Section: Regplot and Lmplot | Data Visualization on Seaborn

Section outline

If you want to perform linear regressions, Seaborn provides a dedicated function: regplot().

Parameter name	Explanations	Required type	Example
data	Table you are planning to use	DataFrame, Series, dict, array, or list of arrays	data=table
x	Variable of the table for the x-axis	string corresponding to a variable of the table	x="weight"
y	Variable of the table for the y-axis	string corresponding to a variable of the table	y=”height”
ci	Variable allowing to control the confidence interval	Integer between 1 and 100	ci=99
nboot	Variable allowing to choose the number of bootstrap resampling done.	Integer	nboot=100
seed	Variable allowing reproductibility by choosing the seed of the bootstrap.	Integer	seed=42
logistic	Variable allowing to do a logistic regression	Boolean	logistic=True
lowess	Variable allowing to do a LOWESS regression	Boolean	lowess=True
robust	Variable allowing to do a robust regression. Higher computing cost	Boolean	robust=True

regplot() can also display the confidence interval around the regression line, which is set to 95% by default.

Here is an editable example of code:

 Importation code
import pandas as pd
import seaborn as sns
import numpy as np
import matplotlib.pyplot as plt
import statsmodels
from pyodide.http import open_url

url = "https://raw.githubusercontent.com/mwaskom/seaborn-data/master/penguins.csv"
data = pd.read_csv(open_url(url))
← Running

 Cell 2
sns.regplot(data=data, x="bill_length_mm",y="bill_depth_mm", ci=70)
← Running

Here, we are 70% confident that the true regression curve lies within the interval displayed on the graph. By default, n_boot is set to 1000. Increasing this value will necessarily increase the code's execution time, as additional resampling iterations will be performed. seed allows you to reproduce the same resampling results by using an integer as a seed, which is useful for reproducibility when writing a scientific paper or verifying that a method works correctly.

The regression method can also be changed by selecting a different approach, for example by setting the lowess parameter to True:

 Cell 3
sns.regplot(data=data, x="bill_length_mm", y="bill_depth_mm",
 ci=99, lowess=True)
← Running

The confidence interval is not displayed when using LOWESS.

Another option is lmplot(), which is better suited for performing regressions across multiple plots:

signature lmplot

Parameter name	Explanations	Required type	Example
data	Table you are planning to use	DataFrame, Series, dict, array, or list of arrays	data=tableau
x	Variable of the table for the x-axis	string corresponding to a variable of the table	x="weight"
y	Variable of the table for the y-axis	string corresponding to a variable of the table	y=”height”
hue	Variable of the table to distinguish with colors	string corresponding to a variable of the table	hue=”age”
row	Variable of the table that will be used to make different plots, in rows	string corresponding to a variable of the table	row=”category”
col	Variable of the table that will be used to make different plots, in columns	string corresponding to a variable of the table	col=”job”
ci	Variable allowing to control the confidence interval	Integer between 1 and 100	ci=99
nboot	Variable allowing to choose the number of bootstrap resampling done.	Integer	nboot=100
lowess	Variable allowing to do a LOWESS regression	Boolean	lowess=True

Here is an example of code:

 Cell 4
sns.lmplot(data=data, x="bill_length_mm", y="bill_depth_mm", ci=95, hue="island", robust=True, col="sex")
← Running

Robust and logistic regressions are also available, just as with regplot(). The nboot and seed parameters are also available.