Quickstart

This guide will walk you through the basics of creating schemas for serializing and deserializing data.

Declaring Schemas

Let’s start with a basic user “model”.

import datetime as dt


class User:
    def __init__(self, name, email):
        self.name = name
        self.email = email
        self.created_at = dt.datetime.now()

    def __repr__(self):
        return "<User(name={self.name!r})>".format(self=self)

Create a schema by defining a class with variables mapping attribute names to Field objects.

from marshmallow import Schema, fields


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

See also

For a full reference on the available field classes, see the API Docs.

Creating Schemas From Dictionaries

You can create a schema from a dictionary of fields using the from_dict method.

from marshmallow import Schema, fields

UserSchema = Schema.from_dict(
    {"name": fields.Str(), "email": fields.Email(), "created_at": fields.DateTime()}
)

from_dict is especially useful for generating schemas at runtime.

Serializing Objects (“Dumping”)

Serialize objects by passing them to your schema’s dump method, which returns the formatted result.

from marshmallow import pprint

user = User(name="Monty", email="monty@python.org")
schema = UserSchema()
result = schema.dump(user)
pprint(result)
# {"name": "Monty",
#  "email": "monty@python.org",
#  "created_at": "2014-08-17T14:54:16.049594+00:00"}

You can also serialize to a JSON-encoded string using dumps.

json_result = schema.dumps(user)
pprint(json_result)
# '{"name": "Monty", "email": "monty@python.org", "created_at": "2014-08-17T14:54:16.049594+00:00"}'

Filtering Output

You may not need to output all declared fields every time you use a schema. You can specify which fields to output with the only parameter.

summary_schema = UserSchema(only=("name", "email"))
summary_schema.dump(user)
# {"name": "Monty Python", "email": "monty@python.org"}

You can also exclude fields by passing in the exclude parameter.

Deserializing Objects (“Loading”)

The reverse of the dump method is load, which validates and deserializes an input dictionary to an application-level data structure.

By default, load will return a dictionary of field names mapped to deserialized values (or raise a ValidationError with a dictionary of validation errors, which we’ll revisit later).

from pprint import pprint

user_data = {
    "created_at": "2014-08-11T05:26:03.869245",
    "email": "ken@yahoo.com",
    "name": "Ken",
}
schema = UserSchema()
result = schema.load(user_data)
pprint(result)
# {'name': 'Ken',
#  'email': 'ken@yahoo.com',
#  'created_at': datetime.datetime(2014, 8, 11, 5, 26, 3, 869245)},

Notice that the datetime string was converted to a datetime object.

Deserializing to Objects

In order to deserialize to an object, define a method of your Schema and decorate it with post_load. The method receives a dictionary of deserialized data.

from marshmallow import Schema, fields, post_load


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

    @post_load
    def make_user(self, data, **kwargs):
        return User(**data)

Now, the load method return a User instance.

user_data = {"name": "Ronnie", "email": "ronnie@stones.com"}
schema = UserSchema()
result = schema.load(user_data)
print(result)  # => <User(name='Ronnie')>

Handling Collections of Objects

Set many=True when dealing with iterable collections of objects.

user1 = User(name="Mick", email="mick@stones.com")
user2 = User(name="Keith", email="keith@stones.com")
users = [user1, user2]
schema = UserSchema(many=True)
result = schema.dump(users)  # OR UserSchema().dump(users, many=True)
pprint(result)
# [{'name': u'Mick',
#   'email': u'mick@stones.com',
#   'created_at': '2014-08-17T14:58:57.600623+00:00'}
#  {'name': u'Keith',
#   'email': u'keith@stones.com',
#   'created_at': '2014-08-17T14:58:57.600623+00:00'}]

Validation

Schema.load() (and its JSON-decoding counterpart, Schema.loads()) raises a ValidationError error when invalid data are passed in. You can access the dictionary of validation errors from the ValidationError.messages attribute. The data that were correctly deserialized are accessible in ValidationError.valid_data. Some fields, such as the Email and URL fields, have built-in validation.

from marshmallow import ValidationError

try:
    result = UserSchema().load({"name": "John", "email": "foo"})
except ValidationError as err:
    print(err.messages)  # => {"email": ['"foo" is not a valid email address.']}
    print(err.valid_data)  # => {"name": "John"}

When validating a collection, the errors dictionary will be keyed on the indices of invalid items.

from pprint import pprint

from marshmallow import Schema, fields, ValidationError


class BandMemberSchema(Schema):
    name = fields.String(required=True)
    email = fields.Email()


user_data = [
    {"email": "mick@stones.com", "name": "Mick"},
    {"email": "invalid", "name": "Invalid"},  # invalid email
    {"email": "keith@stones.com", "name": "Keith"},
    {"email": "charlie@stones.com"},  # missing "name"
]

try:
    BandMemberSchema(many=True).load(user_data)
except ValidationError as err:
    pprint(err.messages)
    # {1: {'email': ['Not a valid email address.']},
    #  3: {'name': ['Missing data for required field.']}}

You can perform additional validation for a field by passing the validate argument. There are a number of built-in validators in the marshmallow.validate module.

from pprint import pprint

from marshmallow import Schema, fields, validate, ValidationError


class UserSchema(Schema):
    name = fields.Str(validate=validate.Length(min=1))
    permission = fields.Str(validate=validate.OneOf(["read", "write", "admin"]))
    age = fields.Int(validate=validate.Range(min=18, max=40))


in_data = {"name": "", "permission": "invalid", "age": 71}
try:
    UserSchema().load(in_data)
except ValidationError as err:
    pprint(err.messages)
    # {'age': ['Must be greater than or equal to 18 and less than or equal to 40.'],
    #  'name': ['Shorter than minimum length 1.'],
    #  'permission': ['Must be one of: read, write, admin.']}

You may implement your own validation functions.

from marshmallow import Schema, fields, ValidationError


def validate_quantity(n):
    if n < 0:
        raise ValidationError("Quantity must be greater than 0.")
    if n > 30:
        raise ValidationError("Quantity must not be greater than 30.")


class ItemSchema(Schema):
    quantity = fields.Integer(validate=validate_quantity)


in_data = {"quantity": 31}
try:
    result = ItemSchema().load(in_data)
except ValidationError as err:
    print(err.messages)  # => {'quantity': ['Quantity must not be greater than 30.']}

You may also pass a collection (list, tuple, generator) of callables to validate.

Warning

Validation occurs on deserialization but not on serialization. To improve serialization performance, data passed to Schema.dump() are considered valid.

See also

You can register a custom error handler function for a schema by overriding the handle_error method. See the Extending Schemas page for more info.

See also

Need schema-level validation? See the Extending Schemas page.

Field Validators as Methods

It is sometimes convenient to write validators as methods. Use the validates decorator to register field validator methods.

from marshmallow import fields, Schema, validates, ValidationError


class ItemSchema(Schema):
    quantity = fields.Integer()

    @validates("quantity")
    def validate_quantity(self, value):
        if value < 0:
            raise ValidationError("Quantity must be greater than 0.")
        if value > 30:
            raise ValidationError("Quantity must not be greater than 30.")

Required Fields

Make a field required by passing required=True. An error will be raised if the the value is missing from the input to Schema.load().

To customize the error message for required fields, pass a dict with a required key as the error_messages argument for the field.

from pprint import pprint

from marshmallow import Schema, fields, ValidationError


class UserSchema(Schema):
    name = fields.String(required=True)
    age = fields.Integer(required=True, error_messages={"required": "Age is required."})
    city = fields.String(
        required=True,
        error_messages={"required": {"message": "City required", "code": 400}},
    )
    email = fields.Email()


try:
    result = UserSchema().load({"email": "foo@bar.com"})
except ValidationError as err:
    pprint(err.messages)
    # {'age': ['Age is required.'],
    # 'city': {'code': 400, 'message': 'City required'},
    # 'name': ['Missing data for required field.']}

Partial Loading

When using the same schema in multiple places, you may only want to skip required validation by passing partial.

class UserSchema(Schema):
    name = fields.String(required=True)
    age = fields.Integer(required=True)


result = UserSchema().load({"age": 42}, partial=("name",))
# OR UserSchema(partial=('name',)).load({'age': 42})
print(result)  # => {'age': 42}

You can ignore missing fields entirely by setting partial=True.

class UserSchema(Schema):
    name = fields.String(required=True)
    age = fields.Integer(required=True)


result = UserSchema().load({"age": 42}, partial=True)
# OR UserSchema(partial=True).load({'age': 42})
print(result)  # => {'age': 42}

Specifying Defaults

missing specifies the default deserialization value for a field. Likewise, default specifies the default serialization value.

class UserSchema(Schema):
    id = fields.UUID(missing=uuid.uuid1)
    birthdate = fields.DateTime(default=dt.datetime(2017, 9, 29))


UserSchema().load({})
# {'id': UUID('337d946c-32cd-11e8-b475-0022192ed31b')}
UserSchema().dump({})
# {'birthdate': '2017-09-29T00:00:00+00:00'}

Handling Unknown Fields

By default, load will raise a ValidationError if it encounters a key with no matching Field in the schema.

This behavior can be modified with the unknown option, which accepts one of the following:

  • RAISE (default): raise a ValidationError if there are any unknown fields

  • EXCLUDE: exclude unknown fields

  • INCLUDE: accept and include the unknown fields

You can specify unknown in the class Meta of your Schema,

from marshmallow import Schema, INCLUDE


class UserSchema(Schema):
    class Meta:
        unknown = INCLUDE

at instantiation time,

schema = UserSchema(unknown=INCLUDE)

or when calling load.

UserSchema().load(data, unknown=INCLUDE)

The unknown option value set in load will override the value applied at instantiation time, which itself will override the value defined in the class Meta.

This order of precedence allows you to change the behavior of a schema for different contexts.

Validation Without Deserialization

If you only need to validate input data (without deserializing to an object), you can use Schema.validate().

errors = UserSchema().validate({"name": "Ronnie", "email": "invalid-email"})
print(errors)  # {'email': ['Not a valid email address.']}

“Read-only” and “Write-only” Fields

In the context of a web API, the dump_only and load_only parameters are conceptually equivalent to “read-only” and “write-only” fields, respectively.

class UserSchema(Schema):
    name = fields.Str()
    # password is "write-only"
    password = fields.Str(load_only=True)
    # created_at is "read-only"
    created_at = fields.DateTime(dump_only=True)

Warning

When loading, dump-only fields are considered unknown. If the unknown option is set to INCLUDE, values with keys corresponding to those fields are therefore loaded with no validation.

Specifying Serialization/Deserialization Keys

Schemas will (de)serialize an input dictionary from/to an output dictionary whose keys are identical to the field names. If you are consuming and producing data that does not match your schema, you can specify the output keys via the data_key argument.

class UserSchema(Schema):
    name = fields.String()
    email = fields.Email(data_key="emailAddress")


s = UserSchema()

data = {"name": "Mike", "email": "foo@bar.com"}
result = s.dump(data)
# {'name': u'Mike',
# 'emailAddress': 'foo@bar.com'}

data = {"name": "Mike", "emailAddress": "foo@bar.com"}
result = s.load(data)
# {'name': u'Mike',
# 'email': 'foo@bar.com'}

Implicit Field Creation

When your model has many attributes, specifying the field type for every attribute can get repetitive, especially when many of the attributes are already native Python datatypes.

The fields option allows you to specify implicitly-created fields. Marshmallow will choose an appropriate field type based on the attribute’s type.

Let’s refactor our User schema to be more concise.

class UserSchema(Schema):
    uppername = fields.Function(lambda obj: obj.name.upper())

    class Meta:
        fields = ("name", "email", "created_at", "uppername")

Note that name will be automatically formatted as a String and created_at will be formatted as a DateTime.

Note

If instead you want to specify which field names to include in addition to the explicitly declared fields, you can use the additional option.

The schema below is equivalent to above:

class UserSchema(Schema):
    uppername = fields.Function(lambda obj: obj.name.upper())

    class Meta:
        # No need to include 'uppername'
        additional = ("name", "email", "created_at")

Ordering Output

To maintain field ordering, set the ordered option to True. This will instruct marshmallow to serialize data to a collections.OrderedDict.

from collections import OrderedDict

from marshmallow import Schema, fields, pprint


class UserSchema(Schema):
    uppername = fields.Function(lambda obj: obj.name.upper())

    class Meta:
        fields = ("name", "email", "created_at", "uppername")
        ordered = True


u = User("Charlie", "charlie@stones.com")
schema = UserSchema()
result = schema.dump(u)
assert isinstance(result, OrderedDict)
# marshmallow's pprint function maintains order
pprint(result, indent=2)
# {
#   "name": "Charlie",
#   "email": "charlie@stones.com",
#   "created_at": "2014-10-30T08:27:48.515735+00:00",
#   "uppername": "CHARLIE"
# }

Next Steps

  • Need to represent relationships between objects? See the Nesting Schemas page.

  • 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.