import pandas as pd
import numpy as np
import scipy.stats as spicydf = pd.read_csv("https://raw.githubusercontent.com/roualdes/data/refs/heads/master/penguins.csv")df.head()| species | island | bill_length_mm | bill_depth_mm | flipper_length_mm | body_mass_g | sex | year | |
|---|---|---|---|---|---|---|---|---|
| 0 | Adelie | Torgersen | 39.1 | 18.7 | 181.0 | 3750.0 | male | 2007 |
| 1 | Adelie | Torgersen | 39.5 | 17.4 | 186.0 | 3800.0 | female | 2007 |
| 2 | Adelie | Torgersen | 40.3 | 18.0 | 195.0 | 3250.0 | female | 2007 |
| 3 | Adelie | Torgersen | NaN | NaN | NaN | NaN | NaN | 2007 |
| 4 | Adelie | Torgersen | 36.7 | 19.3 | 193.0 | 3450.0 | female | 2007 |
xbar = np.mean(df["bill_length_mm"])
s = np.std(df["bill_length_mm"])
N = np.sum(~df["bill_length_mm"].isna())
t = spicy.t(df = N - 1).ppf([0.025, 0.975]) # ppf(0.975)
xbar + t * s / np.sqrt(N)array([43.34209692, 44.50176273])np.shape(df)(344, 8)N342