Downsampling strategies in Matplotlib

Introduction

Downsampling helps to reduce the number of points in a plot. This makes graphs faster to draw and easier to understand when you have lots of data.

You have a very large time series and want to plot it quickly.

Your plot looks cluttered because there are too many points.

You want to save memory and speed up rendering in interactive plots.

You want to focus on general trends instead of every detail.

You need to prepare data for a small screen or report.

Syntax

Matplotlib

plt.plot(x, y, markevery=step)
plt.plot(x, y, linestyle='-', marker='o', markersize=4, markevery=step)
plt.plot(x, y, drawstyle='steps-post')

markevery controls which points get markers, effectively downsampling markers.

drawstyle='steps-post' changes line drawing to step style, useful for some downsampling effects.

Examples

Shows markers only every 10 points, reducing marker clutter.

Matplotlib

plt.plot(x, y, markevery=10)

Draws line with markers every 5 points, making the plot clearer.

Matplotlib

plt.plot(x, y, linestyle='-', marker='o', markersize=4, markevery=5)

Draws the line as steps, which can simplify the visual shape.

Matplotlib

plt.plot(x, y, drawstyle='steps-post')

Sample Program

This code creates a noisy sine wave with 1000 points. It shows three plots: full data, data with markers every 50 points, and a step style plot. This demonstrates simple downsampling strategies in matplotlib.

Matplotlib

import matplotlib.pyplot as plt
import numpy as np

# Create large data
x = np.linspace(0, 10, 1000)
y = np.sin(x) + np.random.normal(0, 0.1, 1000)

plt.figure(figsize=(10, 6))

# Plot full data
plt.subplot(3, 1, 1)
plt.plot(x, y)
plt.title('Full data (1000 points)')

# Plot with markers every 50 points
plt.subplot(3, 1, 2)
plt.plot(x, y, linestyle='-', marker='o', markersize=4, markevery=50)
plt.title('Markers every 50 points')

# Plot with step style
plt.subplot(3, 1, 3)
plt.plot(x, y, drawstyle='steps-post')
plt.title('Step style plot')

plt.tight_layout()
plt.show()

Important Notes

Downsampling with markevery only affects markers, not the line itself.

For very large datasets, consider reducing data points before plotting for better performance.

Step plots can help visualize data changes clearly without showing every point.

Summary

Downsampling reduces the number of points shown to make plots clearer and faster.

Use markevery to show fewer markers on lines.

Step styles can simplify line shapes for better understanding.