Descriptive Statistics

Before visualising, let’s look at the “Bird’s Eye View” of our data. The describe() function gives us the range, mean, and standard deviation for all numeric columns.

# Get a statistical summary
stats = df_clean.describe()
display(stats)

Table 1: Descriptive Statistics for Numeric Variables

	bill_length_mm	bill_depth_mm	flipper_length_mm	body_mass_g
count	333.00	333.00	333.00	333.00
mean	43.99	17.16	200.97	4,207.06
std	5.47	1.97	14.02	805.22
min	32.10	13.10	172.00	2,700.00
25%	39.50	15.60	190.00	3,550.00
50%	44.50	17.30	197.00	4,050.00
75%	48.60	18.70	213.00	4,775.00
max	59.60	21.50	231.00	6,300.00

Grouping by Species

The statistics above are for all penguins mixed together. Let’s group them by species to see if there are distinct size differences.

Question: Which species has the highest average body mass?

# Corrected: Group by species, select columns, THEN calculate mean
species_means = df_clean.groupby("species")[
    ["bill_length_mm", "bill_depth_mm", "flipper_length_mm", "body_mass_g"]
].mean()

display(species_means)

Table 2: Average Measurements by Species

	bill_length_mm	bill_depth_mm	flipper_length_mm	body_mass_g
species
Adelie	38.82	18.35	190.10	3,706.16
Chinstrap	48.83	18.42	195.82	3,733.09
Gentoo	47.57	15.00	217.24	5,092.44