We can pass various parameters to regplot like confidence interval (ci), estimators (mean, median etc.), jitter, color, marker (diamond, plus sign, circle, square), linewidth etc. We can also change style of scattering points and regression lines differently using scatter_kws and line_kws functions.
Lets explore Regression Plot using Tips dataset.
Step 1: Import required libraries
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
%matplotlib inline
Step 2: Load Tips datasets
tips=sns.load_dataset(‘tips')
tips.head()
Step 3: Explore data using Regression Plot
sns.regplot(x='total_bill', y='tip', data=tips)
Specify confidence interval
sns.regplot(x='total_bill', y='tip', data=tips, ci=95)
Specify estimators like mean, median etc.
sns.regplot(x='total_bill', y='tip', data=tips, x_estimator=np.mean)
sns.regplot(x='total_bill', y='tip', data=tips, x_estimator=np.median)
Specify jitter parameter
sns.regplot(x='size', y='total_bill', data=tips)
sns.regplot(x='size', y='total_bill', data=tips, x_jitter=True)
sns.regplot(x='size', y='total_bill', data=tips, x_jitter=0.3)
Cosmetic parameters like color, marker, line width etc.
sns.regplot(x='total_bill', y='tip', data=tips, color='purple')
sns.regplot(x='total_bill', y='tip', data=tips, marker='D') #diamond
sns.regplot(x='total_bill', y='tip', data=tips, marker='+') #plus sign
sns.regplot(x='total_bill', y='tip', data=tips, marker='o') #circle
sns.regplot(x='total_bill', y='tip', data=tips, marker='s') #square
sns.regplot(x='total_bill', y='tip', data=tips, marker='D',
scatter_kws={‘color‘ : ‘blue'},
line_kws={‘color‘ : ‘red', ‘linewidth‘ : 3.1})
