Standard error and 95% confidence intervals in Python

uncertainty.py

# Dealing with uncertainty in Python
import pandas as pd
import numpy as np
from scipy.stats import t
from palmerpenguins import load_penguins

penguins = load_penguins().dropna()

# Split the data frame into groups
groups = penguins.groupby('species')

# Calculate sample means, standard deviations, and standard errors for each group
means = groups['bill_length_mm'].mean()
std_devs = groups['bill_length_mm'].std()
sizes = groups['bill_length_mm'].size()
sems = groups['bill_length_mm'].sem()

# Calculate 95% CIs for each group
cis = {}
for group, mean, std_dev, size, sem in zip(means.index, means, std_devs, sizes, sems):
    ci_1 = mean - (2 * sem)
    ci_2 = mean + (2 * sem)
    cis[group] = ci_1, ci_2

print(cis)