流程图（三）利用python绘制桑基图

news/2025/1/8 2:59:01/

python_0">流程图（三）利用python绘制桑基图

桑基图（Sankey diagram）简介

桑基图经常用于能源、金融行业，对材料、成本的流动进行可视化分析。现在很多互联网行业还使用桑基图做用户流动性分析，能很好地观察数据成分的变动大小及变动方向。

快速绘制

基于plotly

python">import plotly.graph_objects as go
import urllib, json# 导入数据
url = 'https://raw.githubusercontent.com/plotly/plotly.js/master/test/image/mocks/sankey_energy.json'
response = urllib.request.urlopen(url)
data = json.loads(response.read())# 将所有的"magenta"颜色更改为rgba(255,0,255, 0.8)，并将所有连接颜色更改为其对应的'source'节点颜色且透明度是0.4
opacity = 0.4
data['data'][0]['node']['color'] = ['rgba(255,0,255, 0.8)' if color == "magenta" else color for color in data['data'][0]['node']['color']]
data['data'][0]['link']['color'] = [data['data'][0]['node']['color'][src].replace("0.8", str(opacity))for src in data['data'][0]['link']['source']]fig = go.Figure(data=[go.Sankey(valueformat = ".0f",valuesuffix = "TWh",# 定义节点node = dict(pad = 15,thickness = 15,line = dict(color = "black", width = 0.5),label =  data['data'][0]['node']['label'],color =  data['data'][0]['node']['color']),# 添加连接link = dict(source =  data['data'][0]['link']['source'],target =  data['data'][0]['link']['target'],value =  data['data'][0]['link']['value'],label =  data['data'][0]['link']['label'],color =  data['data'][0]['link']['color']
))])fig.update_layout(title_text="Energy forecast for 2050<br>Source: Department of Energy & Climate Change, Tom Counsell via <a href='https://bost.ocks.org/mike/sankey/'>Mike Bostock</a>",font_size=10)

基于pyecharts

python">import pyecharts.options as opts
from pyecharts.charts import Sankey
import urllib, json# 导入数据
url = 'https://echarts.apache.org/examples/data/asset/data/energy.json'
response = urllib.request.urlopen(url)
data = json.loads(response.read())c = (Sankey().add(series_name="",nodes=data["nodes"],links=data["links"],itemstyle_opts=opts.ItemStyleOpts(border_width=1, border_color="#aaa"),linestyle_opt=opts.LineStyleOpts(color="source", curve=0.5, opacity=0.5),tooltip_opts=opts.TooltipOpts(trigger_on="mousemove"),).set_global_opts(title_opts=opts.TitleOpts(title=""))
)c.render_notebook()

基于pysankey

python">import pandas as pd
from pySankey.sankey import sankey# 基于source和target，数据可重复出现，出现次数越多，权重越大（即线越粗）
url = "https://raw.githubusercontent.com/anazalea/pySankey/master/pysankey/fruits.txt"
df = pd.read_csv(url, sep=" ", names=["true", "predicted"])colors = {"apple": "#f71b1b","blueberry": "#1b7ef7","banana": "#f3f71b","lime": "#12e23f","orange": "#f78c1b"
}sankey(df["true"], df["predicted"], aspect=20, colorDict=colors, fontsize=12)

python">import pandas as pd
from pySankey.sankey import sankey# 基于source和、target和value，数据可仅出现一次，value即权重
url = "https://raw.githubusercontent.com/anazalea/pySankey/master/pysankey/customers-goods.csv"
df = pd.read_csv(url, sep=",")sankey(left=df["customer"], right=df["good"], leftWeight= df["revenue"], rightWeight=df["revenue"], aspect=20, fontsize=20
)