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Python三种方法计算皮尔逊相关系数

1、皮尔逊相关系数

在统计学中，皮尔逊相关系数( Pearson correlation coefficient），又称皮尔逊积矩相关系数（Pearson product-moment correlation coefficient，简称 PPMCC或PCCs）。用于衡量两个变量X和Y之间的线性相关相关关系，值域在-1与1之间。

2、根据公式手写

import numpy as np
import pandas as pd
from math import *
def multipl(a,b):
    sumofab=0.0
    for i in range(len(a)):
        temp=a[i]*b[i]
        sumofab+=temp
    return sumofab
 
def cal_pccs(x,y):
    n=len(x)
    #求和
    sum1=sum(x)
    sum2=sum(y)
    #求乘积之和
    sumofxy=multipl(x,y)
    #求平方和
    sumofx2 = sum([pow(i,2) for i in x])
    sumofy2 = sum([pow(j,2) for j in y])
    num=sumofxy-(float(sum1)*float(sum2)/n)
    #计算皮尔逊相关系数
    den=sqrt((sumofx2-float(sum1**2)/n)*(sumofy2-float(sum2**2)/n))
    return num/den
pct_chg = pd.Series({'000001.SZ':-1.7391, '000002.SZ':0.6250,'000004.SZ':1.1378,'002600.SZ':0.0000,'000010.SZ':-1.0013})
vol = pd.Series({'000001.SZ':249326.31,'000002.SZ':338224.97,'000004.SZ':211876.00,'000010.SZ':222782.00,'002600.SZ':342096.76})
print(cal_pccs(pct_chg, vol))

3、talib的函数

import talib as ta
import pandas as pd
pct_chg = pd.Series({'000001.SZ':-1.7391, '000002.SZ':0.6250,'000004.SZ':1.1378,'002600.SZ':0.0000,'000010.SZ':-1.0013})
vol = pd.Series({'000001.SZ':249326.31,'000002.SZ':338224.97,'000004.SZ':211876.00,'000010.SZ':222782.00,'002600.SZ':342096.76})
print(ta.CORREL(pct_chg,vol,5)[-1])

4、numpy的函数

import numpy as np
import pandas as pd
pct_chg = pd.Series({'000001.SZ':-1.7391, '000002.SZ':0.6250,'000004.SZ':1.1378,'002600.SZ':0.0000,'000010.SZ':-1.0013})
vol = pd.Series({'000001.SZ':249326.31,'000002.SZ':338224.97,'000004.SZ':211876.00,'000010.SZ':222782.00,'002600.SZ':342096.76})
pccs = np.corrcoef(pct_chg, vol)
print(pccs[0][1])

5、scipy的函数

import numpy as np
import pandas as pd
from scipy.stats import pearsonr
pct_chg = pd.Series({'000001.SZ':-1.7391, '000002.SZ':0.6250,'000004.SZ':1.1378,'002600.SZ':0.0000,'000010.SZ':-1.0013})
vol = pd.Series({'000001.SZ':249326.31,'000002.SZ':338224.97,'000004.SZ':211876.00,'000010.SZ':222782.00,'002600.SZ':342096.76})
pccs = pearsonr(pct_chg, vol)
print(pccs[0])

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