transform-data-schema 1.2.3

Define the shape and validate data

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transform-data-schema: simple transform and validate schema

---

transform-data-schema is a library for converting complex datatypes, such as objects, to and from native Python datatypes.

from transform_data_schema import (
    BaseSchemaTransform, 
    EXCLUDE, 
    fields, 
    transform_fields, 
    ValidationError
)


class EventJBTransformSchema(BaseSchemaTransform):
    class Meta:
        unknown = EXCLUDE

    ID = fields.Str()
    PROFILE_ID = transform_fields.NestedValueField(
        nested_key='data_event.profile_id',
        type_class=fields.Str,
        required=True
    )
    JOURNEY_ID = transform_fields.NestedValueField(
        nested_key='data_event.journey_id',
        type_class=fields.Str,
        required=True
    )
    MC_ID = transform_fields.NestedValueField(
        nested_key='data_event.master_campaign_id',
        type_class=fields.Str,
        required=True
    )
    MERCHANT_ID = transform_fields.NestedValueField(
        nested_key='data_event.merchant_id',
        type_class=fields.Str,
        required=True
    )
    DATA_TYPE = fields.Str(data_key='event_type')
    ACTION_TIME = transform_fields.NestedValueField(
        nested_key='data_event.action_time',
        type_class=transform_fields.DatetimeFromTimeStamp,
        required=True
    )
    NODE_CODE = transform_fields.NestedValueField(
        nested_key='data_event.node_code',
        type_class=fields.Str,
    )
    NODE_ID = transform_fields.NestedValueField(
        nested_key='data_event.node_id',
        type_class=fields.Str
    )

if __name__ == '__main__':
    raw_data = {
        "message_id": "19db4e86-0cef-11ed-9d73-aea73dddcfef",
        "data_event": {
            "action_time": 1658845656.471722,
            "journey_id": "1a596c15-3ccb-4d51-8d75-03223c5bce8e",
            "merchant_id": "1b99bdcf-d582-4f49-9715-1b61dfff3924",
            "node_id": "64690a9e-e807-4b2a-8da2-f2d28d085fd2",
            "node_code": "WEB_PUSH",
            "event_type": "IN_NODE",
            "profile_id": "ff5d808c-5a28-415f-b60d-9070519e7f1a",
            "master_campaign_id": "a065a9fd-2341-468b-8580-dba1cc4b3459",
            "event_id": "061dbe38-b5db-4470-a1e5-8e2ce2e14851"
        },
        "event_type": "profile_in_node"
    }
    result = EventJBTransformSchema.transform(raw_data)
    pprint(result, indent=2)
    
    """
    { 
        'ACTION_TIME': datetime.datetime(2022, 7, 26, 14, 27, 36, 471722, tzinfo=datetime.timezone.utc),
        'DATA_TYPE': 'profile_in_node',
        'JOURNEY_ID': '1a596c15-3ccb-4d51-8d75-03223c5bce8e',
        'MC_ID': 'a065a9fd-2341-468b-8580-dba1cc4b3459',
        'MERCHANT_ID': '1b99bdcf-d582-4f49-9715-1b61dfff3924',
        'NODE_CODE': 'WEB_PUSH',
        'NODE_ID': '64690a9e-e807-4b2a-8da2-f2d28d085fd2',
        'PROFILE_ID': 'ff5d808c-5a28-415f-b60d-9070519e7f1a'
    }
    """

In short, transform-data-schema can be used to:

• Validate input data.
• Deserialize input data to app-level objects.

Get It Now

$ pip install -U transform-data-schema

Requirements

• Python >= 3.7