局部整体（六）利用python绘制树状图

HsuHeinrich

发布于 2024-11-23 08:42:54

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局部整体（六）利用python绘制树状图

树状图（ Dendrogram）简介

由一个根节点组成，根节点产生多个通过分支连接的子节点。常用于表示层次结构或显示聚类算法的结果。树状图既可以看明白数据的层次结构，也能明白指标间的「对比」。

快速绘制

基于dendrogram

import pandas as pd
from matplotlib import pyplot as plt
from scipy.cluster.hierarchy import dendrogram, linkage
import numpy as np
 
# 导入数据
url = 'https://raw.githubusercontent.com/holtzy/The-Python-Graph-Gallery/master/static/data/mtcars.csv'
df = pd.read_csv(url)
df = df.set_index('model')
df = df.reset_index(drop=True)

# 计算样本距离：对df执行层次聚类，并将结果存储在Z中
# Ward方差最小化算法用来计算样本距离
Z = linkage(df, 'ward')

# 树状图
dendrogram(Z, labels=df.index, leaf_rotation=90)

# 标题
plt.title('Hierarchical Clustering Dendrogram')
# 轴标签
plt.xlabel('sample index')
plt.ylabel('distance (Ward)')

plt.show()

基于pyecharts

# 基本树图

from pyecharts import options as opts
from pyecharts.charts import Tree

# 自定义数据
data = [
    {
        "children": [
            {"name": "B"},
            {
                "children": [{"children": [{"name": "I"}], "name": "E"}, {"name": "F"}],
                "name": "C",
            },
            {
                "children": [
                    {"children": [{"name": "J"}, {"name": "K"}], "name": "G"},
                    {"name": "H"},
                ],
                "name": "D",
            },
        ],
        "name": "A",
    }
]
c = (
    Tree()
    .add("", data)
    .set_global_opts(title_opts=opts.TitleOpts(title="基本树图"))
)
c.render_notebook()

定制多样化的树状图

自定义树状图一般是结合使用场景对相关参数进行修改，并辅以其他的绘图知识。参数信息可以通过官网进行查看，其他的绘图知识则更多来源于实战经验，大家不妨将接下来的绘图作为一种学习经验，以便于日后总结。

通过dendrogram绘制多样化的树状图

更多用法可参考scipy.cluster.hierarchy.dendrogram[1]

修改参数

import pandas as pd
from matplotlib import pyplot as plt
from scipy.cluster.hierarchy import dendrogram, linkage, set_link_color_palette
import numpy as np

plt.rcParams['font.sans-serif'] = ['SimHei'] # 用来正常显示中文标签
 
# 导入数据
url = 'https://raw.githubusercontent.com/holtzy/The-Python-Graph-Gallery/master/static/data/mtcars.csv'
df = pd.read_csv(url)
df = df.set_index('model')

# 计算样本距离
Z = linkage(df, 'ward')
 

# 初始化布局
fig = plt.figure(figsize=(12,12))

# 自定义标签
plt.subplot(3, 2, 1) 
dendrogram(Z, leaf_rotation=90, leaf_font_size=8, labels=df.index)
plt.title('自定义标签')

# 自定义颜色和颜色阈值
plt.subplot(3, 2, 2) 
set_link_color_palette(['#b30000','#996600', '#b30086']) # 颜色
dendrogram(Z, color_threshold=240, above_threshold_color='grey') # 颜色阈值
plt.axhline(y=240, c='grey', lw=1, linestyle='dashed')
plt.title('自定义颜色和颜色阈值')

# 簇截断-叶子数量
plt.subplot(3, 2, 3) 
dendrogram(Z, truncate_mode = 'lastp', p=4 ) 
plt.title('簇截断-叶子数量')

# 簇截断-显示层数
plt.subplot(3, 2, 4) 
dendrogram(Z, truncate_mode = 'level', p=2 ) 
plt.title('簇截断-显示层数')

# 水平方向-右
plt.subplot(3, 2, 5) 
dendrogram(Z, orientation="right", labels=df.index) 
plt.title('水平方向-右')
    
# 水平方向-左
plt.subplot(3, 2, 6) 
dendrogram(Z, orientation="left", labels=df.index) 
plt.title('水平方向-左')


fig.tight_layout() # 自动调整间距
plt.show()

通过pyecharts绘制多样化的树状图

更多用法可参考树图 Tree[2]

径向树图 import requests import pyecharts.options as opts from pyecharts.charts import Tree # 获取官方的数据 url = "https://echarts.apache.org/examples/data/asset/data/flare.json" response = requests.get(url) data = response.json() # 将响应内容解析为JSON # 绘制树图 c = ( Tree() .add( series_name="", data=[data], pos_top="18%", pos_bottom="14%", layout="radial", symbol="emptyCircle", symbol_size=7, ) .set_global_opts( tooltip_opts=opts.TooltipOpts(trigger="item", trigger_on="mousemove"), title_opts=opts.TitleOpts(title="径向树图") ) ) c.render_notebook()

发散树图 import requests import pyecharts.options as opts from pyecharts.charts import Tree # 获取官方的数据 url = "https://echarts.apache.org/examples/data/asset/data/flare.json" response = requests.get(url) data = response.json() # 将响应内容解析为JSON c = ( Tree() .add("", [data], collapse_interval=2, layout="radial") .set_global_opts(title_opts=opts.TitleOpts(title="发散树图")) ) c.render_notebook()