Generation
The d42
package includes tools for generating fake data based on defined data schemas, offering a straightforward and flexible approach for creating test data for applications.
Generating Fake Data
To generate fake data, a schema is first defined using the data modeling capabilities of d42
, followed by calling the fake
function with the defined schema.
from d42 import schema, fake
UserSchema = schema.dict({
"id": schema.int.min(1),
"username": schema.str.len(1, 8),
"is_deleted": schema.bool,
})
user = fake(UserSchema)
print(user)
# {'id': 7207320, 'username': 'ANk', 'is_deleted': False}
In this example, fake user data is generated, including an integer id
, a string username
, and a boolean is_deleted
. The values are generated randomly, adhering to the constraints set by the schema.