How to plot a dashed line in matplotlib?

Matplotlib is used to create visualizations and plotting dashed lines is used to enhance the style and readability of graphs. A dashed line can represent trends, relationships or boundaries in data. Below we will explore how to plot and customize dashed lines using Matplotlib. To plot dashed line:

Syntax: matplotlib.pyplot.plot(x, y, linestyle='dashed')

where:

• x:  X-axis points on the line.
• y:  Y-axis points on the line.
• linestyle: Change the style of the line.

1. Plotting a Basic Dashed Line

The simplest way to plot a dashed line in Matplotlib is by setting the linestyle parameter to 'dashed' or using the shorthand '--'

import matplotlib.pyplot as plt
x = [1.5, 2.6, 3.5, 4, 9]
y = [3.25, 6.3, 4.23, 1.35, 3]

plt.plot(x, y, linestyle='dashed')  # or linestyle='--'
plt.xlabel("X-axis")
plt.ylabel("Y-axis")
plt.show()

Output

2. Customizing the Color of the Dashed Line

You can easily change the color of your dashed line to match your design or to make different lines distinguishable.

import matplotlib.pyplot as plt
x = [1.5, 2.6, 3.5, 4, 9]
y = [3.25, 6.3, 4.23, 1.35, 3]

plt.plot(x, y, linestyle='--', color='gold')
plt.xlabel("X-axis")
plt.ylabel("Y-axis")
plt.show()

Output

3. Adjusting the Width (Thickness) of the Dashed Line

To make the dashed line thicker or thinner, use the linewidth or lw parameter.

import matplotlib.pyplot as plt
x = [1.5, 5.6, 3.5, 4, 9]
y1 = [1, 4, 3, 4, 5]
y2 = [6, 7, 4, 9, 10]

plt.plot(x, y1, linestyle='--', color='black', linewidth=1)  # thinner line
plt.plot(x, y2, linestyle='--', color='red', linewidth=4)    # thicker line
plt.show()

Output

4. Creating Custom Dash Patterns

In addition to predefined styles, Matplotlib lets you create custom dash patterns using set_dashes() which takes a list of dash and gap lengths (in points) for greater control over line appearance.

import matplotlib.pyplot as plt
x = [1.5, 2.6, 3.5, 4, 9]
y = [3.25, 6.3, 4.23, 1.35, 3]
line, = plt.plot(x, y, color='blue')

line.set_dashes([5, 10, 15, 5]) 
plt.show()

Output