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Sankey Diagram in Python

How to make Sankey Diagrams in Python with Plotly.


If you're using Dash Enterprise's Data Science Workspaces, you can copy/paste any of these cells into a Workspace Jupyter notebook.
Alternatively, download this entire tutorial as a Jupyter notebook and import it into your Workspace.
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New to Plotly?

Plotly is a free and open-source graphing library for Python. We recommend you read our Getting Started guide for the latest installation or upgrade instructions, then move on to our Plotly Fundamentals tutorials or dive straight in to some Basic Charts tutorials.

A Sankey diagram is a flow diagram, in which the width of arrows is proportional to the flow quantity.

Basic Sankey Diagram

Sankey diagrams visualize the contributions to a flow by defining source to represent the source node, target for the target node, value to set the flow volum, and label that shows the node name.

In [1]:
import plotly.graph_objects as go

fig = 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()
In [2]:
import plotly.graph_objects as go
import urllib, json

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())

# 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 nodes
    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']
    ),
    # Add links
    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)
fig.show()

Sankey Diagram in Dash

Dash is the best way to build analytical apps in Python using Plotly figures. To run the app below, run pip install dash, click "Download" to get the code and run python app.py.

Get started with the official Dash docs and learn how to effortlessly style & deploy apps like this with Dash Enterprise.

Out[3]:

Style Sankey Diagram

This example also uses hovermode to enable multiple tooltips.

In [4]:
import plotly.graph_objects as go
import urllib, json

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())

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()

Hovertemplate and customdata of Sankey diagrams

Links and nodes have their own hovertemplate, in which link- or node-specific attributes can be displayed. To add more data to links and nodes, it is possible to use the customdata attribute of link and nodes, as in the following example. For more information about hovertemplate and customdata, please see the tutorial on hover text.

In [5]:
import plotly.graph_objects as go

fig = 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"],
      customdata = ["Long name A1", "Long name A2", "Long name B1", "Long name B2",
                    "Long name C1", "Long name C2"],
      hovertemplate='Node %{customdata} has total value %{value}<extra></extra>',
      color = "blue"
    ),
    link = dict(
      source = [0, 1, 0, 2, 3, 3], # indices correspond to labels, eg A1, A2, A2, B1, ...
      target = [2, 3, 3, 4, 4, 5],
      value = [8, 4, 2, 8, 4, 2],
      customdata = ["q","r","s","t","u","v"],
      hovertemplate='Link from node %{source.customdata}<br />'+
        'to node%{target.customdata}<br />has value %{value}'+
        '<br />and data %{customdata}<extra></extra>',
  ))])

fig.update_layout(title_text="Basic Sankey Diagram", font_size=10)
fig.show()

Define Node Position

The following example sets node.x and node.y to place nodes in the specified locations, except in the snap arrangement (default behaviour when node.x and node.y are not defined) to avoid overlapping of the nodes, therefore, an automatic snapping of elements will be set to define the padding between nodes via nodepad. The other possible arrangements are: 1) perpendicular 2) freeform 3) fixed

In [6]:
import plotly.graph_objects as go

fig = go.Figure(go.Sankey(
    arrangement = "snap",
    node = {
        "label": ["A", "B", "C", "D", "E", "F"],
        "x": [0.2, 0.1, 0.5, 0.7, 0.3, 0.5],
        "y": [0.7, 0.5, 0.2, 0.4, 0.2, 0.3],
        'pad':10},  # 10 Pixels
    link = {
        "source": [0, 0, 1, 2, 5, 4, 3, 5],
        "target": [5, 3, 4, 3, 0, 2, 2, 3],
        "value": [1, 2, 1, 1, 1, 1, 1, 2]}))

fig.show()

Reference

See https://plotly.com/python/reference/sankey for more information and options!

What About Dash?

Dash is an open-source framework for building analytical applications, with no Javascript required, and it is tightly integrated with the Plotly graphing library.

Learn about how to install Dash at https://dash.plot.ly/installation.

Everywhere in this page that you see fig.show(), you can display the same figure in a Dash application by passing it to the figure argument of the Graph component from the built-in dash_core_components package like this:

import plotly.graph_objects as go # or plotly.express as px
fig = go.Figure() # or any Plotly Express function e.g. px.bar(...)
# fig.add_trace( ... )
# fig.update_layout( ... )

import dash
import dash_core_components as dcc
import dash_html_components as html

app = dash.Dash()
app.layout = html.Div([
    dcc.Graph(figure=fig)
])

app.run_server(debug=True, use_reloader=False)  # Turn off reloader if inside Jupyter