Matplotlib入门09-绘制多子图

1.方式一：subplots

import pandas as pd
import seaborn as sns  # 基于matplotlib和pandas的画图库 
import matplotlib.pyplot as plt

#构建pandas对象
from sklearn import datasets
california = datasets.fetch_california_housing()
data = pd.DataFrame(california.data, columns=california.feature_names)
target = pd.DataFrame(california.target, columns=california.target_names)

# 获取数值列
numeric_cols = data.select_dtypes(include=['number']).columns.tolist()

n_features = len(numeric_cols)
n_cols = 4 # 一行有几个图
n_rows = (n_features + n_cols - 1)// n_cols # 一共分几行

# 构建画布
fig, axes = plt.subplots(
    nrows=n_rows, # 行数
    ncols=n_cols, # 列书
    figsize=(18, 5 * n_rows),  # 单个图表大小
    constrained_layout=True     # 启用自动布局
)
axes = axes.flatten() # 转置

# 循环绘制图像
for idx, feature in enumerate(numeric_cols):
    ax = axes[idx]  # 获取当前子图对象
    ax.scatter(data[feature], target['MedHouseVal'],s=10) # 设置x，y轴及点的大小
    ax.set_title(f"{feature}-MedHouseVal")
    ax.set_xlabel(feature)
    ax.set_ylabel("MedHouseVal")
plt.show()

2.方式二：seaborn

import pandas as pd
import seaborn as sns  # 基于matplotlib和pandas的画图库 
import matplotlib.pyplot as plt

#构建pandas对象
from sklearn import datasets
california = datasets.fetch_california_housing()
data = pd.DataFrame(california.data, columns=california.feature_names)
target = pd.DataFrame(california.target, columns=california.target_names)
new_data = pd.concat([data,target],axis=1)
# 获取数值列
numeric_cols = new_data.select_dtypes(include=['number']).columns.tolist()
#绘制图形
sns.set(style='ticks')
sns.pairplot(new_data[numeric_cols],hue=None, kind="reg",height = 2.5)
plt.show();

会绘制数据的两两相关关系