plotting in Python with Seaborn: bar plot

Analysis
Visualization
Python
Author
Affiliation

Masumbuko Semba

Nelson Mandela African Institution of Science and Technology

Published

February 23, 2023

Introduction

In Visualization with Seaborn of this series, we were introduced on the power visualization and dove into distributions plot. In this post we are going learn barplot. The bar plot is used to capture the relationship between a categorical and numerical column. For each unique value in a categorical column, a bar is plotted, which by default, displays the mean value for the data in a numeric column specified by the bar plot. Seaborn has nifty function called barplot(), which is dedicated for this type of plot.

Loading libraries

Though most people are familiar with plotting using matplot, as it inherited most of the functions from MatLab. Python has an extremely nady library for data visualiztion called seaborn. The Seaborn library is based on the Matplotlib library. Therefore, you will also need to import the Matplotlib library.

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

sns.set_theme()

Dataset

We are going to use a penguin dataset from palmerpenguins package (Horst, Hill, and Gorman 2020). We do not need to download this dataset as it comes with the seaborn package. We only need to load it from the package into our session using sns.load_dataset function and specify the penguins as the name of the dataset and assign it as df;

df = sns.load_dataset("penguins")
df.head()
  species     island  bill_length_mm  ...  body_mass_g     sex  year
0  Adelie  Torgersen            39.1  ...         3750    male  2007
1  Adelie  Torgersen            39.5  ...         3800  female  2007
2  Adelie  Torgersen            40.3  ...         3250  female  2007
3  Adelie  Torgersen             NaN  ...  -2147483648     NaN  2007
4  Adelie  Torgersen            36.7  ...         3450  female  2007

[5 rows x 8 columns]

A printed df dataset shows that is made up of various measurements of three different penguin species — Adelie, Gentoo, and Chinstrap. The dataset contains seven variables – species, island, bill_length_mm, bill_depth_mm, flipper_length_mm, body_mass_g, sex, and year.

Next, we will call the barplot() function from the Seaborn library to plot a bar plot that displays the average length of penguin species.

fig = plt.figure()
sns.barplot(data=df, x="species", y="bill_length_mm")
plt.show()
Figure 1: Average length by island

The Figure 1 shows that the average length of penguins from the three island. We can parse an argument hue = "sex" to stack the plot as Figure 2 shows.


fig = plt.figure()
sns.barplot(data=df, x="species", y="bill_length_mm", hue = "sex")
plt.show()
Figure 2: Average length by species and island

Cited Materials

Horst, Allison Marie, Alison Presmanes Hill, and Kristen B Gorman. 2020. Palmerpenguins: Palmer Archipelago (Antarctica) Penguin Data. https://doi.org/10.5281/zenodo.3960218.