这篇教程Python如何利用pandas读取csv数据并绘图写得很实用，希望能帮到您。
如何利用pandas读取csv数据并绘图导包，常用的numpy和pandas，绘图模块matplotlib， import matplotlib.pyplot as pltimport pandas as pdimport numpy as npfig = plt.figure()ax = fig.add_subplot(111) 读取csv文件的数据，保存到numpy数组内 path_csv = "E://python//python//2021//202104//04091//path_data.csv"xa = np.array([42.0, 44.4, 43.1, 40.6])ya = np.array([21.6, 21.2, 13.5, 14.0])xa1 = np.array([10, 40])ya1 = np.array([10, 40])path_data_x = pd.read_csv(path_csv, header=None, usecols=[0])path_data_y = pd.read_csv(path_csv, header=None, usecols=[1])path_x = np.array(path_data_x)[:, 0]path_y = np.array(path_data_y)[:, 0]
绘制图像print(path_x[0])print(path_y[0])ax.plot(xa1, ya1, color='g', linestyle='', marker='.')ax.plot(xa, ya, color='g', linestyle='-', marker='.')ax.plot(path_x, path_y, color='m', linestyle='', marker='.')plt.show()
展示结果
pandas画pearson相关系数热力图
pearson相关系数计算函数该方法支持空值：np.nan import seaborn as snsimport numpy as npimport matplotlib.pyplot as pltdata = pd.DataFrame({"A":[np.nan,2,9], "B":[4,14,6], "c":[987,8,9]})f, ax= plt.subplots(figsize = (14, 10))corr = data.corr()# print(corr)sns.heatmap(corr,cmap='RdBu', linewidths = 0.05, ax = ax)# 设置Axes的标题ax.set_title('Correlation between features')plt.show()plt.close()f.savefig('sns_style_origin.jpg', dpi=100, bbox_inches='tight')
其中heatmap()方法中有annot参数，默认为False，不显示每个颜色的数字，如果设置为：annot=True， 则在每个热力图上显示数字。 效果如下： 
以上为个人经验，希望能给大家一个参考，也希望大家多多支持wanshiok.com。
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