python 求相关系数，,两组序列数据，求两者

python 求相关系数，,两组序列数据，求两者

两组序列数据，求两者的线性相关系数。

1：使用numpy

import randomimport numpy as npa = [random.randint(0, 10) for t in range(20)]b = [random.randint(0, 10) for t in range(20)]# 先构造一个矩阵ab = np.array([a, b])# 计算协方差矩阵print(np.cov(ab))print(np.corrcoef(ab))

2：使用pandas

import pandas as pd# 使用 pandas 计算协方差、相关系数# 使用 DataFrame 作为数据结构，为方便计算，我们会将 ab 矩阵转置dfab = pd.DataFrame(ab.T, columns=[‘A‘, ‘B‘])# A B 协方差print(dfab.A.cov(dfab.B))# A B 相关系数print(dfab.A.corr(dfab.B))

3：使用原生函数

import randomimport matha = [random.randint(0, 10) for t in range(20)]b = [random.randint(0, 10) for t in range(20)]#计算平均值def mean(x):  return sum(x) / len(x)# 计算每一项数据与均值的差def de_mean(x):  x_bar = mean(x)  return [x_i - x_bar for x_i in x]# 辅助计算函数 dot product 、sum_of_squaresdef dot(v, w):  return sum(v_i * w_i for v_i, w_i in zip(v, w))def sum_of_squares(v):  return dot(v, v)# 方差def variance(x):  n = len(x)  deviations = de_mean(x)  return sum_of_squares(deviations) / (n - 1)# 标准差def standard_deviation(x):  return math.sqrt(variance(x))# 协方差def covariance(x, y):  n = len(x)  return dot(de_mean(x), de_mean(y)) / (n -1)# 相关系数def correlation(x, y):  stdev_x = standard_deviation(x)  stdev_y = standard_deviation(y)  if stdev_x > 0 and stdev_y > 0:    return covariance(x, y) / stdev_x / stdev_y  else:    return 0print(a)print(b)print(standard_deviation(a))print(standard_deviation(b))print(correlation(a,b))

4：使用R，spss，excel

python 求相关系数