数据可视化第二版-03部分-12章-网络
总结
本系列博客为基于《数据可视化第二版》一书的教学资源博客。本文主要是第12章，网络案例相关。
可视化视角-相关
代码实现
安装依赖
pip install scikit-learn -i https://pypi.tuna.tsinghua.edu.cn/simple pip install seaborn -i https://pypi.tuna.tsinghua.edu.cn/simple pip install tushare==1.2.89 -i https://pypi.tuna.tsinghua.edu.cn/simple pip install mplfinance==0.12.9b7 -i https://pypi.tuna.tsinghua.edu.cn/simple pip install pyheatmap==0.1.12 -i https://pypi.tuna.tsinghua.edu.cn/simple pip install networkx==3.1
网络图
网络图1-networkx
# 网络图 from matplotlib import pyplot as plt import networkx as nx G = nx.Graph() G.add_nodes_from(["A", "B", "C", "D", "E", "F"]) G.add_edges_from([("A", "B"), ("A", "C"), ("A", "D"), ("B", "C"), ("B", "F"), ("C", "E"), ("D", "F")]) # with_labels是否显示标签，node_size节点大小，node_color节点颜色，node_shape节点形状，alpha透明度，linewidths线条宽度 # 左：跳跃式布局 nx.draw(G, with_labels=True, node_size=400, node_color="skyblue", node_shape="o", alpha=1, width=1, font_size=12, font_color="black") plt.show() # 中：环形布局 nx.draw(G, with_labels=True, node_size=400, node_color="skyblue", node_shape="o", alpha=1, width=1, font_size=12, font_color="black", pos=nx.circular_layout(G)) plt.show() # 右：随机布局 nx.draw(G, with_labels=True, node_size=400, node_color="skyblue", node_shape="o", alpha=1, width=1, font_size=12, font_color="black", pos=nx.random_layout(G)) plt.show() ''' networkx 画图参数： - node_size: 指定节点的尺寸大小(默认是300) - node_color: 指定节点的颜色 (默认是红色，可以用字符串简单标识颜色，例如'r'为红色，'b'为绿色等，具体可查看手册)，用“数据字典”赋值的时候必须对字典取值（.values()）后再赋值 - node_shape: 节点的形状（默认是圆形，用字符串'o'标识，具体可查看手册） - alpha: 透明度 (默认是1.0，不透明，0为完全透明) - width: 边的宽度 (默认为1.0) - edge_color: 边的颜色(默认为黑色) - style: 边的样式(默认为实现，可选： solid|dashed|dotted,dashdot) - with_labels: 节点是否带标签（默认为True） - font_size: 节点标签字体大小 (默认为12) - font_color: 节点标签字体颜色（默认为黑色） e.g. nx.draw(G,node_size = 30, with_label = False) 绘制节点的尺寸为30，不带标签的网络图。 布局指定节点排列形式 pos = nx.spring_layout 建立布局，对图进行布局美化，networkx 提供的布局方式有： - circular_layout：节点在一个圆环上均匀分布 - random_layout：节点随机分布 - shell_layout：节点在同心圆上分布 - spring_layout： 用Fruchterman-Reingold算法排列节点 - spectral_layout：根据图的拉普拉斯特征向量排列节 布局也可用pos参数指定，例如，nx.draw(G, pos = spring_layout(G)) 这样指定了networkx上以中心放射状分布. '''
输出为：
弧形图
环形弧形长链接图
# -*- coding: utf-8 -*- """ @reference https://gallery.pyecharts.org/#/Graph/graph_les_miserables https://github.com/pyecharts/pyecharts-gallery/blob/master/Graph/les-miserables.json """ import json from pyecharts import options as opts from pyecharts.charts import Graph import os os.chdir(os.path.dirname(__file__)) 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") ) import os os.system("graph_les_miserables.html")
输出为：
桑基图
桑基图1-
# -*- coding: utf-8 -*- """ @reference: https://pyecharts.org/#/zh-cn/basic_charts?id=sankey%ef%bc%9a%e6%a1%91%e5%9f%ba%e5%9b%be https://gallery.pyecharts.org/#/Sankey/sankey_base """ from pyecharts import options as opts from pyecharts.charts import Sankey # 内置主题类型可查看 pyecharts.globals.ThemeType """ from pyecharts.globals import ThemeType """ nodes = [ {"name": "category1"}, {"name": "category2"}, {"name": "category3"}, {"name": "category4"}, {"name": "category5"}, {"name": "category6"}, ] links = [ {"source": "category1", "target": "category3", "value": 10}, {"source": "category1", "target": "category4", "value": 15}, {"source": "category2", "target": "category3", "value": 10}, {"source": "category2", "target": "category4", "value": 10}, {"source": "category3", "target": "category5", "value": 20}, {"source": "category4", "target": "category5", "value": 10}, {"source": "category4", "target": "category6", "value": 15}, ] # pyecharts V1 版本开始所有方法均支持链式调用。 sankey = ( Sankey() # 试试变换主题：Sankey(init_opts=opts.InitOpts(theme=ThemeType.LIGHT))，参考：进阶话题-定制主题 .add( "sankey", nodes, links, linestyle_opt=opts.LineStyleOpts(opacity=0.2, curve=0.5, color="source"), label_opts=opts.LabelOpts(position="right"), # 节点标签位置可选，参考:配置项-系列配置项-标签配置项 ) .set_global_opts(title_opts=opts.TitleOpts(title="Sankey-基本示例")) # 或者直接使用字典参数 # .set_global_opts(title_opts={"text": "主标题", "subtext": "副标题"}) .render("sankey_base_2.html") # render 会生成本地 HTML 文件，默认会在当前目录生成 render.html 文件 # 也可以传入路径参数，如 Sankey.render("sankey_base.html") ) import os os.system("sankey_base_2.html") # 不习惯链式调用的开发者依旧可以单独调用方法 """ sankey = Sankey() sankey.add("sankey", nodes, links, linestyle_opt=opts.LineStyleOpts(opacity=0.2, curve=0.5, color="source"), label_opts=opts.LabelOpts(position="right"), ) sankey.set_global_opts(title_opts=opts.TitleOpts(title="Sankey-基本示例")) sankey.render("sankey_base.html") """ # 渲染成图片文件 """ from pyecharts.render import make_snapshot # 使用 snapshot-selenium 渲染图片（需安装） from snapshot_selenium import snapshot make_snapshot(snapshot, sankey, "sankey_base.png")#sankey为html文件 #snapshot-selenium 报错处理可参考：https://blog.csdn.net/snwang_miss/article/details/117728949 """
桑基图2-
from pyecharts import options as opts from pyecharts.charts import Sankey nodes = [ {"name": "category1"}, {"name": "category2"}, {"name": "category3"}, {"name": "category4"}, {"name": "category5"}, {"name": "category6"}, ] links = [ {"source": "category1", "target": "category3", "value": 10}, {"source": "category1", "target": "category4", "value": 15}, {"source": "category2", "target": "category3", "value": 10}, {"source": "category2", "target": "category4", "value": 10}, {"source": "category3", "target": "category5", "value": 20}, {"source": "category4", "target": "category5", "value": 10}, {"source": "category4", "target": "category6", "value": 15}, ] sankey = ( Sankey() .add( "sankey", nodes, links, linestyle_opt=opts.LineStyleOpts(opacity=0.2, curve=0.5, color="source"), label_opts=opts.LabelOpts(position="right"),#节点标签位置 ) .set_global_opts(title_opts=opts.TitleOpts(title="Sankey-基本示例")) .render("sankey_base.html") ) import os os.system("sankey_base.html")
桑基图3-
from pyecharts import options as opts from pyecharts.charts import Sankey # 内置主题类型可查看 pyecharts.globals.ThemeType from pyecharts.globals import ThemeType nodes = [ {"name": "category1"}, {"name": "category2"}, {"name": "category3"}, {"name": "category4"}, {"name": "category5"}, {"name": "category6"}, ] links = [ {"source": "category1", "target": "category3", "value": 10}, {"source": "category1", "target": "category4", "value": 15}, {"source": "category2", "target": "category3", "value": 10}, {"source": "category2", "target": "category4", "value": 10}, {"source": "category3", "target": "category5", "value": 20}, {"source": "category4", "target": "category5", "value": 10}, {"source": "category4", "target": "category6", "value": 15}, ] sankey_vertical = ( Sankey(init_opts=opts.InitOpts(theme=ThemeType.LIGHT)) .add( "sankey", nodes, links, # Sankey 组件离容器外侧的距离 types.Union[str, types.Numeric]：默认值：pos_left="5%",pos_right="20%", pos_left="20%", orient="vertical", linestyle_opt=opts.LineStyleOpts(opacity=0.2, curve=0.5, color="source"), label_opts=opts.LabelOpts(position="inside"),#节点标签位置可选，参考:配置项-系列配置项-标签配置项 ) .set_global_opts(title_opts=opts.TitleOpts(title="Sankey-Vertical")) .render("sankey_vertical.html") ) import os os.system("sankey_vertical.html")
有趣的可视化
https://plotapi.com/#billing_interval
教材截图
