import matplotlib.pyplot as plt
plt.figure()
plt.scatter(x_1[:,0] , x_1[:,2],marker=‘.‘,color=‘r‘)
plt.scatter(x_2[:,0] , x_2[:,2],marker=‘.‘,color=‘g‘)
plt.scatter(x_3[:,0] , x_3[:,2],marker=‘.‘,color=‘b‘)
plt.show()

import matplotlib.pyplot as plt
fig=plt.figure()
import mpl_toolkits.mplot3d as mp3d
ax=mp3d.Axes3D(fig)
ax.scatter(x_1[:,0],x_1[:,1],x_1[:,2])
ax.plot3D(x_1[:,0],x_1[:,1],x_1[:,2])
ax.set_xlabel(‘x‘)
ax.set_ylabel(‘y‘)
ax.set_zlabel(‘z‘)
ax.view_init(elev=45,azim=45)#从y俯视

import seaborn as sns
# 检验新的自变量和charges的相关性
corr_df = pd.DataFrame(x_poly, columns=[‘one‘,‘two‘,‘three‘,‘four‘])
corr_df[‘charges‘] = y
plt.figure()
sns.heatmap(corr_df.corr(), annot=True)

import matplotlib.pyplot as plt
kinds = "m", "n", "l", "k"
colors = ["red", "pink", "blue", "yellow"]        
soldNums = [1,1, 1, 2]
plt.pie(soldNums, labels=kinds, autopct="%3.1f%%", startangle=90, colors=colors)
plt.title("不同类型箱子的销售数量占比")
plt.show()