如何在Matplotlib散点图中仅绘制一次均值而非重复绘制？

我有一个包含三列的数据集：第一列为实际数据，第二列为第一列的均值，第三列为类别。我希望在散点图中同时展示数据与均值，但目前均值被重复绘制N次，难以控制可视化效果。请问是否有办法仅绘制一次均值？

以下是示例代码，其中代表均值的橙色点被重复绘制14次叠加在一起：

import matplotlib.pyplot as plt
import pandas as pd

df = pd.DataFrame({
    'Numbers': [10,20,30,40,50,60,11,59,12,58,13,57,25,45],
    'Mean':    [35,35,35,35,35,35,35,35,35,35,35,35,35,35],
    'y':       [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1 ]    
})

fig, ax = plt.subplots(1)
ax.scatter(y="y", x= "Numbers", data=df, s = 200, alpha = .5, label = "实际数据")
ax.scatter(y="y", x= "Mean", data=df, s = 200, alpha = .15, label = "均值")
legend = ax.legend(loc="lower right", framealpha = .4)

方法1：提取均值列的唯一值绘制 #

直接从Mean列中提取唯一值，确保只绘制一次均值点：

import matplotlib.pyplot as plt
import pandas as pd

df = pd.DataFrame({
    'Numbers': [10,20,30,40,50,60,11,59,12,58,13,57,25,45],
    'Mean':    [35,35,35,35,35,35,35,35,35,35,35,35,35,35],
    'y':       [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1 ]    
})

fig, ax = plt.subplots(1)
# 绘制实际数据散点
ax.scatter(y="y", x= "Numbers", data=df, s = 200, alpha = .5, label = "实际数据")
# 提取唯一均值并对应类别值绘制
mean_value = df['Mean'].unique()[0]
category_value = df['y'].iloc[0]
ax.scatter(y=category_value, x=mean_value, s = 200, alpha = 1, label = "均值")
legend = ax.legend(loc="lower right", framealpha = .4)
plt.show()

方法2：直接传入标量值绘制 #

如果已知均值和对应的类别是固定值，直接传入标量参数即可，无需从整个DataFrame取数：

import matplotlib.pyplot as plt
import pandas as pd

df = pd.DataFrame({
    'Numbers': [10,20,30,40,50,60,11,59,12,58,13,57,25,45],
    'Mean':    [35,35,35,35,35,35,35,35,35,35,35,35,35,35],
    'y':       [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1 ]    
})

fig, ax = plt.subplots(1)
ax.scatter(y="y", x= "Numbers", data=df, s = 200, alpha = .5, label = "实际数据")
# 直接传入标量x=35，y=1绘制均值点
ax.scatter(y=1, x=35, s = 200, alpha = 1, label = "均值")
legend = ax.legend(loc="lower right", framealpha = .4)
plt.show()

方法3：多类别场景的通用处理 #

如果数据集包含多个类别（y列有不同取值），可以按类别分组后提取每组的均值，再分别绘制：

import matplotlib.pyplot as plt
import pandas as pd

# 构造多类别示例数据
df = pd.DataFrame({
    'Numbers': [10,20,30,40,50,60,11,59,12,58,13,57,25,45, 100,110,120],
    'Mean':    [35,35,35,35,35,35,35,35,35,35,35,35,35,35, 110,110,110],
    'y':       [1,1,1,1,1,1,1,1,1,1,1,1,1,1,2,2,2]    
})

fig, ax = plt.subplots(1)
# 绘制所有实际数据
ax.scatter(y="y", x= "Numbers", data=df, s = 200, alpha = .5, label = "实际数据")

# 按类别分组，提取每组的均值和类别值
grouped_data = df.groupby('y').agg({'Mean': 'first', 'y': 'first'})
# 遍历分组绘制每个类别的均值点
for _, row in grouped_data.iterrows():
    ax.scatter(y=row['y'], x=row['Mean'], s=200, alpha=1, label=f"类别{row['y']}均值")

legend = ax.legend(loc="lower right", framealpha = .4)
plt.show()

内容的提问来源于stack exchange，提问作者Aristide Herve