我想要这个Pandas代码的等价物。下面的PANDA代码生成atable名称和在其中找到atable名称的索引：

import pandas as pd
df1 = pd.DataFrame({
    'atable':     ['Users', 'Users', 'Domains', 'Domains', 'Locks'],
    'column':     ['col_1', 'col_2', 'col_a', 'col_b', 'col'],
    'column_type':['varchar', 'varchar', 'int', 'varchar', 'varchar'],
    'is_null':    ['No', 'No', 'Yes', 'No', 'Yes'],
})
df1_grouped = df1.groupby('atable')
# iterate over each group
for group_name, df_group in df1_grouped.groups.items():
    print(group_name, df_group)

产出：

Domains Int64Index([2, 3], dtype='int64')
Locks Int64Index([4], dtype='int64')
Users Int64Index([0, 1], dtype='int64')

Spark输出应如下所示：

# +-------+--------------------+
# |atable |             sources|
# +-------+--------------------+
# |Domains|    [2, 3]          |
# |Locks  |     [4]            |
# | Users |    [0, 1]          |
# +-------+--------------------+