treemap in python

2 min read 17-10-2024

Visualizing Data Hierarchies: A Guide to Treemaps in Python

Treemaps are an excellent way to visually represent hierarchical data, providing a clear picture of proportions and relationships within a complex dataset. They excel at showcasing how different parts contribute to the whole, making them ideal for financial data, market analysis, and project management.

In this article, we'll explore how to create effective treemaps in Python using the matplotlib and squarify libraries. Let's dive in!

Understanding Treemaps

Imagine you're analyzing sales data for different product categories. A treemap can help you visualize the overall market share of each category, and further drill down to see the individual product contributions within each category.

Here's how treemaps achieve this:

Hierarchical Structure: Data is organized into nested levels, like folders within folders.
Rectangles: Each data point is represented by a rectangle, with the size of the rectangle proportional to the value it represents.
Color Coding: Colors can be used to represent additional information, such as product type or sales region.

Building Your First Treemap in Python

Let's create a basic treemap using matplotlib and squarify.

1. Installation

pip install matplotlib squarify

2. Sample Data

For this example, we'll use data about different types of fruits and their respective quantities.

import matplotlib.pyplot as plt
import squarify

data = {
    'Fruits': [
        {'Name': 'Apples', 'Quantity': 25},
        {'Name': 'Oranges', 'Quantity': 18},
        {'Name': 'Bananas', 'Quantity': 12},
        {'Name': 'Grapes', 'Quantity': 10},
    ],
    'Vegetables': [
        {'Name': 'Carrots', 'Quantity': 15},
        {'Name': 'Tomatoes', 'Quantity': 10},
        {'Name': 'Cucumbers', 'Quantity': 8},
    ]
}

3. Data Preparation

We need to format our data for the squarify library. This involves extracting the labels (fruit names) and values (quantities) from our dictionary.

labels = []
sizes = []

for category, items in data.items():
    for item in items:
        labels.append(f"{item['Name']} ({category})")
        sizes.append(item['Quantity'])

4. Creating the Treemap

Now, we can use squarify.plot() to create the treemap:

plt.figure(figsize=(10, 6))
squarify.plot(sizes=sizes, label=labels, alpha=.8)
plt.title("Fruit and Vegetable Quantities")
plt.axis('off')
plt.show()

This code generates a simple treemap where the size of each rectangle represents the quantity of each fruit or vegetable.

Adding Enhancements

Let's make our treemap more informative and visually appealing:

Color Coding: Assign specific colors to each category or data point using the color parameter in squarify.plot().
Labels: Add labels inside each rectangle to show the exact value. You can modify the label placement and size using label_kwargs in squarify.plot().
Customization: You can further customize the treemap's appearance by using matplotlib functions to set the background color, font sizes, and other styling elements.

Real-World Applications

Treemaps find applications in various domains:

Financial Analysis: Visualizing asset allocation, portfolio performance, or expense breakdowns.
Market Research: Understanding market share distribution, competitor analysis, or product category trends.
Project Management: Tracking project progress, task dependencies, or resource allocation.

Conclusion

Treemaps are a valuable tool for visualizing hierarchical data, offering a clear and concise representation of proportions and relationships. By mastering this technique in Python, you can effectively analyze complex data and gain insights into its structure and distribution.

Remember to adapt your treemap design based on the specific data and its intended audience, and always strive for clarity and readability in your visualizations.