data = {'code': ['a', 'b', 'a', 'c', 'c', 'c', 'c'],
        'cost': [10, 20, 100, 10, 10, 500, 10]}
df = pd.DataFrame(data)

grouped = df.groupby('code')['cost'].agg(['sum', 'mean']).apply(pd.Series)

def is_outlier(s):
    # Only calculate outliers when we have more than 100 observations
    if s.count() >= 100:
        return np.where(s >= s.quantile(0.75) + 1.5 * iqr(s), 1, 0).mean()
    else:
        return np.nan