Treemap Charts in Python

How to make Treemap Charts with Plotly


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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.

Treemap charts visualize hierarchical data using nested rectangles. Same as Sunburst 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 "tidy" 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.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=['day', 'time', 'sex'], values='total_bill')
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")
df["world"] = "world" # in order to have a single root node
fig = px.treemap(df, path=['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.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()
df["all"] = "all" # in order to have a single root node
fig = px.treemap(df, path=['all', 'sex', 'day', 'time'], values='total_bill', color='day')
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()
df["all"] = "all" # in order to have a single root node
fig = px.treemap(df, path=['all', 'sex', 'day', 'time'], values='total_bill', color='time')
fig.show()

Using an explicit mapping for discrete colors

For more information about discrete colors, see the dedicated page.

In [6]:
import plotly.express as px
df = px.data.tips()
fig = px.treemap(df, path=['sex', 'day', 'time'], values='total_bill', color='time',
                  color_discrete_map={'(?)':'black', 'Lunch':'gold', 'Dinner':'darkblue'})
fig.show()

Rectangular data with missing values

If the dataset is not fully rectangular, missing values should be supplied as None.

In [7]:
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", "Other",
           "Tech", "Tech", "Finance", "Finance", "Other"]
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)
)
df["all"] = "all" # in order to have a single root node
print(df)
fig = px.treemap(df, path=['all', 'regions', 'sectors', 'vendors'], values='sales')
fig.show()
  vendors  sectors regions  sales  all
0       A     Tech   North      1  all
1       B     Tech   North      3  all
2       C  Finance   North      2  all
3       D  Finance   North      4  all
4    None    Other   North      1  all
5       E     Tech   South      2  all
6       F     Tech   South      2  all
7       G  Finance   South      1  all
8       H  Finance   South      4  all
9    None    Other   South      1  all

Basic Treemap with go.Treemap

If Plotly Express does not provide a good starting point, it is also possible to use the more generic go.Treemap function from plotly.graph_objects.

In [8]:
import plotly.graph_objects as go

fig = go.Figure(go.Treemap(
    labels = ["Eve","Cain", "Seth", "Enos", "Noam", "Abel", "Awan", "Enoch", "Azura"],
    parents = ["", "Eve", "Eve", "Seth", "Seth", "Eve", "Eve", "Awan", "Eve"]
))

fig.show()

Set Different Attributes in Treemap

This example uses the following attributes:

  1. values: sets the values associated with each of the sectors.
  2. textinfo: determines which trace information appear on the graph that can be 'text', 'value', 'current path', 'percent root', 'percent entry', and 'percent parent', or any combination of them.
  3. pathbar: a main extra feature of treemap to display the current path of the visible portion of the hierarchical map. It may also be useful for zooming out of the graph.
  4. branchvalues: determines how the items in values are summed. When set to "total", items in values are taken to be value of all its descendants. In the example below Eva = 65, which is equal to 14 + 12 + 10 + 2 + 6 + 6 + 1 + 4. When set to "remainder", items in values corresponding to the root and the branches sectors are taken to be the extra part not part of the sum of the values at their leaves.
In [9]:
import plotly.graph_objects as go
from plotly.subplots import make_subplots

labels = ["Eve", "Cain", "Seth", "Enos", "Noam", "Abel", "Awan", "Enoch", "Azura"]
parents = ["", "Eve", "Eve", "Seth", "Seth", "Eve", "Eve", "Awan", "Eve"]

fig = make_subplots(
    cols = 2, rows = 1,
    column_widths = [0.4, 0.4],
    subplot_titles = ('branchvalues: <b>remainder<br />&nbsp;<br />', 'branchvalues: <b>total<br />&nbsp;<br />'),
    specs = [[{'type': 'treemap', 'rowspan': 1}, {'type': 'treemap'}]]
)

fig.add_trace(go.Treemap(
    labels = labels,
    parents = parents,
    values =  [10, 14, 12, 10, 2, 6, 6, 1, 4],
    textinfo = "label+value+percent parent+percent entry+percent root",
    ),
              row = 1, col = 1)

fig.add_trace(go.Treemap(
    branchvalues = "total",
    labels = labels,
    parents = parents,
    values = [65, 14, 12, 10, 2, 6, 6, 1, 4],
    textinfo = "label+value+percent parent+percent entry",
    outsidetextfont = {"size": 20, "color": "darkblue"},
    marker = {"line": {"width": 2}},
    pathbar = {"visible": False}),
              row = 1, col = 2)

fig.show()

Set Color of Treemap Sectors

There are three different ways to change the color of the sectors in Treemap:

  1. marker.colors, 2) colorway, 3) colorscale. The following examples show how to use each of them.
In [10]:
import plotly.graph_objects as go

labels = ["A1", "A2", "A3", "A4", "A5", "B1", "B2"]
parents = ["", "A1", "A2", "A3", "A4", "", "B1"]

fig = go.Figure(go.Treemap(
    labels = labels,
    parents = parents,
    marker_colors = ["pink", "royalblue", "lightgray", "purple", "cyan", "lightgray", "lightblue"]))

fig.show()

This example uses treemapcolorway attribute, which should be set in layout.

In [11]:
import plotly.graph_objects as go

labels = ["A1", "A2", "A3", "A4", "A5", "B1", "B2"]
parents = ["", "A1", "A2", "A3", "A4", "", "B1"]

fig = go.Figure(go.Treemap(
    labels = labels,
    parents = parents
))

fig.update_layout(treemapcolorway = ["pink", "lightgray"])

fig.show()
In [12]:
import plotly.graph_objects as go

values = ["11", "12", "13", "14", "15", "20", "30"]
labels = ["A1", "A2", "A3", "A4", "A5", "B1", "B2"]
parents = ["", "A1", "A2", "A3", "A4", "", "B1"]

fig = go.Figure(go.Treemap(
    labels = labels,
    values = values,
    parents = parents,
    marker_colorscale = 'Blues'))

fig.show()

Treemap chart with a continuous colorscale

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.

In [13]:
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()
        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.Treemap(
    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.Treemap(
    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()
   Unnamed: 0 region   county salesperson  calls  sales
0           0  North   Dallam          JE     35     23
1           1  North   Dallam          ZQ     49     13
2           2  North   Dallam          IJ     20      6
3           3  North  Hartley          WE     39     37
4           4  North  Hartley          PL     42     37

Nested Layers in Treemap

The following example uses hierarchical data that includes layers and grouping. Treemap and Sunburst charts reveal insights into the data, and the format of your hierarchical data. maxdepth attribute sets the number of rendered sectors from the given level.

In [14]:
import plotly.graph_objects as go
from plotly.subplots import make_subplots

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 = make_subplots(
    rows = 1, cols = 2,
    column_widths = [0.4, 0.4],
    specs = [[{'type': 'treemap', 'rowspan': 1}, {'type': 'treemap'}]]
)

fig.add_trace(
    go.Treemap(
        ids = df1.ids,
        labels = df1.labels,
        parents = df1.parents),
    col = 1, row = 1)

fig.add_trace(
    go.Treemap(
        ids = df2.ids,
        labels = df2.labels,
        parents = df2.parents,
        maxdepth = 3),
    col = 2, row = 1)

fig.update_layout(
    margin = {'t':0, 'l':0, 'r':0, 'b':0}
)

fig.show()

Controlling text fontsize with uniformtext

If you want all the text labels to have the same size, you can use the uniformtext layout parameter. The minsize attribute sets the font size, and the mode attribute sets what happens for labels which cannot fit with the desired fontsize: either hide them or show them with overflow.

In [15]:
import plotly.graph_objects as go
import pandas as pd

df = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/718417069ead87650b90472464c7565dc8c2cb1c/sunburst-coffee-flavors-complete.csv')

fig = go.Figure(go.Treemap(
        ids = df.ids,
        labels = df.labels,
        parents = df.parents))
fig.update_layout(uniformtext=dict(minsize=10, mode='hide'))
fig.show()

Reference

See https://plotly.com/python/reference/#treemap for more information and chart attribute 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