Jun Ryu - Palmer Penguins

In this blog post, we will explain how to construct an interesting data visualization of the Palmer Penguins data set.

First, we read the data…

import pandas as pd
url = "https://raw.githubusercontent.com/PhilChodrow/PIC16B/master/datasets/palmer_penguins.csv"
penguins = pd.read_csv(url)

Let’s see what this data holds!

using .head() will display the first 5 rows of the data frame.

penguins.head()

	studyName	Sample Number	Species	Region	Island	Stage	Individual ID	Clutch Completion	Date Egg	Culmen Length (mm)	Culmen Depth (mm)	Flipper Length (mm)	Body Mass (g)	Sex	Delta 15 N (o/oo)	Delta 13 C (o/oo)	Comments
0	PAL0708	1	Adelie Penguin (Pygoscelis adeliae)	Anvers	Torgersen	Adult, 1 Egg Stage	N1A1	Yes	11/11/07	39.1	18.7	181.0	3750.0	MALE	NaN	NaN	Not enough blood for isotopes.
1	PAL0708	2	Adelie Penguin (Pygoscelis adeliae)	Anvers	Torgersen	Adult, 1 Egg Stage	N1A2	Yes	11/11/07	39.5	17.4	186.0	3800.0	FEMALE	8.94956	-24.69454	NaN
2	PAL0708	3	Adelie Penguin (Pygoscelis adeliae)	Anvers	Torgersen	Adult, 1 Egg Stage	N2A1	Yes	11/16/07	40.3	18.0	195.0	3250.0	FEMALE	8.36821	-25.33302	NaN
3	PAL0708	4	Adelie Penguin (Pygoscelis adeliae)	Anvers	Torgersen	Adult, 1 Egg Stage	N2A2	Yes	11/16/07	NaN	NaN	NaN	NaN	NaN	NaN	NaN	Adult not sampled.
4	PAL0708	5	Adelie Penguin (Pygoscelis adeliae)	Anvers	Torgersen	Adult, 1 Egg Stage	N3A1	Yes	11/16/07	36.7	19.3	193.0	3450.0	FEMALE	8.76651	-25.32426	NaN

Now, suppose we wanted to create a plot that shows the distribution of the body mass based on the penguin’s species.

We will import the correct packages for plotting…

matplotlib is a plotting library and seaborn is a data visualization library based on matplotlib. the following code is how we import these packages:

from matplotlib import pyplot as plt 
import seaborn as sns

We first create our empty plot using pyplot…

we use plt.subplots() as described here: https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.subplots.html

the method returns two variables “figures” and “axes”, which we store under fig and ax, respectively. our first argument for plt.subplots() is 1 because we want to create 1 subplot. we also specify the size of our figure using the figsize argument: the first number represents how wide and the second number represents how tall the plot will be.

fig, ax = plt.subplots(1, figsize = (8,5))

Then, we use sns.boxplot() to plot “body mass” split along different species…

here, we call our penguins data using data = penguins and we set our x-axis data to draw from the “Body Mass (g)” column and y-axis to draw from the “Species” column. we will also set width = 0.5, which controls the size of the boxes. lastly, we will store this result under fig, which we created earlier with matplotlib.

for further documentation: https://seaborn.pydata.org/generated/seaborn.boxplot.html

fig = sns.boxplot(data = penguins, x="Body Mass (g)", y="Species", width=0.5)

For funsies, we will also produce the strip plot…

the intention of adding a strip plot is to see the spread of the individual data points, thus we utilize sns.stripplot(). we use color = "black" to make the dots black and we set size = 3 to reduce the size of the dots.

for further documentation: https://seaborn.pydata.org/generated/seaborn.stripplot.html

fig = sns.stripplot(data = penguins, x="Body Mass (g)", y="Species", color = "black", size = 3)

Combine the two plots with a title and a figure caption…

now, for our final step, we combine the previous three code chunks, but we add an extra line using ax.set_title() to create a title for our plot. remember that ax was formed when we originally created our plot using matplotlib and represents our “axes”.

fig, ax = plt.subplots(1, figsize = (8,5))
ax.set_title("Body Mass vs. Species")
fig = sns.boxplot(data = penguins, x="Body Mass (g)", y="Species", width=0.5)
fig = sns.stripplot(data = penguins, x="Body Mass (g)", y="Species", color = "black", size = 3)

Figure 1: Body Mass (g) vs. Penguin Species