When I started working with Python data visualization, I often needed to highlight specific thresholds in my plots.
Sometimes, a horizontal line was the best way to do it. But instead of a solid line, I wanted a dashed horizontal line to make the chart more readable.
At first, it wasn’t obvious how to do this in Matplotlib. But after experimenting with different methods, I found several simple ways to draw dashed horizontal lines in Python.
In this tutorial, I’ll show you step-by-step how I do it. I’ll cover different methods so you can choose the one that works best for your project.
Methods to Make a Dashed Horizontal Line in Python Matplotlib
A dashed horizontal line is useful when you want to:
- Highlight a target value (like a sales goal in USD).
- Show a threshold (like a passing score in an exam).
- Mark a zero baseline in financial charts.
- Separate sections in a graph visually without making the line too bold.
As a Python developer with more than 10 years of experience, I can say that dashed lines are one of the simplest but most effective ways to improve chart readability.
Method 1 – Use axhline() in Matplotlib
The easiest way to draw a dashed horizontal line in Python Matplotlib is by using the axhline() function. This function lets you specify the y-position and style of the line.
Here’s the full Python code:
import matplotlib.pyplot as plt
# Sample data: Average monthly electricity bills in USD
months = ["Jan", "Feb", "Mar", "Apr", "May", "Jun"]
bills = [120, 135, 150, 160, 155, 170]
# Create the plot
plt.plot(months, bills, marker="o", label="Monthly Bills")
# Add a dashed horizontal line at $150 (budget threshold)
plt.axhline(y=150, color="red", linestyle="--", linewidth=2, label="Budget Limit")
# Add labels and title
plt.title("Average Monthly Electricity Bills in the USA")
plt.xlabel("Month")
plt.ylabel("Bill Amount (USD)")
plt.legend()
# Show the plot
plt.show()You can see the output in the screenshot below.
This code plots monthly electricity bills and adds a dashed horizontal line at $150. The linestyle=”–” makes the line dashed, and linewidth=2 makes it thicker for better visibility.
Method 2 – Use hlines() in Matplotlib
Another way to create a dashed horizontal line in Python is with the hlines() function. This method is useful when you want to control the start and end points of the line.
Here’s an example:
import matplotlib.pyplot as plt
# Sample data: Average daily temperatures in New York (°F)
days = list(range(1, 11))
temps = [72, 74, 76, 78, 80, 82, 79, 77, 75, 73]
# Create the plot
plt.plot(days, temps, marker="o", label="Daily Temp")
# Add a dashed horizontal line at 78°F
plt.hlines(y=78, xmin=1, xmax=10, colors="blue", linestyles="--", linewidth=2, label="Comfort Level")
# Add labels and title
plt.title("Daily Temperatures in New York")
plt.xlabel("Day")
plt.ylabel("Temperature (°F)")
plt.legend()
# Show the plot
plt.show()You can see the output in the screenshot below.
Here, the dashed horizontal line is drawn only from day 1 to day 10. This gives you more flexibility compared to axhline().
Method 3 – Use plot() with constant y-values
Sometimes I prefer to use the regular plot() function in Python Matplotlib to create a dashed line horizontally. This method is handy when I want full control over the line style.
Here’s how I do it:
import matplotlib.pyplot as plt
# Sample data: Stock prices of a company (USD)
days = [1, 2, 3, 4, 5]
prices = [210, 220, 215, 225, 230]
# Create the plot
plt.plot(days, prices, marker="o", label="Stock Price")
# Add a dashed horizontal line at $220
plt.plot([1, 5], [220, 220], linestyle="--", color="green", linewidth=2, label="Target Price")
# Add labels and title
plt.title("Stock Prices of a Tech Company (USD)")
plt.xlabel("Day")
plt.ylabel("Price (USD)")
plt.legend()
# Show the plot
plt.show()You can see the output in the screenshot below.
In this case, I manually set the x-range [1, 5] and kept the y-value constant at 220 to draw the dashed line. This method gives me maximum customization.
Customize dashed horizontal lines in Python
Once you know how to draw a dashed horizontal line, you can customize it further.
Here are some useful options:
- Change dash style: linestyle=”–“, linestyle=”-.”, or linestyle=”:”.
- Change color: Use names like “red”, “blue”, or hex codes like “#FF5733”.
- Change thickness: Use linewidth=1.5 or linewidth=3 for thicker lines.
- Add transparency: Use alpha=0.5 to make the line semi-transparent.
Example with custom dash style:
import matplotlib.pyplot as plt
# Create a simple plot
plt.plot([0, 10], [0, 10], label="Data")
# Add a custom dashed horizontal line
plt.axhline(y=5, color="purple", linestyle=(0, (5, 10)), linewidth=2, label="Custom Dash")
plt.title("Custom Dashed Horizontal Line in Python Matplotlib")
plt.legend()
plt.show()Here, linestyle=(0, (5, 10)) creates a custom dash pattern (5 pixels on, 10 pixels off).
Real-world example: Highlight passing scores in exams
Let’s take a practical example that many of us can relate to. Suppose we have exam scores of students in a U.S. high school, and we want to highlight the passing score (60).
Here’s how I would do it in Python:
import matplotlib.pyplot as plt
# Student names and scores
students = ["Alice", "Bob", "Charlie", "David", "Eva", "Frank"]
scores = [55, 65, 72, 48, 90, 60]
# Create the plot
plt.bar(students, scores, color="skyblue")
# Add a dashed horizontal line at passing score (60)
plt.axhline(y=60, color="red", linestyle="--", linewidth=2, label="Passing Score")
# Add labels and title
plt.title("High School Exam Scores (USA)")
plt.xlabel("Students")
plt.ylabel("Scores")
plt.legend()
# Show the plot
plt.show()This makes it very easy to see who passed and who didn’t. The dashed horizontal line at 60 acts as a clear visual threshold.
Key takeaways
- Use axhline() for quick full-width dashed horizontal lines.
- Use hlines() when you want to control the start and end points.
- Use plot() for maximum customization of dashed lines.
- Always customize color, thickness, and dash style to match your chart’s needs.
When I first learned Python Matplotlib, I underestimated how powerful a simple dashed horizontal line could be. Now, I use them all the time to highlight thresholds, targets, and baselines in my charts.
If you’re working on data visualization in Python, I recommend trying out these methods in your next project. It will make your charts not only more professional but also easier to understand.
You may also read:
- Matplotlib fill_between
- Matplotlib set_xticklabels
- Matplotlib set_xticks
- Matplotlib Plot NumPy Array
Bijay Kumar is an experienced Python and AI professional who enjoys helping developers learn modern technologies through practical tutorials and examples. His expertise includes Python development, Machine Learning, Artificial Intelligence, automation, and data analysis using libraries like Pandas, NumPy, TensorFlow, Matplotlib, SciPy, and Scikit-Learn. At PythonGuides.com, he shares in-depth guides designed for both beginners and experienced developers. More about us.
