Python：如何从元组创建嵌套字典？

Question

I think this problem is similar to the previous questions in this forum. However, I still have a problem with how to create a nested dictionary from a list of tuples.

Suppose I have the following tuple:

my_list = [
 ('actor', 'actor_id', 'integer', 'NO'),
 ('actor', 'first_name', 'character varying', 'NO'),
 ('actor_info', 'actor_id', 'integer', 'YES'),
 ('actor_info', 'first_name', 'character varying', 'YES')]

# convert into DataFrame
col = ['table', 'col_name', 'dtype', 'isnull']
df = pd.DataFrame(mylist, columns=col)

print(df)

        table     col_name                        dtype   isnull
0       actor     actor_id                      integer     NO
1       actor   first_name            character varying     NO
2  actor_info     actor_id                      integer    YES
3  actor_info   first_name            character varying    YES

Current result:

{
 'actor': {
    'actor_id': {'dtype': 'integer', 'isnull': 'NO'},
    'first_name': {'dtype': 'character varying', 'isnull': 'NO'}
    },
 'actor_info': {
    'actor_id': {'dtype': 'integer', 'isnull': 'YES'},
    'first_name': {'dtype': 'character varying', 'isnull': 'YES'}
    }
}

The expected result (it should be grouped by table_name):

{
 'actor': 
    [
        {'column': 'actor_id', 'dtype': 'integer', 'isnull': 'NO'},
        {'column': 'first_name', 'dtype': 'character varying', 'isnull': 'NO'}
    ],
 'actor_info': 
    [
        {'column': 'actor_id', 'dtype': 'integer', 'isnull': 'YES'},
        {'column': 'first_name', 'dtype': 'character varying', 'isnull': 'YES'}
    ]
}

I've tried to make a nested dictionary by converting my_list to DataFrame . However, I cannot get the desired output result. This is my current code :

# convert to nested dictionary
ff = df.iloc.groupby(['table'])[['col_name','dtype','isnull']].apply(lambda x: x.set_index('col_name').to_dict(orient='index')).to_dict()

# convert to JSON
print(json.dumps(ff, indent=1))

Could you help me to solve this kind of problem?

I'm also curious about how to solve this problem without converting to DataFrame (eg, list comprehension, nested looping) . Any help to clear this problem up would be much appreciated. Thanks

Answer 1

As per my comment, I'm assuming you'll want a list of columns metadata per table name, rather dict of dicts.

If so, this is one straightforward approach.

from collections import defaultdict

d = defaultdict(list)

for tablename, col, dtype, isnull in my_list:
    d[tablename].append({ 'column': col, 'dtype': dtype, 'isnull': isnull })

Output (in ipython):

In [19]: d
Out[19]:
defaultdict(list,
            {'actor': [{'column': 'actor_id',
               'dtype': 'integer',
               'isnull': 'NO'},
              {'column': 'first_name',
               'dtype': 'character varying',
               'isnull': 'NO'}],
             'actor_info': [{'column': 'actor_id',
               'dtype': 'integer',
               'isnull': 'YES'},
              {'column': 'first_name',
               'dtype': 'character varying',
               'isnull': 'YES'}]})

In [20]: d['actor']
Out[20]:
[{'column': 'actor_id', 'dtype': 'integer', 'isnull': 'NO'},
 {'column': 'first_name', 'dtype': 'character varying', 'isnull': 'NO'}]

In [21]: d['actor_info']
Out[21]:
[{'column': 'actor_id', 'dtype': 'integer', 'isnull': 'YES'},
 {'column': 'first_name', 'dtype': 'character varying', 'isnull': 'YES'}]

Answer 2

Quite easy with a dictionary comprehension:

from itertools import groupby

{k: {a: dict(zip(('column', 'dtype', 'isnull'), b])) for _,a,*b in g}
 for k,g in groupby(my_list, lambda t: t[0])}

NB. groupby assumes that the initial array is sorted by the grouping key, if not you need to sort it first

Output:

{'actor': {'actor_id': {'column': 'integer', 'dtype': 'NO'},
  'first_name': {'column': 'character varying', 'dtype': 'NO'}},
 'actor_info': {'actor_id': {'column': 'integer', 'dtype': 'YES'},
  'first_name': {'column': 'character varying', 'dtype': 'YES'}}}