源数据在结尾处：
import math import numpy as np import xlrd import scipy.stats as stats from sklearn.metrics import mean_squared_error # 用于评估模型 from sklearn.metrics import r2_score workbook = xlrd.open_workbook('F:\\2020TreeClassification\\GFS2Analyze\\20201215R2RMSE.xlsx', 'r') sheet1 = workbook.sheet_by_index(0) for i in range(6): if i<=2: a = sheet1.col_values(i+3)[2:] #计算第二行以后的数据 b = sheet1.col_values(i+9)[2:] GF_RMSE = math.sqrt(mean_squared_error(a, b)) print('The GF_B' + str(i+1) + ' rmse is', GF_RMSE) Per = stats.pearsonr(a, b) print('The GF_B' + str(i + 1) + 'persson is', Per) elif i==3: a = sheet1.col_values(i + 3)[2:] b = sheet1.col_values(i + 10)[2:] GF_RMSE = math.sqrt(mean_squared_error(a, b)) print('The GF_B' + str(i + 1) + ' rmse is', GF_RMSE) Per = stats.pearsonr(a, b) print('The GF_B' + str(i + 1) + 'persson is', Per) elif i==4: a = sheet1.col_values(i + 3)[2:] b = sheet1.col_values(i + 8)[2:] GF_RMSE = math.sqrt(mean_squared_error(a, b)) print('The GF_B' + str(i + 1) + ' rmse is', GF_RMSE) Per = stats.pearsonr(a, b) print('The GF_B' + str(i + 1) + 'persson is', Per) else: a = sheet1.col_values(i + 3)[2:] b = sheet1.col_values(i + 10)[2:] GF_RMSE = math.sqrt(mean_squared_error(a, b)) print('The GF_B' + str(i + 1) + ' rmse is', GF_RMSE) Per = stats.pearsonr(a, b) print('The GF_B' + str(i + 1) + 'persson is', Per) print(u'结束！')输出结果： Pearson系数为一个元组，包括两个值，第一个值为相关系数，值越接近1，表明两组数据拟合结果越好；第二个值为显著性，值越小，表明显著性越强
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