Appendix E — Seaborn tutorial¶

WIP

See planned outline here https://docs.google.com/document/d/1fwep23-95U-w1QMPU31nOvUnUXE2X3s_Dbk5JuLlKAY/edit#bookmark=id.3i7cktuf1u3i

In this tutorial, we'll learn about Seaborn data visualizations. We'll discuss Seaborn plot functions

TODO: LIST / TABLE

We'll also describe the various options for customize plots' the appearance, add annotations, and export plots as publication-quality images.

If you want to pursue a career in a data-related field, I highly recommend you get to know Seaborn by reading this tutorial and the other resources in the links section.

Introduction to Seaborn¶

In [1]:

import seaborn as sns
import pandas as pd

import seaborn as sns import pandas as pd

In [2]:

days = [1, 2, 3, 4]
cakes = [2, 5, 3, 4]
sns.lineplot(x=days, y=cakes)

days = [1, 2, 3, 4] cakes = [2, 5, 3, 4] sns.lineplot(x=days, y=cakes)

Out[2]:

<AxesSubplot: >

In [3]:

# (optional) use Matplotlib axis methods to add labels
ax = sns.lineplot(x=days, y=cakes)
ax.set_xlabel("days")
ax.set_ylabel("cakes")

# (optional) use Matplotlib axis methods to add labels ax = sns.lineplot(x=days, y=cakes) ax.set_xlabel("days") ax.set_ylabel("cakes")

Out[3]:

Text(0, 0.5, 'cakes')

In [4]:

df = pd.DataFrame({"days":days, "cakes":cakes})
df

df = pd.DataFrame({"days":days, "cakes":cakes}) df

Out[4]:

	days	cakes
0	1	2
1	2	5
2	3	3
3	4	4

In [5]:

df.columns

df.columns

Out[5]:

Index(['days', 'cakes'], dtype='object')

In [6]:

sns.lineplot(x="days", y="cakes", data=df)

sns.lineplot(x="days", y="cakes", data=df)

Out[6]:

<AxesSubplot: xlabel='days', ylabel='cakes'>

In [7]:

# # ALT. hybrid approach
# sns.lineplot(x=df["days"], y=df["cakes"])

# # ALT. hybrid approach # sns.lineplot(x=df["days"], y=df["cakes"])

Links¶

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

Official docs¶

• An introduction to seaborn
  http://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¶

• Python Seaborn Tutorial For Beginners
  https://www.datacamp.com/community/tutorials/seaborn-python-tutorial

• The Ultimate Python Seaborn Tutorial
  https://elitedatascience.com/python-seaborn-tutorial

• Python 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.

In [ ]: