:doc:`Django 数据库抽象 API ` 描述了使用 Django queries 来增删查改单个对象的方法。 然而,有时候你要获取的值需要根据一组对象聚合后才能得到。这个主题指南描述了如何使用 Django queries 来生成和返回聚合值的方法。


from django.db import models

class Author(models.Model):
    name = models.CharField(max_length=100)
    age = models.IntegerField()

class Publisher(models.Model):
    name = models.CharField(max_length=300)
    num_awards = models.IntegerField()

class Book(models.Model):
    name = models.CharField(max_length=300)
    pages = models.IntegerField()
    price = models.DecimalField(max_digits=10, decimal_places=2)
    rating = models.FloatField()
    authors = models.ManyToManyField(Author)
    publisher = models.ForeignKey(Publisher, on_delete=models.CASCADE)
    pubdate = models.DateField()

class Store(models.Model):
    name = models.CharField(max_length=300)
    books = models.ManyToManyField(Book)
    registered_users = models.PositiveIntegerField()



# Total number of books.
>>> Book.objects.count()

# Total number of books with publisher=BaloneyPress
>>> Book.objects.filter(publisher__name='BaloneyPress').count()

# Average price across all books.
>>> from django.db.models import Avg
>>> Book.objects.all().aggregate(Avg('price'))
{'price__avg': 34.35}

# Max price across all books.
>>> from django.db.models import Max
>>> Book.objects.all().aggregate(Max('price'))
{'price__max': Decimal('81.20')}

# Difference between the highest priced book and the average price of all books.
>>> from django.db.models import FloatField
>>> Book.objects.aggregate(
...     price_diff=Max('price', output_field=FloatField()) - Avg('price'))
{'price_diff': 46.85}

# All the following queries involve traversing the Book<->Publisher
# foreign key relationship backwards.

# Each publisher, each with a count of books as a "num_books" attribute.
>>> from django.db.models import Count
>>> pubs = Publisher.objects.annotate(num_books=Count('book'))
>>> pubs
<QuerySet [<Publisher: BaloneyPress>, <Publisher: SalamiPress>, ...]>
>>> pubs[0].num_books

# Each publisher, with a separate count of books with a rating above and below 5
>>> from django.db.models import Q
>>> above_5 = Count('book', filter=Q(book__rating__gt=5))
>>> below_5 = Count('book', filter=Q(book__rating__lte=5))
>>> pubs = Publisher.objects.annotate(below_5=below_5).annotate(above_5=above_5)
>>> pubs[0].above_5
>>> pubs[0].below_5

# The top 5 publishers, in order by number of books.
>>> pubs = Publisher.objects.annotate(num_books=Count('book')).order_by('-num_books')[:5]
>>> pubs[0].num_books

在 QuerySet 上生成聚合¶

Django 提供了两种生成聚合的方法。第一种方法是从整个 QuerySet 生成汇总值。比如你想要计算所有在售书的平均价格。Django 的查询语法提供了一种用来描述所有图书集合的方法:

>>> Book.objects.all()

可以通过在 QuerySet 后添加 aggregate() 子句来计算 QuerySet 对象的汇总值。

>>> from django.db.models import Avg
>>> Book.objects.all().aggregate(Avg('price'))
{'price__avg': 34.35}

The all() is redundant in this example, so this could be simplified to:

>>> Book.objects.aggregate(Avg('price'))
{'price__avg': 34.35}

The argument to the aggregate() clause describes the aggregate value that
we want to compute – in this case, the average of the price field on the
Book model. A list of the aggregate functions that are available can be
found in the QuerySet reference.

aggregate() is a terminal clause for a QuerySet that, when invoked,
returns a dictionary of name-value pairs. The name is an identifier for the
aggregate value; the value is the computed aggregate. The name is
automatically generated from the name of the field and the aggregate function.
If you want to manually specify a name for the aggregate value, you can do so
by providing that name when you specify the aggregate clause:

>>> Book.objects.aggregate(average_price=Avg('price'))
{'average_price': 34.35}

If you want to generate more than one aggregate, you just add another
argument to the aggregate() clause. So, if we also wanted to know
the maximum and minimum price of all books, we would issue the query:

>>> from django.db.models import Avg, Max, Min
>>> Book.objects.aggregate(Avg('price'), Max('price'), Min('price'))
{'price__avg': 34.35, 'price__max': Decimal('81.20'), 'price__min': Decimal('12.99')}

Generating aggregates for each item in a QuerySet¶

The second way to generate summary values is to generate an independent
summary for each object in a QuerySet. For example, if you are retrieving
a list of books, you may want to know how many authors contributed to
each book. Each Book has a many-to-many relationship with the Author; we
want to summarize this relationship for each book in the QuerySet.

Per-object summaries can be generated using the annotate() clause.
When an annotate() clause is specified, each object in the QuerySet
will be annotated with the specified values.

The syntax for these annotations is identical to that used for the
aggregate() clause. Each argument to annotate() describes an
aggregate that is to be calculated. For example, to annotate books with
the number of authors:

# Build an annotated queryset
>>> from django.db.models import Count
>>> q = Book.objects.annotate(Count('authors'))
# Interrogate the first object in the queryset
>>> q[0]
<Book: The Definitive Guide to Django>
>>> q[0].authors__count
# Interrogate the second object in the queryset
>>> q[1]
<Book: Practical Django Projects>
>>> q[1].authors__count

As with aggregate(), the name for the annotation is automatically derived
from the name of the aggregate function and the name of the field being
aggregated. You can override this default name by providing an alias when you
specify the annotation:

>>> q = Book.objects.annotate(num_authors=Count('authors'))
>>> q[0].num_authors
>>> q[1].num_authors

Unlike aggregate(), annotate() is not a terminal clause. The output
of the annotate() clause is a QuerySet; this QuerySet can be
modified using any other QuerySet operation, including filter(),
order_by(), or even additional calls to annotate().

Combining multiple aggregations¶

Combining multiple aggregations with annotate() will yield the wrong
because joins are used
instead of subqueries:

>>> book = Book.objects.first()
>>> book.authors.count()
>>> book.store_set.count()
>>> q = Book.objects.annotate(Count('authors'), Count('store'))
>>> q[0].authors__count
>>> q[0].store__count

For most aggregates, there is no way to avoid this problem, however, the
Count aggregate has a distinct parameter that
may help:

>>> q = Book.objects.annotate(Count('authors', distinct=True), Count('store', distinct=True))
>>> q[0].authors__count
>>> q[0].store__count

If in doubt, inspect the SQL query!

In order to understand what happens in your query, consider inspecting the
query property of your QuerySet.

Joins and aggregates¶

So far, we have dealt with aggregates over fields that belong to the
model being queried. However, sometimes the value you want to aggregate
will belong to a model that is related to the model you are querying.

When specifying the field to be aggregated in an aggregate function, Django
will allow you to use the same double underscore notation that is used when referring to related fields in
filters. Django will then handle any table joins that are required to retrieve
and aggregate the related value.

For example, to find the price range of books offered in each store,
you could use the annotation:

>>> from django.db.models import Max, Min
>>> Store.objects.annotate(min_price=Min('books__price'), max_price=Max('books__price'))

This tells Django to retrieve the Store model, join (through the
many-to-many relationship) with the Book model, and aggregate on the
price field of the book model to produce a minimum and maximum value.

The same rules apply to the aggregate() clause. If you wanted to
know the lowest and highest price of any book that is available for sale
in any of the stores, you could use the aggregate:

>>> Store.objects.aggregate(min_price=Min('books__price'), max_price=Max('books__price'))

Join chains can be as deep as you require. For example, to extract the
age of the youngest author of any book available for sale, you could
issue the query:

>>> Store.objects.aggregate(youngest_age=Min('books__authors__age'))

Following relationships backwards¶

In a way similar to Lookups that span relationships, aggregations and
annotations on fields of models or models that are related to the one you are
querying can include traversing “reverse” relationships. The lowercase name
of related models and double-underscores are used here too.

For example, we can ask for all publishers, annotated with their respective
total book stock counters (note how we use 'book' to specify the
Publisher -> Book reverse foreign key hop):

>>> from django.db.models import Avg, Count, Min, Sum
>>> Publisher.objects.annotate(Count('book'))

(Every Publisher in the resulting QuerySet will have an extra attribute
called book__count.)

We can also ask for the oldest book of any of those managed by every publisher:

>>> Publisher.objects.aggregate(oldest_pubdate=Min('book__pubdate'))

(The resulting dictionary will have a key called 'oldest_pubdate'. If no
such alias were specified, it would be the rather long 'book__pubdate__min'.)

This doesn’t apply just to foreign keys. It also works with many-to-many
relations. For example, we can ask for every author, annotated with the total
number of pages considering all the books the author has (co-)authored (note how we
use 'book' to specify the Author -> Book reverse many-to-many hop):

>>> Author.objects.annotate(total_pages=Sum('book__pages'))

(Every Author in the resulting QuerySet will have an extra attribute
called total_pages. If no such alias were specified, it would be the rather
long book__pages__sum.)

Or ask for the average rating of all the books written by author(s) we have on

>>> Author.objects.aggregate(average_rating=Avg('book__rating'))

(The resulting dictionary will have a key called 'average_rating'. If no
such alias were specified, it would be the rather long 'book__rating__avg'.)

Aggregations and other QuerySet clauses¶

filter() and exclude()¶

Aggregates can also participate in filters. Any filter() (or
exclude()) applied to normal model fields will have the effect of
constraining the objects that are considered for aggregation.

When used with an annotate() clause, a filter has the effect of
constraining the objects for which an annotation is calculated. For example,
you can generate an annotated list of all books that have a title starting
with “Django” using the query:

>>> from django.db.models import Avg, Count
>>> Book.objects.filter(name__startswith="Django").annotate(num_authors=Count('authors'))

When used with an aggregate() clause, a filter has the effect of
constraining the objects over which the aggregate is calculated.
For example, you can generate the average price of all books with a
title that starts with “Django” using the query:

>>> Book.objects.filter(name__startswith="Django").aggregate(Avg('price'))

Filtering on annotations

Annotated values can also be filtered. The alias for the annotation can be
used in filter() and exclude() clauses in the same way as any other
model field.

For example, to generate a list of books that have more than one author,
you can issue the query:

>>> Book.objects.annotate(num_authors=Count('authors')).filter(num_authors__gt=1)

This query generates an annotated result set, and then generates a filter
based upon that annotation.

If you need two annotations with two separate filters you can use the
filter argument with any aggregate. For example, to generate a list of
authors with a count of highly rated books:

>>> highly_rated = Count('books', filter=Q(books__rating__gte=7))
>>> Author.objects.annotate(num_books=Count('books'), highly_rated_books=highly_rated)

Each Author in the result set will have the num_books and
highly_rated_books attributes.

Choosing between filter and QuerySet.filter()

Avoid using the filter argument with a single annotation or
aggregation. It’s more efficient to use QuerySet.filter() to exclude
rows. The aggregation filter argument is only useful when using two or
more aggregations over the same relations with different conditionals.

Changed in Django 2.0:

The filter argument was added to aggregates.

Order of annotate() and filter() clauses

When developing a complex query that involves both annotate() and
filter() clauses, pay particular attention to the order in which the
clauses are applied to the QuerySet.

When an annotate() clause is applied to a query, the annotation is computed
over the state of the query up to the point where the annotation is requested.
The practical implication of this is that filter() and annotate() are
not commutative operations.


  • Publisher A has two books with ratings 4 and 5.
  • Publisher B has two books with ratings 1 and 4.
  • Publisher C has one book with rating 1.

Here’s an example with the Count aggregate:

>>> a, b = Publisher.objects.annotate(num_books=Count('book', distinct=True)).filter(book__rating__gt=3.0)
>>> a, a.num_books
(<Publisher: A>, 2)
>>> b, b.num_books
(<Publisher: B>, 2)

>>> a, b = Publisher.objects.filter(book__rating__gt=3.0).annotate(num_books=Count('book'))
>>> a, a.num_books
(<Publisher: A>, 2)
>>> b, b.num_books
(<Publisher: B>, 1)

Both queries return a list of publishers that have at least one book with a
rating exceeding 3.0, hence publisher C is excluded.

In the first query, the annotation precedes the filter, so the filter has no
effect on the annotation. distinct=True is required to avoid a query

The second query counts the number of books that have a rating exceeding 3.0
for each publisher. The filter precedes the annotation, so the filter
constrains the objects considered when calculating the annotation.

Here’s another example with the Avg aggregate:

>>> a, b = Publisher.objects.annotate(avg_rating=Avg('book__rating')).filter(book__rating__gt=3.0)
>>> a, a.avg_rating
(<Publisher: A>, 4.5)  # (5+4)/2
>>> b, b.avg_rating
(<Publisher: B>, 2.5)  # (1+4)/2

>>> a, b = Publisher.objects.filter(book__rating__gt=3.0).annotate(avg_rating=Avg('book__rating'))
>>> a, a.avg_rating
(<Publisher: A>, 4.5)  # (5+4)/2
>>> b, b.avg_rating
(<Publisher: B>, 4.0)  # 4/1 (book with rating 1 excluded)

The first query asks for the average rating of all a publisher’s books for
publisher’s that have at least one book with a rating exceeding 3.0. The second
query asks for the average of a publisher’s book’s ratings for only those
ratings exceeding 3.0.

It’s difficult to intuit how the ORM will translate complex querysets into SQL
queries so when in doubt, inspect the SQL with str(queryset.query) and
write plenty of tests.


Annotations can be used as a basis for ordering. When you
define an order_by() clause, the aggregates you provide can reference
any alias defined as part of an annotate() clause in the query.

For example, to order a QuerySet of books by the number of authors
that have contributed to the book, you could use the following query:

>>> Book.objects.annotate(num_authors=Count('authors')).order_by('num_authors')


通常,注解值会添加到每个对象上,即一个被注解的 QuerySet 将会为初始 QuerySet 的每个对象返回一个结果集。然而,当使用 values() 子句来对结果集进行约束时,生成注解值的方法会稍有不同。不是在原始 QuerySet 中对每个对象添加注解并返回,而是根据定义在 values() 子句中的字段组合先对结果进行分组,再对每个单独的分组进行注解,这个注解值是根据分组中所有的对象计算得到的。


>>> Author.objects.annotate(average_rating=Avg('book__rating'))


但是如果你使用 values() 子句,结果会稍有不同:

>>> Author.objects.values('name').annotate(average_rating=Avg('book__rating'))


annotate()values() 的顺序

和使用 filter() 一样,作用于某个查询的 annotate()values() 子句的顺序非常重要。如果 values() 子句在 annotate() 之前,就会根据 values() 子句产生的分组来计算注解。

然而如果 annotate() 子句在 values() 之前,就会根据整个查询集生成注解。这种情况下,values() 子句只能限制输出的字段。

举个例子,如果我们颠倒上个例子中 values()annotate() 的顺序:

>>> Author.objects.annotate(average_rating=Avg('book__rating')).values('name', 'average_rating')

这段代码将为每个作者添加一个唯一注解,但只有作者姓名和 average_rating 注解会返回在输出结果中。

You should also note that average_rating has been explicitly included
in the list of values to be returned. This is required because of the
ordering of the values() and annotate() clause.

If the values() clause precedes the annotate() clause, any annotations
will be automatically added to the result set. However, if the values()
clause is applied after the annotate() clause, you need to explicitly
include the aggregate column.

Interaction with default ordering or order_by()

Fields that are mentioned in the order_by() part of a queryset (or which
are used in the default ordering on a model) are used when selecting the
output data, even if they are not otherwise specified in the values()
call. These extra fields are used to group “like” results together and they
can make otherwise identical result rows appear to be separate. This shows up,
particularly, when counting things.

By way of example, suppose you have a model like this:

from django.db import models

class Item(models.Model):
    name = models.CharField(max_length=10)
    data = models.IntegerField()

    class Meta:
        ordering = ["name"]

The important part here is the default ordering on the name field. If you
want to count how many times each distinct data value appears, you might
try this:

# Warning: not quite correct!

…which will group the Item objects by their common data values and
then count the number of id values in each group. Except that it won’t
quite work. The default ordering by name will also play a part in the
grouping, so this query will group by distinct (data, name) pairs, which
isn’t what you want. Instead, you should construct this queryset:


…clearing any ordering in the query. You could also order by, say, data
without any harmful effects, since that is already playing a role in the

This behavior is the same as that noted in the queryset documentation for
distinct() and the general rule is the
same: normally you won’t want extra columns playing a part in the result, so
clear out the ordering, or at least make sure it’s restricted only to those
fields you also select in a values() call.


You might reasonably ask why Django doesn’t remove the extraneous columns
for you. The main reason is consistency with distinct() and other
places: Django never removes ordering constraints that you have
specified (and we can’t change those other methods’ behavior, as that
would violate our API 的稳定性 policy).

Aggregating annotations¶

You can also generate an aggregate on the result of an annotation. When you
define an aggregate() clause, the aggregates you provide can reference
any alias defined as part of an annotate() clause in the query.

For example, if you wanted to calculate the average number of authors per
book you first annotate the set of books with the author count, then
aggregate that author count, referencing the annotation field:

>>> from django.db.models import Avg, Count
>>> Book.objects.annotate(num_authors=Count('authors')).aggregate(Avg('num_authors'))
{'num_authors__avg': 1.66}