Appendix E — Seaborn tutorial

In this tutorial, we’ll learn about the Seaborn library for data visualizations. We’ll start by explaining the plotting function interface that is common to all Seaborn functions, then list all the Seaborn functions thought examples. We’ll also describe the various options for customize plots’ the appearance, add adding annotations and labels.

Click the binder button or this link bit.ly/4twDwZu to play with the tutorial notebook interactively.

Notebook setup

Before we begin the tutorial, we we must take care of some preliminary tasks to prepare the notebook environment. Feel free to skip this commands

Installing Seaborn and other libraries

We can install Python package pandas in the current environment using the %pip Jupyter magic command.

%pip install --quiet seaborn matplotlib pandas ministats

Note: you may need to restart the kernel to use updated packages.

import seaborn as sns
import matplotlib.pyplot as plt

Setting display options

Next, we run some commands to configure the display of figures and number formatting.

sns.set_theme(
    context="paper",
    style="whitegrid",
    palette="colorblind",
    rc={"font.family": "serif",
        "font.serif": ["Palatino", "DejaVu Serif", "serif"],
        "figure.figsize": (5, 1.7)},
)
%config InlineBackend.figure_format = 'retina'

# simple float __repr__
import numpy as np
np.set_printoptions(legacy='1.25')

Download datasets

import pandas as pd

The ministats package provides a helper function for downloading datasets. We’ll use this function now to make sure the datasets/ folder that accompanies this tutorial is present.

# download datasets/ directory if necessary
from ministats import ensure_datasets
ensure_datasets()

datasets/ directory already exists.

With all these preliminaries in place, we can now get the Pandas show started!

Loading datasets

healthexp = sns.load_dataset("healthexp")
healthexp
# pyg.walk(healthexp)

# TODO: show how to add per-datum labels

	Year	Country	Spending_USD	Life_Expectancy
0	1970	Germany	252.311	70.6
1	1970	France	192.143	72.2
2	1970	Great Britain	123.993	71.9
3	1970	Japan	150.437	72.0
4	1970	USA	326.961	70.9
...	...	...	...	...
269	2020	Germany	6938.983	81.1
270	2020	France	5468.418	82.3
271	2020	Great Britain	5018.700	80.4
272	2020	Japan	4665.641	84.7
273	2020	USA	11859.179	77.0

274 rows × 4 columns

# dots = sns.load_dataset("dots")
# data = dots[(dots["choice"]=="T1") & (dots["align"]=="dots")]
# sns.lineplot(data=data, x="time", y="firing_rate")
# # data["align"].unique()

# attention = sns.load_dataset("attention", index_col=0)
# attention

Seaborn overview

The Seaborn library is a powerful toolbox for generating statistical data visualizations. Seaborn makes it very easy to visualize data stored in Pandas data frames. You can generate standard statistical plots like barplots, stripplots, scatterplots, using just a few lines of code. If you plan to pursue a career in a data-related field, learning a bit about Seeaborn is highly recommend.

Learning objectives

In this tutorial, I’m going to show you how to …

Seaborn line plots

We’ll start by playing with the Seaborn function for generating line plots sns.lineplot. We’ll use this function to learn the the syntax for mapping data to graph attributes, which is common to all Seaborn plotting function.

Simple line plot

xs = np.linspace(-2, 2, 100)
ys = xs**2 
quadfun = pd.DataFrame({"x":xs, "y":ys})
sns.lineplot(data=quadfun, x="x", y="y", label="$f(x) = x^2$")

<Axes: xlabel='x', ylabel='y'>

Line plot with statistical calculations

wloss = pd.read_csv("datasets/wloss.csv")
wloss.sample(3)

	week	weight
365	15	103.3
264	11	105.4
122	5	110.2

sns.lineplot(data=wloss, x="week", y="weight")

<Axes: xlabel='week', ylabel='weight'>

Pandas datasets

import pandas as pd

The iris dataset

iris = sns.load_dataset("iris")
iris.head()

	sepal_length	sepal_width	petal_length	petal_width	species
0	5.1	3.5	1.4	0.2	setosa
1	4.9	3.0	1.4	0.2	setosa
2	4.7	3.2	1.3	0.2	setosa
3	4.6	3.1	1.5	0.2	setosa
4	5.0	3.6	1.4	0.2	setosa

The tips dataset

tips = sns.load_dataset("tips")
tips.head()

	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

The students dataset

students = pd.read_csv("datasets/students.csv",
                       index_col="student_ID")
students

	background	curriculum	effort	score
student_ID
1	arts	debate	10.96	75.0
2	science	lecture	8.69	75.0
3	arts	debate	8.60	67.0
4	arts	lecture	7.92	70.3
5	science	debate	9.90	76.1
6	business	debate	10.80	79.8
7	science	lecture	7.81	72.7
8	business	lecture	9.13	75.4
9	business	lecture	5.21	57.0
10	science	lecture	7.71	69.0
11	business	debate	9.82	70.4
12	arts	debate	11.53	96.2
13	science	debate	7.10	62.9
14	science	lecture	6.39	57.6
15	arts	debate	12.00	84.3

sns.lineplot(data=students,
             x="effort",
             y="score")

<Axes: xlabel='effort', ylabel='score'>

Inventory of Seaborn plotting functions

Strip plots

Use the function sns.stripplot

sns.stripplot(data=students, x="score");

plt.figure(figsize=(6,1))
sns.stripplot(data=students, x="score");

ALT. sns.swarmplot or sns.rugplot

Point plots

Use the function sns.pointplot

Scatter plots

Use the function sns.scatterplot

Line plot

Use the function sns.lineplot

Histograms

Use the function sns.histplot

Kernel density plots

Use the function sns.kdeplot

Box plots

Use the function sns.boxplot

Violin plots

Use the function sns.violinplot

Empirical Cumulative Distribution Function (ECDF) plot

Use the function sns.ecdfplot

Count plot

Use the function sns.countplot

titanic = sns.load_dataset("titanic")
sns.countplot(titanic, x="class", hue="sex")

<Axes: xlabel='class', ylabel='count'>

titanic

	survived	pclass	sex	age	sibsp	parch	fare	embarked	class	who	adult_male	deck	embark_town	alive	alone
0	0	3	male	22.0	1	0	7.2500	S	Third	man	True	NaN	Southampton	no	False
1	1	1	female	38.0	1	0	71.2833	C	First	woman	False	C	Cherbourg	yes	False
2	1	3	female	26.0	0	0	7.9250	S	Third	woman	False	NaN	Southampton	yes	True
3	1	1	female	35.0	1	0	53.1000	S	First	woman	False	C	Southampton	yes	False
4	0	3	male	35.0	0	0	8.0500	S	Third	man	True	NaN	Southampton	no	True
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
886	0	2	male	27.0	0	0	13.0000	S	Second	man	True	NaN	Southampton	no	True
887	1	1	female	19.0	0	0	30.0000	S	First	woman	False	B	Southampton	yes	True
888	0	3	female	NaN	1	2	23.4500	S	Third	woman	False	NaN	Southampton	no	False
889	1	1	male	26.0	0	0	30.0000	C	First	man	True	C	Cherbourg	yes	True
890	0	3	male	32.0	0	0	7.7500	Q	Third	man	True	NaN	Queenstown	no	True

891 rows × 15 columns

sns.barplot(data=titanic, x="class", hue="sex", y="survived", estimator=np.size);

titanic_counts = titanic["class"].value_counts().reset_index()
sns.barplot(data=titanic_counts, x="class",  y="count")

<Axes: xlabel='class', ylabel='count'>

Bar plots

Use the function sns.barplot

Plotting function graphs

def g(x):
    return 0.5 * x**2

import numpy as np
xs = np.linspace(0, 10, 1000)
gxs = g(xs)
sns.lineplot(x=xs, y=gxs, label="Graph of g(x)");

# # FIGURES ONLY
# from ministats.utils import savefigure
# ax = sns.lineplot(x=xs, y=gxs, label="Graph of g(x)");
# filename = "figures/tutorials/seaborn/graph_of_function_g_eq_halfx2.pdf"
# savefigure(ax, filename)

Linear model plots

Linear regression plot

sns.regplot(data=tips, x="total_bill", y="tip");

Residuals plot

sns.residplot(data=tips, x="total_bill", y="tip");

Linear model plots from scratch

Linear model plots using statsmodels

Plotting probability distributions

Line plots for continuous random variables

Stem plots for discrete random variables

Customizing plots

Anotomy of a figure

from matplotlib.ticker import MultipleLocator, FuncFormatter

np.random.seed(123)

X = np.linspace(0.5, 3.5, 100)
Y1 = 3+np.cos(X)
Y2 = 1+np.cos(1+X/0.75)/2
Y3 = np.random.uniform(Y1, Y2, len(X))

fig = plt.figure(figsize=(8, 8), facecolor="w")
ax = fig.add_subplot(1, 1, 1, aspect=1)


def minor_tick(x, pos):
    if not x % 1.0:
        return ""
    return "%.2f" % x

ax.xaxis.set_major_locator(MultipleLocator(1.000))
ax.xaxis.set_minor_locator(MultipleLocator(0.250))
ax.yaxis.set_major_locator(MultipleLocator(1.000))
ax.yaxis.set_minor_locator(MultipleLocator(0.250))
ax.xaxis.set_minor_formatter(FuncFormatter(minor_tick))

ax.set_xlim(0, 4)
ax.set_ylim(0, 4)

ax.tick_params(which='major', width=1.0)
ax.tick_params(which='major', length=10)
ax.tick_params(which='minor', width=1.0, labelsize=10)
ax.tick_params(which='minor', length=5, labelsize=10, labelcolor='0.25')

ax.grid(linestyle="--", linewidth=0.5, color='.25', zorder=-10)

ax.plot(X, Y1, c=(0.25, 0.25, 1.00), lw=2, label="Blue signal", zorder=10)
ax.plot(X, Y2, c=(1.00, 0.25, 0.25), lw=2, label="Red signal")
ax.scatter(X, Y3, c='w')

ax.set_title("Anatomy of a figure", fontsize=20)
ax.set_xlabel("X axis label")
ax.set_ylabel("Y axis label")

ax.legend(frameon=False)


def circle(x, y, radius=0.15):
    from matplotlib.patches import Circle
    from matplotlib.patheffects import withStroke
    circle = Circle((x, y), radius, clip_on=False, zorder=10, linewidth=1,
                    edgecolor='black', facecolor=(0, 0, 0, .0125),
                    path_effects=[withStroke(linewidth=5, foreground='w')])
    ax.add_artist(circle)


def text(x, y, text):
    ax.text(x, y, text, backgroundcolor="white",
            ha='center', va='top', weight='bold', color='blue')


# Minor tick
circle(0.50, -.05)
text(0.50, -0.25, "Minor tick label")

# Major tick
circle(4.00, 2.00)
text(4.00, 1.80, "Major tick")

# Minor tick
circle(0.25, 4.00)
text(0.25, 3.80, "Minor tick")

# Major tick label
circle(-0.05, 3.00)
text(-0.05, 2.80, "Major tick label")

# X Label
circle(1.80, -0.22)
text(1.80, -0.4, "X axis label")

# Y Label
circle(-0.20, 1.80)
text(-0.20, 1.6, "Y axis label")

# Title
circle(1.60, 4.10)
text(1.60, 3.9, "Title")

# Blue plot
circle(1.75, 2.80)
text(1.75, 2.60, "Line\n(line plot)")

# Red plot
circle(1.20, 0.60)
text(1.20, 0.40, "Line\n(line plot)")

# Scatter plot
circle(3.20, 1.75)
text(3.20, 1.55, "Markers\n(scatter plot)")

# Grid
circle(3.00, 3.00)
text(3.00, 2.80, "Grid")

# Legend
circle(3.70, 3.75)
text(3.70, 3.55, "Legend")

# Axes
circle(0.5, 0.5)
text(0.5, 0.3, "Axes")

# Figure
circle(-0.3, 0.65)
text(-0.3, 0.45, "Figure")

color = 'blue'
ax.annotate('Spines', xy=(4.0, 0.35), xycoords='data',
            xytext=(3.3, 0.5), textcoords='data',
            weight='bold', color=color,
            arrowprops=dict(arrowstyle='->',
                            connectionstyle="arc3",
                            color=color))

ax.annotate('', xy=(3.15, 0.0), xycoords='data',
            xytext=(3.45, 0.45), textcoords='data',
            weight='bold', color=color,
            arrowprops=dict(arrowstyle='->',
                            connectionstyle="arc3",
                            color=color))

ax.legend(loc="upper right")

<matplotlib.legend.Legend at 0x7f7935162bf0>

Adding text annotations

healthexp = sns.load_dataset("healthexp")
healthexp_2020 = healthexp[healthexp["Year"]==2020]
ax = sns.scatterplot(data=healthexp_2020,
                     x="Spending_USD",
                     y="Life_Expectancy")
for _, row in healthexp_2020.iterrows():
    x_pos = row["Spending_USD"]
    y_pos = row["Life_Expectancy"] + 0.1
    ax.text(x_pos, y_pos, row["Country"], ha="center", va="bottom")

# ha supported values are 'center', 'right', 'left'
# va supported values are 'center', 'top', 'bottom', 

sns.scatterplot(data=healthexp,
                x="Spending_USD",
                y="Life_Expectancy",
                hue="Country");

healthexp.groupby("Country")["Year"].last()

Country
Canada           2020
France           2020
Germany          2020
Great Britain    2020
Japan            2020
USA              2020
Name: Year, dtype: int64

Layout options

option	meaning	values / example	applies to
color	single colour override	"k"	sca, lin, his, kde, ecdf, str, swa, box, vio, poi, bar, cnt, reg, res
orient	vertical/horizontal	"v"/"h" or "x"/"y"	lin, str, swa, box, vio, poi, bar, cnt
dodge	separate hue groups	True/False/"auto"	his, str, swa, box, vio, poi, bar, cnt
jitter	random x spread	True, 0.2	str
width	element width	0.8	box, vio, bar, cnt
log_scale	log axes	True, 10, (True, False)	his, kde, ecdf, str, swa, box, vio, poi, bar, cnt

Matplotlib pass through options

Seaborn plot functions will “forward” keywords arguments to the underlying Matplotlib plotting function. The following list of Matplotlib options are often used to change plot appearance.

option	meaning	values / example	applies to
fill	filled vs outlines	True/False	his, box, vio, bar, cnt
alpha	transparency	0.3	sca, lin, his, kde, ecdf, str, swa, box, vio, poi, bar, cnt, reg, res, hea
linewidth	line/edge width	1.5	sca, lin, his, kde, ecdf, str, swa, box, vio, poi, bar, cnt, reg, res, hea
marker/markers	marker style	"o", ".", dict	sca, lin, str, swa, poi, reg, res
linestyle/linestyles	line style	"-", "--"	lin, kde, ecdf, poi, reg

Bonus topics

Data visualization tips

Links

Here are some links to learning resources for Seaborn and data visualization techniques.

Official docs

[ Seaborn documentation website ]
https://seaborn.pydata.org/
https://seaborn.pydata.org/introduction.html

[ Seaborn tutorials featuring lots of useful plot examples ]
https://seaborn.pydata.org/tutorial.html

[ Gallery of data visualizations produced using Seaborn ] https://seaborn.pydata.org/examples/index.html

Tutorials

[ Seaborn tutorial for beginners ]
https://www.datacamp.com/community/tutorials/seaborn-python-tutorial

[ The ultimate Python Seaborn tutorial ]
https://elitedatascience.com/python-seaborn-tutorial

[ Seaborn Tutorial ]
https://www.geeksforgeeks.org/python-seaborn-tutorial/

Video tutorials

[ Intro to Seaborn by Kimberly Fessel (excellent!) ]
https://www.youtube.com/playlist?list=PLtPIclEQf-3cG31dxSMZ8KTcDG7zYng1j
see also notebooks from the videos.

[ Seaborn Tutorial 2021 by Derek Banas ]
https://www.youtube.com/watch?v=6GUZXDef2U0

[ Data Visualisation with Seaborn Crash Course by Valentine Mwangi ]
https://www.youtube.com/watch?v=zafPvR4MmBA
See also the colab notebook for the course.

Other plotting libraries:

• altair vega/altair

• plotnine [1,2,3,4]

• PyGwalker Kanaries/pygwalker

CUT MATERIAL

%pip install -q pygwalker 

Note: you may need to restart the kernel to use updated packages.

import pygwalker as pyg

exercise = sns.load_dataset("exercise", index_col=0)
exercise.head()
# pyg.walk(exercise)

	id	diet	pulse	time	kind
0	1	low fat	85	1 min	rest
1	1	low fat	85	15 min	rest
2	1	low fat	88	30 min	rest
3	2	low fat	90	1 min	rest
4	2	low fat	92	15 min	rest

sns.catplot(
    data=exercise, x="time", y="pulse", hue="kind", col="diet",
    capsize=.2, palette="YlGnBu_d", errorbar="se",
    kind="point", height=6, aspect=.75,
)

<seaborn.axisgrid.FacetGrid at 0x7f79425eb040>

seaice = sns.load_dataset("seaice")
seaice
# pyg.walk(healthexp)

	Date	Extent
0	1980-01-01	14.200
1	1980-01-03	14.302
2	1980-01-05	14.414
3	1980-01-07	14.518
4	1980-01-09	14.594
...	...	...
13170	2019-12-27	12.721
13171	2019-12-28	12.712
13172	2019-12-29	12.780
13173	2019-12-30	12.858
13174	2019-12-31	12.889

13175 rows × 2 columns

FMRI

fmri = sns.load_dataset("fmri")
fmri.head()
# fmri.shape
# fmri["subject"].value_counts()

	subject	timepoint	event	region	signal
0	s13	18	stim	parietal	-0.017552
1	s5	14	stim	parietal	-0.080883
2	s12	18	stim	parietal	-0.081033
3	s11	18	stim	parietal	-0.046134
4	s10	18	stim	parietal	-0.037970

# Plot the responses for different events and regions
sns.lineplot(data=fmri, x="timepoint", y="signal",
             hue="region", style="event");

tips = sns.load_dataset("tips")
print(tips.shape)
tips.head()

(244, 7)

	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

The Dow Jones dataset

dowjones = sns.load_dataset("dowjones")
dowjones["Year"] = dowjones["Date"].dt.year
sns.lineplot(data=dowjones, x="Year", y="Price", estimator="mean", errorbar=("sd",1))

<Axes: xlabel='Year', ylabel='Price'>

plt.figure(figsize=(5,3))
ax = sns.pointplot(data=titanic, x="class", y="survived", hue="sex",
                   markers=["^", "o"], linestyles=["-", "--"])

# sns.despine(top=True)

# [attr for attr in dir(sns) if attr.endswith("plot")]