Python pandas groupby后如何在同一张画布绘制多条折线而非独立图表

问题根因 #

groupby对象直接调用plot方法时，默认会为每个分组单独创建独立的绘图实例，因此会输出3张独立图表。 #

修复代码 #

方案1：基于matplotlib原生实现 #

import pandas as pd
import matplotlib.pyplot as plt

df = pd.DataFrame({ 
'A': ['aaa','aaa','aaa','aaa','aaa','aaa','aaa','aaa','aaa','aaa','aaa','aaa'],
'B': ['bbb','bbb','bbb','bbb','bbb','bbb','bbb','bbb','bbb','bbb','bbb','bbb'],
'DC': ['L','L','L','L','Q','Q','Q','Q','ZL','ZL','ZL','ZL'],
'score' : [0.1,0.2,0.3,0.4,0.11,0.21,0.31,0.39,0.1,0.22,0.3,0.42],
'max_sel' : [2.0,3.3,6.0,7.1,3.1,4.0,8.0,8.9,1.2,3.0,5.0,6.6]
})

# 提前创建公共的绘图轴对象
fig, ax = plt.subplots(figsize=(7,4))

# 遍历所有分组，指定在同一个ax上绘图
for (a_val, b_val, dc_val), group_df in df.groupby(["A","B","DC"]):
    group_df.plot(
        ax=ax, 
        x="score", 
        y="max_sel", 
        label=f"{a_val}_{b_val}_{dc_val}",
        xlabel="score",
        ylabel="max_sel"
    )

plt.show()

方案2：基于seaborn简化实现 #

如果可以使用seaborn库，代码会更简洁：

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

df = pd.DataFrame({ 
'A': ['aaa','aaa','aaa','aaa','aaa','aaa','aaa','aaa','aaa','aaa','aaa','aaa'],
'B': ['bbb','bbb','bbb','bbb','bbb','bbb','bbb','bbb','bbb','bbb','bbb','bbb'],
'DC': ['L','L','L','L','Q','Q','Q','Q','ZL','ZL','ZL','ZL'],
'score' : [0.1,0.2,0.3,0.4,0.11,0.21,0.31,0.39,0.1,0.22,0.3,0.42],
'max_sel' : [2.0,3.3,6.0,7.1,3.1,4.0,8.0,8.9,1.2,3.0,5.0,6.6]
})

# 生成分组标签列
df["group_name"] = df["A"] + "_" + df["B"] + "_" + df["DC"]

# 直接绘图，hue参数按分组区分折线
sns.lineplot(data=df, x="score", y="max_sel", hue="group_name")
plt.xlabel("score")
plt.ylabel("max_sel")
plt.show()

内容的提问来源于stack exchange，提问作者pytonnan