Often when you perform simple linear regression, you may be interested in creating a scatterplot to visualize the various combinations of x and y values along with the estimation regression line.

Fortunately there are two easy ways to create this type of plot in Python. This tutorial explains both methods using the following data:

import numpyas np 

#create data
x = np.array([1, 1, 2, 3, 4, 4, 5, 6, 7, 7, 8, 9])
y = np.array([13, 14, 17, 12, 23, 24, 25, 25, 24, 28, 32, 33])

Method 1: Using Matplotlib

The following code shows how to create a scatterplot with an estimated regression line for this data using Matplotlib:

import matplotlib.pyplot as plt

#create basic scatterplot
plt.plot(x, y, 'o')

#obtain m (slope) and b(intercept) of linear regression line
m, b = np.polyfit(x, y, 1)

#add linear regression line to scatterplot 
plt.plot(x, m*x+b)

Feel free to modify the colors of the graph as you’d like. For example, here’s how to change the individual points to green and the line to red:

#use green as color for individual points
plt.plot(x, y, 'o', color='green')

#obtain m (slope) and b(intercept) of linear regression line
m, b = np.polyfit(x, y, 1)

#use red as color for regression line
plt.plot(x, m*x+b, color='red')

Method 2: Using Seaborn

You can also use the regplot() function from the Seaborn visualization library to create a scatterplot with a regression line:

import seaborn as sns

#create scatterplot with regression line
sns.regplot(x, y, ci=None)

Note that ci=None tells Seaborn to hide the confidence interval bands on the plot. You can choose to show them if you’d like, though:

import seaborn as sns

#create scatterplot with regression line and confidence interval lines
sns.regplot(x, y)