import numpy as np

np.set_printoptions(precision=2, suppress=True)

scores = np.array(
    [
        [72.0, 75.0, 79.0],
        [80.0, 82.0, 88.0],
        [68.0, 74.0, 77.0],
        [88.0, 93.0, 96.0],
    ]
)

scores_with_missing = scores.copy()
scores_with_missing[1, 2] = np.nan

print("dataset shape:", scores.shape)
print("overall mean:", scores.mean())
print("overall median:", np.median(scores))
print("population std:", round(float(scores.std()), 2))
print("sample std:", round(float(scores.std(ddof=1)), 2))
print("column means:", scores.mean(axis=0))
print("column percentiles:")
print(np.percentile(scores, [25, 50, 75], axis=0))
print("row means with NaN ignored:", np.nanmean(scores_with_missing, axis=1))
print("ordinary mean with NaN:", np.mean(scores_with_missing))