Matplotlib - Add an Average Line to the Plot

In this tutorial, we will look at how to add an average line to a matplotlib plot with the help of some examples.

What is an average line on a plot?

Generally, an average line is a (horizontal) line that represents the average value of the data points on the y-axis. Thus, to plot an average line (irrespective of the tool or library), first, find out the average y-value and then plot a horizontal line for that y-value.

Average line in matplotlib

To add an average line to a plot in matplotlib, you can use the matplotlib.pyplot.axhline() function and pass the average y-value as an argument. You can use the numpy.mean() function to get the average of the y-values. The following is the syntax –

import numpy as np
import matplotlib.pyplot as plt

# add an average line to a plot
plt.axhline(np.mean(y_values))

Let’s now look at some examples of using the above syntax –

First, we will create a scatter plot to which we will later add an average line.

Highlighted programs for you

Put simply, All Star is in the business of connecting students and schools. We help students find schools that are great matches for their educational needs, personal preferences and lifestyles, and schools pay us for our work.

Here's a quick Q & A of information we think you should know if you choose to use our websites. The main takeaways: Using our services is free, but starting and completing a degree, certificate or diploma program takes commitment and effort and should be considered carefully.

1. How do you provide your services for free?

Schools pay to advertise on our sites. This allows us to offer our service to prospective students like you for free.

Schools place a very high priority on enrolling students who go on to graduate. By using All Star's focused, information-rich services, schools can be more confident that the prospective students we introduce them to are truly committed to their education and to succeeding once they're enrolled in school.

2. How do you differ from your competitors?

There are other companies out there that do what we do. But there is no one that does it with more integrity or respect for our users and their choices. We put you, the student, in control. You get to choose the schools you're interested in, and you get to choose what programs you'd like to learn more about. We will never submit your contact information to a school without your consent.

3. Do you really work with ALL schools?

No. Our listings are not exhaustive, we do not list all schools, but they do include a rich selection of options. We believe this wide variety of options from hundreds of schools meets the needs of most prospective students who visit our sites. We partner with smaller schools that specialize in one particular career field, such as Pima Medical Institute, as well as world-class, multi-discipline institutions like the University of Southern California.

4. Is getting a degree really going to open up doors for me?

We really want you to succeed in the program you choose. So does your school. But we feel compelled to be very frank and upfront: You must work hard and stay committed to graduate. And assuming you do graduate, there is no guarantee you will find a job in your chosen field, or any job for that matter.

Obtaining an education has many personal benefits and can also improve your financial prospects. However, it's important to keep in mind that obtaining an education does not guarantee financial success or even a job. Job markets vary greatly by region, state, and even locally, and are affected by trends in the national economy and even international events. Other factors affecting your job-hunting success may include your job history and experience, and your level of education, degree, or certificate type. Your area of specialization plays a role, too.

5. If I do find a job, what can I expect in terms of salary?

The government publishes a great deal of information related to hundreds of jobs on the Bureau of Labor Statistics (BLS) website. We use this as a resource for many of the articles on the All Star websites. As comprehensive as the BLS information is, it is neither complete nor entirely accurate. The BLS salary information we publish is a national average. Actual salaries for a particular job or skill may be different where you live. For example, if you live in a large city or where the job market for particular skills is especially competitive, such as Silicon Valley in California, you're likely to receive a higher wage or salary for the same job than you would in a rural or economically depressed market. Other factors that can affect salary rates include the size of the employer, union contracts and governmental regulations to name a few.

6. Where are you located? How can I contact you?

All Star Directories is located at P.O. Box 1677 Renton, WA 98057. You can reach us at (206) 436-7500 or at customerservice@allstardirectories.com.

Flatiron School

Data Science Bootcamp

Product Design UX/UI Bootcamp

University of Maryland Global Campus

Cloud Computing Systems Master's

Digital Forensics & Cyber Investigation Master's

Creighton University

Health Informatics Master's

import matplotlib.pyplot as plt

# x values - years
x = [2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020]
# y values - 1 USD in INR
y = [46.67, 53.44, 56.57, 62.33, 62.97, 66.46, 67.79, 70.09, 70.39, 76.38]

# plot x and y on scatter plot
plt.scatter(x, y)

# add axes labels
plt.xlabel('Year')
plt.ylabel('1USD in INR')

Output:

scatter plot of year vs usd to rupee conversion data

The above scatter plot depicts the price of 1 USD in INR from 2011 to 2020. Let’s add an average line to this plot, which will depict the average USD to INR conversion rate from 2011 to 2020.

import numpy as np

# plot x and y on scatter plot
plt.scatter(x, y)

# add axes labels
plt.xlabel('Year')
plt.ylabel('1USD in INR')

# add an average line
plt.axhline(np.mean(y))

Output:

You can see that the horizontal line is around 63.

You can customize the horizontal line with additional parameters. For example, let’s make the line dashed and give it a green color.

import numpy as np

# plot x and y on scatter plot
plt.scatter(x, y)

# add axes labels
plt.xlabel('Year')
plt.ylabel('1USD in INR')

# add an average line
plt.axhline(np.mean(y), linestyle='dashed', color='g')

Output:

average line formatted with custom color and style added to plot

Author

Piyush Raj

Piyush is a data professional passionate about using data to understand things better and make informed decisions. He has experience working as a Data Scientist in the consulting domain and holds an engineering degree from IIT Roorkee. His hobbies include watching cricket, reading, and working on side projects.

View all posts