Matplotlib Polar Charts

last modified March 7, 2025

Matplotlib is a versatile Python library for creating a wide range of visualizations, including polar charts. Polar charts, also known as radial or circular charts, plot data in a circular coordinate system, making them ideal for displaying cyclic or directional data. This tutorial explores how to create various types of polar charts using Matplotlib.

Polar charts are particularly useful for visualizing periodic phenomena, such as wind directions, daily activity patterns, or seasonal trends. Matplotlib's polar plotting capabilities allow for flexible customization, enabling users to represent data in an intuitive and visually appealing way.

Basic Polar Chart

This example demonstrates how to create a basic polar chart showing wind speed by direction.

import matplotlib.pyplot as plt
import numpy as np

# Data: Directions (degrees) and wind speed (m/s)
theta = np.radians([0, 90, 180, 270, 360])  # N, E, S, W, N
wind_speed = [5, 8, 6, 7, 5]

# Create a polar chart
ax = plt.subplot(111, projection="polar")
ax.plot(theta, wind_speed, color="blue", label="Wind Speed")
ax.fill_between(theta, 0, wind_speed, color="skyblue", alpha=0.4)

# Customize the chart
ax.set_theta_zero_location("N")  # North at top
ax.set_theta_direction(-1)  # Clockwise
ax.set_title("Wind Speed by Direction (March 2025)")
ax.set_xlabel("Direction")
ax.legend()

# Display the chart
plt.show()

In this example, we plot wind speed across cardinal directions (North, East, South, West) for a day in March 2025. The np.radians function converts degrees to radians, as Matplotlib's polar plots use radians. The subplot function with projection="polar" sets up the circular coordinate system. The fill_between method fills the area from the center (0) to the wind speed values, creating a radial effect.

The chart is customized with set_theta_zero_location("N") to align North at the top and set_theta_direction(-1) for clockwise rotation, mimicking a compass. Wind speed peaks at 8 m/s from the East, providing a clear visual of directional variation—useful for meteorologists or renewable energy analysts studying wind patterns.

Polar Bar Chart

This example shows how to create a polar bar chart to display daily activity hours.

import matplotlib.pyplot as plt
import numpy as np

# Data: Hours of the day and activity duration (hours)
theta = np.linspace(0, 2 * np.pi, 24, endpoint=False)  # 24 hours
activity = [1, 2, 1, 0, 0, 1, 2, 3, 4, 5, 6, 5, 4, 3, 2, 2, 3, 4, 5, 4, 3, 2, 1, 1]

# Create a polar bar chart
ax = plt.subplot(111, projection="polar")
width = 2 * np.pi / 24  # Width of each bar
bars = ax.bar(theta, activity, width=width, color="skyblue", alpha=0.7)

# Customize the chart
ax.set_theta_zero_location("N")
ax.set_theta_direction(-1)
ax.set_title("Daily Activity Hours (March 7, 2025)")
ax.set_xlabel("Time of Day")

# Display the chart
plt.show()

This polar bar chart represents a person's activity duration across 24 hours on March 7, 2025. The np.linspace function divides the circle into 24 equal segments (one per hour), and ax.bar creates bars radiating from the center, with heights corresponding to activity hours. The width parameter ensures bars span one hour each, and alpha=0.7 adds slight transparency.

Activity peaks between 9 AM and 5 PM (up to 6 hours), likely reflecting work or exercise, and tapers off at night. The polar layout naturally conveys the cyclical nature of a day, making it ideal for time-based data like sleep patterns, energy usage, or traffic flow. The chart's compass-like orientation enhances its intuitive appeal.

Polar Area Chart with Multiple Datasets

This example demonstrates how to create a polar area chart comparing sales by product category across seasons.

import matplotlib.pyplot as plt
import numpy as np

# Data: Angles (seasons) and sales (thousands $)
theta = np.radians([0, 90, 180, 270, 360])  # Winter, Spring, Summer, Fall, Winter
electronics = [30, 35, 40, 45, 30]
clothing = [25, 20, 15, 30, 25]

# Create a polar chart
ax = plt.subplot(111, projection="polar")
ax.fill_between(theta, electronics, color="skyblue", alpha=0.4, label="Electronics")
ax.fill_between(theta, clothing, color="lightgreen", alpha=0.4, label="Clothing")

# Customize the chart
ax.set_theta_zero_location("N")
ax.set_theta_direction(-1)
ax.set_xticks(theta[:-1])  # Exclude the last tick to avoid overlap
ax.set_xticklabels(["Winter", "Spring", "Summer", "Fall"])
ax.set_title("Seasonal Sales by Category (2025)")
ax.legend()

# Display the chart
plt.show()

This chart compares seasonal sales for electronics and clothing in 2025. Two fill_between calls create overlapping filled areas, with alpha=0.4 ensuring visibility of both datasets. The angles represent seasons, converted to radians, and custom xticklabels replace numerical ticks with season names for clarity.

Electronics sales peak in Summer (40) and Fall (45), possibly due to tech releases, while clothing sales spike in Fall (30) and Winter (25), reflecting seasonal demand. This visualization highlights cyclical trends and comparisons, making it valuable for retail analysis or marketing strategy planning.

Polar Scatter Chart with Size Variation

This example shows how to create a polar scatter chart with varying point sizes to represent earthquake magnitudes by direction.

import matplotlib.pyplot as plt
import numpy as np

# Data: Directions (degrees), distances (km), and magnitudes
theta = np.radians([45, 135, 225, 315])  # NE, SE, SW, NW
distances = [50, 80, 60, 70]  # Distance from epicenter
magnitudes = [4.5, 6.0, 5.2, 4.8]  # Richter scale

# Create a polar scatter chart
ax = plt.subplot(111, projection="polar")
scatter = ax.scatter(theta, distances, s=np.array(magnitudes) * 50, color="red", alpha=0.6, label="Earthquakes")

# Customize the chart
ax.set_theta_zero_location("N")
ax.set_theta_direction(-1)
ax.set_title("Earthquake Magnitudes by Direction (March 2025)")
ax.set_xlabel("Direction from Epicenter")
ax.set_ylabel("Distance (km)")
ax.legend()

# Display the chart
plt.show()

This polar scatter chart maps earthquakes around an epicenter in March 2025, with directions in degrees, distances in kilometers, and magnitudes on the Richter scale. The scatter function plots points, where s=np.array(magnitudes) * 50 scales point sizes by magnitude for visual impact. The alpha=0.6 parameter adds transparency to overlapping points.

The largest quake (6.0) occurs 80 km southeast, while the smallest (4.5) is 50 km northeast. This chart type excels at showing spatial relationships and intensity, useful for seismologists or emergency planners assessing quake distribution relative to a central point.

Best Practices for Polar Charts

• Set Clear Directions: Use set_theta_zero_location and set_theta_direction to orient the chart intuitively.
• Label Axes: Provide meaningful labels for angles and radii to aid interpretation.
• Use Transparency: Apply alpha for overlapping areas or points to maintain visibility.
• Simplify Data: Limit the number of categories or points to avoid clutter in the circular layout.

Source

Matplotlib Polar Chart Documentation

In this article, we have explored various types of polar charts using Matplotlib, including basic polar charts, polar bar charts, multi-dataset area charts, and scatter charts with size variation.

Author

My name is Jan Bodnar, and I am a passionate programmer with extensive programming experience. I have been writing programming articles since 2007. To date, I have authored over 1,400 articles and 8 e-books. I possess more than ten years of experience in teaching programming.

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