pycharm——树状图

复制代码
from pyecharts import options as opts
from pyecharts.charts import Tree


data = [
    {
        "children": [
            {"name": "计算机"},
            {
                "children": [{"children": [{"name": "主机"}], "name": "硬盘"}, {"name": "鼠标和键盘"}],
                "name": "硬件",
            },
            {
                "children": [
                    {"children": [{"name": "操作系统"}, {"name": "数据结构"}], "name": "组成原理"},
                    {"name": "基础"},
                ],
                "name": "软件",
            },
        ],
        "name": "数学",
    }
]
c = (
    Tree()
    .add("", data)
    .set_global_opts(title_opts=opts.TitleOpts(title="Tree-基本示例"))
    .render("tree_base.html")
)
复制代码
import json

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

with open("flare.json", "r", encoding="utf-8") as f:
    j = json.load(f)
c = (
    Tree()
    .add("", [j], collapse_interval=2, layout="radial")
    .set_global_opts(title_opts=opts.TitleOpts(title="Tree-Layout"))
    .render("tree_layout.html")
)

flare.json文件

复制代码
 {
    "name": "My Library",
    "children": [
        {
            "name": "Book",
            "children": [
                {"name": "Title", "value": "The Great Gatsby"},
                {"name": "Author", "value": "F. Scott Fitzgerald"},
                {"name": "Publication Date", "value": "1925-04-10"}
            ]
        },
        {
            "name": "Library",
            "children": [
                {"name": "Name", "value": "Central Library"},
                {"name": "Location", "value": "New York"}
            ]
        },
        {
            "name": "Characters",
            "children": [
                {"name": "Jay Gatsby", "value": "Wealthy Gambler"},
                {"name": "Nick Carraway", "value": "Narrator"},
                {"name": "Daisy Buchanan", "value": "Socialite"}
            ]
        },
        {
          "name": "competer",
          "children": [
            {"name": "数据结构","value": "50"},
            {"name": "数据库原理","value": "60"},
            {"name": "计算机组成网络","value": "40"}
          ]
         }
    ]
}
相关推荐
触底反弹30 分钟前
面试被问到 Text2SQL,我用 DeepSeek 自己实现了一个
python·sqlite
2601_9622955840 分钟前
python+selenium实现自动化测试
自动化测试·python·selenium·学习心得·web端测试
青 春 记 忆1 小时前
零基础入门python66:FastAPI AI标题、摘要和标签
python·fastapi·后端开发
qq_22589174661 小时前
基于Python+Django的LangGraph智能旅游规划系统
python·django·旅游
青 春 记 忆1 小时前
零基础入门python65:FastAPI 安全调用大模型API
python·fastapi·后端开发
维克兜率天2 小时前
【维克】特征归一化与标准化:为什么模型对数据的“尺度“很敏感?
人工智能·笔记·python·机器学习·量化
2601_962298672 小时前
Python multiprocessing PicklingError: Can't pickle &l
python·module·multiprocessing·function·picklingerror
CV山月2 小时前
大模型强化学习对齐:从 RLHF 框架到 PPO 算法原理
人工智能·python·大模型·强化学习·多模态·研究生
用户739548002062 小时前
本地 CSV 清洗的小细节:先标准化,再去重,并保留可检查的报告
python
AC赳赳老秦3 小时前
文旅市场公开数据分析:基于 OpenClaw 采集景区客流与门票公示数据,生成区域文旅热度监测报告
java·c语言·python·php·symfony·deepseek·openclaw