Creating Nested JSON from Dataframe - python-3.x

Question

I have a dataframe and have to convert it into nested JSON.
countryname name text score
UK ABC Hello 5
Right now, I have some code that generates JSON, grouping countryname and name.
However, I want to firstly group by countryname and then group by name. Below is the code and output:
cols = test.columns.difference(['countryname','name'])
j = (test.groupby(['countryname','name'])[cols]
.apply(lambda x: x.to_dict('r'))
.reset_index(name='results')
.to_json(orient='records'))
test_json = json.dumps(json.loads(j), indent=4)
Output:
[
{
"countryname":"UK"
"name":"ABC"
"results":[
{
"text":"Hello"
"score":"5"
}
]
}
]
However, I am expecting an output like this:
[
{
"countryname":"UK"
{
"name":"ABC"
"results":[
{
"text":"Hello"
"score":"5"
}
]
}
}
]
Can anyone please help in fixing this?

Answer 1

This would be the valid JSON. Note the comma , usage, is required as you may check here.
[
{
"countryname":"UK",
"name":"ABC",
"results":[
{
"text":"Hello",
"score":"5"
}
]
}
]
The other output you try to achieve is also not according to the standard:
[{
"countryname": "UK",
"you need a name in here": {
"name": "ABC",
"results": [{
"text": "Hello",
"score": "5"
}]
}
}]
I improved that so you can figure out what name to use.
For custom JSON output you will need to use custom function to reformat your object first.
l=df.to_dict('records')[0] #to get the list
print(l, type(l)) #{'countryname': 'UK', 'name': 'ABC', 'text': 'Hello', 'score': 5} <class 'dict'>
e = l['countryname']
print(e) # UK
o=[{
"countryname": l['countryname'],
"you need a name in here": {
"name": l['name'],
"results": [{
"text": l['text'],
"score": l['score']
}]
}
}]
print(o) #[{'countryname': 'UK', 'you need a name in here': {'name': 'ABC', 'results': [{'text': 'Hello', 'score': 5}]}}]