Declaration

The d42 package offers a data description language for defining data models. It provides a simple yet expressive way to outline the structure and constraints of data.

Defining Schemas

d42 includes a variety of built-in data types, such as booleans, integers, strings, and more complex types like lists and dictionaries.

Here's an example of defining a simple schema using d42:

from d42 import schema

UserSchema = schema.dict({
    "id": schema.int.min(1),
    "username": schema.str.len(1, 8),
    "is_deleted": schema.bool,
})

print(UserSchema)

# schema.dict({
#     'id': schema.int.min(1),
#     'username': schema.str.len(1, 8),
#     'is_deleted': schema.bool
# })

This example demonstrates defining a user schema with an integer id (minimum value 1), a string username (length between 1 and 8 characters), and a boolean is_deleted.

info

A comprehensive list of all available data types is provided in the types chapter.

Creating Schemas from Native Types

d42 also enables the creation of schemas directly from native Python objects. This is particularly useful for quickly creating schemas based on existing data structures.

For instance:

from d42 import from_native

data = {
    "id": 1,
    "username": "Bob",
    "is_deleted": False,
}

schema = from_native(data)
print(schema)

# schema.dict({
#     'id': schema.int(1),
#     'username': schema.str('Bob'),
#     'is_deleted': schema.bool(False)
# })

This code snippet shows how to create a schema mirroring the structure and data types of a native Python object.