Treemap Charts in Python

How to make Treemap Charts with Plotly

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Treemap charts visualize hierarchical data using nested rectangles. The input data format is the same as for Sunburst Charts and Icicle Charts: the hierarchy is defined by labels (names for px.treemap) and parents attributes. Click on one sector to zoom in/out, which also displays a pathbar in the upper-left corner of your treemap. To zoom out you can use the path bar as well.

Basic Treemap with plotly.express¶

Plotly Express is the easy-to-use, high-level interface to Plotly, which operates on a variety of types of data and produces easy-to-style figures.

With px.treemap, each row of the DataFrame is represented as a sector of the treemap.

In [1]:

import plotly.express as px
fig = px.treemap(
    names = ["Eve","Cain", "Seth", "Enos", "Noam", "Abel", "Awan", "Enoch", "Azura"],
    parents = ["", "Eve", "Eve", "Seth", "Seth", "Eve", "Eve", "Awan", "Eve"]
)
fig.update_traces(root_color="lightgrey")
fig.update_layout(margin = dict(t=50, l=25, r=25, b=25))
fig.show()

Treemap of a rectangular DataFrame with plotly.express¶

Hierarchical data are often stored as a rectangular dataframe, with different columns corresponding to different levels of the hierarchy. px.treemap can take a path parameter corresponding to a list of columns. Note that id and parent should not be provided if path is given.

In [2]:

import plotly.express as px
df = px.data.tips()
fig = px.treemap(df, path=[px.Constant("all"), 'day', 'time', 'sex'], values='total_bill')
fig.update_traces(root_color="lightgrey")
fig.update_layout(margin = dict(t=50, l=25, r=25, b=25))
fig.show()

Treemap of a rectangular DataFrame with continuous color argument in px.treemap¶

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.

Note: for best results, ensure that the first path element is a single root node. In the examples below we are creating a dummy column containing identical values for each row to achieve this.

In [3]:

import plotly.express as px
import numpy as np
df = px.data.gapminder().query("year == 2007")
fig = px.treemap(df, path=[px.Constant("world"), '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.update_layout(margin = dict(t=50, l=25, r=25, b=25))
fig.show()

Treemap of a rectangular DataFrame with discrete color argument in px.treemap¶

When the argument of color corresponds to non-numerical data, discrete colors are used. If a sector has the same value of the color column for all its children, then the corresponding color is used, otherwise the first color of the discrete color sequence is used.

In [4]:

import plotly.express as px
df = px.data.tips()
fig = px.treemap(df, path=[px.Constant("all"), 'sex', 'day', 'time'],
                 values='total_bill', color='day')
fig.update_layout(margin = dict(t=50, l=25, r=25, b=25))
fig.show()

In the example below the color of Saturday and Sunday sectors is the same as Dinner because there are only Dinner entries for Saturday and Sunday. However, for Female -> Friday there are both lunches and dinners, hence the "mixed" color (blue here) is used.

In [5]:

import plotly.express as px
df = px.data.tips