| jupyter | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|
Sunburst plots visualize hierarchical data spanning outwards radially from root to leaves. The sunburst sector hierarchy is determined by the entries in labels (names in px.sunburst) and in parents. The root starts from the center and children are added to the outer rings.
Main arguments:
labels(namesinpx.sunburstsincelabelsis reserved for overriding columns names): sets the labels of sunburst sectors.parents: sets the parent sectors of sunburst sectors. An empty string''is used for the root node in the hierarchy. In this example, the root is "Eve".values: sets the values associated with sunburst sectors, determining their width (See thebranchvaluessection below for different modes for setting the width).
Plotly Express is the easy-to-use, high-level interface to Plotly, which operates on "tidy" data.
With px.sunburst, each row of the DataFrame is represented as a sector of the sunburst.
import plotly.express as px
data = dict(
character=["Eve", "Cain", "Seth", "Enos", "Noam", "Abel", "Awan", "Enoch", "Azura"],
parent=["", "Eve", "Eve", "Seth", "Seth", "Eve", "Eve", "Awan", "Eve" ],
value=[10, 14, 12, 10, 2, 6, 6, 4, 4])
fig =px.sunburst(
data,
names='character',
parents='parent',
values='value',
)
fig.show()Hierarchical data are often stored as a rectangular dataframe, with different columns corresponding to different levels of the hierarchy. px.sunburst can take a path parameter corresponding to a list of columns. Note that id and parent should not be provided if path is given.
import plotly.express as px
df = px.data.tips()
fig = px.sunburst(df, path=['day', 'time', 'sex'], values='total_bill')
fig.show()If a color argument is passed, the color of a node is computed as the average of the color values of its children, weighted by their values.
import plotly.express as px
import numpy as np
df = px.data.gapminder().query("year == 2007")
fig = px.sunburst(df, path=['continent', 'country'], values='pop',
color='lifeExp', hover_data=['iso_alpha'],
color_continuous_scale='RdBu',
color_continuous_midpoint=np.average(df['lifeExp'], weights=df['pop']))
fig.show()If the dataset is not fully rectangular, missing values should be supplied as None.
import plotly.express as px
import pandas as pd
vendors = ["A", "B", "C", "D", None, "E", "F", "G", "H", None]
sectors = ["Tech", "Tech", "Finance", "Finance", None,
"Tech", "Tech", "Finance", "Finance", "Finance"]
regions = ["North", "North", "North", "North", "North",
"South", "South", "South", "South", "South"]
sales = [1, 3, 2, 4, 1, 2, 2, 1, 4, 1]
df = pd.DataFrame(
dict(vendors=vendors, sectors=sectors, regions=regions, sales=sales)
)
print(df)
fig = px.sunburst(df, path=['regions', 'sectors', 'vendors'], values='sales')
fig.show()If Plotly Express does not provide a good starting point, it is also possible to use the more generic go.Sunburst function from plotly.graph_objects.
import plotly.graph_objects as go
fig =go.Figure(go.Sunburst(
labels=["Eve", "Cain", "Seth", "Enos", "Noam", "Abel", "Awan", "Enoch", "Azura"],
parents=["", "Eve", "Eve", "Seth", "Seth", "Eve", "Eve", "Awan", "Eve" ],
values=[10, 14, 12, 10, 2, 6, 6, 4, 4],
))
# Update layout for tight margin
# See https://plot.ly/python/creating-and-updating-figures/
fig.update_layout(margin = dict(t=0, l=0, r=0, b=0))
fig.show()import plotly.graph_objects as go
fig =go.Figure(go.Sunburst(
ids=[
"North America", "Europe", "Australia", "North America - Football", "Soccer",
"North America - Rugby", "Europe - Football", "Rugby",
"Europe - American Football","Australia - Football", "Association",
"Australian Rules", "Autstralia - American Football", "Australia - Rugby",
"Rugby League", "Rugby Union"
],
labels= [
"North<br>America", "Europe", "Australia", "Football", "Soccer", "Rugby",
"Football", "Rugby", "American<br>Football", "Football", "Association",
"Australian<br>Rules", "American<br>Football", "Rugby", "Rugby<br>League",
"Rugby<br>Union"
],
parents=[
"", "", "", "North America", "North America", "North America", "Europe",
"Europe", "Europe","Australia", "Australia - Football", "Australia - Football",
"Australia - Football", "Australia - Football", "Australia - Rugby",
"Australia - Rugby"
],
))
fig.update_layout(margin = dict(t=0, l=0, r=0, b=0))
fig.show()With branchvalues "total", the value of the parent represents the width of its wedge. In the example below, "Enoch" is 4 and "Awan" is 6 and so Enoch's width is 4/6ths of Awans. With branchvalues "remainder", the parent's width is determined by its own value plus those of its children. So, Enoch's width is 4/10ths of Awan's (4 / (6 + 4)).
Note that this means that the sum of the values of the children cannot exceed the value of their parent when branchvalues "total". When branchvalues "relative" (the default), children will not take up all of the space below their parent (unless the parent is the root and it has a value of 0).
import plotly.graph_objects as go
fig =go.Figure(go.Sunburst(
labels=[ "Eve", "Cain", "Seth", "Enos", "Noam", "Abel", "Awan", "Enoch", "Azura"],
parents=["", "Eve", "Eve", "Seth", "Seth", "Eve", "Eve", "Awan", "Eve" ],
values=[ 65, 14, 12, 10, 2, 6, 6, 4, 4],
branchvalues="total",
))
fig.update_layout(margin = dict(t=0, l=0, r=0, b=0))
fig.show()This example uses a plotly grid attribute for the suplots. Reference the row and column destination using the domain attribute.
import plotly.graph_objects as go
import pandas as pd
df1 = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/718417069ead87650b90472464c7565dc8c2cb1c/sunburst-coffee-flavors-complete.csv')
df2 = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/718417069ead87650b90472464c7565dc8c2cb1c/coffee-flavors.csv')
fig = go.Figure()
fig.add_trace(go.Sunburst(
ids=df1.ids,
labels=df1.labels,
parents=df1.parents,
domain=dict(column=0)
))
fig.add_trace(go.Sunburst(
ids=df2.ids,
labels=df2.labels,
parents=df2.parents,
domain=dict(column=1),
maxdepth=2
))
fig.update_layout(
grid= dict(columns=2, rows=1),
margin = dict(t=0, l=0, r=0, b=0)
)
fig.show()The insidetextorientation attribute controls the orientation of text inside sectors. With
"auto" the texts may automatically be rotated to fit with the maximum size inside the slice. Using "horizontal" (resp. "radial", "tangential") forces text to be horizontal (resp. radial or tangential). Note that plotly may reduce the font size in order to fit the text with the requested orientation.
For a figure fig created with plotly express, use fig.update_traces(insidetextorientation='...') to change the text orientation.
import plotly.graph_objects as go
import pandas as pd
df = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/718417069ead87650b90472464c7565dc8c2cb1c/coffee-flavors.csv')
fig = go.Figure()
fig.add_trace(go.Sunburst(
ids=df.ids,
labels=df.labels,
parents=df.parents,
domain=dict(column=1),
maxdepth=2,
insidetextorientation='radial'
))
fig.update_layout(
margin = dict(t=10, l=10, r=10, b=10)
)
fig.show()The example below visualizes a breakdown of sales (corresponding to sector width) and call success rate (corresponding to sector color) by region, county and salesperson level. For example, when exploring the data you can see that although the East region is behaving poorly, the Tyler county is still above average -- however, its performance is reduced by the poor success rate of salesperson GT.
In the right subplot which has a maxdepth of two levels, click on a sector to see its breakdown to lower levels.
import plotly.graph_objects as go
from plotly.subplots import make_subplots
import pandas as pd
df = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/sales_success.csv')
print(df.head())
levels = ['salesperson', 'county', 'region'] # levels used for the hierarchical chart
color_columns = ['sales', 'calls']
value_column = 'calls'
def build_hierarchical_dataframe(df, levels, value_column, color_columns=None):
"""
Build a hierarchy of levels for Sunburst or Treemap charts.
Levels are given starting from the bottom to the top of the hierarchy,
ie the last level corresponds to the root.
"""
df_all_trees = pd.DataFrame(columns=['id', 'parent', 'value', 'color'])
for i, level in enumerate(levels):
df_tree = pd.DataFrame(columns=['id', 'parent', 'value', 'color'])
dfg = df.groupby(levels[i:]).sum(numerical_only=True)
dfg = dfg.reset_index()
df_tree['id'] = dfg[level].copy()
if i < len(levels) - 1:
df_tree['parent'] = dfg[levels[i+1]].copy()
else:
df_tree['parent'] = 'total'
df_tree['value'] = dfg[value_column]
df_tree['color'] = dfg[color_columns[0]] / dfg[color_columns[1]]
df_all_trees = df_all_trees.append(df_tree, ignore_index=True)
total = pd.Series(dict(id='total', parent='',
value=df[value_column].sum(),
color=df[color_columns[0]].sum() / df[color_columns[1]].sum()))
df_all_trees = df_all_trees.append(total, ignore_index=True)
return df_all_trees
df_all_trees = build_hierarchical_dataframe(df, levels, value_column, color_columns)
average_score = df['sales'].sum() / df['calls'].sum()
fig = make_subplots(1, 2, specs=[[{"type": "domain"}, {"type": "domain"}]],)
fig.add_trace(go.Sunburst(
labels=df_all_trees['id'],
parents=df_all_trees['parent'],
values=df_all_trees['value'],
branchvalues='total',
marker=dict(
colors=df_all_trees['color'],
colorscale='RdBu',
cmid=average_score),
hovertemplate='<b>%{label} </b> <br> Sales: %{value}<br> Success rate: %{color:.2f}',
name=''
), 1, 1)
fig.add_trace(go.Sunburst(
labels=df_all_trees['id'],
parents=df_all_trees['parent'],
values=df_all_trees['value'],
branchvalues='total',
marker=dict(
colors=df_all_trees['color'],
colorscale='RdBu',
cmid=average_score),
hovertemplate='<b>%{label} </b> <br> Sales: %{value}<br> Success rate: %{color:.2f}',
maxdepth=2
), 1, 2)
fig.update_layout(margin=dict(t=10, b=10, r=10, l=10))
fig.show()See https://plot.ly/python/reference/#sunburst for more information and chart attribute options!