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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 youâ€™d like. For example, hereâ€™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 theÂ **regplot()Â **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 thatÂ **ci=NoneÂ **tells Seaborn to hide the confidence interval bands on the plot. You can choose to show them if youâ€™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.

**Additional Resources**

How to Perform Simple Linear Regression in Python

How to Create a Residual Plot in Python