beta() is a beta continuous random variable that is defined with a standard format and some shape parameters to complete its specification. [Tex]f(x,Î±,Î²)=(Î“(Î±+Î²)xÎ±âˆ’1(1âˆ’x)Î²âˆ’1â€‹)/Î“(Î±)Î“(Î²) [/Tex] where: [Tex]Î±>0 and Î²>0Î²>0[/Tex] are the shape parameters of the Beta distribution.

sciPy stats.alpha() | Python

scipy.stats.alpha() is an alpha continuous random variable that is defined with a standard format and some shape parameters to complete its specification. Parameters : q : lower and upper tail probability x : quantiles a : shape parameter loc : [optional] location parameter. Default = 0 scale : [opt

sciPy stats.tmean() | Python

scipy.stats.tmean(array, limits=None, inclusive=(True, True)) calculates the trimmed mean of the array elements along the specified axis of the array. It's formula - Parameters : array: Input array or object having the elements to calculate the trimmed mean. axis: Axis along which the trimmed mean i

sciPy stats.hmean() | Python

scipy.stats.hmean(array, axis=0, dtype=None) calculates the harmonic mean of the array elements along the specified axis of the array (list in python). It's formula - Parameters : array: Input array or object having the elements to calculate the harmonic mean. axis: Axis along which the mean is to b

scipy stats.frechet_l() | Python

scipy.stats.frechet_l() is an Frechet left (or Weibull maximum) continuous random variable that is defined with a standard format and some shape parameters to complete its specification. Parameters : -> q : lower and upper tail probability -> a : shape parameters -> x : quantiles -> loc

sciPy stats.signaltonoise() function | Python

scipy.stats.signaltonoise(arr, axis=0, ddof=0) function computes the signal-to-noise ratio of the input data. Its formula : Parameters :arr : [array_like]Input array or object having the elements to calculate the signal-to-noise ratio axis : Axis along which the mean is to be computed. By default ax

sciPy stats.mean() function | Python

scipy.stats.mean(array, axis=0) function calculates the arithmetic mean of the array elements along the specified axis of the array (list in python). It's formula - Parameters : array: Input array or object having the elements to calculate the arithmetic mean. axis: Axis along which the mean is to b

sciPy stats.tsem() function | Python

scipy.stats.tsem(array, limits=None, inclusive=(True, True)) calculates the trimmed standard error of the mean of array elements along the specified axis of the array. Its formula :- Parameters : array: Input array or object having the elements to calculate the trimmed standard error of the mean. ax