桑基图是一种数据可视化技术或流程图，强调从一种状态到另一种状态或从一个时间到另一个时间的流动/移动/变化，其中箭头的宽度与所描绘的广泛属性的流速成正比。桑基图强调系统内的主要转移或流动。它们有助于确定流动中最重要的贡献。它们通常显示定义的系统边界内的守恒量。
对比2023年和2024年最受欢迎编程语言
均以百分数显示，左侧是2023年各语言使用比，右侧是2024年各语言使用比，中间是各语言在2023年至2024年后的变化：上升或下降
🍇Python桑基图
桑基图通过定义源来表示源节点、目标来表示目标节点、值来设置流量以及显示节点名称的标签来可视化对流的贡献。
import plotly.graph_objects as gofig = go.Figure(data=[go.Sankey(node = dict(pad = 15,thickness = 20,line = dict(color = "black", width = 0.5),label = ["A1", "A2", "B1", "B2", "C1", "C2"],color = "blue"),link = dict(source = [0, 1, 0, 2, 3, 3], # indices correspond to labels, eg A1, A2, A1, B1, ...target = [2, 3, 3, 4, 4, 5],value = [8, 4, 2, 8, 4, 2]))])fig.update_layout(title_text="Basic Sankey Diagram", font_size=10)
fig.show()
带有彩色链接的更复杂的桑基图
import plotly.graph_objects as go
import urllib, jsonurl = 'https://raw.githubusercontent.com/plotly/plotly.js/master/test/image/mocks/sankey_energy.json'
response = urllib.request.urlopen(url)
data = json.loads(response.read())# override gray link colors with 'source' colors
opacity = 0.4
# change 'magenta' to its 'rgba' value to add opacity
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",# Define nodesnode = dict(pad = 15,thickness = 15,line = dict(color = "black", width = 0.5),label = data['data'][0]['node']['label'],color = data['data'][0]['node']['color']),# Add linkslink = 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)
fig.show()
Dash 是使用 Plotly 图表在 Python 中构建分析应用程序的最佳方式。
from dash import Dash, dcc, html, Input, Output
import plotly.graph_objects as go
import json, urllibapp = Dash(__name__)app.layout = html.Div([html.H4('Supply chain of the energy production'),dcc.Graph(id="graph"),html.P("Opacity"),dcc.Slider(id='slider', min=0, max=1, value=0.5, step=0.1)
])@app.callback(Output("graph", "figure"), Input("slider", "value"))
def display_sankey(opacity):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()) # replace with your own data sourcenode = data['data'][0]['node']node['color'] = [f'rgba(255,0,255,{opacity})' if c == "magenta" else c.replace('0.8', str(opacity)) for c in node['color']]link = data['data'][0]['link']link['color'] = [node['color'][src] for src in link['source']]fig = go.Figure(go.Sankey(link=link, node=node))fig.update_layout(font_size=10)return figapp.run_server(debug=True)
此示例还使用悬停模式来启用多个工具提示。
import plotly.graph_objects as go
import urllib, jsonurl = 'https://raw.githubusercontent.com/plotly/plotly.js/master/test/image/mocks/sankey_energy.json'
response = urllib.request.urlopen(url)
data = json.loads(response.read())fig = go.Figure(data=[go.Sankey(valueformat = ".0f",valuesuffix = "TWh",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']),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']))])fig.update_layout(hovermode = 'x',title="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=dict(size = 10, color = 'white'),plot_bgcolor='black',paper_bgcolor='black'
)fig.show()
