Pearson相关性分析 & plot绘图（相关性系数柱状图、绘制非空值数量柱状图）
1.Pearson相关性分析
- Pearson相关性分析是一种用于检测两个变量之间线性关系强度的统计方法，其结果介于-1和1之间。一个相关系数为1表示完全正相关，-1表示完全负相关，0则表示没有线性关系。 Pearson相关性分析假设数据来自正态分布，并且对异常值敏感。
2.Pearson相关性分析实例
# 计算pearsonr相关系数def calculate_pearsonr(pd): head = pd.head().columns.values GDM = pd["目标变量"].tolist() coefficient_of_association = {} significance_level = {} feature_cnt = {} for feature in head: if feature != "目标变量": ftc = 0 feature_values = pd[feature].tolist() GDM_temp, feature_temp, tag = [], [], 0 for v in feature_values: if str(v) != "nan": ftc += 1 GDM_temp.append(GDM[tag]) feature_temp.append(v) tag += 1 feature_cnt[feature] = ftc if len(feature_temp) > 1: pc = pearsonr(np.array(feature_temp), np.array(GDM_temp)) if str(pc[0]) != "nan": ca = pc[0] if ca < -0.0001: ca = ca*-1 coefficient_of_association[feature] = ca significance_level[feature] = pc[1] elif ca > 0.0001: coefficient_of_association[feature] = ca significance_level[feature] = pc[1] dp_ca = sorted( coefficient_of_association.items(), key=lambda x: x[1], reverse=True) print("pearsonr-相关系数:",dp_ca) dp_ca_Nempty=[(i[0], feature_cnt[i[0]]) for i in dp_ca] print("非空值的数量:",dp_ca_Nempty) return dp_caimport matplotlib.pyplot as pltdef plot1(dp_ca): # 将元组列表转换为字典 dp_ca_dict = dict(dp_ca) # 创建子图 # fig, ax = plt.subplots() fig = plt.figure(figsize=(16, 10)) ax = fig.add_subplot(1, 1, 1) # 绘制相关性系数柱状图 ax.bar(dp_ca_dict.keys(), dp_ca_dict.values()) ax.set_title('Correlation between Feature and 目标变量') ax.set_xlabel('Features') ax.set_ylabel('Correlation Coefficient') # 调整布局并显示图形 plt.xticks(rotation=45,ha='right') ## # 将x轴标签旋转45度，并以最后一个字符为旋转中心 # 设置x轴刻度标签字体大小为8 ax.tick_params(axis='x', labelsize=10) plt.tight_layout() plt.savefig("./Pearson.jpeg") plt.show()if __name__ == '__main__': file = pd.read_excel("./filename.xlsx") dp_ca=calculate_pearsonr(file) plot1(dp_ca)
3.plot绘图（相关性系数柱状图、绘制非空值数量柱状图）
import matplotlib.pyplot as plt# 获取数据dp_ca = [('feature1', 0.8), ('feature2', 0.6), ('feature3', 0.4),('feature4', 0.77), ('feature5', 0.2), ('feature6', 0.4)]dp_ca_Nempty = [('feature1', 100), ('feature3', 50), ('feature2', 20),('feature4', 70), ('feature5', 10), ('feature6', 26)]# 将元组列表转换为字典dp_ca_dict = dict(dp_ca)dp_ca_Nempty_dict = dict(dp_ca_Nempty)# 创建子图fig, axs = plt.subplots(1, 2, figsize=(10, 5))# 绘制相关性系数柱状图axs[0].bar(dp_ca_dict.keys(), dp_ca_dict.values())axs[0].set_title('Pearson correlation coefficients')axs[0].set_xlabel('Features')axs[0].set_ylabel('Correlation coefficient')# 绘制非空值数量柱状图axs[1].bar(dp_ca_Nempty_dict.keys(), dp_ca_Nempty_dict.values())axs[1].set_title('Number of non-empty values')axs[1].set_xlabel('Features')axs[1].set_ylabel('Count')# 调整布局并显示图形plt.xticks(rotation=45,ha='right') ## # 将x轴标签旋转45度，并以最后一个字符为旋转中心# 设置x轴刻度标签字体大小为10axs[0].tick_params(axis='x', labelsize=10)axs[1].tick_params(axis='x', labelsize=10)# 调整布局并显示图形plt.tight_layout()plt.show()
来源地址：https://blog.csdn.net/crist_meng/article/details/129840456
