You can call .as_dict() on a row object inside an iteration through
.render(). Here is a one-liner to replace all your code:
json.dumps(dict(data=[{k: v for sub_row in row.as_dict().values() for (k, v)
in sub_row.iteritems()}
for row in persons.render()]))
The list comprehension iterates through persons.render(), which yields a
Row object for each record (with the represent functions applied to each
field). For each Row object, there is then a dictionary comprehension with
two "for" clauses -- the first converts the Row to a nested dict via
.as_dict and then uses .values() to extract the two nested dicts into a
list containing the two dicts, and the second then calls .iteritems() on
each of the dicts in order to generate a new dictionary.
Note, you still have a problem -- you have two fields with the same name
(i.e., "name"), so in the combined dictionary, the second will overwrite
the first.
Anthony
On Thursday, October 12, 2017 at 5:19:26 AM UTC-4, Ricardo Oliveira wrote:
>
> Hi Anthony,
>
> Thanks for your reply.
> That was also my initial solution.
> Problem is I need to use render() in order to force the lambdas of the
> table fields to be executed, and I can't seem to find a way to combine
> render() and as_dict(), that's why I used the approach of iterating through
> the Rows.
>
> Is there some way to do this? That would also work.
>
> Thanks again.
> Ricardo.
>
>
> On Wednesday, October 11, 2017 at 11:05:56 PM UTC+1, Anthony wrote:
>>
>> The problem is here:
>>
>>
>>> for field in person[table]:
>>>
>>
>> person[table] is a Row object, and when the Row object includes the
>> record ID, it also includes the special .update_record and .delete_record
>> attributes. So, you'll either have to specify the fields explicitly, or
>> check the types (the Row.as_dict method takes the latter approach).
>>
>> Anthony
>>
>
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