Convert JSON to set of nested Python classes

mabalenk
November 27, 2024
182 views
1 vote
2 Answers

I need to process multiple messages in JSON format. Each message has its own nested structure. I would like to create a Python SDK to process these messages. My idea is to map each JSON structure into a set of nested Python classes. Currently, I’m doing it manually. But it is a tedious task.

Please find an example JSON message below:

{
    "GlobalGnbId": {
        "PlmnIdentity": {
            "Data": [
                 19,
                241,
                132
            ]
        },
        "GnbId": {
            "Value":   1,
            "Length": 22
        }
    },
    "OptionalGnbDuId": 1
}

Please find below my own handcrafted set of Python classes to work with this JSON message:

class PlmnIdentity(BaseModel):
    """Class for PLMN identity"""
    Data: list[int]

class GnbId(BaseModel):
    """Class for gNodeB ID"""
    Value:  int
    Length: int

class GlobalGnbId(BaseModel):
    """Class for global gNodeB ID"""
    PlmnIdentity:    PlmnIdentity
    GnbId:           GnbId

class NodeId(BaseModel):
    """Class for node ID"""
    GlobalGnbId:     GlobalGnbId
    OptionalGnbDuId: int

Finally, please find below a full minimal example:

from pydantic import BaseModel, TypeAdapter

import json

class PlmnIdentity(BaseModel):
    """Class for PLMN identity"""
    Data: list[int]

class GnbId(BaseModel):
    """Class for gNodeB ID"""
    Value:  int
    Length: int

class GlobalGnbId(BaseModel):
    """Class for global gNodeB ID"""
    PlmnIdentity:    PlmnIdentity
    GnbId:           GnbId

class NodeId(BaseModel):
    """Class for node ID"""
    GlobalGnbId:     GlobalGnbId
    OptionalGnbDuId: int

node_id_str = 
"""
{
    "GlobalGnbId": {
        "PlmnIdentity": {
            "Data": [
                 19,
                241,
                132
            ]
        },
        "GnbId": {
            "Value":   1,
            "Length": 22
        }
    },
    "OptionalGnbDuId": 1
}
"""

# NodeId as class
node_id_class = TypeAdapter(NodeId).validate_json(node_id_str)

print(node_id_class)

print(node_id_class.GlobalGnbId)
print(node_id_class.GlobalGnbId.PlmnIdentity)
print(node_id_class.GlobalGnbId.PlmnIdentity.Data)

print(node_id_class.GlobalGnbId.GnbId)
print(node_id_class.GlobalGnbId.GnbId.Value)
print(node_id_class.GlobalGnbId.GnbId.Length)

print(node_id_class.OptionalGnbDuId)

# NodeId as dictionary
node_id_dict = node_id_class.model_dump()

print(node_id_dict)

My question is there an automatic or semi-automatic way to map a nested JSON message to a set of Python classes?

Answers

- user26979062
- November 27, 2024 at 8:07 pm
- 0 votes
0
You can use Python’s dataclasses module alongside libraries like pydantic or dataclasses-json to map JSON to nested Python classes automatically.

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I strongly recommend exploring dataclass-wizard! This fast, strongly-typed de/serialization library is built on dataclasses and features minimal dependencies. It’s well-tested and ready for production use.

Disclaimer: I am the author/maintainer of this library.

Generating Dataclass Schema

First, install the package:

pip install dataclass-wizard

Next, use the included wiz CLI utility to generate a schema for your JSON data:

echo '{
    "GlobalGnbId": {
        "PlmnIdentity": {
            "Data": [
                 19,
                241,
                132
            ]
        },
        "GnbId": {
            "Value":   1,
            "Length": 22
        }
    },
    "OptionalGnbDuId": 1
}' | wiz gs -x

That gave me Python dataclass schema to use:

from __future__ import annotations

from dataclasses import dataclass

from dataclass_wizard import JSONWizard


@dataclass
class Data(JSONWizard):
    """
    Data dataclass

    """
    global_gnb_id: GlobalGnbId
    optional_gnb_du_id: int


@dataclass
class GlobalGnbId:
    """
    GlobalGnbId dataclass

    """
    plmn_identity: PlmnIdentity
    gnb_id: GnbId


@dataclass
class PlmnIdentity:
    """
    PlmnIdentity dataclass

    """
    data: list[int]


@dataclass
class GnbId:
    """
    GnbId dataclass

    """
    value: int
    length: int

Now I can proceed to de/serialize the data using the Dataclass Wizard library:

from __future__ import annotations

from dataclasses import dataclass

from dataclass_wizard import JSONWizard


@dataclass
class Data(JSONWizard):
    """
    Data dataclass

    """
    global_gnb_id: GlobalGnbId
    optional_gnb_du_id: int


@dataclass
class GlobalGnbId:
    """
    GlobalGnbId dataclass

    """
    plmn_identity: PlmnIdentity
    gnb_id: GnbId


@dataclass
class PlmnIdentity:
    """
    PlmnIdentity dataclass

    """
    data: list[int]


@dataclass
class GnbId:
    """
    GnbId dataclass

    """
    value: int
    length: int

d = Data.from_json("""
{
    "GlobalGnbId": {
        "PlmnIdentity": {
            "Data": [
                 19,
                241,
                132
            ]
        },
        "GnbId": {
            "Value":   1,
            "Length": 22
        }
    },
    "OptionalGnbDuId": 1
}
""")

print(repr(d))

Result:

Data(global_gnb_id=GlobalGnbId(plmn_identity=PlmnIdentity(data=[19, 241, 132]), gnb_id=GnbId(value=1, length=22)), optional_gnb_du_id=1)

As you can see, one of the main benefits is the automatic letter-case transformation. For example, TitleCase in your JSON input is seamlessly mapped to a snake_case field name, following Python’s convention.

Hope this helps!

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