如何为2D散点图中的每个分组设置统一颜色

我在使用Matplotlib绘制2D散点图时，希望为分组A、B、C…各自设置统一的颜色，但当前代码实现的是每个分组内的点颜色不同。

原代码 #

import matplotlib.pyplot as plt
import numpy as np

# Data
group_labels = ['A', 'B', 'C', 'D', 'E', 'F', 'G']

data = [
    [0.735721594, 0.619603837, 0.87785673, 0.482125754, 0.0894892, 0.133485767, 0.995450247],
    [0.666117198, 0.52923401, 0.499589112, 0.096963416, 0.308461174, 0.130418723, 0.195501054],
    [0.696378042, 0.437459297, 0.033071186, 0.645614608, 0.99425186, 0.097360026, 0.354376981],
    [0.552392974, 0.668845104, 0.079569268, 0.455465795, 0.353141333, 0.147198273, 0.249947862],
    [0.591065904, 0.34886412, 0.821742243, 0.008845512, 0.259947361, 0.063514992, 0.040540063],
    [0.016209069, 0.092671819, 0.195080351, 0.886493551, 0.745661888, 0.504613173, 0.593546542],
    [0.536218451, 0.466140392, 0.721903277, 0.426671591, 0.648579902, 0.823047029, 0.922809018]
]

# Transpose the data to have groups on the x-axis
data = np.array(data).T

# Create a 2D scatter plot with unique colors for each data point
color_map = plt.cm.get_cmap('tab10', len(group_labels))  # Choose a color map
for i in range(len(group_labels)):
    colors = color_map(np.linspace(0, 1, len(data[i])))
    for j in range(len(data[i])):
        plt.scatter(group_labels[i], data[i][j], label=f'Group {group_labels[i]}', marker='o', s=50, c=colors[j])

# Customize the plot
plt.xticks(rotation=0)
plt.ylabel('Y Values')
plt.tight_layout()

# Show the plot
plt.show()

当前效果：每个分组内的点呈现不同渐变颜色，无法清晰区分分组整体。

修改后的代码 #

import matplotlib.pyplot as plt
import numpy as np

# Data
group_labels = ['A', 'B', 'C', 'D', 'E', 'F', 'G']

data = [
    [0.735721594, 0.619603837, 0.87785673, 0.482125754, 0.0894892, 0.133485767, 0.995450247],
    [0.666117198, 0.52923401, 0.499589112, 0.096963416, 0.308461174, 0.130418723, 0.195501054],
    [0.696378042, 0.437459297, 0.033071186, 0.645614608, 0.99425186, 0.097360026, 0.354376981],
    [0.552392974, 0.668845104, 0.079569268, 0.455465795, 0.353141333, 0.147198273, 0.249947862],
    [0.591065904, 0.34886412, 0.821742243, 0.008845512, 0.259947361, 0.063514992, 0.040540063],
    [0.016209069, 0.092671819, 0.195080351, 0.886493551, 0.745661888, 0.504613173, 0.593546542],
    [0.536218451, 0.466140392, 0.721903277, 0.426671591, 0.648579902, 0.823047029, 0.922809018]
]

# Transpose the data to have groups on the x-axis
data = np.array(data).T

# Create a 2D scatter plot with unified color per group
color_map = plt.cm.get_cmap('tab10', len(group_labels))  # Choose a color map
for i in range(len(group_labels)):
    # 获取当前分组的单一固定颜色
    group_color = color_map(i)
    # 一次性绘制当前分组所有点，使用统一颜色
    plt.scatter(group_labels[i], data[i], label=f'Group {group_labels[i]}', 
                marker='o', s=50, c=[group_color]*len(data[i]))

# Customize the plot
plt.xticks(rotation=0)
plt.ylabel('Y Values')
plt.legend()  # 添加分组图例
plt.tight_layout()

# Show the plot
plt.show()

关键修改点 #

• 移除内层循环：不再逐个绘制点，直接对整个分组的数据批量绘制，提升代码效率
• 固定分组颜色：每个分组通过color_map(i)获取单一颜色，而非生成渐变颜色数组
• 统一颜色传递：用[group_color]*len(data[i])确保当前分组所有点使用同一颜色
• 修复图例：每个分组仅添加一次label，配合plt.legend()正常显示分组图例

内容的提问来源于stack exchange，提问作者newtopy