python 相关系数（全）
1.皮尔逊相关系数
#两个变量计算#
import pandas as pd
A=[1,3,6,9,0,3]
B=[3,5,1,4,11,3]
A1=pd.Series(A)
B1=pd.Series(B)
corr=B1.corr(A1,method=‘pearson’)
print(corr)
#dataframe计算#
import pandas as pd
data=pd.DataFrame({‘a’:[1,3,6,9,0,3],‘b’:[3,5,1,4,11,3]})
corr=data.corr(method=‘pearson’)
print(corr)
2.斯皮尔曼相关系数
#两个变量计算#
import pandas as pd
A=[1,3,6,9,0,3]
B=[3,5,1,4,11,3]
A1=pd.Series(A)
B1=pd.Series(B)
corr=B1.corr(A1,method=‘spearman’)
print(corr)
#dataframe计算#
import pandas as pd
data=pd.DataFrame({‘a’:[1,3,6,9,0,3],‘b’:[3,5,1,4,11,3]})
corr=data.corr(method=‘spearman’)
print(corr)
3.肯德尔秩相关系数
#两个变量计算#
import pandas as pd
A=[1,3,6,9,0,3]
B=[3,5,1,4,11,3]
A1=pd.Series(A)
B1=pd.Series(B)
corr=B1.corr(A1,method=‘kendall’)
print(corr)
#dataframe计算#
import pandas as pd
data=pd.DataFrame({‘a’:[1,3,6,9,0,3],‘b’:[3,5,1,4,11,3]})
corr=data.corr(method=‘kendall’)
print(corr)
4.距离相关系数
import dcor
import numpy as np
a1=np.array([11,2,56,34])
b1=np.array([45,15,26,24])
dcor.distance_correlation(a1,b1)
关于 dcor 安装包及距离相关系数的详细介绍我主页写过一篇文章：https://mp.csdn.net/editor/html/108356586