def detect_outliers(df,n,features): 
    outlier_indices = []
    for col in features:
        Q1 = np.percentile(df[col],25)
        Q3 = np.percentile(df[col],75)
        IQR = Q3 - Q1
        outlier_step = 1.5 * IQR 
        outlier_list_col = df[(df[col] < Q1 - outlier_step) | (df[col] > Q3 + outlier_step)].index
        outlier_indices.extend(outlier_list_col)

    outlier_indices = Counter(outlier_indices)
    multiple_outliers = list(k for k, v in outlier_indices.items() if v>n)
    return multiple_outliers

Outliers_to_drop = detect_outliers(data1,2,['Age','Parch','Fare','SibSp'])
data1.iloc[Outliers_to_drop]