--- jupyter: jupytext: notebook_metadata_filter: all text_representation: extension: .md format_name: markdown format_version: '1.3' jupytext_version: 1.14.1 kernel_info: name: python2 kernelspec: display_name: Python 3 (ipykernel) language: python name: python3 language_info: codemirror_mode: name: ipython version: 3 file_extension: .py mimetype: text/x-python name: python nbconvert_exporter: python pygments_lexer: ipython3 version: 3.8.8 plotly: description: How to make Sankey Diagrams in Python with Plotly. display_as: basic language: python layout: base name: Sankey Diagram order: 12 page_type: u-guide permalink: python/sankey-diagram/ thumbnail: thumbnail/sankey.jpg --- A [Sankey diagram](https://en.wikipedia.org/wiki/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](https://plotly.com/python/reference/sankey/#sankey-link-source) to represent the source node, [target](https://plotly.com/python/reference/sankey/#sankey-link-target) for the target node, [value](https://plotly.com/python/reference/sankey/#sankey-link-value) to set the flow volume, and [label](https://plotly.com/python/reference/sankey/#sankey-node-label) that shows the node name. ```python 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() ``` ### More complex Sankey diagram with colored links ```python 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
Source: Department of Energy & Climate Change, Tom Counsell via Mike Bostock", font_size=10) fig.show() ``` ### Sankey Diagram in Dash [Dash](https://plotly.com/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](https://dash.plotly.com/installation) and **learn how to effortlessly [style](https://plotly.com/dash/design-kit/) & [deploy](https://plotly.com/dash/app-manager/) apps like this with Dash Enterprise.** ```python hide_code=true from IPython.display import IFrame snippet_url = 'https://python-docs-dash-snippets.herokuapp.com/python-docs-dash-snippets/' IFrame(snippet_url + 'sankey-diagram', width='100%', height=1200) ```

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### Style Sankey Diagram This example also uses [hovermode](https://plotly.com/python/reference/layout/#layout-hovermode) to enable multiple tooltips. ```python 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
Source: Department of Energy & Climate Change, Tom Counsell via Mike Bostock", 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](/python/hover-text-and-formatting/). ```python 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}', 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}
'+ 'to node%{target.customdata}
has value %{value}'+ '
and data %{customdata}', ))]) fig.update_layout(title_text="Basic Sankey Diagram", font_size=10) fig.show() ``` ### Define Node Position The following example sets [node.x](https://plotly.com/python/reference/sankey/#sankey-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](https://plotly.com/python/reference/sankey/#sankey-node-pad). The other possible arrangements are: 1) perpendicular 2) freeform 3) fixed ```python 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() ``` ### Sankey Diagram with Arrow Links *New in 5.10* Create a Sankey diagram with arrow links by setting the `arrowlen` attribute of `link`: ```python import plotly.graph_objects as go fig = go.Figure(go.Sankey( arrangement='snap', node=dict( 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 ), link=dict( arrowlen=15, 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](https://plotly.com/python/reference/sankey/) for more information and options!