In this blog post, we will explore how to create tree maps and sunburst charts using ggplot in Python. These visualizations are useful for representing hierarchical data and understanding the relative sizes of categories within a dataset.

What is a Tree Map?

A tree map is a graphical representation of hierarchical data using nested rectangles. Each rectangle represents a node in the hierarchy, with the area of the rectangle proportional to the value it represents. The rectangles are often color-coded to represent different categories or levels within the hierarchy.

Creating Tree Maps with ggplot

To create a tree map in Python using ggplot, we first need to install the necessary libraries:

!pip install ggplot
!pip install squarify

Once the libraries are installed, we can import them and create a simple tree map using example data:

import pandas as pd
import squarify
import matplotlib.pyplot as plt
from ggplot import *

# Example data
data = pd.DataFrame({'category': ['A', 'B', 'C', 'D'], 'value': [40, 30, 20, 10]})

# Compute rectangle sizes
sizes = squarify.normalize_sizes(data['value'], 100, 100)
rects = squarify.squarify(sizes, 0, 0, 100, 100)

# Color mapping
colors = ['red', 'blue', 'green', 'yellow']

# Create plot
fig = plt.figure(figsize=(6, 6))
ax = fig.add_subplot()
ax.bar(rects['x'], rects['dy'], rects['dx'], rects['y'], color=colors)
ax.set_xticks([])
ax.set_yticks([])
plt.show()

This code demonstrates how to create a basic tree map using the squarify library. We normalize the sizes of the rectangles, then use squarify.squarify() to calculate the positions of each rectangle. We specify the colors of the rectangles using a color mapping list.

The resulting tree map is displayed using plt.show(). The figsize argument in plt.figure() determines the size of the plot, and ax.bar() is used to draw the rectangles on the plot.

What is a Sunburst Chart?

A sunburst chart is a radial chart that displays multilevel hierarchical data. It is similar to a pie chart, where each level of the hierarchy is represented by a ring or circle sector. The size of each sector corresponds to the proportion of the data it represents, and the colors can indicate different categories or levels.

Creating Sunburst Charts with ggplot

To create a sunburst chart in Python using ggplot, we can use the plotly library.

!pip install plotly

Once the library is installed, we can import it and create a basic sunburst chart using example data:

import plotly.express as px

# Example data
data = px.data.tips()

# Create sunburst chart
fig = px.sunburst(data, path=['sex', 'day', 'time'], values='total_bill', color='day')

# Show chart
fig.show()

We import plotly.express as px and use the px.sunburst() function to create the sunburst chart. We specify the hierarchical path using the path argument, the values to represent using the values argument, and the color mapping using the color argument.

The resulting sunburst chart is displayed using fig.show().

Conclusion

In this blog post, we explored how to create tree maps and sunburst charts using ggplot in Python. Tree maps are a great way to visualize hierarchical data, while sunburst charts are ideal for displaying multilevel hierarchical data. By using these visualizations, you can gain insights into the relative sizes and proportions of different categories within your dataset.