===========

Aggregation

===========

.. versionadded:: 1.1

.. currentmodule:: django.db.models

The topic guide on :doc:`Django's database-abstraction API </topics/db/queries>`

described the way that you can use Django queries that create,

retrieve, update and delete individual objects. However, sometimes you will

need to retrieve values that are derived by summarizing or *aggregating* a

collection of objects. This topic guide describes the ways that aggregate values

can be generated and returned using Django queries.

Throughout this guide, we'll refer to the following models. These models are

used to track the inventory for a series of online bookstores:

.. _queryset-model-example:

.. code-block:: python

    class Author(models.Model):

       name = models.CharField(max_length=100)

       age = models.IntegerField()

       friends = models.ManyToManyField('self', blank=True)

    class Publisher(models.Model):

       name = models.CharField(max_length=300)

       num_awards = models.IntegerField()

    class Book(models.Model):

       isbn = models.CharField(max_length=9)

       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)

       pubdate = models.DateField()

    class Store(models.Model):

       name = models.CharField(max_length=300)

       books = models.ManyToManyField(Book)

Generating aggregates over a QuerySet

=====================================

Django provides two ways to generate aggregates. The first way is to generate

summary values over an entire ``QuerySet``. For example, say you wanted to

calculate the average price of all books available for sale. Django's query

syntax provides a means for describing the set of all books::

    >>> Book.objects.all()

What we need is a way to calculate summary values over the objects that

belong to this ``QuerySet``. This is done by appending an ``aggregate()``

clause onto the ``QuerySet``::

    >>> 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 :ref:`QuerySet reference <aggregation-functions>`.

``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, Count

    >>> 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

    >>> 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

    2

    # Interrogate the second object in the queryset

    >>> q[1]

    <Book: Practical Django Projects>

    >>> q[1].authors__count

    1

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

    2

    >>> q[1].num_authors

    1

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()``.

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 :ref:`double underscore notation

<field-lookups-intro>` 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::

    >>> 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 a store, 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'))

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::

    >>> 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.

Order of ``annotate()`` and ``filter()`` clauses

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

When developing a complex query that involves both ``annotate()`` and

``filter()`` clauses, particular attention should be paid 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 -- that is, there is a

difference between the query::

    >>> Publisher.objects.annotate(num_books=Count('book')).filter(book__rating__gt=3.0)

and the query::

    >>> Publisher.objects.filter(book__rating__gt=3.0).annotate(num_books=Count('book'))

Both queries will return a list of Publishers that have at least one good

book (i.e., a book with a rating exceeding 3.0). However, the annotation in

the first query will provide the total number of all books published by the

publisher; the second query will only include good books in the annotated

count. In the first query, the annotation precedes the filter, so the

filter has no effect on the annotation. In the second query, the filter

preceeds the annotation, and as a result, the filter constrains the objects

considered when calculating the annotation.

``order_by()``

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

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')

``values()``

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

Ordinarily, annotations are generated on a per-object basis - an annotated

``QuerySet`` will return one result for each object in the original

``QuerySet``. However, when a ``values()`` clause is used to constrain the

columns that are returned in the result set, the method for evaluating

annotations is slightly different. Instead of returning an annotated result

for each result in the original ``QuerySet``, the original results are

grouped according to the unique combinations of the fields specified in the

``values()`` clause. An annotation is then provided for each unique group;

the annotation is computed over all members of the group.

For example, consider an author query that attempts to find out the average

rating of books written by each author:

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

This will return one result for each author in the database, annotated with

their average book rating.

However, the result will be slightly different if you use a ``values()`` clause::

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

In this example, the authors will be grouped by name, so you will only get

an annotated result for each *unique* author name. This means if you have

two authors with the same name, their results will be merged into a single

result in the output of the query; the average will be computed as the

average over the books written by both authors.

Order of ``annotate()`` and ``values()`` clauses

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

As with the ``filter()`` clause, the order in which ``annotate()`` and

``values()`` clauses are applied to a query is significant. If the

``values()`` clause precedes the ``annotate()``, the annotation will be

computed using the grouping described by the ``values()`` clause.

However, if the ``annotate()`` clause precedes the ``values()`` clause,

the annotations will be generated over the entire query set. In this case,

the ``values()`` clause only constrains the fields that are generated on

output.

For example, if we reverse the order of the ``values()`` and ``annotate()``

clause from our previous example::

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

This will now yield one unique result for each author; however, only

the author's name and the ``average_rating`` annotation will be returned

in the output data.

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::

    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!

    Item.objects.values("data").annotate(Count("id"))

...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::

    Item.objects.values("data").annotate(Count("id")).order_by()

...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

query.

This behavior is the same as that noted in the queryset documentation for

:meth:`~django.db.models.QuerySet.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.

.. note::

    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 :doc:`/misc/api-stability` 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::

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

    {'num_authors__avg': 1.66}