Homework 0: Penguins

in Blog / Programming / Pic16b
Homework 0: Penguins

A quick tutorial on PIC 16’s favorite dataset. Image credit to Allison Horst.

Introduction to the Dataset

My penguins-, um, enthused Python professor just gave us this dataset all about penguins! In fact, we had an entire project dedicated to penguins last quarter in PIC 16A… Either way, let’s get started on making a cool dataset visualization about these penguins, and see if we can learn anything just from some pretty plots.

Of course, even though we spend many more hours that I’m willing to admit staring about concentrations of isotopes in penguin blood last quarter, we’d better start by giving an introduction tot he data. In particular, it contains measurements on three penguin species: Chinstrap, Gentoo, and Adelie.

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

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

Neat, so as you might expect, we have each row corresponding to a particular penguin, then we get all its info: species, region, physiological measurements, etc.

For our purposes, we won’t need to worry too much about cleaning (no fancy machine learning yet), but we still need to do just one small step: the species name is currently pretty bulky, so let’s just take the first word.

# replace species with just its first word
penguins['Species'] = penguins['Species'].str.split(' ').str.get(0)

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 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 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 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 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 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

Professor helped me get this table looking a lot better.

I think that looks a lot better already.

Making the Visualization

But now, onto the actual data visualization. I’d like to create a histogram for each species that has in two different colors the frequencies for the Culmen Depths of each sex male and female. That way, we’ll be able to see overall if there’s a significant difference between male and female penguins, and if that difference is consistent across all the species.

First and foremost though, we’ll need matplotlib.

from matplotlib import pyplot as plt

Now let’s create a function that plots two histograms on top of each other. The idea will be to use a groupby and apply later so that we can create this double histogram for each species.

# Overlay 2 histograms to compare them
def overlaid_histogram(data1, data2, n_bins = 10, data1_name=None, data1_color="firebrick", data2_name=None, data2_color="blue", **kwargs):
    # get axes in the standard way
    fig, ax = plt.subplots()

    # create the two histograms
    ax.hist(data1, bins = bins, color = data1_color, alpha = 1, label = data1_name or "")
    ax.hist(data2, bins = bins, color = data2_color, alpha = 0.75, label = data2_name or "")
    ax.set(**kwargs)
    ax.legend(loc = 'best')

I can only use SpeedChat.

Just for fun, we can test this on a single species, say Adelie.

adelie_df = penguins[penguins['Species'] == 'Adelie'] # glad we shortened the species name...
overlaid_histogram(adelie_df[adelie_df['Sex'] == 'MALE']['Culmen Depth (mm)'], adelie_df[adelie_df['Sex'] == 'FEMALE']['Culmen Depth (mm)'],
n_bins=10,
data1_name="Adelie Males",
data2_name="Adelie Females",
xlabel="Culmen Depth (mm)",
ylabel="Frequency",
title="Adelie Gender Culmen Comparison")

svg

I think that looks great. So now, let’s just generalize this in a function, and then run our groupby + apply.

def plot_species_histogram(df):
    species_name = df['Species'].iloc[0]
    overlaid_histogram(df[df['Sex'] == 'MALE']['Culmen Depth (mm)'], df[df['Sex'] == 'FEMALE']['Culmen Depth (mm)'],
    n_bins=10,
    data1_name=species_name+" Males",
    data2_name=species_name+" Females",
    xlabel="Culmen Depth (mm)",
    ylabel="Frequency",
    title=species_name+" Gender Culmen Comparison")

penguins.groupby("Species").apply(plot_species_histogram)

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