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 numpy as 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)
You can find the complete documentation for the regplot() function here.