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".

.. code-block:: python

    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 :class:`Field <fields.Field>` objects.

.. code-block:: python

    from marshmallow import Schema, fields


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

.. seealso::

    For a full reference on the available field classes, see the :ref:`fields module documentation <api_fields>`.

Creating schemas from dictionaries
----------------------------------

You can create a schema from a dictionary of fields using the `from_dict <marshmallow.Schema.from_dict>` method.

.. code-block:: python

    from marshmallow import Schema, fields

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

`from_dict <marshmallow.Schema.from_dict>` is especially useful for generating schemas at runtime.

Serializing objects ("dumping")
-------------------------------

Serialize objects by passing them to your schema's :meth:`dump <marshmallow.Schema.dump>` method, which returns the formatted result.

.. code-block:: python

    from pprint 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 :meth:`dumps <marshmallow.Schema.dumps>`.

.. code-block:: python

    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.

.. code-block:: python

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

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


Deserializing objects ("loading")
---------------------------------

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

By default, :meth:`load <Schema.load>` will return a dictionary of field names mapped to deserialized values (or raise a :exc:`ValidationError <marshmallow.exceptions.ValidationError>` 
with a dictionary of validation errors, which we'll :ref:`revisit later <validation>`).

.. code-block:: python

    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 :class:`Schema` and decorate it with `post_load <marshmallow.decorators.post_load>`. The method receives a dictionary of deserialized data.

.. code-block:: python

    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 <Schema.load>` method return a ``User`` instance.

.. code-block:: python

    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.

.. code-block:: python

    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:

Validation
----------

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

.. code-block:: python

    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.

.. code-block:: python

    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 :ref:`marshmallow.validate <api_validators>` module.

.. code-block:: python

    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 validators.
A validator is a callable that accepts a single argument, the value to validate.
If validation fails, the callable should raise a :exc:`ValidationError <marshmallow.exceptions.ValidationError>`
with a useful error message or return ``False`` (for a generic error message).

.. code-block:: python

    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 <marshmallow.Schema.dump>` 
    are considered valid.

.. seealso::

    You can register a custom error handler function for a schema by overriding the 
    :func:`handle_error <Schema.handle_error>` method. 
    See the :doc:`extending` page for more info.

.. seealso::

    Need schema-level validation? See the :ref:`Extending Schemas <schema_validation>` page.


Field validators as methods
+++++++++++++++++++++++++++

It is sometimes convenient to write validators as methods. Use the `validates <marshmallow.decorators.validates>` decorator to register field validator methods.

.. code-block:: python

    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 <marshmallow.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.

.. code-block:: python

    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``.

.. code-block:: python

    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``.

.. code-block:: python

    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
-------------------

`load_default` specifies the default deserialization value for a field.
Likewise, `dump_default` specifies the default serialization value.

.. code-block:: python

    class UserSchema(Schema):
        id = fields.UUID(load_default=uuid.uuid1)
        birthdate = fields.DateTime(dump_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'}

.. _unknown:

Handling unknown fields
-----------------------

By default, :meth:`load <Schema.load>` will raise a :exc:`ValidationError <marshmallow.exceptions.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 <marshmallow.utils.RAISE>` (default): raise a :exc:`ValidationError <marshmallow.exceptions.ValidationError>`
  if there are any unknown fields
- `EXCLUDE <marshmallow.utils.EXCLUDE>`: exclude unknown fields
- `INCLUDE <marshmallow.utils.INCLUDE>`: accept and include the unknown fields

You can specify ``unknown`` in the *class Meta* of your `Schema <marshmallow.Schema>`,

.. code-block:: python

    from marshmallow import Schema, INCLUDE


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

at instantiation time,

.. code-block:: python

    schema = UserSchema(unknown=INCLUDE)

or when calling `load <Schema.load>`.

.. code-block:: python

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

The ``unknown`` option value set in :meth:`load <Schema.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 <marshmallow.Schema.validate>`.

.. code-block:: python

    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.

.. code-block:: python

    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.

.. code-block:: python

    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'}


.. _meta_options:

Implicit field creation
-----------------------

.. warning::

    Implicit field creation is deprecated and is removed in marshmallow 4.
    Fields should be declared explicitly.

    .. code-block:: python

        # 3.x
        class UserSchema(Schema):
            class Meta:
                fields = ("name", "birthdate")


        # 4.x
        class UserSchema(Schema):
            name = fields.String()
            email = fields.Date()

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.

.. code-block:: python

    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 :class:`String <marshmallow.fields.String>` and ``created_at`` will be formatted as a :class:`DateTime <marshmallow.fields.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:

    .. code-block:: python

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

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

Next steps
----------
- Need to represent relationships between objects? See the :doc:`nesting` page.
- Want to create your own field type? See the :doc:`custom_fields` page.
- Need to add schema-level validation, post-processing, or error handling behavior? See the :doc:`extending` page.
- For example applications using marshmallow, check out the :doc:`examples` page.
