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Data Visualization and Analysis With Seaborn Library

Visualizing the Reg Plots

Learn how to plot, design, and interpret reg plots for data visualizations.

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Overview

Reg plot stands for regression plot; we use it to build a linear regression model of our data to see how variables are related.

Plotting reg plot

Let’s get started by importing the required libraries and the mpg dataset. Then, we draw a reg plot using the sns.regplot() function and pass displacement and mpg to the x and y parameters, respectively. The regplot() function draws a scatter plot for the displacement and mpg variables and then fits a regression line on top. It also computes the 95% confidence interval, represented as a shaded line below the main regression line. We can see a downward trend in the plot; as the displacement increases, the mpg decreases.

Python
mpg_df = sns.load_dataset("mpg")
sns.regplot(x = 'displacement', y='mpg', data= mpg_df)
plt.savefig('output/graph.png')

We can set truncate=False in the sns.regplot() function to extend the regression line to the axis limit. By default, it’s set to True, which enforces the regression line within the data points.

Python
sns.regplot(y='mpg', x = 'displacement', data= mpg_df, truncate=False) 

plt.savefig('output/graph.png')

To plot a regression line without a scatter plot, we specify scatter=False in the sns.regplot() ...