How to Create Sunburst Chart/Diagram in Python [Plotly]?

The sunburst diagram can be used to visualize the distribution of hierarchical variables of data. It represents distribution with a list of rings around the center circle. The central circle represents the total quantity of a particular attribute and then each ring around it represents distribution at that level to a relationship with parent ring which is inside of it. The common example to explain the usage of sunburst chart would population distribution of world where the central circle represents total world distribution, ring around it represents distribution per continent, ring around it represents distribution per country of each continent and ring around it can further used to for distribution per state of each country.

The sunburst chart is very similar to treemap charts with the only difference that data is laid out radially. If you are interested in learning treemap plotting using python then feel free to go through our tutorial on treemap which explains various ways to draw treemap in python.

We'll start by importing necessary libraries.

import pandas as pd
import numpy as np

pd.set_option("max_columns", 30)

import plotly.express as px
import plotly.graph_objects as go

We'll also be using 3 datasets available from kaggle to include further data for analysis and plotting.

• world happiness report dataset - Dataset also holds information about GDP per capita, social support, life expectancy, generosity, and corruption.
• starbucks store location data - It has information about each Starbucks store locations as well as their address, city, country, phone number, latitude and longitude data.
• Indian census data - It has information aboutthe population of Indian districts in 2001 and 2011.

We suggest that you download allthe datasets to follow along with us through the tutorial.

starbucks_locations = pd.read_csv("datasets/starbucks_store_locations.csv")
starbucks_locations.head()

	Brand	Store Number	Store Name	Ownership Type	Street Address	City	State/Province	Country	Postcode	Phone Number	Timezone	Longitude	Latitude
0	Starbucks	47370-257954	Meritxell, 96	Licensed	Av. Meritxell, 96	Andorra la Vella	7	AD	AD500	376818720	GMT+1:00 Europe/Andorra	1.53	42.51
1	Starbucks	22331-212325	Ajman Drive Thru	Licensed	1 Street 69, Al Jarf	Ajman	AJ	AE	NaN	NaN	GMT+04:00 Asia/Dubai	55.47	25.42
2	Starbucks	47089-256771	Dana Mall	Licensed	Sheikh Khalifa Bin Zayed St.	Ajman	AJ	AE	NaN	NaN	GMT+04:00 Asia/Dubai	55.47	25.39
3	Starbucks	22126-218024	Twofour 54	Licensed	Al Salam Street	Abu Dhabi	AZ	AE	NaN	NaN	GMT+04:00 Asia/Dubai	54.38	24.48
4	Starbucks	17127-178586	Al Ain Tower	Licensed	Khaldiya Area, Abu Dhabi Island	Abu Dhabi	AZ	AE	NaN	NaN	GMT+04:00 Asia/Dubai	54.54	24.51

world_countries_data = pd.read_csv("datasets/countries of the world.csv")
world_countries_data["World"] = "World"
world_countries_data.head()

	Country	Region	Population	Area (sq. mi.)	Pop. Density (per sq. mi.)	Coastline (coast/area ratio)	Net migration	Infant mortality (per 1000 births)	GDP ($ per capita)	Literacy (%)	Phones (per 1000)	Arable (%)	Crops (%)	Other (%)	Climate	Birthrate	Deathrate	Agriculture	Industry	Service	World
0	Afghanistan	ASIA (EX. NEAR EAST)	31056997	647500	48,0	0,00	23,06	163,07	700.0	36,0	3,2	12,13	0,22	87,65	1	46,6	20,34	0,38	0,24	0,38	World
1	Albania	EASTERN EUROPE	3581655	28748	124,6	1,26	-4,93	21,52	4500.0	86,5	71,2	21,09	4,42	74,49	3	15,11	5,22	0,232	0,188	0,579	World
2	Algeria	NORTHERN AFRICA	32930091	2381740	13,8	0,04	-0,39	31	6000.0	70,0	78,1	3,22	0,25	96,53	1	17,14	4,61	0,101	0,6	0,298	World
3	American Samoa	OCEANIA	57794	199	290,4	58,29	-20,71	9,27	8000.0	97,0	259,5	10	15	75	2	22,46	3,27	NaN	NaN	NaN	World
4	Andorra	WESTERN EUROPE	71201	468	152,1	0,00	6,6	4,05	19000.0	100,0	497,2	2,22	0	97,78	3	8,71	6,25	NaN	NaN	NaN	World

indian_district_population = pd.read_csv("datasets/indian-census-data-with-geospatial-indexing/district wise population for year 2001 and 2011.csv")
indian_district_population["Country"] = "India"
indian_district_population.head()

	State	District	Population in 2001	Population in 2011	Country
0	Andaman & Nicobar Islands	Nicobar	42068	36842	India
1	Andaman & Nicobar Islands	North & Middle Andaman	105613	105597	India
2	Andaman & Nicobar Islands	South Andaman	208471	238142	India
3	Andhra Pradesh	Anantapur	3640478	4081148	India
4	Andhra Pradesh	Chittoor	3745875	4174064	India

There are two ways to generate a sunburst chart using plotly. It provides two APIs for generating sunburst charts.

• plotly.express - It provides method named sunburst() to create sunburst charts.
• plotly.graph_objects - It provides method named Sunburst() to create charts.

We'll be explaining both ways one by one below.

Plotly Express

The plotly has a module named express which provides easy to use method named sunburst() which can be used to create sunburst charts. It accepts dataframe containing data, columns to use for hierarchical, and column to use for actual values of the distribution. We can provide a list of columns with hierarchical relations as list to the pathattribute of the method. The values to use to decide sizes of distribution circles can be provided as a column name to thevaluesattribute. We can also providetitle,width, and height attribute of the figure. The sunburst() method returns figure object which can be used to show a chart by calling show() method on it.

Starbucks Store Count Distribution World Wide Sunburst Chart

We'll need to prepare the dataset first in order to show Starbucks store counts distribution per city, and country worldwide. We'll be grouping the original Starbucks dataset according to Country, and City. Then we'll call count() on it which will count entry for each possible combination of Country and City. We also have introduceda new column named World which has all valuesthe same containing string World. We have created this column to createa circle inthe center to seethe total worldwide count.

starbucks_dist = starbucks_locations.groupby(by=["Country", "State/Province", "City"]).count()[["Store Number"]].rename(columns={"Store Number":"Count"})
starbucks_dist["World"] = "World"
starbucks_dist = starbucks_dist.reset_index()
starbucks_dist.head()

	Country	State/Province	City	Count	World
0	AD	7	Andorra la Vella	1	World
1	AE	AJ	Ajman	2	World
2	AE	AZ	Abu Dhabi	40	World
3	AE	AZ	Al Ain	8	World
4	AE	DU	Abu Dhabi	3	World

fig = px.sunburst(starbucks_dist,
                  path=["World", "Country", "State/Province", "City"],
                  values='Count',
                  title="Starbucks Store Count Distribution World Wide [Country, State, City]",
                  width=750, height=750)
fig.show()

Indian Districts Population Distribution Per State [2011] Sunburst Chart

Below we are creating a sunburst chart depicting population distribution per district per the of India in 2011. We have passed the path parameter list of columns necessary to createa hierarchy. We have covered this in our tutorial on treemap as well.

fig = px.sunburst(indian_district_population,
                  path=["Country", "State", "District",],
                  values='Population in 2011',
                  width=750, height=750,
                  title="Indian District Population Per State",
                  )
fig.show()

World Population Distribution Per Country Per Region Sunburst Chart

Below we have created a sunburst chart showing population count per country per region of the world. We have provided necessary columns having a hierarchical relationship to the path parameter of the method.

fig = px.sunburst(world_countries_data,
                  path=["World", "Region", "Country"],
                  values='Population',
                  width=750, height=750,
                  title="World Population Per Country Per Region",
                  )
fig.show()

World Area Distribution Per Country Per Region Sunburst Chart

Below the sunburst chart explains area distribution per country per region worldwide.

fig = px.sunburst(world_countries_data,
                  path=["World", "Region", "Country"],
                  values='Area (sq. mi.)',
                  width=750, height=750,
                  title="World Area Per Country Per Region",
                  )
fig.show()

World Population Distribution Per Country Per Region Color-Encoded By GDP Sunburst Chart

Below we have again plotted sunburst chart explaining population distribution per country per region but we have also color encoded each distribution according to GDP of that country/region. We can compare the population and GDP of the country based on this sunburst chart. We can notice that countries like India and China have less GDP even though having more population whereas countries like the US, Japan, Germany, UK, France, Australia, Hong Kong have less population but more GDP.

fig = px.sunburst(world_countries_data,
                  path=["World", "Region", "Country"],
                  values='Population',
                  width=750, height=750,
                  color_continuous_scale="BrBG",
                  color='GDP ($ per capita)',
                  title="World Population Per Country Per Region Color-Encoded By GDP"
                  )
fig.show()

World Population Per Country Per Region Color-Encoded By Area Sunburst Chart

Below we have again plotted population distribution per country per region of the world but this time we have color encoded data to the area of countries and region. This helps us compare the relationship between population and area. We can notice that countries like India are more but has less area compared to countries like Russia, the United States, Brazil which has visibly more area with less population.

fig = px.sunburst(world_countries_data,
                  path=["World", "Region", "Country"],
                  values='Population',
                  width=750, height=750,
                  color_continuous_scale="RdYlGn",
                  color='Area (sq. mi.)',
                  title="World Population Per Country Per Region Color-Encoded By Area"
                  )
fig.show()

Plotly Graph Objects

The second way of creating a sunburst chart using plotly is using the Sunburst() method of the graph_objects module. We need to provide it a list of all possible combination of parent and child combination and their values in order to create a chart using this method.

World Population Per Country Per Region Sunburst Chart

In order to create a sunburst chart using graph_objects.Sunburst() method, we have done little preprocessing with data. The Sunburst() method expects that we provided all possible parent-child relationship labels and their values to it. We have region-country relation labels and values ready in the dataset but for getting world-region relationship labels and values we have grouped dataframe according to the region in order to get region-wise population counts. We have then combined labels in order to generate all possible parent-child relationship labels as well as values.

region_wise_pop = world_countries_data.groupby(by="Region").sum()[["Population"]].reset_index()

parents = [""] + ["World"] *region_wise_pop.shape[0] + world_countries_data["Region"].values.tolist()
labels = ["World"] + region_wise_pop["Region"].values.tolist() + world_countries_data["Country"].values.tolist()
values  = [world_countries_data["Population"].sum()] + region_wise_pop["Population"].values.tolist() + world_countries_data["Population"].values.tolist()

fig =go.Figure(go.Sunburst(
    parents=parents,
    labels= labels,
    values= values,
))

fig.update_layout(title="World Population Per Country Per Region",
                  width=700, height=700)

fig.show()

World Population Per Country Per Region Sunburst Chart

Below we have again created a sunburst chart of population distribution but this time it looks completely like the plotly.express module. We have set the branchvalues parameter to string value total which fills the whole circle. By default, the Sunburst() method does not create full circle sunburst charts.

region_wise_pop = world_countries_data.groupby(by="Region").sum()[["Population"]].reset_index()

parents = [""] + ["World"] *region_wise_pop.shape[0] + world_countries_data["Region"].values.tolist()
labels = ["World"] + region_wise_pop["Region"].values.tolist() + world_countries_data["Country"].values.tolist()
values  = [world_countries_data["Population"].sum()] + region_wise_pop["Population"].values.tolist() + world_countries_data["Population"].values.tolist()

fig =go.Figure(go.Sunburst(
    parents=parents,
    labels= labels,
    values= values,
    branchvalues="total",
))

fig.update_layout(title="World Population Per Country Per Region",
                  width=700, height=700)

fig.show()

World Population and Area Distribution Per Country Per Region Sunburst Chart Subplots

Below we have combined two sunburst charts into a single figure. One sunburst chart is about world population distribution per country per region and another is about area distribution per country per region. We can combine many related sunburst charts this way to show possible relationships. Please go through code to understand little preprocessing in order to create charts.

fig = go.Figure()

parents = [""] + ["World"] *region_wise_pop.shape[0] + world_countries_data["Region"].values.tolist()
labels = ["World"] + region_wise_pop["Region"].values.tolist() + world_countries_data["Country"].values.tolist()
values  = [world_countries_data["Population"].sum()] + region_wise_pop["Population"].values.tolist() + world_countries_data["Population"].values.tolist()

fig.add_trace(go.Sunburst(
    parents=parents,
    labels= labels,
    values= values,
    domain=dict(column=0),
    name="Population Distribution"
))


region_wise_area = world_countries_data.groupby(by="Region").sum()[["Area (sq. mi.)"]].reset_index()

parents = [""] + ["World"] *region_wise_area.shape[0] + world_countries_data["Region"].values.tolist()
labels = ["World"] + region_wise_area["Region"].values.tolist() + world_countries_data["Country"].values.tolist()
values  = [world_countries_data["Area (sq. mi.)"].sum()] + region_wise_area["Area (sq. mi.)"].values.tolist() + world_countries_data["Area (sq. mi.)"].values.tolist()


fig.add_trace(go.Sunburst(
    parents=parents,
    labels= labels,
    values= values,
    domain=dict(column=1)
))


fig.update_layout(
    grid= dict(columns=2, rows=1),
    margin = dict(t=0, l=0, r=0, b=0),
    width=900, height=700
)

fig.show()

This ends our small tutorial explaining how to plot a sunburst chart in python using plotly. Please feel free to let us know your views in the comments section.

References

• Treemap in Python using Plotly
• Sunburst Chart using R
• Sunburst Chart Definition