关系图（1 of 3）

GraphNode：关系图的节点数据项

①name="结点1"数据项名称
②symbol_size: Union[Numeric, Sequence, None] = None： 该类目节点标记的大小，可以设置成诸如 10 这样单一的数字，也可以用数组分开表示宽和高，例如 [20, 10] 表示标记宽为 20，高为 10。

GraphLink：节点间的关系数据

①source: Union[str, int, None] = None：边的源节点名称的字符串，也支持使用数字表示源节点的索引；
②target="结点2：边的目标节点名称的字符串，也支持使用数字表示源节点的索引；
③value=3：边的数值，可以在力引导布局中用于映射到边的长度；
④linestyle_opts=opts.LineStyleOpts(width=6)：关系边的线条样式。

from pyecharts import options as opts
from pyecharts.charts import Graph
nodes = [
    opts.GraphNode(name="结点1", symbol_size=10),
    opts.GraphNode(name="结点2", symbol_size=20),
    opts.GraphNode(name="结点3", symbol_size=30),
    opts.GraphNode(name="结点4", symbol_size=40),
    opts.GraphNode(name="结点5", symbol_size=50),
]
links = [
    opts.GraphLink(source="结点1", target="结点2", value=2, linestyle_opts=opts.LineStyleOpts(width=2),),
    opts.GraphLink(source="结点2", target="结点3", value=3, linestyle_opts=opts.LineStyleOpts(width=3),),
    opts.GraphLink(source="结点3", target="结点4", value=4, linestyle_opts=opts.LineStyleOpts(width=4),),
    opts.GraphLink(source="结点4", target="结点5", value=5, linestyle_opts=opts.LineStyleOpts(width=5)),
    opts.GraphLink(source="结点5", target="结点3", value=6, linestyle_opts=opts.LineStyleOpts(width=6)),
]
c = (
    Graph()
    .add("", nodes, links, repulsion=4000,
         edge_label=opts.LabelOpts(is_show=True,position="middle",formatter="{c}")
         )
    .set_global_opts(title_opts=opts.TitleOpts(title="Graph-GraphNode-GraphLink"))
    .render("graph_with_options.html")
)

关系图（2 of 3）

repulsion=50：节点之间的斥力因子，支持设置成数组表达斥力的范围，此时不同大小的值会线性映射到不同的斥力。值越大则斥力越大

import json
from pyecharts import options as opts
from pyecharts.charts import Graph

with open("weibo.json", "r", encoding="utf-8") as f:
    j = json.load(f)
    nodes, links, categories, cont, mid, userl = j
c = (
    Graph()
    .add(
        "",
        nodes,
        links,
        categories,
        repulsion=50,
        linestyle_opts=opts.LineStyleOpts(curve=0.2),
        label_opts=opts.LabelOpts(is_show=False),
    )
    .set_global_opts(
        legend_opts=opts.LegendOpts(is_show=False),
        title_opts=opts.TitleOpts(title="Graph-微博转发关系图"),
    )
    .render("graph_weibo.html")
)

关系图（3 of 3）

is_rotate_label: bool = False 是否旋转标签，默认不旋转。
layout="circular"

import json
from pyecharts import options as opts
from pyecharts.charts import Graph
with open("les-miserables.json", "r", encoding="utf-8") as f:
    j = json.load(f)
    nodes = j["nodes"]
    links = j["links"]
    categories = j["categories"]

c = (
    Graph(init_opts=opts.InitOpts(width="1000px", height="600px"))
    .add(
        "",
        nodes=nodes,
        links=links,
        categories=categories,
        layout="circular",
        is_rotate_label=True,
        linestyle_opts=opts.LineStyleOpts(color="source", curve=0.3),
        label_opts=opts.LabelOpts(position="right"),
    )
    .set_global_opts(
        title_opts=opts.TitleOpts(title="Graph-Les Miserables"),
        legend_opts=opts.LegendOpts(orient="vertical", pos_left="2%", pos_top="20%"),
    )
    .render("graph_les_miserables.html")
)

涟漪特效

适用场景：利用动画特效将某些想要突出的数据进行视觉突出，比如地图上某个地区。
①导入不同的涟漪特效的一个包：from pyecharts.globals import SymbolType
②默认样式：涟漪散点
③函数EffectScatter()

from pyecharts import options as opts
from pyecharts.charts import EffectScatter
from pyecharts.faker import Faker
from pyecharts.globals import SymbolType #不同symbol的包
c = (
    EffectScatter()
    .add_xaxis(Faker.choose())
    .add_yaxis("基本示例", Faker.values()) #默认无样式
    .add_yaxis("不同Symbol", Faker.values(), symbol=SymbolType.ARROW)
    .set_global_opts(title_opts=opts.TitleOpts(title="EffectScatter-基本示例"))
    .render("effectscatter_base.html")
)

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