# 设置子图 fig, axs = plt.subplots(nrows=2, ncols=3, figsize=(15, 10)) # 绘制分布密度图 sns.histplot(data=data, x="PM2.5", bins=20, kde=True) sns.histplot(data=data, x="PM10", bins=20, kde=True) sns.histplot(data=data, x="SO2", bins=20, kde=True) sns.histplot(data=data, x="NO2", bins=20, kde=True) sns.histplot(data=data, x="CO", bins=20, kde=True) sns.histplot(data=data, x="O3", bins=20, kde=True) plt.show()优化这段代码,各可视化结果分别显示在子图中
时间: 2024-02-18 15:01:23 浏览: 26
以下是优化后的代码,其中每个子图都有标题,x轴标签和y轴标签:
```python
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
# 读取数据
data = pd.read_excel('北京市空气数据.xlsx',usecols=['PM2.5','PM10','SO2','NO2','CO','O3'])
# 设置子图
fig, axs = plt.subplots(nrows=2, ncols=3, figsize=(15, 10))
# 绘制子图
sns.histplot(data=data, x="PM2.5", bins=20, kde=True, ax=axs[0,0])
axs[0,0].set_title("PM 2.5 Distribution")
axs[0,0].set_xlabel("PM 2.5")
axs[0,0].set_ylabel("Count")
sns.histplot(data=data, x="PM10", bins=20, kde=True, ax=axs[0,1])
axs[0,1].set_title("PM 10 Distribution")
axs[0,1].set_xlabel("PM 10")
axs[0,1].set_ylabel("Count")
sns.histplot(data=data, x="SO2", bins=20, kde=True, ax=axs[0,2])
axs[0,2].set_title("SO2 Distribution")
axs[0,2].set_xlabel("SO2")
axs[0,2].set_ylabel("Count")
sns.histplot(data=data, x="NO2", bins=20, kde=True, ax=axs[1,0])
axs[1,0].set_title("NO2 Distribution")
axs[1,0].set_xlabel("NO2")
axs[1,0].set_ylabel("Count")
sns.histplot(data=data, x="CO", bins=20, kde=True, ax=axs[1,1])
axs[1,1].set_title("CO Distribution")
axs[1,1].set_xlabel("CO")
axs[1,1].set_ylabel("Count")
sns.histplot(data=data, x="O3", bins=20, kde=True, ax=axs[1,2])
axs[1,2].set_title("O3 Distribution")
axs[1,2].set_xlabel("O3")
axs[1,2].set_ylabel("Count")
# 显示子图
plt.show()
```