The bare python/stdlib example used (as well as bare python and avoiding add-on data science oriented libraries not being the way most people would use python for data science) is just...bad? (And, by bad here I mean showing signs of deliberately avoiding stdlib features in order to increase the appearance of the things the author then complains about.)

A better stdlib-only version would be:

    from palmerpenguins import load_penguins
    import math
    from itertools import groupby
    from statistics import fmean, stdev

    penguins = load_penguins()

    # Convert DataFrame to list of dictionaries
    penguins_list = penguins.to_dict('records')

    # create key function for grouping/sorting by species/island
    def key_func(x):
        return x['species'], x['island']

    # Filter out rows where body_mass_g is missing and sort by species and island
    filtered = sorted((row for row in penguins_list if not math.isnan(row['body_mass_g'])), key=key_func)

    # Group by species and island
    groups = groupby(filtered, key=key_func)

    # Calculate mean and standard deviation for each group
    results = []
    for (species, island), group in groups:
        values = [row['body_mass_g'] for row in group]
        mean_value = fmean(values)
        sd_value = stdev(values, xbar=mean_value)
        results.append({
            'species': species,
            'island': island,
            'body_weight_mean': mean_value,
            'body_weight_sd': sd_value
        })