LM - Linear Model plot

seaborn.lmplot() plots data points (scatter) along with a linear regression model fit, just like sns.regplot().
However, unlike regplot(), lmplot() is a figure-level function, meaning it can create:

• Multiple subplots (facets) based on categorical variables
• Grouped regressions using color (hue)

It’s built on top of sns.regplot() and adds faceting (via FacetGrid) for multi-group or multi-category analysis.

Syntax:

sns.lmplot(    data=None,    *,    x=None,    y=None,    hue=None,    col=None,    row=None,    palette=None,    col_wrap=None,    height=5,    aspect=1,    markers=None,    scatter=True,    fit_reg=True,    ci=95,    n_boot=1000,    order=1,    robust=False,    logx=False,    truncate=True,    x_estimator=None,    x_bins=None,    line_kws=None,    scatter_kws=None,    legend=True,    legend_out=True,    facet_kws=None,    **kwargs)

Parameters:

• data = DataFrame containing the data
• x, y = Numeric variables for regression
• hue = Adds subgroups (different colours and regression lines)
• col, row = Facet the data into multiple subplots
• palette = Colour palette for hue groups
• col_wrap = Wrap facet columns into multiple rows
• height = Height (in inches) of each facet
• aspect = Aspect ratio (width/height) of each facet
• order = Degree of polynomial regression (1 = linear)
• robust = Use robust regression to reduce outlier influence
• ci = Confidence interval width (in %)
• line_kws = Keyword args for line (colour, style, width, etc.)
• scatter_kws = Keyword args for scatter points
• legend = Show/hide legend
• legend_out = Place legend outside the plot grid
• facet_kws = Pass additional parameters to the FacetGrid

Basic example

import seaborn as snsimport matplotlib.pyplot as plt​tips = sns.load_dataset("tips")​sns.lmplot(data=tips, x="total_bill", y="tip")plt.show()

Plots a scatterplot with a regression line and 95% confidence interval. Figure-level → creates its own figure and axes.

Facet into multiple columns

import seaborn as snsimport matplotlib.pyplot as plt​tips = sns.load_dataset("tips")​sns.lmplot(data=tips, x="total_bill", y="tip", col="day")plt.show()

Creates one subplot per day. Each shows its own scatter + regression line.

Facet by rows and columns

import seaborn as snsimport matplotlib.pyplot as plt​tips = sns.load_dataset("tips")​sns.lmplot(data=tips, x="total_bill", y="tip", col="day", row="sex")plt.show()

Creates a grid of subplots, split by both day (columns) and sex (rows).

Wrap columns across rows

import seaborn as snsimport matplotlib.pyplot as plt​tips = sns.load_dataset("tips")​sns.lmplot(    data=tips,    x="total_bill",    y="tip",    col="day",    col_wrap=2,    height=4)plt.show()

Creates 2 columns per row (better layout for many facets).

Compare groups side-by-side

import seaborn as snsimport matplotlib.pyplot as plt​tips = sns.load_dataset("tips")​sns.lmplot(    data=tips,    x="total_bill",    y="tip",    hue="sex",    col="day",    palette="Set2")plt.show()

Multiple regression lines across columns for day, coloured by sex.