如何为Seaborn双轴图中的折线图添加数据标签

我用Matplotlib/Seaborn绘制带折线图的双轴图表，尝试给折线图添加数据标签，但始终显示不出来。相关代码、数据和当前图表效果如下：

代码 #

# Pandas 用于数据管理
import pandas as pd

# Matplotlib 用于自定义设置
from matplotlib import pyplot as plt
%matplotlib inline

# Seaborn 用于绘图和样式设置
import seaborn as sns

# 示例数据
data = {'Performance Score': ['PIP', 'To Improve', 'Meet', 'Exceed'],
        'Employees by performance score': [2, 17, 201, 30],
        'Average Satisfaction': [1.5, 3.6, 3.9, 4.1]}
df = pd.DataFrame(data)

# 设置图表主题
sns.set_theme(style='dark', rc={'axes.facecolor':'White', 'figure.facecolor':'White'})
fig, ax1 = plt.subplots(figsize=(12,6))

# 绘制双轴图表
# 左Y轴为折线图
sns.lineplot(data = df, y='Average Satisfaction', x='Performance Score', marker='o', sort = False, ax=ax1, color='#97b0df', label='Average Satisfaction')
ax2 = ax1.twinx()

# 右Y轴为柱状图
sns.barplot(data = df, x = 'Performance Score', y = 'Employees by performance score', alpha=0.8, ax=ax2, color='#2c5494', label='Employees by Performance Score')

# 显示柱状图数据标签
for i in ax2.containers:
    ax2.bar_label(i,)

数据集 df #

Performance Score  Employees by performance score  Average Satisfaction
0               PIP                               2                   1.5
1        To Improve                              17                   3.6
2              Meet                             201                   3.9
3            Exceed                              30                   4.1

当前图表效果 #

Seaborn的lineplot没有像barplot那样的containers属性可直接遍历添加标签，需要手动获取折线的坐标点，逐个添加文本标签。

修改后的完整代码如下：

# Pandas 用于数据管理
import pandas as pd

# Matplotlib 用于自定义设置
from matplotlib import pyplot as plt
%matplotlib inline

# Seaborn 用于绘图和样式设置
import seaborn as sns

# 示例数据
data = {'Performance Score': ['PIP', 'To Improve', 'Meet', 'Exceed'],
        'Employees by performance score': [2, 17, 201, 30],
        'Average Satisfaction': [1.5, 3.6, 3.9, 4.1]}
df = pd.DataFrame(data)

# 设置图表主题
sns.set_theme(style='dark', rc={'axes.facecolor':'White', 'figure.facecolor':'White'})
fig, ax1 = plt.subplots(figsize=(12,6))

# 绘制左Y轴折线图，并捕获返回的折线对象
line_plot = sns.lineplot(data = df, y='Average Satisfaction', x='Performance Score', marker='o', sort = False, ax=ax1, color='#97b0df', label='Average Satisfaction')
ax2 = ax1.twinx()

# 绘制右Y轴柱状图
sns.barplot(data = df, x = 'Performance Score', y = 'Employees by performance score', alpha=0.8, ax=ax2, color='#2c5494', label='Employees by Performance Score')

# 显示柱状图数据标签
for i in ax2.containers:
    ax2.bar_label(i,)

# 为折线图添加数据标签
# 从折线对象获取所有数据点的坐标
x_data = line_plot.lines[0].get_xdata()
y_data = line_plot.lines[0].get_ydata()

# 遍历每个数据点添加格式化标签
for x, y in zip(x_data, y_data):
    ax1.text(x, y, f'{y:.1f}', ha='center', va='bottom', fontsize=10, color='#97b0df')

# 合并两个轴的图例，避免重叠
lines1, labels1 = ax1.get_legend_handles_labels()
lines2, labels2 = ax2.get_legend_handles_labels()
ax1.legend(lines1 + lines2, labels1 + labels2, loc='upper left')

plt.show()

关键说明： #

• 捕获lineplot返回的对象，通过lines[0].get_xdata()和get_ydata()获取折线的所有数据点坐标
• 使用ax1.text()在每个坐标点添加格式化数值标签，通过ha（水平对齐）和va（垂直对齐）调整位置，避免与折线标记重叠
• 合并双轴的图例，让图表布局更清晰

内容的提问来源于stack exchange，提问作者Tricia Ang