You can use the seaborn **regplot** function to plot a linear regression model fit to a dataset.

Unfortunately there is no built-in feature in seaborn to extract the regression equation of the line, but you can use the **scipy.stats.linregress** function to quickly find the regression coefficients:

import scipy import seaborn as sns #create regplot p = sns.regplot(data=df, x=df.x, y=df.y) #calculate slope and intercept of regression equation slope, intercept, r, p, sterr = scipy.stats.linregress(x=p.get_lines()[0].get_xdata(), y=p.get_lines()[0].get_ydata())

The following example shows how to use this syntax in practice.

**Example: Display Regression Equation in Seaborn Regplot**

Suppose we have the following pandas DataFrame that contains information about the hours studied and final exam score of various students:

import pandas as pd #create DataFrame df = pd.DataFrame({'hours': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], 'score': [77, 79, 84, 80, 81, 89, 95, 90, 83, 89]}) #view DataFrame print(df) hours score 0 1 77 1 2 79 2 3 84 3 4 80 4 5 81 5 6 89 6 7 95 7 8 90 8 9 83 9 10 89

Suppose we would like to plot the data points and add a fitted regression line to the data.

We can use the following syntax to do so:

import scipy import seaborn as sns #create regplot p = sns.regplot(data=df, x=df.hours, y=df.score) #calculate slope and intercept of regression equation slope, intercept, r, p, sterr = scipy.stats.linregress(x=p.get_lines()[0].get_xdata(), y=p.get_lines()[0].get_ydata()) #display slope and intercept of regression equation print(intercept, slope) 77.39999999999995 1.3272727272727356

From the output we can see that the regression line has the following equation:

**y = 77.4 + 1.327**

If we would like to display this equation on the seaborn **regplot**, we can use the **text()** function from matplotlib:

import matplotlib.pyplot as plt import scipy import seaborn as sns #create regplot p = sns.regplot(data=df, x=df.hours, y=df.score) #calculate slope and intercept of regression equation slope, intercept, r, p, sterr = scipy.stats.linregress(x=p.get_lines()[0].get_xdata(), y=p.get_lines()[0].get_ydata()) #add regression equation to plot plt.text(2, 95, 'y = ' + str(round(intercept,3)) + ' + ' + str(round(slope,3)) + 'x')

Notice that the regression equation is now displayed in the top left corner of the plot.

Note that within the **text()** function, we specified that the regression equation should be displayed starting at the (x, y) coordinates of (2, 95).

Feel free to modify these coordinates to display the regression equation where you’d like in your own plot.

**Note**: You can find the complete documentation for the seaborn **regplot** function here.

**Additional Resources**

The following tutorials explain how to perform other common tasks in seaborn:

How to Adjust the Figure Size of a Seaborn Plot

How to Change the Position of a Legend in Seaborn

How to Change Axis Labels on a Seaborn Plot