icaoberg / Visualization - Intro to seaborn

Created Sun, 17 May 2026 00:00:00 +0000 Modified Sun, 17 May 2026 17:14:28 -0400

Jupyter Notebook python

Visualization - Intro to seaborn¶

Seaborn is a Python data-visualization library built on top of Matplotlib. It provides a high-level interface for drawing attractive statistical graphics with minimal code, and integrates tightly with pandas DataFrames.

Key advantages over raw Matplotlib:

Sensible defaults and beautiful built-in themes
Native DataFrame / tidy-data support
Built-in statistical estimation (means, confidence intervals, regression lines)
Figure-level functions (relplot, displot, catplot) that automatically handle faceting

What you'll learn in this notebook:

Installing and importing Seaborn
Seaborn themes and color palettes
Relational plots: scatter, line
Distribution plots: histogram, KDE, ECDF, rug, violin
Categorical plots: box, bar, strip, swarm, point
Matrix / regression plots: heatmap, clustermap, pairplot, lmplot

Installation¶

If Seaborn is not already installed, run the cell below.

In [1]:

!pip install seaborn -q

Imports¶

In [2]:

import seaborn as sns
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np

print(f"Seaborn version : {sns.__version__}")

sns.set_theme(style='whitegrid', palette='muted', font_scale=1.1)

Seaborn version : 0.13.2

Built-in Example Datasets¶

Seaborn ships with several classic datasets that are loaded as pandas DataFrames.

In [3]:

print(sns.get_dataset_names())

['anagrams', 'anscombe', 'attention', 'brain_networks', 'car_crashes', 'diamonds', 'dots', 'dowjones', 'exercise', 'flights', 'fmri', 'geyser', 'glue', 'healthexp', 'iris', 'mpg', 'penguins', 'planets', 'seaice', 'taxis', 'tips', 'titanic']

In [4]:

tips    = sns.load_dataset('tips')
iris    = sns.load_dataset('iris')
flights = sns.load_dataset('flights')
penguins = sns.load_dataset('penguins').dropna()
fmri    = sns.load_dataset('fmri')

tips.head()

Out[4]:

	total_bill	tip	sex	smoker	day	time	size
0	16.99	1.01	Female	No	Sun	Dinner	2
1	10.34	1.66	Male	No	Sun	Dinner	3
2	21.01	3.50	Male	No	Sun	Dinner	3
3	23.68	3.31	Male	No	Sun	Dinner	2
4	24.59	3.61	Female	No	Sun	Dinner	4

Themes and Color Palettes¶

Seaborn provides five built-in themes (darkgrid, whitegrid, dark, white, ticks) and a rich palette system.

In [5]:

styles = ['darkgrid', 'whitegrid', 'dark', 'white', 'ticks']
x = np.linspace(0, 2 * np.pi, 100)

fig, axes = plt.subplots(1, 5, figsize=(16, 3), sharey=True)
for ax, style in zip(axes, styles):
    with sns.axes_style(style):
        ax2 = fig.add_axes(ax.get_position(), label=style)
        ax2.plot(x, np.sin(x))
        ax2.set_title(style, fontsize=10)
        ax2.set_xticks([])
    ax.set_visible(False)

fig.suptitle('Seaborn Themes', fontsize=13, y=1.02)
plt.tight_layout()
plt.show()

/var/folders/nx/gq421vkn2h1cxq_cljnl3r1m0000gn/T/ipykernel_60397/2517397815.py:14: UserWarning: This figure includes Axes that are not compatible with tight_layout, so results might be incorrect.
  plt.tight_layout()

No description has been provided for this image

In [6]:

palettes = ['deep', 'muted', 'pastel', 'bright', 'dark', 'colorblind']
fig, axes = plt.subplots(len(palettes), 1, figsize=(8, 4))
for ax, pal in zip(axes, palettes):
    colors = sns.color_palette(pal)
    for j, color in enumerate(colors):
        ax.add_patch(plt.Rectangle((j, 0), 1, 1, color=color))
    ax.set_xlim(0, len(colors))
    ax.set_ylim(0, 1)
    ax.set_ylabel(pal, rotation=0, labelpad=55, va='center', fontsize=9)
    ax.set_xticks([]); ax.set_yticks([])
fig.suptitle('Built-in Color Palettes', fontsize=12)
plt.tight_layout()
plt.show()

Relational Plots¶

Relational plots (scatterplot, lineplot, relplot) show relationships between two numerical variables.

Scatter Plot¶

scatterplot maps variables to x/y position and supports hue, size, and style semantics.

In [7]:

fig, axes = plt.subplots(1, 2, figsize=(12, 5))

sns.scatterplot(data=tips, x='total_bill', y='tip',
                hue='time', style='smoker', size='size',
                sizes=(30, 200), alpha=0.8, ax=axes[0])
axes[0].set_title('Tips – total bill vs tip')

sns.scatterplot(data=penguins, x='flipper_length_mm', y='body_mass_g',
                hue='species', style='island', alpha=0.8, ax=axes[1])
axes[1].set_title('Penguins – flipper length vs body mass')

plt.tight_layout()
plt.show()

Line Plot¶

lineplot aggregates repeated measurements and draws a confidence band by default.

In [8]:

fig, axes = plt.subplots(1, 2, figsize=(13, 5))

sns.lineplot(data=fmri, x='timepoint', y='signal',
             hue='event', style='event', markers=True,
             dashes=False, ax=axes[0])
axes[0].set_title('fMRI signal over time (95% CI shaded)')

flights_pivot = flights.pivot(index='month', columns='year', values='passengers')
sns.lineplot(data=flights, x='year', y='passengers',
             hue='month', palette='tab20', legend=False, ax=axes[1])
axes[1].set_title('Flights – passengers per year by month')

plt.tight_layout()
plt.show()

Distribution Plots¶

Distribution plots (histplot, kdeplot, ecdfplot, rugplot, displot) visualise the shape of a variable's distribution.

Histogram¶

In [9]:

fig, axes = plt.subplots(1, 2, figsize=(12, 4))

sns.histplot(data=tips, x='total_bill', hue='time',
             multiple='stack', bins=25, ax=axes[0])
axes[0].set_title('Histogram – stacked by time')

sns.histplot(data=penguins, x='body_mass_g', hue='species',
             multiple='dodge', shrink=0.85, bins=20, ax=axes[1])
axes[1].set_title('Histogram – dodged by species')

plt.tight_layout()
plt.show()

KDE Plot¶

Kernel Density Estimation smooths the histogram into a continuous probability density curve.

In [10]:

fig, axes = plt.subplots(1, 2, figsize=(12, 4))

sns.kdeplot(data=tips, x='total_bill', hue='time',
            fill=True, alpha=0.4, linewidth=1.5, ax=axes[0])
axes[0].set_title('KDE – total bill by time')

sns.kdeplot(data=penguins, x='flipper_length_mm', y='body_mass_g',
            hue='species', fill=True, alpha=0.3, ax=axes[1])
axes[1].set_title('Bivariate KDE – flipper vs body mass')

plt.tight_layout()
plt.show()

ECDF Plot¶

The Empirical CDF shows the cumulative proportion of observations below each value.

In [11]:

fig, ax = plt.subplots(figsize=(7, 4))
sns.ecdfplot(data=penguins, x='body_mass_g', hue='species', ax=ax)
ax.set_title('ECDF – penguin body mass by species')
ax.set_xlabel('Body mass (g)')
plt.tight_layout()
plt.show()

Rug Plot¶

Rug plots add a small tick for each individual observation along an axis, often combined with a KDE.

In [12]:

fig, ax = plt.subplots(figsize=(7, 4))
sns.kdeplot(data=tips, x='total_bill', fill=True, alpha=0.4, ax=ax)
sns.rugplot(data=tips, x='total_bill', height=0.06, ax=ax)
ax.set_title('KDE + Rug Plot – total bill')
plt.tight_layout()
plt.show()

Violin Plot¶

Violin plots combine a KDE with a box plot to show both the distribution shape and summary statistics.

In [13]:

fig, axes = plt.subplots(1, 2, figsize=(13, 5))

sns.violinplot(data=tips, x='day', y='total_bill', hue='sex',
               split=True, inner='quart', palette='pastel', ax=axes[0])
axes[0].set_title('Violin – split by sex')

sns.violinplot(data=penguins, x='species', y='flipper_length_mm',
               hue='sex', inner='box', palette='muted', ax=axes[1])
axes[1].set_title('Violin – penguins flipper by sex')

plt.tight_layout()
plt.show()

Categorical Plots¶

Categorical plots (boxplot, barplot, stripplot, swarmplot, pointplot) compare distributions or aggregate statistics across discrete groups.

Box Plot¶

In [14]:

fig, axes = plt.subplots(1, 2, figsize=(13, 5))

sns.boxplot(data=tips, x='day', y='total_bill', hue='smoker',
            palette='Set2', ax=axes[0])
axes[0].set_title('Box Plot – tips by day and smoker')

sns.boxplot(data=penguins, x='species', y='body_mass_g',
            hue='sex', palette='coolwarm', ax=axes[1])
axes[1].set_title('Box Plot – penguin mass by species & sex')

plt.tight_layout()
plt.show()

Bar Plot¶

barplot shows the mean (default) of a numeric variable per category, with a 95% confidence interval.

In [15]:

fig, axes = plt.subplots(1, 2, figsize=(13, 5))

sns.barplot(data=tips, x='day', y='total_bill', hue='sex',
            palette='Set1', capsize=0.1, ax=axes[0])
axes[0].set_title('Bar Plot – mean total bill by day')

sns.barplot(data=penguins, x='species', y='body_mass_g',
            order=['Gentoo', 'Chinstrap', 'Adelie'],
            palette='viridis', capsize=0.1, ax=axes[1])
axes[1].set_title('Bar Plot – mean body mass by species')

plt.tight_layout()
plt.show()

/var/folders/nx/gq421vkn2h1cxq_cljnl3r1m0000gn/T/ipykernel_60397/525494336.py:7: FutureWarning: 

Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.

  sns.barplot(data=penguins, x='species', y='body_mass_g',

Strip Plot & Swarm Plot¶

Strip plots show every individual observation; swarm plots spread overlapping points to avoid overplotting.

In [16]:

fig, axes = plt.subplots(1, 2, figsize=(13, 5))

sns.stripplot(data=tips, x='day', y='total_bill', hue='smoker',
              dodge=True, alpha=0.6, jitter=True, ax=axes[0])
axes[0].set_title('Strip Plot – tips by day')

sns.swarmplot(data=iris, x='species', y='petal_length',
              hue='species', dodge=False, size=4, ax=axes[1])
axes[1].set_title('Swarm Plot – iris petal length')

plt.tight_layout()
plt.show()

Point Plot¶

pointplot draws the mean and CI as a dot + error bar and connects groups with lines — great for showing interactions.

In [17]:

fig, ax = plt.subplots(figsize=(8, 5))
sns.pointplot(data=tips, x='day', y='total_bill', hue='smoker',
              dodge=0.3, capsize=0.12, markers=['o', 's'],
              linestyles=['-', '--'], palette='tab10', ax=ax)
ax.set_title('Point Plot – mean tip by day and smoker status')
plt.tight_layout()
plt.show()

Matrix and Regression Plots¶

Heatmap¶

heatmap renders a rectangular data array as a color-encoded matrix. Ideal for correlation matrices and pivot tables.

In [18]:

fig, axes = plt.subplots(1, 2, figsize=(14, 5))

# Correlation matrix
corr = penguins.select_dtypes('number').corr()
sns.heatmap(corr, annot=True, fmt='.2f', cmap='coolwarm',
            vmin=-1, vmax=1, square=True, linewidths=0.5, ax=axes[0])
axes[0].set_title('Correlation Matrix – Penguins')

# Pivot table
pivot = flights.pivot(index='month', columns='year', values='passengers')
sns.heatmap(pivot, annot=True, fmt='d', cmap='YlOrRd',
            linewidths=0.3, ax=axes[1])
axes[1].set_title('Flights – Passengers Pivot Table')

plt.tight_layout()
plt.show()

Clustermap¶

clustermap performs hierarchical clustering on rows and columns then draws a heatmap sorted by the resulting dendrograms.

In [19]:

iris_data = iris.drop('species', axis=1)
iris_norm = (iris_data - iris_data.mean()) / iris_data.std()
species_lut = dict(zip(iris['species'].unique(), ['#e74c3c','#2ecc71','#3498db']))
row_colors = iris['species'].map(species_lut)

g = sns.clustermap(iris_norm, row_colors=row_colors,
                   cmap='vlag', figsize=(8, 7),
                   row_cluster=True, col_cluster=True,
                   linewidths=0, yticklabels=False)
g.fig.suptitle('Clustermap – Iris (z-scored)', y=1.02)
plt.show()

Pair Plot¶

pairplot draws a grid of scatter plots for every pair of numerical variables, with univariate distributions on the diagonal.

In [20]:

g = sns.pairplot(penguins, hue='species', diag_kind='kde',
                 plot_kws=dict(alpha=0.5, edgecolors='none'),
                 diag_kws=dict(fill=True, alpha=0.4))
g.fig.suptitle('Pair Plot – Penguins', y=1.02)
plt.show()

Linear Regression Plot (lmplot)¶

lmplot combines a scatter plot with a fitted regression line and uncertainty band. It supports faceting via col and row.

In [21]:

g = sns.lmplot(data=tips, x='total_bill', y='tip',
               hue='smoker', col='time',
               scatter_kws=dict(alpha=0.5, s=30),
               height=4, aspect=1.1)
g.set_axis_labels('Total Bill ($)', 'Tip ($)')
g.figure.suptitle('lmplot – tip vs total bill, faceted by time', y=1.04)
plt.show()

$No description has been provided for this image$

2D Plots¶

This section covers Seaborn's figure-level functions and composite 2D plot types that combine multiple views into a single figure.

Joint Plot¶

jointplot draws a bivariate scatter (or other plot) in the center and the two marginal univariate distributions on the edges — all in one figure.

In [22]:

# kind options: 'scatter', 'kde', 'hist', 'hex', 'reg', 'resid'
kinds = ['scatter', 'kde', 'hist', 'hex']
fig, axes_outer = plt.subplots(1, 4, figsize=(20, 5))
plt.close()  # jointplot manages its own figures

for kind in kinds:
    g = sns.jointplot(data=penguins, x='flipper_length_mm', y='body_mass_g',
                      hue='species' if kind in ('scatter','kde') else None,
                      kind=kind, height=4,
                      marginal_kws=dict(fill=True) if kind=='kde' else {})
    g.figure.suptitle(f'jointplot  kind={kind!r}', y=1.02)

plt.show()

relplot — Faceted Relational Plot¶

relplot is the figure-level wrapper for scatterplot and lineplot. Use col / row to create small-multiple facets.

In [23]:

g = sns.relplot(
    data=tips, x='total_bill', y='tip',
    hue='smoker', size='size', style='sex',
    col='time', row='day',
    height=3, aspect=1.0,
    sizes=(20, 150), alpha=0.7
)
g.set_axis_labels('Total Bill ($)', 'Tip ($)')
g.figure.suptitle('relplot — Tips faceted by time × day', y=1.02)
plt.show()

displot — Faceted Distribution Plot¶

displot is the figure-level wrapper for histplot, kdeplot, and ecdfplot, with full faceting support.

In [24]:

fig_kws = dict(height=3.5, aspect=1.2)

g1 = sns.displot(data=penguins, x='flipper_length_mm',
                 hue='species', col='sex',
                 kind='kde', fill=True, **fig_kws)
g1.figure.suptitle('displot  kind=kde — flipper length by sex', y=1.04)

g2 = sns.displot(data=penguins, x='body_mass_g', y='flipper_length_mm',
                 hue='species', col='species',
                 kind='kde', fill=True, **fig_kws)
g2.figure.suptitle('displot  kind=kde bivariate — faceted by species', y=1.04)

plt.show()

catplot — Faceted Categorical Plot¶

catplot is the figure-level wrapper for all categorical plots (box, violin, bar, strip, swarm, point, count), with faceting.

In [25]:

g = sns.catplot(
    data=tips, x='day', y='total_bill',
    hue='smoker', col='time',
    kind='violin', split=True,
    inner='quart', palette='pastel',
    height=4, aspect=1.0
)
g.set_axis_labels('Day', 'Total Bill ($)')
g.figure.suptitle('catplot  kind=violin — tips by day, faceted by time', y=1.04)
plt.show()

FacetGrid¶

FacetGrid is the underlying engine behind all figure-level functions. Use it directly when you need full control over what goes into each panel.

In [26]:

g = sns.FacetGrid(penguins, col='species', row='sex',
                  height=3, aspect=1.1, margin_titles=True)
g.map_dataframe(sns.histplot, x='body_mass_g',
                bins=15, kde=True, color='steelblue', alpha=0.7)
g.set_axis_labels('Body mass (g)', 'Count')
g.set_titles(col_template='{col_name}', row_template='{row_name}')
g.figure.suptitle('FacetGrid — body mass distribution by species × sex', y=1.03)
plt.show()

3D Plots¶

Seaborn does not have native 3D support, but its color palettes, themes, and style settings integrate seamlessly with Matplotlib's mpl_toolkits.mplot3d. The examples below use Seaborn palettes and the active theme to produce styled 3D figures.

In [27]:

from mpl_toolkits.mplot3d import Axes3D   # noqa: F401
from scipy.stats import gaussian_kde

3D Scatter Plot¶

A 3D scatter plot built with Matplotlib's scatter on a 3D axes, coloured with a Seaborn palette so it respects the active theme.

In [28]:

species_list = penguins['species'].unique()
palette = sns.color_palette('deep', n_colors=len(species_list))

fig = plt.figure(figsize=(8, 6))
ax = fig.add_subplot(111, projection='3d')

for sp, col in zip(species_list, palette):
    sub = penguins[penguins['species'] == sp]
    ax.scatter(sub['bill_length_mm'], sub['bill_depth_mm'], sub['body_mass_g'],
               c=[col], label=sp, s=30, alpha=0.8, edgecolors='none')

ax.set_xlabel('Bill length (mm)')
ax.set_ylabel('Bill depth (mm)')
ax.set_zlabel('Body mass (g)')
ax.set_title('3D Scatter – Penguins (Seaborn deep palette)')
ax.legend()
plt.tight_layout()
plt.show()

3D KDE Surface¶

A bivariate KDE (computed with scipy.stats.gaussian_kde) rendered as a 3D surface using a Seaborn colormap — equivalent to Seaborn's kdeplot lifted into the third dimension.

In [29]:

sub = penguins[['flipper_length_mm','body_mass_g']].dropna()
xv = sub['flipper_length_mm'].values
yv = sub['body_mass_g'].values

xi = np.linspace(xv.min(), xv.max(), 60)
yi = np.linspace(yv.min(), yv.max(), 60)
Xi, Yi = np.meshgrid(xi, yi)
positions = np.vstack([Xi.ravel(), Yi.ravel()])
kernel = gaussian_kde(np.vstack([xv, yv]))
Zi = kernel(positions).reshape(Xi.shape)

fig = plt.figure(figsize=(9, 6))
ax = fig.add_subplot(111, projection='3d')
cmap = sns.color_palette('rocket', as_cmap=True)
surf = ax.plot_surface(Xi, Yi, Zi, cmap=cmap, linewidth=0, antialiased=True, alpha=0.9)
fig.colorbar(surf, ax=ax, shrink=0.45, aspect=10, label='Density')

ax.set_xlabel('Flipper length (mm)')
ax.set_ylabel('Body mass (g)')
ax.set_zlabel('KDE density')
ax.set_title('3D KDE Surface – Penguins (Seaborn rocket palette)')
plt.tight_layout()
plt.show()

3D Histogram (bar3d)¶

A 3D histogram bins two variables simultaneously and draws each bin as a 3D bar. Bars are coloured by height using a Seaborn palette.

In [30]:

sub = penguins[['flipper_length_mm','body_mass_g']].dropna()
n_bins = 10
hist, xedges, yedges = np.histogram2d(
    sub['flipper_length_mm'], sub['body_mass_g'], bins=n_bins)

xpos, ypos = np.meshgrid(
    (xedges[:-1] + xedges[1:]) / 2,
    (yedges[:-1] + yedges[1:]) / 2, indexing='ij')
xpos = xpos.ravel(); ypos = ypos.ravel(); zpos = np.zeros_like(xpos)
dx = (xedges[1] - xedges[0]) * 0.8
dy = (yedges[1] - yedges[0]) * 0.8
dz = hist.ravel()

norm = plt.Normalize(dz.min(), dz.max())
cmap = sns.color_palette('mako', as_cmap=True)
colors = cmap(norm(dz))

fig = plt.figure(figsize=(9, 6))
ax = fig.add_subplot(111, projection='3d')
ax.bar3d(xpos, ypos, zpos, dx, dy, dz, color=colors, zsort='average', alpha=0.85)

sm = plt.cm.ScalarMappable(cmap=cmap, norm=norm)
fig.colorbar(sm, ax=ax, shrink=0.45, aspect=10, label='Count')
ax.set_xlabel('Flipper length (mm)')
ax.set_ylabel('Body mass (g)')
ax.set_zlabel('Count')
ax.set_title('3D Histogram – Penguins (Seaborn mako palette)')
plt.tight_layout()
plt.show()

3D Line Plot¶

A 3D parametric curve styled with Seaborn's active colour palette — useful for trajectories, time-series in phase space, or embedding paths.

In [31]:

t = np.linspace(0, 4 * np.pi, 400)
curves = [
    (np.cos(t),          np.sin(t),          t / (4*np.pi),   'Helix'),
    (np.cos(t) * (1-t/(4*np.pi)), np.sin(t) * (1-t/(4*np.pi)), t/(4*np.pi), 'Cone helix'),
    (np.cos(2*t),        np.sin(3*t),         t/(4*np.pi),    'Lissajous'),
]
palette = sns.color_palette('tab10', n_colors=len(curves))

fig = plt.figure(figsize=(12, 4))
for idx, (xs, ys, zs, label) in enumerate(curves):
    ax = fig.add_subplot(1, 3, idx + 1, projection='3d')
    ax.plot(xs, ys, zs, color=palette[idx], linewidth=1.5)
    ax.set_title(label, fontsize=10)
    ax.set_xlabel('x'); ax.set_ylabel('y'); ax.set_zlabel('t')
    ax.tick_params(labelsize=7)

fig.suptitle('3D Line Plots – Parametric Curves (Seaborn tab10 palette)', y=1.02)
plt.tight_layout()
plt.show()

3D Surface with Seaborn Diverging Palette¶

Seaborn's diverging palettes (vlag, icefire, coolwarm) work as Matplotlib colourmaps and are ideal for surfaces that span positive and negative values.

In [32]:

x = np.linspace(-3, 3, 100)
y = np.linspace(-3, 3, 100)
X, Y = np.meshgrid(x, y)
Z = np.sin(np.sqrt(X**2 + Y**2)) * np.exp(-0.15 * (X**2 + Y**2))

fig, axes_3d = plt.subplots(1, 3, figsize=(15, 5),
                             subplot_kw={'projection': '3d'})
div_palettes = ['vlag', 'icefire', 'RdBu_r']

for ax, pal in zip(axes_3d, div_palettes):
    cmap = sns.color_palette(pal, as_cmap=True) if pal in ('vlag','icefire') else pal
    surf = ax.plot_surface(X, Y, Z, cmap=cmap, linewidth=0,
                           antialiased=True, alpha=0.9)
    fig.colorbar(surf, ax=ax, shrink=0.4, aspect=10)
    ax.set_title(f'palette: {pal!r}', fontsize=9)
    ax.set_xlabel('x', fontsize=8); ax.set_ylabel('y', fontsize=8)
    ax.set_zlabel('z', fontsize=8)

fig.suptitle('3D Surface – Seaborn Diverging Palettes', y=1.01)
plt.tight_layout()
plt.show()

Summary¶

Category	Function	Best for
Relational	`scatterplot`	Two numeric variables, optional groupings
Relational	`lineplot`	Ordered/time-series data with CI
Distribution	`histplot`	Counts / frequency of a variable
Distribution	`kdeplot`	Smooth density, bivariate density
Distribution	`ecdfplot`	Cumulative distribution
Distribution	`rugplot`	Individual observations along an axis
Distribution	`violinplot`	Distribution shape + box-plot summary
Categorical	`boxplot`	Quartiles + outliers across categories
Categorical	`barplot`	Mean + CI per category
Categorical	`stripplot` / `swarmplot`	All data points per category
Categorical	`pointplot`	Mean + CI with group connections
Matrix	`heatmap`	Matrices, correlation tables
Matrix	`clustermap`	Clustered heatmap with dendrograms
Multi-plot	`pairplot`	All pairwise scatter + diagonal dists
Regression	`lmplot`	Scatter + regression line, optional facets

Further reading:

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This work is distributed WITHOUT ANY WARRANTY.
Happy computing.