基于Pandas列值为绘图区间添加分段着色背景的实现方法

你手头的DataFrame包含分段标识segment、样本编号sample和数据值data_value，需要绘制sample与data_value的散点图，并给不同segment对应的x轴区间添加不同颜色的背景，同时显示对应图例。你已经实现了散点图绘制，想知道是否能用fill_between()实现背景分段着色——答案是肯定的，下面是具体实现步骤：

1. 准备数据与自动获取分段区间 #

先通过分组计算，自动获取每个segment对应的sample区间，避免硬编码适配数据变化：

import pandas as pd

# 构建示例DataFrame
data = {
    'segment': ['first', 'first', 'first', 'second', 'second', 'second', 'third', 'third', 'third'],
    'sample': [1, 2, 3, 4, 5, 6, 7, 8, 9],
    'data_value': [28, 53, 19, 50, 39, 61, 22, 49, 34]
}
df = pd.DataFrame(data)

# 计算每个segment的sample最小/最大值，得到区间
segment_ranges = df.groupby('segment')['sample'].agg(['min', 'max']).reset_index()

2. 定义分段颜色映射 #

给每个segment分配对应背景色：

color_map = {
    'first': '#63aaff',   # 蓝色
    'second': '#70d675',  # 绿色
    'third': '#ff9e64'    # 橙色
}

3. 使用fill_between()绘制背景色块 #

利用fill_between()填充x轴区间内的y轴范围，设置透明度避免遮挡散点：

import matplotlib.pyplot as plt

# 设置y轴范围（给数据上下留5个单位的边距）
y_min = df['data_value'].min() - 5
y_max = df['data_value'].max() + 5

fig, ax = plt.subplots(figsize=(8, 5))

# 遍历每个分段，绘制背景色块
for _, row in segment_ranges.iterrows():
    seg = row['segment']
    # 让色块覆盖sample的完整区间（比如sample=1对应x从0.5到1.5）
    x_start = row['min'] - 0.5
    x_end = row['max'] + 0.5
    ax.fill_between([x_start, x_end], y_min, y_max, color=color_map[seg], alpha=0.2)

4. 绘制散点图并添加图例 #

通过zorder确保散点在背景之上，手动创建图例项来对应分段背景色：

from matplotlib.patches import Patch

# 绘制散点图，zorder设置为2让散点在背景（默认zorder=1）之上
ax.scatter(df['sample'], df['data_value'], color='black', zorder=2)

# 创建背景色块对应的图例元素
legend_elements = [Patch(facecolor=color_map[seg], alpha=0.2, label=seg) for seg in color_map.keys()]
ax.legend(handles=legend_elements, title='Segment')

# 设置轴标签与标题
ax.set_xlabel('Sample')
ax.set_ylabel('Data Value')
ax.set_title('Scatter Plot with Segment Backgrounds')

# 显示所有sample的x轴刻度
ax.set_xticks(df['sample'])

plt.tight_layout()
plt.show()

完整代码整合 #

import pandas as pd
import matplotlib.pyplot as plt
from matplotlib.patches import Patch

# 构建示例DataFrame
data = {
    'segment': ['first', 'first', 'first', 'second', 'second', 'second', 'third', 'third', 'third'],
    'sample': [1, 2, 3, 4, 5, 6, 7, 8, 9],
    'data_value': [28, 53, 19, 50, 39, 61, 22, 49, 34]
}
df = pd.DataFrame(data)

# 计算每个segment的sample区间
segment_ranges = df.groupby('segment')['sample'].agg(['min', 'max']).reset_index()

# 定义颜色映射
color_map = {
    'first': '#63aaff',
    'second': '#70d675',
    'third': '#ff9e64'
}

# 设置y轴范围
y_min = df['data_value'].min() - 5
y_max = df['data_value'].max() + 5

fig, ax = plt.subplots(figsize=(8, 5))

# 绘制分段背景
for _, row in segment_ranges.iterrows():
    seg = row['segment']
    x_start = row['min'] - 0.5
    x_end = row['max'] + 0.5
    ax.fill_between([x_start, x_end], y_min, y_max, color=color_map[seg], alpha=0.2)

# 绘制散点图
ax.scatter(df['sample'], df['data_value'], color='black', zorder=2)

# 添加图例
legend_elements = [Patch(facecolor=color_map[seg], alpha=0.2, label=seg) for seg in color_map.keys()]
ax.legend(handles=legend_elements, title='Segment')

# 设置轴标签和标题
ax.set_xlabel('Sample')
ax.set_ylabel('Data Value')
ax.set_title('Scatter Plot with Segment Backgrounds')

ax.set_xticks(df['sample'])

plt.tight_layout()
plt.show()

内容的提问来源于stack exchange，提问作者MAtennis9