django-rest-framework/docs/api-guide/serializers.md

source: serializers.py

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Note: This is the documentation for the version 3.0 of REST framework. Documentation for version 2.4 is also available.

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Serializers

Expanding the usefulness of the serializers is something that we would like to address. However, it's not a trivial problem, and it will take some serious design work.

— Russell Keith-Magee, Django users group

Serializers allow complex data such as querysets and model instances to be converted to native Python datatypes that can then be easily rendered into JSON, XML or other content types. Serializers also provide deserialization, allowing parsed data to be converted back into complex types, after first validating the incoming data.

The serializers in REST framework work very similarly to Django's Form and ModelForm classes. We provide a Serializer class which gives you a powerful, generic way to control the output of your responses, as well as a ModelSerializer class which provides a useful shortcut for creating serializers that deal with model instances and querysets.

Declaring Serializers

Let's start by creating a simple object we can use for example purposes:

class Comment(object):
    def __init__(self, email, content, created=None):
        self.email = email
        self.content = content
        self.created = created or datetime.datetime.now()

comment = Comment(email='leila@example.com', content='foo bar')

We'll declare a serializer that we can use to serialize and deserialize data that corresponds to Comment objects.

Declaring a serializer looks very similar to declaring a form:

from rest_framework import serializers

class CommentSerializer(serializers.Serializer):
    email = serializers.EmailField()
    content = serializers.CharField(max_length=200)
    created = serializers.DateTimeField()

Serializing objects

We can now use CommentSerializer to serialize a comment, or list of comments. Again, using the Serializer class looks a lot like using a Form class.

serializer = CommentSerializer(comment)
serializer.data
# {'email': u'leila@example.com', 'content': u'foo bar', 'created': datetime.datetime(2012, 8, 22, 16, 20, 9, 822774)}

At this point we've translated the model instance into Python native datatypes. To finalise the serialization process we render the data into json.

from rest_framework.renderers import JSONRenderer

json = JSONRenderer().render(serializer.data)
json
# '{"email": "leila@example.com", "content": "foo bar", "created": "2012-08-22T16:20:09.822"}'

Deserializing objects

Deserialization is similar. First we parse a stream into Python native datatypes...

from StringIO import StringIO
from rest_framework.parsers import JSONParser

stream = StringIO(json)
data = JSONParser().parse(stream)

...then we restore those native datatypes into a dictionary of validated data.

serializer = CommentSerializer(data=data)
serializer.is_valid()
# True
serializer.validated_data
# {'content': 'foo bar', 'email': 'leila@example.com', 'created': datetime.datetime(2012, 08, 22, 16, 20, 09, 822243)}

Saving instances

If we want to be able to return complete object instances based on the validated data we need to implement one or both of the .create() and update() methods. For example:

class CommentSerializer(serializers.Serializer):
    email = serializers.EmailField()
    content = serializers.CharField(max_length=200)
    created = serializers.DateTimeField()

    def create(self, validated_data):
        return Comment(**validated_data)

    def update(self, instance, validated_data):
        instance.email = validated_data.get('email', instance.email)
        instance.content = validated_data.get('content', instance.content)
        instance.created = validated_data.get('created', instance.created)
        return instance

If your object instances correspond to Django models you'll also want to ensure that these methods save the object to the database. For example, if Comment was a Django model, the methods might look like this:

    def create(self, validated_data):
        return Comment.objects.create(**validated_data)

    def update(self, instance, validated_data):
        instance.email = validated_data.get('email', instance.email)
        instance.content = validated_data.get('content', instance.content)
        instance.created = validated_data.get('created', instance.created)
        instance.save()
        return instance

Now when deserializing data, we can call .save() to return an object instance, based on the validated data.

comment = serializer.save()

Calling .save() will either create a new instance, or update an existing instance, depending on if an existing instance was passed when instantiating the serializer class:

# .save() will create a new instance.
serializer = CommentSerializer(data=data)

# .save() will update the existing `comment` instance.
serializer = CommentSerializer(comment, data=data)

Both the .create() and .update() methods are optional. You can implement either neither, one, or both of them, depending on the use-case for your serializer class.

Passing additional attributes to .save()

Sometimes you'll want your view code to be able to inject additional data at the point of saving the instance. This additional data might include information like the current user, the current time, or anything else that is not part of the request data.

You can do so by including additional keyword arguments when calling .save(). For example:

serializer.save(owner=request.user)

Any additional keyword arguments will be included in the validated_data argument when .create() or .update() are called.

Overriding .save() directly.

In some cases the .create() and .update() method names may not be meaningful. For example, in a contact form we may not be creating new instances, but instead sending an email or other message.

In these cases you might instead choose to override .save() directly, as being more readable and meaningful.

For example:

class ContactForm(serializers.Serializer):
    email = serializers.EmailField()
    message = serializers.CharField()
    
    def save(self):
        email = self.validated_data['email']
        message = self.validated_data['message']
        send_email(from=email, message=message)

Note that in the case above we're now having to access the serializer .validated_data property directly.

Validation

When deserializing data, you always need to call is_valid() before attempting to access the validated data, or save an object instance. If any validation errors occur, the .errors property will contain a dictionary representing the resulting error messages. For example:

serializer = CommentSerializer(data={'email': 'foobar', 'content': 'baz'})
serializer.is_valid()
# False
serializer.errors
# {'email': [u'Enter a valid e-mail address.'], 'created': [u'This field is required.']}

Each key in the dictionary will be the field name, and the values will be lists of strings of any error messages corresponding to that field. The non_field_errors key may also be present, and will list any general validation errors. The name of the non_field_errors key may be customized using the NON_FIELD_ERRORS_KEY REST framework setting.

When deserializing a list of items, errors will be returned as a list of dictionaries representing each of the deserialized items.

#### Raising an exception on invalid data

The .is_valid() method takes an optional raise_exception flag that will cause it to raise a serializers.ValidationError exception if there are validation errors.

These exceptions are automatically dealt with by the default exception handler that REST framework provides, and will return HTTP 400 Bad Request responses by default.

# Return a 400 response if the data was invalid.
serializer.is_valid(raise_exception=True)

Field-level validation

You can specify custom field-level validation by adding .validate_<field_name> methods to your Serializer subclass. These are similar to the .clean_<field_name> methods on Django forms.

These methods take a single argument, which is the field value that requires validation.

Your validate_<field_name> methods should return the validated value or raise a serializers.ValidationError. For example:

from rest_framework import serializers

class BlogPostSerializer(serializers.Serializer):
    title = serializers.CharField(max_length=100)
    content = serializers.CharField()

    def validate_title(self, value):
        """
        Check that the blog post is about Django.
        """
        if 'django' not in value.lower():
            raise serializers.ValidationError("Blog post is not about Django")
        return value

Object-level validation

To do any other validation that requires access to multiple fields, add a method called .validate() to your Serializer subclass. This method takes a single argument, which is a dictionary of field values. It should raise a ValidationError if necessary, or just return the validated values. For example:

from rest_framework import serializers

class EventSerializer(serializers.Serializer):
    description = serializers.CharField(max_length=100)
    start = serializers.DateTimeField()
    finish = serializers.DateTimeField()

    def validate(self, data):
        """
        Check that the start is before the stop.
        """
        if data['start'] > data['finish']:
            raise serializers.ValidationError("finish must occur after start")
        return data

Validators

Individual fields on a serializer can include validators, by declaring them on the field instance, for example:

def multiple_of_ten(value):
    if value % 10 != 0:
        raise serializers.ValidationError('Not a multiple of ten')

class GameRecord(serializers.Serializer):
    score = IntegerField(validators=[multiple_of_ten])
    ...

Serializer classes can also include reusable validators that are applied to the complete set of field data. These validators are included by declaring them on an inner Meta class, like so:

class EventSerializer(serializers.Serializer):
    name = serializers.CharField()
    room_number = serializers.IntegerField(choices=[101, 102, 103, 201])
    date = serializers.DateField()
    
    class Meta:
        # Each room only has one event per day.
        validators = UniqueTogetherValidator(
            queryset=Event.objects.all(),
            fields=['room_number', 'date']
        )

For more information see the validators documentation.

Partial updates

By default, serializers must be passed values for all required fields or they will raise validation errors. You can use the partial argument in order to allow partial updates.

# Update `comment` with partial data
serializer = CommentSerializer(comment, data={'content': u'foo bar'}, partial=True)

Dealing with nested objects

The previous examples are fine for dealing with objects that only have simple datatypes, but sometimes we also need to be able to represent more complex objects, where some of the attributes of an object might not be simple datatypes such as strings, dates or integers.

The Serializer class is itself a type of Field, and can be used to represent relationships where one object type is nested inside another.

class UserSerializer(serializers.Serializer):
    email = serializers.EmailField()
    username = serializers.CharField(max_length=100)

class CommentSerializer(serializers.Serializer):
    user = UserSerializer()
    content = serializers.CharField(max_length=200)
    created = serializers.DateTimeField()

If a nested representation may optionally accept the None value you should pass the required=False flag to the nested serializer.

class CommentSerializer(serializers.Serializer):
    user = UserSerializer(required=False)  # May be an anonymous user.
    content = serializers.CharField(max_length=200)
    created = serializers.DateTimeField()

Similarly if a nested representation should be a list of items, you should pass the many=True flag to the nested serialized.

class CommentSerializer(serializers.Serializer):
    user = UserSerializer(required=False)
    edits = EditItemSerializer(many=True)  # A nested list of 'edit' items.
    content = serializers.CharField(max_length=200)
    created = serializers.DateTimeField()

Writable nested representations

When dealing with nested representations that support deserializing the data, an errors with nested objects will be nested under the field name of the nested object.

serializer = CommentSerializer(data={'user': {'email': 'foobar', 'username': 'doe'}, 'content': 'baz'})
serializer.is_valid()
# False
serializer.errors
# {'user': {'email': [u'Enter a valid e-mail address.']}, 'created': [u'This field is required.']}

Similarly, the .validated_data property will include nested data structures.

Writing .create() methods for nested representations

If you're supporting writable nested representations you'll need to write .create() or .update() methods that handle saving multiple objects.

The following example demonstrates how you might handle creating a user with a nested profile object.

class UserSerializer(serializers.ModelSerializer):
    profile = ProfileSerializer()

    class Meta:
        model = User
        fields = ('username', 'email', 'profile')

    def create(self, validated_data):
        profile_data = validated_data.pop('profile')
        user = User.objects.create(**validated_data)
        Profile.objects.create(user=user, **profile_data)
        return user

Writing .update() methods for nested representations

For updates you'll want to think carefully about how to handle updates to relationships. For example if the data for the relationship is None, or not provided, which of the following should occur?

• Set the relationship to NULL in the database.
• Delete the associated instance.
• Ignore the data and leave the instance as it is.
• Raise a validation error.

Here's an example for an update() method on our previous UserSerializer class.

    def update(self, instance, validated_data):
        profile_data = validated_data.pop('profile')
        # Unless the application properly enforces that this field is
        # always set, the follow could raise a `DoesNotExist`, which
        # would need to be handled.
        profile = instance.profile

        user.username = validated_data.get('username', instance.username)
        user.email = validated_data.get('email', instance.email)
        user.save()

        profile.is_premium_member = profile_data.get(
            'is_premium_member',
            profile.is_premium_member
        )
        profile.has_support_contract = profile_data.get(
            'has_support_contract',
            profile.has_support_contract
         )
        profile.save()

        return user

Because the behavior of nested creates and updates can be ambiguous, and may require complex dependancies between related models, REST framework 3 requires you to always write these methods explicitly. The default ModelSerializer .create() and .update() methods do not include support for writable nested representations.

It is possible that a third party package, providing automatic support some kinds of automatic writable nested representations may be released alongside the 3.1 release.

Handling saving related instances in model manager classes

An alternative to saving multiple related instances in the serializer is to write custom model manager classes handle creating the correct instances.

For example, suppose we wanted to ensure that User instances and Profile instances are always created together as a pair. We might write a custom manager class that looks something like this:

class UserManager(models.Manager):
    ...

    def create(self, username, email, is_premium_member=False, has_support_contract=False):
        user = User(username=username, email=email)
        user.save()
        profile = Profile(
            user=user,
            is_premium_member=is_premium_member,
            has_support_contract=has_support_contract
        )
        profile.save()
        return user

This manager class now more nicely encapsulates that user instances and profile instances are always created at the same time. Our .create() method on the serializer class can now be re-written to use the new manager method.

def create(self, validated_data):
    return User.objects.create(
        username=validated_data['username'],
        email=validated_data['email']
        is_premium_member=validated_data['profile']['is_premium_member']
        has_support_contract=validated_data['profile']['has_support_contract']
    )

For more details on this approach see the Django documentation on model managers, and this blogpost on using model and manger classes.

Dealing with multiple objects

The Serializer class can also handle serializing or deserializing lists of objects.

Serializing multiple objects

To serialize a queryset or list of objects instead of a single object instance, you should pass the many=True flag when instantiating the serializer. You can then pass a queryset or list of objects to be serialized.

queryset = Book.objects.all()
serializer = BookSerializer(queryset, many=True)
serializer.data
# [
#     {'id': 0, 'title': 'The electric kool-aid acid test', 'author': 'Tom Wolfe'},
#     {'id': 1, 'title': 'If this is a man', 'author': 'Primo Levi'},
#     {'id': 2, 'title': 'The wind-up bird chronicle', 'author': 'Haruki Murakami'}
# ]

Deserializing multiple objects

The default behavior for deserializing multiple objects is to support multiple object creation, but not support multiple object updates. For more information on how to support or customize either of these cases, see the ListSerializer documentation below.

Including extra context

There are some cases where you need to provide extra context to the serializer in addition to the object being serialized. One common case is if you're using a serializer that includes hyperlinked relations, which requires the serializer to have access to the current request so that it can properly generate fully qualified URLs.

You can provide arbitrary additional context by passing a context argument when instantiating the serializer. For example:

serializer = AccountSerializer(account, context={'request': request})
serializer.data
# {'id': 6, 'owner': u'denvercoder9', 'created': datetime.datetime(2013, 2, 12, 09, 44, 56, 678870), 'details': 'http://example.com/accounts/6/details'}

The context dictionary can be used within any serializer field logic, such as a custom .to_representation() method, by accessing the self.context attribute.

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ModelSerializer

Often you'll want serializer classes that map closely to Django model definitions.

The ModelSerializer class provides a shortcut that lets you automatically create a Serializer class with fields that correspond to the Model fields.

The ModelSerializer class is the same as a regular Serializer class, except that:

• It will automatically generate a set of fields for you, based on the model.
• It will automatically generate validators for the serializer, such as unique_together validators.
• It includes simple default implementations of .create() and .update().

Declaring a ModelSerializer looks like this:

class AccountSerializer(serializers.ModelSerializer):
    class Meta:
        model = Account

By default, all the model fields on the class will be mapped to a corresponding serializer fields.

Any relationships such as foreign keys on the model will be mapped to PrimaryKeyRelatedField. Reverse relationships are not included by default unless explicitly included as described below.

Inspecting a ModelSerializer

Serializer classes generate helpful verbose representation strings, that allow you to fully inspect the state of their fields. This is particularly useful when working with ModelSerializers where you want to determine what set of fields and validators are being automatically created for you.

To do so, open the Django shell, using python manage.py shell, then import the serializer class, instantiate it, and print the object representation…

>>> from myapp.serializers import AccountSerializer
>>> serializer = AccountSerializer()
>>> print repr(serializer)  # Or `print(repr(serializer))` in Python 3.x.
AccountSerializer():
    id = IntegerField(label='ID', read_only=True)
    name = CharField(allow_blank=True, max_length=100, required=False)
    owner = PrimaryKeyRelatedField(queryset=User.objects.all())

Specifying which fields should be included

If you only want a subset of the default fields to be used in a model serializer, you can do so using fields or exclude options, just as you would with a ModelForm.

For example:

class AccountSerializer(serializers.ModelSerializer):
    class Meta:
        model = Account
        fields = ('id', 'account_name', 'users', 'created')

The names in the fields option will normally map to model fields on the model class.

Alternatively names in the fields options can map to properties or methods which take no arguments that exist on the model class.

Specifying nested serialization

The default ModelSerializer uses primary keys for relationships, but you can also easily generate nested representations using the depth option:

class AccountSerializer(serializers.ModelSerializer):
    class Meta:
        model = Account
        fields = ('id', 'account_name', 'users', 'created')
        depth = 1

The depth option should be set to an integer value that indicates the depth of relationships that should be traversed before reverting to a flat representation.

If you want to customize the way the serialization is done (e.g. using allow_add_remove) you'll need to define the field yourself.

Specifying fields explicitly

You can add extra fields to a ModelSerializer or override the default fields by declaring fields on the class, just as you would for a Serializer class.

class AccountSerializer(serializers.ModelSerializer):
    url = serializers.CharField(source='get_absolute_url', read_only=True)
    groups = serializers.PrimaryKeyRelatedField(many=True)

    class Meta:
        model = Account

Extra fields can correspond to any property or callable on the model.

Specifying which fields should be read-only

You may wish to specify multiple fields as read-only. Instead of adding each field explicitly with the read_only=True attribute, you may use the shortcut Meta option, read_only_fields.

This option should be a list or tuple of field names, and is declared as follows:

class AccountSerializer(serializers.ModelSerializer):
    class Meta:
        model = Account
        fields = ('id', 'account_name', 'users', 'created')
        read_only_fields = ('account_name',)

Model fields which have editable=False set, and AutoField fields will be set to read-only by default, and do not need to be added to the read_only_fields option.

Specifying additional keyword arguments for fields.

There is also a shortcut allowing you to specify arbitrary additional keyword arguments on fields, using the extra_kwargs option. Similarly to read_only_fields this means you do not need to explicitly declare the field on the serializer.

This option is a dictionary, mapping field names to a dictionary of keyword arguments. For example:

class CreateUserSerializer(serializers.ModelSerializer):
    class Meta:
        model = User
        fields = ('email', 'username', 'password')
        extra_kwargs = {'password': {'write_only': True}}
        
    def create(self, validated_data):
        user = User(
            email=validated_data['email'],
            username=validated_data['username']
        )
        user.set_password(validated_data['password'])
        user.save()
        return user

Relational fields

When serializing model instances, there are a number of different ways you might choose to represent relationships. The default representation for ModelSerializer is to use the primary keys of the related instances.

Alternative representations include serializing using hyperlinks, serializing complete nested representations, or serializing with a custom representation.

For full details see the serializer relations documentation.

## Inheritance of the 'Meta' class

The inner Meta class on serializers is not inherited from parent classes by default. This is the same behavior as with Django's Model and ModelForm classes. If you want the Meta class to inherit from a parent class you must do so explicitly. For example:

class AccountSerializer(MyBaseSerializer):
    class Meta(MyBaseSerializer.Meta):
        model = Account

Typically we would recommend not using inheritance on inner Meta classes, but instead declaring all options explicitly.

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HyperlinkedModelSerializer

The HyperlinkedModelSerializer class is similar to the ModelSerializer class except that it uses hyperlinks to represent relationships, rather than primary keys.

By default the serializer will include a url field instead of a primary key field.

The url field will be represented using a HyperlinkedIdentityField serializer field, and any relationships on the model will be represented using a HyperlinkedRelatedField serializer field.

You can explicitly include the primary key by adding it to the fields option, for example:

class AccountSerializer(serializers.HyperlinkedModelSerializer):
    class Meta:
        model = Account
        fields = ('url', 'id', 'account_name', 'users', 'created')

How hyperlinked views are determined

There needs to be a way of determining which views should be used for hyperlinking to model instances.

By default hyperlinks are expected to correspond to a view name that matches the style '{model_name}-detail', and looks up the instance by a pk keyword argument.

You can override a URL field view name and lookup field by using either, or both of, the view_name and lookup_field options in the extra_kwargs setting, like so:

class AccountSerializer(serializers.HyperlinkedModelSerializer):
    class Meta:
        model = Account
        fields = ('account_url', 'account_name', 'users', 'created')
        extra_kwargs = {
            'url': {'view_name': 'accounts', 'lookup_field': 'account_name'}
            'users': {'lookup_field': 'username'}
        }

Alternatively you can set the fields on the serializer explicitly. For example:

class AccountSerializer(serializers.HyperlinkedModelSerializer):
    url = serializers.HyperlinkedIdentityField(
        view_name='accounts',
        lookup_field='slug'
    )
    users = serializers.HyperlinkedRelatedField(
        view_name='user-detail',
        lookup_field='username',
        many=True,
        read_only=True
    )

    class Meta:
        model = Account
        fields = ('url', 'account_name', 'users', 'created')

---

Tip: Properly matching together hyperlinked representations and your URL conf can sometimes be a bit fiddly. Printing the repr of a HyperlinkedModelSerializer instance is a particularly useful way to inspect exactly which view names and lookup fields the relationships are expected to map too.

---

Changing the URL field name

The name of the URL field defaults to 'url'. You can override this globally, by using the URL_FIELD_NAME setting.

---

ListSerializer

The ListSerializer class provides the behavior for serializing and validating multiple objects at once. You won't typically need to use ListSerializer directly, but should instead simply pass many=True when instantiating a serializer.

When a serializer is instantiated and many=True is passed, a ListSerializer instance will be created. The serializer class then becomes a child of the parent ListSerializer

There are a few use cases when you might want to customize the ListSerializer behavior. For example:

• You want to provide particular validation of the lists, such as always ensuring that there is at least one element in a list.
• You want to customize the create or update behavior of multiple objects.

For these cases you can modify the class that is used when many=True is passed, by using the list_serializer_class option on the serializer Meta class.

For example:

class CustomListSerializer(serializers.ListSerializer):
    ...

class CustomSerializer(serializers.Serializer):
    ...
    class Meta:
        list_serializer_class = CustomListSerializer

Customizing multiple create

The default implementation for multiple object creation is to simply call .create() for each item in the list. If you want to customize this behavior, you'll need to customize the .create() method on ListSerializer class that is used when many=True is passed.

For example:

class BookListSerializer(serializers.ListSerializer):
    def create(self, validated_data):
        books = [Book(**item) for item in validated_data]
        return Book.objects.bulk_create(books)

class BookSerializer(serializers.Serializer):
    ...
    class Meta:
        list_serializer_class = BookListSerializer

Customizing multiple update

By default the ListSerializer class does not support multiple updates. This is because the behavior that should be expected for insertions and deletions is ambiguous.

To support multiple updates you'll need to do so explicitly. When writing your multiple update code make sure to keep the following in mind:

• How do you determine which instance should be updated for each item in the list of data?
• How should insertions be handled? Are they invalid, or do they create new objects?
• How should removals be handled? Do they imply object deletion, or removing a relationship? Should they be silently ignored, or are they invalid?
• How should ordering be handled? Does changing the position of two items imply any state change or is it ignored?

Here's an example of how you might choose to implement multiple updates:

class BookListSerializer(serializers.ListSerializer):
    def update(self, instance, validated_data):
        # Maps for id->instance and id->data item.
        book_mapping = {book.id: book for book in instance}
        data_mapping = {item['id']: item for item in validated_data}

        # Perform creations and updates.
        ret = []
        for book_id, data in data_mapping.items():
            book = book_mapping.get(book_id, None):
            if book is None:
                ret.append(self.child.create(data))
            else:
                ret.append(self.child.update(book, data))

        # Perform deletions.
        for book_id, book in book_mapping.items():
            if book_id not in data_mapping:
                book.delete()

        return ret

class BookSerializer(serializers.Serializer):
    ...
    class Meta:
        list_serializer_class = BookListSerializer

It is possible that a third party package may be included alongside the 3.1 release that provides some automatic support for multiple update operations, similar to the allow_add_remove behavior that was present in REST framework 2.

Customizing ListSerializer initialization

When a serializer with many=True is instantiated, we need to determine which arguments and keyword arguments should be passed to the .__init__() method for both the child Serializer class, and for the parent ListSerializer class.

The default implementation is to pass all arguments to both classes, except for validators, and any custom keyword arguments, both of which are assumed to be intended for the child serializer class.

Occasionally you might need to explicitly specify how the child and parent classes should be instantiated when many=True is passed. You can do so by using the many_init class method.

    @classmethod
    def many_init(cls, *args, **kwargs):
        # Instantiate the child serializer.
        kwargs['child'] = cls()
        # Instantiate the parent list serializer.
        return CustomListSerializer(*args, **kwargs)

---

BaseSerializer

BaseSerializer class that can be used to easily support alternative serialization and deserialization styles.

This class implements the same basic API as the Serializer class:

• .data - Returns the outgoing primitive representation.
• .is_valid() - Deserializes and validates incoming data.
• .validated_data - Returns the validated incoming data.
• .errors - Returns an errors during validation.
• .save() - Persists the validated data into an object instance.

There are four methods that can be overridden, depending on what functionality you want the serializer class to support:

• .to_representation() - Override this to support serialization, for read operations.
• .to_internal_value() - Override this to support deserialization, for write operations.
• .create() and .update() - Overide either or both of these to support saving instances.

Because this class provides the same interface as the Serializer class, you can use it with the existing generic class based views exactly as you would for a regular Serializer or ModelSerializer.

The only difference you'll notice when doing so is the BaseSerializer classes will not generate HTML forms in the browsable API. This is because the data they return does not include all the field information that would allow each field to be rendered into a suitable HTML input.

Read-only BaseSerializer classes

To implement a read-only serializer using the BaseSerializer class, we just need to override the .to_representation() method. Let's take a look at an example using a simple Django model:

class HighScore(models.Model):
    created = models.DateTimeField(auto_now_add=True)
    player_name = models.CharField(max_length=10)
    score = models.IntegerField()

It's simple to create a read-only serializer for converting HighScore instances into primitive data types.

class HighScoreSerializer(serializers.BaseSerializer):
    def to_representation(self, obj):
        return {
            'score': obj.score,
            'player_name': obj.player_name
        }

We can now use this class to serialize single HighScore instances:

@api_view(['GET'])
def high_score(request, pk):
    instance = HighScore.objects.get(pk=pk)
    serializer = HighScoreSerializer(instance)
    return Response(serializer.data)

Or use it to serialize multiple instances:

@api_view(['GET'])
def all_high_scores(request):
    queryset = HighScore.objects.order_by('-score')
    serializer = HighScoreSerializer(queryset, many=True)
    return Response(serializer.data)

Read-write BaseSerializer classes

To create a read-write serializer we first need to implement a .to_internal_value() method. This method returns the validated values that will be used to construct the object instance, and may raise a ValidationError if the supplied data is in an incorrect format.

Once you've implemented .to_internal_value(), the basic validation API will be available on the serializer, and you will be able to use .is_valid(), .validated_data and .errors.

If you want to also support .save() you'll need to also implement either or both of the .create() and .update() methods.

Here's a complete example of our previous HighScoreSerializer, that's been updated to support both read and write operations.

class HighScoreSerializer(serializers.BaseSerializer):
    def to_internal_value(self, data):
        score = data.get('score')
        player_name = data.get('player_name')

        # Perform the data validation.
        if not score:
            raise ValidationError({
                'score': 'This field is required.'
            })
        if not player_name:
            raise ValidationError({
                'player_name': 'This field is required.'
            })
        if len(player_name) > 10:
            raise ValidationError({
                'player_name': 'May not be more than 10 characters.'
            })

		# Return the validated values. This will be available as
		# the `.validated_data` property.
        return {
            'score': int(score),
            'player_name': player_name
        }

    def to_representation(self, obj):
        return {
            'score': obj.score,
            'player_name': obj.player_name
        }

    def create(self, validated_data):
        return HighScore.objects.create(**validated_data)

Creating new base classes

The BaseSerializer class is also useful if you want to implement new generic serializer classes for dealing with particular serialization styles, or for integrating with alternative storage backends.

The following class is an example of a generic serializer that can handle coercing arbitrary objects into primitive representations.

class ObjectSerializer(serializers.BaseSerializer):
    """
    A read-only serializer that coerces arbitrary complex objects
    into primitive representations.
    """
    def to_representation(self, obj):
        for attribute_name in dir(obj):
            attribute = getattr(obj, attribute_name)
            if attribute_name('_'):
                # Ignore private attributes.
                pass
            elif hasattr(attribute, '__call__'):
                # Ignore methods and other callables.
                pass
            elif isinstance(attribute, (str, int, bool, float, type(None))):
                # Primitive types can be passed through unmodified.
                output[attribute_name] = attribute
            elif isinstance(attribute, list):
                # Recursively deal with items in lists.
                output[attribute_name] = [
                    self.to_representation(item) for item in attribute
                ]
            elif isinstance(attribute, dict):
                # Recursively deal with items in dictionaries.
                output[attribute_name] = {
                    str(key): self.to_representation(value)
                    for key, value in attribute.items()
                }
            else:
                # Force anything else to its string representation.
                output[attribute_name] = str(attribute)

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Advanced serializer usage

Overriding serialization and deserialization behavior

If you need to alter the serialization, deserialization or validation of a serializer class you can do so by overriding the .to_representation() or .to_internal_value() methods.

Some reasons this might be useful include...

• Adding new behavior for new serializer base classes.
• Modifying the behavior slightly for an existing class.
• Improving serialization performance for a frequently accessed API endpoint that returns lots of data.

The signatures for these methods are as follows:

.to_representation(self, obj)

Takes the object instance that requires serialization, and should return a primitive representation. Typically this means returning a structure of built-in Python datatypes. The exact types that can be handled will depend on the render classes you have configured for your API.

.to_internal_value(self, data)

Takes the unvalidated incoming data as input and should return the validated data that will be made available as serializer.validated_data. The return value will also be passed to the .create() or .update() methods if .save() is called on the serializer class.

If any of the validation fails, then the method should raise a serializers.ValidationError(errors). Typically the errors argument here will be a dictionary mapping field names to error messages.

The data argument passed to this method will normally be the value of request.data, so the datatype it provides will depend on the parser classes you have configured for your API.

Dynamically modifying fields

Once a serializer has been initialized, the dictionary of fields that are set on the serializer may be accessed using the .fields attribute. Accessing and modifying this attribute allows you to dynamically modify the serializer.

Modifying the fields argument directly allows you to do interesting things such as changing the arguments on serializer fields at runtime, rather than at the point of declaring the serializer.

Example

For example, if you wanted to be able to set which fields should be used by a serializer at the point of initializing it, you could create a serializer class like so:

class DynamicFieldsModelSerializer(serializers.ModelSerializer):
    """
    A ModelSerializer that takes an additional `fields` argument that
    controls which fields should be displayed.
    """

    def __init__(self, *args, **kwargs):
        # Don't pass the 'fields' arg up to the superclass
        fields = kwargs.pop('fields', None)

        # Instantiate the superclass normally
        super(DynamicFieldsModelSerializer, self).__init__(*args, **kwargs)

        if fields is not None:
            # Drop any fields that are not specified in the `fields` argument.
            allowed = set(fields)
            existing = set(self.fields.keys())
            for field_name in existing - allowed:
                self.fields.pop(field_name)

This would then allow you to do the following:

>>> class UserSerializer(DynamicFieldsModelSerializer):
>>>     class Meta:
>>>         model = User
>>>         fields = ('id', 'username', 'email')
>>>
>>> print UserSerializer(user)
{'id': 2, 'username': 'jonwatts', 'email': 'jon@example.com'}
>>>
>>> print UserSerializer(user, fields=('id', 'email'))
{'id': 2, 'email': 'jon@example.com'}

Customizing the default fields

REST framework 2 provided an API to allow developers to override how a ModelSerializer class would automatically generate the default set of fields.

This API included the .get_field(), .get_pk_field() and other methods.

Because the serializers have been fundamentally redesigned with 3.0 this API no longer exists. You can still modify the fields that get created but you'll need to refer to the source code, and be aware that if the changes you make are against private bits of API then they may be subject to change.

A new interface for controlling this behavior is currently planned for REST framework 3.1.

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Third party packages

The following third party packages are also available.

MongoengineModelSerializer

The django-rest-framework-mongoengine package provides a MongoEngineModelSerializer serializer class that supports using MongoDB as the storage layer for Django REST framework.

GeoFeatureModelSerializer

The django-rest-framework-gis package provides a GeoFeatureModelSerializer serializer class that supports GeoJSON both for read and write operations.

HStoreSerializer

The django-rest-framework-hstore package provides an HStoreSerializer to support django-hstore DictionaryField model field and its schema-mode feature.