lm plots

The lm plot plots the scatter and fits the regression model on it. A scatter plot is the best way to understand the relationship between two variables. Its output visualization is a joint distribution of two variables. lmplot() takes two column names – x and y – as a string and DataFrame variable. Let's see the following example:

# Import the required libraries
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt

# Create DataFrame
df=pd.DataFrame({'x':[1,3,5,7,9,11],'y':[10,25,35,33,41,59]})

# Create lmplot
sns.lmplot(x='x', y='y', data=df)

# Show figure
plt.show()

This results in the following output:

By default, lmplot() fits the regression line. We can also remove this by setting the fit_reg parameter as False:

# Create lmplot
sns.lmplot(x='x', y='y', data=df, fit_reg=False)

# Show figure
plt.show()

This results in the following output:

Let's take a dataset of HR Analytics...