Difference between regplot(), lmplot() and residplot()?

A matplotlib-based Python data visualisation package is called Seaborn. It offers a sophisticated drawing tool for creating eye-catching and educational statistics visuals. Seaborn assists in resolving Matplotlib's two main issues, which are?

We now believe that teaching students how to generate these representations using ggplot2's methods—which take more coding but are more advanced, adaptable, and transparent—will benefit students. Here, the basic plots made by residPlot() are rebuilt using ggplot2 as a resource to assist users in switching from residPlot() to ggplot2.

Feature	regplot()	lmplot()	residplot()
Purpose	Plot a simple linear regression model between two variables	Plot a linear regression model while grouping one variable by another variable	Plot the residuals of a linear regression model
Visualization	Scatter plot with a regression line and confidence interval	Scatter plot with a regression line and confidence interval, with the ability to group one variable by another variable	Scatter plot of residuals with a zero reference line
Usefulness	Useful for quickly visualizing the relationship between two variables and the linear regression model, fit	Useful for visualizing the relationship between two variables and the linear regression model fit, while taking into account the impact of a third variable	Useful for visualizing the residuals of a linear regression model and checking for pattern or structure in the residuals
Regression Model	Simple linear regression model	Linear regression model	Linear regression model
Output	Scatter plot with a regression line and confidence interval	Scatter plot with a regression line and confidence interval, grouped by a third variable	Scatter plot of residuals with a zero reference line

seaborn.regplot()

Data and a fit to a linear regression model are plotted using the seaborn.regplot() function. There are several options for estimating the regression model, all of which are mutually incompatible.

Syntax

seaborn.regplot( x,  y,  data=None, x_estimator=None, x_bins=None,  
x_ci=’ci’, scatter=True, fit_reg=True, ci=95, n_boot=1000, units=None, 
order=1, logistic=False, lowess=False, robust=False, logx=False, 
x_partial=None, y_partial=None, truncate=False, dropna=True, 
x_jitter=None, y_jitter=None, label=None, color=None, marker=’o’,   
scatter_kws=None, line_kws=None, ax=None)

Parameters − The following is a description of a few key parameters −

• x, y − These are input variables. If strings, these should match the column names in the "data" section. Axes will have the series name indicated when pandas objects are utilized.

• data − This is a dataframe where each row represents an observation, and each column represents a variable.

• lowess − This parameter accepts a boolean value and is optional. If "True," estimate a nonparametric Lowess model using "statsmodels" (locally weighted linear regression).

• color − A colour that should be used for all narrative parts.

• marker − (optional) (optional) glyphs for the scatterplot marker to be used.

Return − The Axes object containing the plot.

seaborn.Implot()

seaborn.lmplot() is a function in the Seaborn library that is used to visualize the relationship between two numerical variables. It creates a scatter plot and fits a linear regression model to the data. It is a convenient way to visualize the relationship between the variables and the regression model, and can also be used to compare the relationship between the variables for different groups or categories.

Here is an example of how to use the lmplot() function in Python −

import seaborn as sns
# Load the data
df = sns.load_dataset('titanic')
# Create an lmplot with fare and age as the x and y variables, and class as the hue
sns.lmplot(x='fare', y='age', hue='class', data=df)
# Show the plot
plt.show()

This will create a scatter plot with a linear regression model fit to the data, and will color the points by the class column.

The lmplot() function has several parameters that you can use to customize the appearance and behavior of the plot. Some of the main parameters are −

• x − The name of the column to use as the x variable.

• y − The name of the column to use as the y variable.

• hue − The name of the column to use to color the points.

• data − The DataFrame to use for the plot.

• col − The column's name to create subplots for each unique value.

• row − The column's name to create subplots for each unique value.

• fit_reg − A boolean value indicating whether to fit a linear regression model to the data.

• scatter_kws − A dictionary of keyword arguments to pass to the scatter() function.

• line_kws − A dictionary of keyword arguments to pass to the plot() function.

Returns − This method returns the FacetGrid object with the plot for further tweaking

seaborn.residplot()

Syntax

seaborn.residplot(x, y, data=None, lowess=False, x_partial=None, y_partial=None, order=1,
   robust=False, dropna=True, label=None, color=None, scatter_kws=None, line_kws=None, ax=None)

Parameters − The description of some main parameters are given below −

• x − The predictor variable's data or column name in the "data" section.

• y − The response variable's data or column name in the "data" section.

• data − A DataFrame with the columns "x" and "y."

• lowess − (Optional) Fit the residual scatterplot with a lowess smoother.

• dropna − This argument, which is optional, accepts a boolean value. If True, then while fitting and charting, disregard observations with missing data.

Conclusion

In conclusion, regplot(), lmplot(), and residplot() are functions in the Python library Seaborn that are used to create regression plots. These plots are used to visualize the relationship between two variables and the strength of that relationship.

Regplot() is a function that plots a scatterplot of the data and fits a linear regression model to the data. It allows you to specify the x and y variables, the data, and various customization options such as the points' color, size, and style.

Lmplot() is a function that combines regplot() with a facet plot, allowing you to plot multiple regression plots in a single figure. It allows you to specify the x and y variables, the data, and the hue, col, and row variables to create the facet plot. It also allows you to specify various customization options, such as the points' color, size, and style.

Residplot() is a function that plots the residuals of a linear regression model. It allows you to specify the x and y variables, the data, and various customization options such as the points' color, size, and style.