python散点图及相关系数矩阵计算和相关性验证

python散点图及相关系数矩阵计算

# -*- coding:utf-8 -*-
# __author__ = "LQ"
import matplotlib.pyplot as plt
import pandas as pd
import scipy.stats as stats
#自定义方法调用，上一篇有该方法
from DataAnalysis.TeachingModel.dbc import dbcConnect

def main(sql):
    df = dbcConnect.selectDf(sql)
    print(df)
    # pearson相关系数矩阵
    corr = df.corr()
    print(corr)
    #计算某一个元素相关
    print(df.corr()[u'avg_getscore'])
    # 输出结果第一个值为pearsonr相关系数，
    # 第二个为p-value，所以这里Guba列和Value值是显著相关的
     pearsonr = stats.pearsonr(df['avg_total_afterrequire_num'], df['avg_getscore'])
     print(pearsonr)

    # 散点图矩阵
    pd.plotting.scatter_matrix(df, figsize=(8, 8),
                               c='k',
                               marker='+',
                               diagonal='hist',
                               alpha=0.8,
                               range_padding=0.1)
    plt.show()


if __name__ == '__main__':
    sql = '''SELECT
    avg_total_afterrequire_num,
    avg_total_beforerequire_num,
    avg_total_homework_num,
    avg_actual_homework_num,
    avg_total_exam_num,
    avg_actual_exam_num,
    avg_evaluate_num,
    avg_question_answer_num,
    avg_note_num,
    avg_error_num,
    avg_login_log_num,
    avg_question_times,
    avg_getscore
    FROM
    t_study_behavior_class
    WHERE
    avg_getscore > 0
    AND avg_getscore < 100'''
    main(sql)

自定义方法dbcConnect
在下面博客中
https://blog.csdn.net/qq_30868737/article/details/103995174

来源：CSDN

作者：安然烟火

链接：https://blog.csdn.net/qq_30868737/article/details/103995270

标签

散点图