Rasterization for complex plots in Matplotlib - Mini Project: Build & Apply

Rasterization for Complex Plots

📖 Scenario: You are working with a large scatter plot that has thousands of points. Plotting all points as vector graphics can make the file very large and slow to display. Rasterization helps by converting complex parts of the plot into images, making the plot faster and lighter.

🎯 Goal: You will create a scatter plot with many points and use rasterization on the scatter points to improve performance while keeping the axes and labels as vector graphics.

📋 What You'll Learn

Create a scatter plot with 10,000 points using matplotlib.

Set a rasterization option on the scatter points only.

Keep the axes and labels as vector graphics.

Display the plot.

💡 Why This Matters

🌍 Real World

Scientists and data analysts often create plots with thousands of points. Rasterization helps keep these plots fast and manageable when saving or sharing.

💼 Career

Knowing how to optimize plots with rasterization is useful for data scientists, researchers, and anyone creating complex visualizations for reports or presentations.

1

Create a scatter plot with 10,000 points

Import matplotlib.pyplot as plt and numpy as np. Create two numpy arrays called x and y each with 10,000 random numbers using np.random.rand(10000).

Matplotlib

# Your code here

Need a hint?

Use np.random.rand(10000) to create arrays of 10,000 random points between 0 and 1.

2

Set up the plot figure and axes

Create a figure and axes using plt.subplots() and save them as fig and ax.

Matplotlib

import matplotlib.pyplot as plt
import numpy as np

x = np.random.rand(10000)
y = np.random.rand(10000)

# Create figure and axes
# Your code here

Need a hint?

Use fig, ax = plt.subplots() to create the plot area.

3

Plot the scatter points with rasterization

Use ax.scatter() to plot x and y. Set the rasterized parameter to True to rasterize the scatter points.

Matplotlib

import matplotlib.pyplot as plt
import numpy as np

x = np.random.rand(10000)
y = np.random.rand(10000)

fig, ax = plt.subplots()

# Plot scatter points with rasterization
# Your code here

Need a hint?

Pass rasterized=True inside ax.scatter() to rasterize the points only.

4

Display the plot

Use plt.show() to display the plot with rasterized scatter points and vector axes.

Matplotlib

import matplotlib.pyplot as plt
import numpy as np

x = np.random.rand(10000)
y = np.random.rand(10000)

fig, ax = plt.subplots()

ax.scatter(x, y, rasterized=True)

# Show the plot
# Your code here

Need a hint?

Call plt.show() to open the plot window.