import numpy as np
import pandas as pd

def f2(seed=789, niter=10):
    rng = np.random.RandomState(seed=seed)
    df = pd.DataFrame(index=np.arange(5))
    df['x'] = rng.uniform(0, 2, size=5)
    df['y'] = rng.uniform(0, 2, size=5)
    df['cost'] = rng.uniform(0, 10, size=5)
    # print the initial df
    print(df)
    # for loop: replace the worst param combo and evaluate
    for i in range(niter):
        # idx of param combo with highest cost
        worst_idx = np.argmax(df.cost.values)
        # IF line below is commented out, I don't get the discrepancy
        centroid = df.loc[rng.choice(np.arange(5), size=1)].mean(axis=0)
        for p in ['x', 'y']:
            new_param = 4
            # THIS IS THE OFFENDING CALL to df.at! 
            # Change it to .loc and it doesn't give the same issue
            df.at[worst_idx, p] = new_param
#             df.loc[worst_idx, p] = new_param
        # The at below is not the offending at
        df.at[worst_idx, 'cost'] = rng.uniform(low=0, high=10)
#         df.loc[worst_idx, 'cost'] = rng.uniform(low=0, high=10, size=(1,))
    return df

test = f2()
test
test.cost
test[['cost']]
test.iloc[4]