如何使用Python绘制含标签的分类XY数据（如BCG矩阵）避免同值标签重叠

Python可以完美实现你的需求，核心通过adjustText库解决同坐标标签重叠问题，完整实现方案如下：

依赖安装 #

首先安装所需第三方库：

pip install pandas matplotlib adjusttext

完整实现代码 #

import pandas as pd
import matplotlib.pyplot as plt
from adjustText import adjust_text

# 读入自有数据可以用以下代码读取csv
# df = pd.read_csv('your_data.csv')
# 以下内置示例数据可直接运行测试
data = [
["A","topic_1",2,4],["B","topic_2",4,2],["C","topic_3",3,3],["D","topic_4",3,5],["E","topic_5",3,4],
["F","topic_6",5,1],["G","topic_7",4,5],["H","topic_8",1,2],["I","topic_9",4,1],["J","topic_10",3,3],
["K","topic_11",5,5],["L","topic_12",5,3],["M","topic_13",3,5],["N","topic_14",1,5],["O","topic_15",4,1],
["P","topic_16",4,2],["Q","topic_17",1,5],["R","topic_18",2,3],["S","topic_19",1,2],["T","topic_20",5,1],
["U","topic_21",3,4],["V","topic_22",2,5],["W","topic_23",1,3],["X","topic_24",3,3],["Y","topic_25",4,1],
["Z","topic_26",2,4],["1","topic_27",2,4],["2","topic_28",5,4],["3","topic_29",3,3],["4","topic_30",4,4],
["5","topic_31",3,2],["6","topic_32",4,2],["7","topic_33",2,3],["8","topic_34",2,3],["9","topic_35",2,5],
["10","topic_36",4,2]
]
df = pd.DataFrame(data, columns=["ID","Name","value_A","value_B"])

# 计算BCG矩阵分割阈值，默认取两个维度的中位数，也可以自定义固定值比如 threshold_a = 3
threshold_a = df['value_A'].median()
threshold_b = df['value_B'].median()

# 初始化画布
plt.figure(figsize=(10,10), dpi=100)
texts = []

# 遍历数据添加标记和ID标签
for idx, row in df.iterrows():
    # 半透明散点标记坐标位置
    plt.scatter(row['value_A'], row['value_B'], c='gray', alpha=0.3, s=50)
    # 收集文本对象用于后续自动调整位置
    texts.append(plt.text(row['value_A'], row['value_B'], row['ID'], fontsize=12))

# 绘制BCG十字分割线
plt.axvline(x=threshold_a, c='black', linestyle='--')
plt.axhline(y=threshold_b, c='black', linestyle='--')

# 自动调整文本位置解决重叠，参数可根据显示效果调整
adjust_text(
    texts,
    force_text=0.5, # 文本之间的排斥力，数值越大间距越宽
    expand=(1.2, 1.2), # 文本和原坐标的距离系数，数值越小越贴近原点位
    arrowprops=dict(arrowstyle='-', color='gray', lw=0.5) # 重叠较多时可加细线连回原坐标，不需要可以删掉该行
)

# 调整图表样式
plt.xlim(df['value_A'].min()-0.5, df['value_A'].max()+0.5)
plt.ylim(df['value_B'].min()-0.5, df['value_B'].max()+0.5)
plt.xlabel('value_A', fontsize=14)
plt.ylabel('value_B', fontsize=14)
plt.title('BCG矩阵', fontsize=16)
plt.grid(alpha=0.2)
plt.show()

补充说明 #

• 上述代码支持超过50个主题的数据集调整，可通过修改adjust_text的参数适配不同密度的数据
• 不需要代码的场景可以用Tableau实现：导入数据后选择散点图，标签设置为ID，打开标签的「避免重叠」开关，选择自动排列即可

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