Json Multiple level to Dataframe flat file in Pandas - json

Question

Trying to convert the json with multiple children in this format to dataframe flat file.
{'name': 'A text',
'children': [{'name': 'A01 text',
'children': [{'name': 'A01A text',
'children': [{'name': 'A01AA text',
'children': [{'name': 'A01AA01 text',
'children': [{'name': 'D00943 text'}]},
{'name': 'A01AA02 text',
'children': [{'name': 'D05864 text'}]},
{'name': 'A01AA03 text',
'children': [{'name': 'D05241 text'}]},
{'name': 'A01AA04 text',
'children': [{'name': 'D05919 text'}]},
{'name': 'A01AA30 text'},
{'name': 'A01AA51 text',
'children': [{'name': 'D08736 text'}]}]},
...
Tried json_normalize and couldn't achieve it.
Expected dataframe
level_1 level_2 level_3 level_4 level_5 level_6
A text A01 text A01A text A01AA text A01AA01 text D00943 text
A text A01 text A01A text A01AA text A01AA03 text D05241 text
A text A01 text A01A text A01AA text A01AA04 text D05919 text
A text A01 text A01A text A01AA text A01AA30 text NA
A text A01 text A01A text A01AA text A01AA51 text D08736 text

Answer 1

Given this horrifying thing:
data = {
'children': [{'children': [{'children': [{'children': [{'children': [{'name': 'D00943 text'}], 'name': 'A01AA01 text'},
{'children': [{'name': 'D05864 text'}], 'name': 'A01AA02 text'},
{'children': [{'name': 'D05241 text'}], 'name': 'A01AA03 text'},
{'children': [{'name': 'D05919 text'}], 'name': 'A01AA04 text'},
{'name': 'A01AA30 text'},
{'children': [{'name': 'D08736 text'}], 'name': 'A01AA51 text'}],
'name': 'A01AA text'}],
'name': 'A01A text'}],
'name': 'A01 text'}],
'name': 'A text'
}
Doing:
prefix = ''.join(['children.']*4)
df = pd.json_normalize(data,
['children', 'children', 'children', 'children'],
['name',
['children', 'name'],
['children', 'children', 'name'],
['children', 'children', 'children', 'name']],
record_prefix=prefix)
prefix += 'children'
df = df.explode(prefix)
df = (df.drop(prefix, axis=1)
.join(df[prefix].apply(pd.Series)
.drop(0, axis=1)
.add_prefix(prefix + '.')))
df.columns = [f"level{x.count('.')+1}" for x in df.columns]
df = df.sort_index(axis=1)
print(df)
Output:
level1 level2 level3 level4 level5 level6
0 A text A01 text A01A text A01AA text A01AA01 text D00943 text
1 A text A01 text A01A text A01AA text A01AA02 text D05864 text
2 A text A01 text A01A text A01AA text A01AA03 text D05241 text
3 A text A01 text A01A text A01AA text A01AA04 text D05919 text
4 A text A01 text A01A text A01AA text A01AA30 text NaN
5 A text A01 text A01A text A01AA text A01AA51 text D08736 text