Customize a treemap in matplotlib with the squarify library

The squarify library serves as a powerful tool for tailoring a treemap visualization within the matplotlib ecosystem.

In this insightful post, we delve into the process of constructing a captivating treemap that boasts a harmonious color palette, along with customized labels that enhance the overall readability. Furthermore, we will explore the addition of an informative title and a corresponding legend that will aid in comprehending the visualized data.

Accompanying the explanations, you will find reproducible code snippets that will enable you to confidently create and customize your own treemaps in Python.

Libraries & Dataset

For creating this chart, we will need to load the following libraries:

matplotlib for creatin the chart
pandas for loading the dataset
pypalettes for the color palette
highlight_text for custom annotations
squarify for creating the treemap

# load libraries
import squarify # pip install squarify (algorithm for treemap)
import matplotlib.pyplot as plt
from pypalettes import load_cmap
import pandas as pd
from highlight_text import fig_text

# set a higher resolution
plt.rcParams['figure.dpi'] = 300

df = pd.read_csv("https://raw.githubusercontent.com/holtzy/The-Python-Graph-Gallery/master/static/data/simple-treemap.csv")
df

	type	name	value	parent
0	leaf	Mark	90	Team Dataviz
1	leaf	Robert	12	Team Dataviz
2	leaf	Emily	34	Team Dataviz
3	leaf	Marion	53	Team Dataviz
4	leaf	Nicolas	98	Team DevOps
5	leaf	Malki	22	Team DevOps
6	leaf	Djé	12	Team DevOps
7	leaf	Mélanie	45	Team Sales
8	leaf	Einstein	76	Team Sales

Simple treemap

The plot() function from squarify works mainly with 3 arguments:

sizes: a list of values that will be represented by the area of the squares
label: a list of labels to display on the squares
ax: the matplotlib axes where to plot the treemap

# create a figure
fig, ax = plt.subplots(figsize=(10,10))
ax.set_axis_off()

# add treemap
squarify.plot(
   sizes=df["value"],
   label=df["name"],
   ax=ax
)

# display plot
plt.show()

Custom colors

Utilizing the convenient pypalette library, you can effortlessly discover a color palette that suits your requirements.

In this instance, we employ the 'Acadia' palette and subsequently associate it with the parent column in the dataframe. This process generates a collection of colors that will be utilized to enhance the visual appeal of the squares.

Furthermore, we introduce some spacing between the rectangles with the help of pad=True and stipulate the preference for white text by using text_kwargs={'color':'white'}.

# create a color palette
cmap = load_cmap('Acadia')
category_codes, unique_categories = pd.factorize(df['parent'])
colors = [cmap(code) for code in category_codes]

# create a treemap
fig, ax = plt.subplots(figsize=(10,10))
ax.set_axis_off()
squarify.plot(
   sizes=df["value"],
   label=df["name"],
   color=colors,
   text_kwargs={'color':'white'},
   pad=True,
   ax=ax
)
plt.show()

Custom labels

In place of providing only the column of labels to the label argument, we establish a new variable called labels. This variable will encompass the name, the parent, and the value of each individual rectangle.

By doing so, we can present all these pertinent details on the treemap, enhancing its overall comprehensiveness.

# create a color palette
cmap = load_cmap('Acadia')
category_codes, unique_categories = pd.factorize(df['parent'])
colors = [cmap(code) for code in category_codes]

# customize the labels
labels = [f"{name} ({parent[5:]})\n{value}" for name, value, parent in zip(df['name'], df['value'], df['parent'])]

# create a treemap
fig, ax = plt.subplots(figsize=(10,10))
ax.set_axis_off()
squarify.plot(
   sizes=df["value"],
   label=labels,
   color=colors,
   text_kwargs={
      'color':'white',
      'fontsize':8,
      'fontweight':'bold'
   },
   pad=True,
   ax=ax
)
plt.show()

Legend with `highlight_text`

The highlight_text library offers the capability to incorporate various styles within a single string in a matplotlib annotation.

In our particular scenario, we leverage this functionality to introduce a title that associates colors with specific categories and their corresponding labels, thereby enhancing the overall clarity and interpretability of the visualization.

# create a color palette
cmap = load_cmap('Acadia')
category_codes, unique_categories = pd.factorize(df['parent'])
colors = [cmap(code) for code in category_codes]

# customize the labels
labels = [f"{name}\n{parent[5:]}\n{value}" for name, value, parent in zip(df['name'], df['value'], df['parent'])]

# create a treemap
fig, ax = plt.subplots(figsize=(10,8))
ax.set_axis_off()
squarify.plot(
   sizes=df["value"],
   label=labels,
   color=colors,
   text_kwargs={
      'color':'white',
      'fontsize':9,
      'fontweight':'bold'
   },
   pad=True,
   ax=ax
)

# add a title and legend
text = """<Treemap of a Simple Dataset>
Each color represents a category: <Dataviz>, <Sales> and <DevOps>.
Each rectangle represents a person.
"""
fig_text(
   x=0.133, y=0.98,
   s=text,
   color="black",
   highlight_textprops=[
      {"fontsize": 20, 'fontweight': 'bold'},
      {"color": cmap(0), 'fontweight': 'bold'},
      {"color": cmap(2), 'fontweight': 'bold'},
      {"color": cmap(1), 'fontweight': 'bold'},
   ],
   fontsize=14,
   ha='left'
)

plt.show()

Going further

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