Why do these different outlier methods fail to detect outliers?

I am trying to find the outliers by group for my dataframe. I have two groups: Group1 and Group2, and I am trying to find the best way to implement an outlier method

data = {'Group1':['A', 'A', 'A', 'B', 'B', 'B','A','A','B','B','B','A','A','A','B','B','B','A','A','A','B','B','B','A','A','A','A','A','B','B'], 'Group2':['C', 'C', 'C', 'C', 'D', 'D','C','D','C','C','D', 'C', 'C', 'D', 'D','C', 'C','D','D','D', 'D','C','D','C','C', 'D','C','D','C','C'], 'Age':[20, 21, 19, 24, 11, 15, 18, 1, 17,23, 35,2000,22,24,24,18,17,19,21,22,20,25,18,24,17,19,16,18,25,23]} 
df = pd.DataFrame(data) 

groups = df.groupby(['Group1', 'Group2'])
means = groups.Age.transform('mean')
stds = groups.Age.transform('std')

df['Flag'] = ~df.Age.between(means-stds*3, means+stds*3)

def flag_outlier(x):
    lower_limit  = np.mean(x) - np.std(x) * 3 
    upper_limit = np.mean(x) + np.std(x) * 3
    return (x>upper_limit)| (x<lower_limit)

df['Flag2'] = df.groupby(['Group1', 'Group2'])['Age'].apply(flag_outlier)

df["Flag3"] = df.groupby(['Group1', 'Group2'])['Age'].transform(lambda x: (x - x.mean()).abs() > 3*x.std())

However, all 3 methods fail to detect obvious outliers – for example, when Age is 2000, none of these methods treat it as an outlier. Is there a reason for this? Or is it possible that my code for all three outlier detection models is incorrect?

I have a strong feeling I’ve made a foolish mistake somewhere but I’m not sure where, so any help would be appreciated, thanks!