Python dataclasses is a great module, but one of the things it doesn't unfortunately handle is parsing a JSON object to a nested dataclass structure.
A few workarounds exist for this:
- You can either roll your own JSON parsing helper method, for example a
from_json which converts a JSON string to an List instance with a nested dataclass.
- You can make use of existing JSON serialization libraries. For example,
pydantic is a popular one that supports this use case.
Here is an example using the dataclass-wizard library that works well enough for your use case. It's more lightweight than pydantic and coincidentally also a little faster. It also supports automatic case transforms and type conversions (for example str to annotated int)
Example below:
from dataclasses import dataclass
from typing import List as PyList
from dataclass_wizard import JSONWizard
@dataclass
class List(JSONWizard):
id: int
statuses: PyList['StatusElement']
# on Python 3.9+ you can use the following syntax:
# statuses: list['StatusElement']
@dataclass
class StatusElement:
status: str
order_index: int
color: str
type: str
json = {
"id": "124",
"statuses": [
{
"status": "to do",
"orderIndex": 0,
"color": "#d3d3d3",
"type": "open"
}]
}
object = List.from_dict(json)
print(repr(object))
# List(id=124, statuses=[StatusElement(status='to do', order_index=0, color='#d3d3d3', type='open')])
Disclaimer: I am the creator (and maintainer) of this library.
You can now skip the class inheritance as of the latest release of dataclass-wizard. It's straightforward enough to use it; using the same example from above, but I've removed the JSONWizard usage from it completely. Just remember to ensure you don't import asdict from the dataclasses module, even though I guess that should coincidentally work.
Here's the modified version of the above without class inheritance:
from dataclasses import dataclass
from typing import List as PyList
from dataclass_wizard import fromdict, asdict
@dataclass
class List:
id: int
statuses: PyList['StatusElement']
@dataclass
class StatusElement:
status: str
order_index: int
color: str
type: str
json = {
"id": "124",
"statuses": [
{
"status": "to do",
"orderIndex": 0,
"color": "#d3d3d3",
"type": "open"
}]
}
# De-serialize the JSON dictionary into a `List` instance.
c = fromdict(List, json)
print(c)
# List(id=124, statuses=[StatusElement(status='to do', order_index=0, color='#d3d3d3', type='open')])
# Convert the instance back to a dictionary object that is JSON-serializable.
d = asdict(c)
print(d)
# {'id': 124, 'statuses': [{'status': 'to do', 'orderIndex': 0, 'color': '#d3d3d3', 'type': 'open'}]}
Also, here's a quick performance comparison with dacite. I wasn't aware of this library before, but it's also very easy to use (and there's also no need to inherit from any class). However, from my personal tests - Windows 10 Alienware PC using Python 3.9.1 - dataclass-wizard seemed to perform much better overall on the de-serialization process.
from dataclasses import dataclass
from timeit import timeit
from typing import List
from dacite import from_dict
from dataclass_wizard import JSONWizard, fromdict
data = {
"id": 124,
"statuses": [
{
"status": "to do",
"orderindex": 0,
"color": "#d3d3d3",
"type": "open"
}]
}
@dataclass
class StatusElement:
status: str
orderindex: int
color: str
type: str
@dataclass
class List:
id: int
statuses: List[StatusElement]
class ListWiz(List, JSONWizard):
...
n = 100_000
# 0.37
print('dataclass-wizard: ', timeit('ListWiz.from_dict(data)', number=n, globals=globals()))
# 0.36
print('dataclass-wizard (fromdict): ', timeit('fromdict(List, data)', number=n, globals=globals()))
# 11.2
print('dacite: ', timeit('from_dict(List, data)', number=n, globals=globals()))
lst_wiz1 = ListWiz.from_dict(data)
lst_wiz2 = from_dict(List, data)
lst = from_dict(List, data)
# True
assert lst.__dict__ == lst_wiz1.__dict__ == lst_wiz2.__dict__
