scipy stats.skew() | Python
**scipy.stats.skew(array, axis=0, bias=True)**函数计算数据集的偏斜度。
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.
其公式–
参数: 数组:输入数组或有元素的对象。 轴:测量偏斜值的轴。默认情况下，轴= 0。 偏差:Bool；如果设置为假，计算将针对统计偏差进行校正。
沿轴返回:数据集的偏斜度值。
代码#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))
输出:
Skewness for data : 1.1108237139164436
代码#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))
输出:
Skewness for data : 1.917677776148478
代码#3: 随机数据
# 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))
输出:
X :
[ 0.03255323 -6.18574775 -0.58430139 ... 3.22112446 1.16543279
0.84083317]
Skewness for data : 0.03248837584866293
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