用Python绘制波士顿数据集中CRIM和MEDV的折线图和散点图，并绘制为一张折线图+一张散点图

以下是代码实现：

import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd

# 加载波士顿房价数据集
from sklearn.datasets import load_boston
boston = load_boston()

# 将数据集转换为DataFrame
df = pd.DataFrame(boston.data, columns=boston.feature_names)
df['MEDV'] = boston.target

# 绘制CRIM和MEDV的折线图
fig, ax = plt.subplots()
ax.plot(df['CRIM'], df['MEDV'])
ax.set_xlabel('CRIM')
ax.set_ylabel('MEDV')
ax.set_title('CRIM vs. MEDV')

# 绘制CRIM和MEDV的散点图
fig, ax = plt.subplots()
sns.scatterplot(x='CRIM', y='MEDV', data=df)
ax.set_xlabel('CRIM')
ax.set_ylabel('MEDV')
ax.set_title('CRIM vs. MEDV')

# 绘制CRIM和MEDV的折线图和散点图
fig, ax = plt.subplots()
ax.plot(df['CRIM'], df['MEDV'])
sns.scatterplot(x='CRIM', y='MEDV', data=df)
ax.set_xlabel('CRIM')
ax.set_ylabel('MEDV')
ax.set_title('CRIM vs. MEDV')
plt.show()

运行代码后，会分别绘制CRIM和MEDV的折线图和散点图，以及折线图和散点图的组合图，效果如下：