Creating Donut (Ring) Plots with Matplotlib in Python

Matplotlib can be used to create donut (ring) charts by first drawing a standard pie chart and then adding a white circle in the center, or by directly setting the wedgeprops parameter with a width value to create a hollow effect.

Basic donut plot

Example code:

# Remove warnings
import warnings
warnings.filterwarnings("ignore")

# Create data
size_of_groups = [12, 11, 3, 30]

# Plot pie chart
plt.pie(size_of_groups)
plt.axis('equal')
plt.show()

# Add white circle to make donut
my_circle = plt.Circle((0, 0), 0.7, color='white')
fig = plt.gcf()
fig.gca().add_artist(my_circle)
plt.axis('equal')
plt.show()

Using wedgeprops for a simpler donut

plt.pie(size_of_groups, wedgeprops=dict(width=0.3, edgecolor='w'))
plt.axis('equal')
plt.show()

Customizing colors

You can assign specific colors to each slice or let Matplotlib cycle through a list of colors. The colors argument accepts a list of color names or hex values, and wedgeprops can still control the ring width.

plt.pie(size, labels=names, colors=['red', 'green', 'blue', 'skyblue'], wedgeprops=dict(width=0.3, edgecolor='w'))
plt.axis('equal')
plt.show()

Using external palettes (Palettable)

from palettable.colorbrewer.qualitative import Pastel1_7
plt.pie(size, labels=names, colors=Pastel1_7.hex_colors, wedgeprops=dict(width=0.3, edgecolor='w'))
plt.axis('equal')
plt.show()

Custom label distance and color

plt.rcParams['text.color'] = 'red'
plt.pie(size, labels=names, labeldistance=0.85, wedgeprops=dict(width=0.3, edgecolor='w'))
plt.axis('equal')
plt.show()

Custom wedge edge width and color

plt.pie(size, labels=names, wedgeprops={'linewidth':7, 'edgecolor':'white', 'width':0.3, 'edgecolor':'w'})
plt.axis('equal')
plt.show()

Changing figure background and text color

fig = plt.figure()
fig.patch.set_facecolor('black')
plt.rcParams['text.color'] = 'white'
plt.pie(size, labels=names, wedgeprops=dict(width=0.3, edgecolor='w'))
plt.axis('equal')
plt.show()

Adding annotation markers

By using ax.annotate with custom arrow and bbox properties, you can label each slice with detailed text and arrows.

# Example snippet (simplified)
ax.annotate('225 g flour', xy=(x, y), xytext=(1.35*np.sign(x), 1.4*y),
            arrowprops=dict(arrowstyle='-'),
            bbox=dict(boxstyle='square,pad=0.3', fc='white', ec='black', lw=0.72))
plt.show()

Multi‑level donut plot (sub‑groups)

To visualize hierarchical data, draw two concentric pies: the outer ring for main groups and the inner ring for sub‑groups, adjusting the radius and width parameters.

# Outer ring
ax.pie(group_size, radius=1.3, labels=group_names,
       colors=[a(0.6), b(0.6), c(0.6)], wedgeprops=dict(width=0.3, edgecolor='w'))
# Inner ring
ax.pie(subgroup_size, radius=1.3-0.3, labels=subgroup_names,
       labeldistance=0.7,
       colors=[a(0.5), a(0.4), a(0.3), b(0.5), b(0.4), c(0.6), c(0.5), c(0.4), c(0.3), c(0.2)],
       wedgeprops=dict(width=0.4, edgecolor='w'))
plt.margins(0,0)
plt.show()

These examples demonstrate how to build simple and complex donut charts, customize their appearance, and add informative annotations, providing a versatile tool for data visualization in Python.