Nesting Schemas

Schemas can be nested to represent relationships between objects (e.g. foreign key relationships). For example, a Blog may have an author represented by a User object.

import datetime as dt

class User(object):
    def __init__(self, name, email):
        self.name = name
        self.email = email
        self.created_at = dt.datetime.now()
        self.friends = []
        self.employer = None

class Blog(object):
    def __init__(self, title, author):
        self.title = title
        self.author = author  # A User object

Use a Nested field to represent the relationship, passing in a nested schema class.

from marshmallow import Schema, fields, pprint

class UserSchema(Schema):
    name = fields.String()
    email = fields.Email()
    created_at = fields.DateTime()

class BlogSchema(Schema):
    title = fields.String()
    author = fields.Nested(UserSchema)

The serialized blog will have the nested user representation.

user = User(name="Monty", email="monty@python.org")
blog = Blog(title="Something Completely Different", author=user)
result = BlogSchema().dump(blog)
pprint(result)
# {'title': u'Something Completely Different',
#  'author': {'name': u'Monty',
#             'email': u'monty@python.org',
#             'created_at': '2014-08-17T14:58:57.600623+00:00'}}

Note

If the field is a collection of nested objects, you must set many=True.

collaborators = fields.Nested(UserSchema, many=True)

Specifying Which Fields to Nest

You can explicitly specify which attributes of the nested objects you want to serialize with the only argument.

class BlogSchema2(Schema):
    title = fields.String()
    author = fields.Nested(UserSchema, only=["email"])

schema = BlogSchema2()
result = schema.dump(blog)
pprint(result)
# {
#     'title': u'Something Completely Different',
#     'author': {'email': u'monty@python.org'}
# }

You can represent the attributes of deeply nested objects using dot delimiters.

class SiteSchema(Schema):
    blog = fields.Nested(BlogSchema2)

schema = SiteSchema(only=['blog.author.email'])
result = schema.dump(site)
pprint(result)
# {
#     'blog': {
#         'author': {'email': u'monty@python.org'}
#     }
# }

Note

If you pass in a string field name to only, only a single value (or flat list of values if many=True) will be (de)serialized.

class UserSchema(Schema):
    name = fields.String()
    email = fields.Email()
    friends = fields.Nested('self', only='name', many=True)
# ... create ``user`` ...
serialized_data = UserSchema().dump(user)
pprint(serialized_data)
# {
#     "name": "Steve",
#     "email": "steve@example.com",
#     "friends": ["Mike", "Joe"]
# }
deserialized_data = UserSchema().load(result)
pprint(deserialized_data)
# {
#     "name": "Steve",
#     "email": "steve@example.com",
#     "friends": [{"name": "Mike"}, {"name": "Joe"}]
# }

You can also exclude fields by passing in an exclude list. This argument also allows representing the attributes of deeply nested objects using dot delimiters.

Two-way Nesting

If you have two objects that nest each other, you can refer to a nested schema by its class name. This allows you to nest Schemas that have not yet been defined.

For example, a representation of an Author model might include the books that have a foreign-key (many-to-one) relationship to it. Correspondingly, a representation of a Book will include its author representation.

class AuthorSchema(Schema):
    # Make sure to use the 'only' or 'exclude' params
    # to avoid infinite recursion
    books = fields.Nested('BookSchema', many=True, exclude=('author', ))
    class Meta:
        fields = ('id', 'name', 'books')

class BookSchema(Schema):
    author = fields.Nested(AuthorSchema, only=('id', 'name'))
    class Meta:
        fields = ('id', 'title', 'author')

from marshmallow import pprint
from mymodels import Author, Book

author = Author(name='William Faulkner')
book = Book(title='As I Lay Dying', author=author)
book_result = BookSchema().dump(book)
pprint(book_result, indent=2)
# {
#   "id": 124,
#   "title": "As I Lay Dying",
#   "author": {
#     "id": 8,
#     "name": "William Faulkner"
#   }
# }

author_result = AuthorSchema().dump(author)
pprint(author_result, indent=2)
# {
#   "id": 8,
#   "name": "William Faulkner",
#   "books": [
#     {
#       "id": 124,
#       "title": "As I Lay Dying"
#     }
#   ]
# }

Note

If you need to, you can also pass the full, module-qualified path to fields.Nested.

books = fields.Nested('path.to.BookSchema',
                      many=True, exclude=('author', ))

Nesting A Schema Within Itself

If the object to be marshalled has a relationship to an object of the same type, you can nest the Schema within itself by passing "self" (with quotes) to the Nested constructor.

class UserSchema(Schema):
    name = fields.String()
    email = fields.Email()
    friends = fields.Nested('self', many=True)
    # Use the 'exclude' argument to avoid infinite recursion
    employer = fields.Nested('self', exclude=('employer', ), default=None)

user = User("Steve", 'steve@example.com')
user.friends.append(User("Mike", 'mike@example.com'))
user.friends.append(User('Joe', 'joe@example.com'))
user.employer = User('Dirk', 'dirk@example.com')
result = UserSchema().dump(user)
pprint(result, indent=2)
# {
#     "name": "Steve",
#     "email": "steve@example.com",
#     "friends": [
#         {
#             "name": "Mike",
#             "email": "mike@example.com",
#             "friends": [],
#             "employer": null
#         },
#         {
#             "name": "Joe",
#             "email": "joe@example.com",
#             "friends": [],
#             "employer": null
#         }
#     ],
#     "employer": {
#         "name": "Dirk",
#         "email": "dirk@example.com",
#         "friends": []
#     }
# }

Next Steps

  • Want to create your own field type? See the Custom Fields page.
  • Need to add schema-level validation, post-processing, or error handling behavior? See the Extending Schemas page.
  • For example applications using marshmallow, check out the Examples page.