Conditional Expressions ¶

Conditional expressions let you use if … elif … else logic within filters, annotations, aggregations, and updates. A conditional expression evaluates a series of conditions for each row of a table and returns the matching result expression. Conditional expressions can also be combined and nested like other expressions .
The conditional expression classes ¶

We’ll be using the following model in the subsequent examples:
from django.db import models
class Client(models.Model):
    REGULAR = "R"
    GOLD = "G"
    PLATINUM = "P"
    ACCOUNT_TYPE_CHOICES = {
        REGULAR: "Regular",
        GOLD: "Gold",
        PLATINUM: "Platinum",
    name = models.CharField(max_length=50)
    registered_on = models.DateField()
    account_type = models.CharField(
        max_length=1,
        choices=ACCOUNT_TYPE_CHOICES,
        default=REGULAR,
When¶
class When(condition=None, then=None, **lookups)¶
A When() object is used to encapsulate a condition and its result for use
in the conditional expression. Using a When() object is similar to using
the filter() method. The condition can
be specified using field lookups,
Q objects, or Expression
objects that have an output_field that is a
BooleanField. The result is provided using the
then keyword.
Some examples:
>>> from django.db.models import F, Q, When
>>> # String arguments refer to fields; the following two examples are equivalent:
>>> When(account_type=Client.GOLD, then="name")
>>> When(account_type=Client.GOLD, then=F("name"))
>>> # You can use field lookups in the condition
>>> from datetime import date
>>> When(
...     registered_on__gt=date(2014, 1, 1),
...     registered_on__lt=date(2015, 1, 1),
...     then="account_type",
... )
>>> # Complex conditions can be created using Q objects
>>> When(Q(name__startswith="John") | Q(name__startswith="Paul"), then="name")
>>> # Condition can be created using boolean expressions.
>>> from django.db.models import Exists, OuterRef
>>> non_unique_account_type = (
...     Client.objects.filter(
...         account_type=OuterRef("account_type"),
...     )
...     .exclude(pk=OuterRef("pk"))
...     .values("pk")
... )
>>> When(Exists(non_unique_account_type), then=Value("non unique"))
>>> # Condition can be created using lookup expressions.
>>> from django.db.models.lookups import GreaterThan, LessThan
>>> When(
...     GreaterThan(F("registered_on"), date(2014, 1, 1))
...     & LessThan(F("registered_on"), date(2015, 1, 1)),
...     then="account_type",
... )
Keep in mind that each of these values can be an expression.
Since the then keyword argument is reserved for the result of the
When(), there is a potential conflict if a
Model has a field named then. This can be
resolved in two ways:
>>> When(then__exact=0, then=1)
>>> When(Q(then=0), then=1)
A Case() expression is like the if … elif …
else statement in Python. Each condition in the provided
When() objects is evaluated in order, until one evaluates to a
truthful value. The result expression from the matching When() object
is returned.
An example:
>>> from datetime import date, timedelta
>>> from django.db.models import Case, Value, When
>>> Client.objects.create(
...     name="Jane Doe",
...     account_type=Client.REGULAR,
...     registered_on=date.today() - timedelta(days=36),
... )
>>> Client.objects.create(
...     name="James Smith",
...     account_type=Client.GOLD,
...     registered_on=date.today() - timedelta(days=5),
... )
>>> Client.objects.create(
...     name="Jack Black",
...     account_type=Client.PLATINUM,
...     registered_on=date.today() - timedelta(days=10 * 365),
... )
>>> # Get the discount for each Client based on the account type
>>> Client.objects.annotate(
...     discount=Case(
...         When(account_type=Client.GOLD, then=Value("5%")),
...         When(account_type=Client.PLATINUM, then=Value("10%")),
...         default=Value("0%"),
...     ),
... ).values_list("name", "discount")
<QuerySet [('Jane Doe', '0%'), ('James Smith', '5%'), ('Jack Black', '10%')]>
Case() accepts any number of When() objects as individual arguments.
Other options are provided using keyword arguments. If none of the conditions
evaluate to TRUE, then the expression given with the default keyword
argument is returned. If a default argument isn’t provided, None is
used.




    

If we wanted to change our previous query to get the discount based on how long
the Client has been with us, we could do so using lookups:
>>> a_month_ago = date.today() - timedelta(days=30)
>>> a_year_ago = date.today() - timedelta(days=365)
>>> # Get the discount for each Client based on the registration date
>>> Client.objects.annotate(
...     discount=Case(
...         When(registered_on__lte=a_year_ago, then=Value("10%")),
...         When(registered_on__lte=a_month_ago, then=Value("5%")),
...         default=Value("0%"),
...     )
... ).values_list("name", "discount")
<QuerySet [('Jane Doe', '5%'), ('James Smith', '0%'), ('Jack Black', '10%')]>
Remember that the conditions are evaluated in order, so in the above
example we get the correct result even though the second condition matches
both Jane Doe and Jack Black. This works just like an if …
elif … else statement in Python.
Case() also works in a filter() clause. For example, to find gold
clients that registered more than a month ago and platinum clients that
registered more than a year ago:
>>> a_month_ago = date.today() - timedelta(days=30)
>>> a_year_ago = date.today() - timedelta(days=365)
>>> Client.objects.filter(
...     registered_on__lte=Case(
...         When(account_type=Client.GOLD, then=a_month_ago),
...         When(account_type=Client.PLATINUM, then=a_year_ago),
...     ),
... ).values_list("name", "account_type")
<QuerySet [('Jack Black', 'P')]>
Advanced queries¶
Conditional expressions can be used in annotations, aggregations, filters,
lookups, and updates. They can also be combined and nested with other
expressions. This allows you to make powerful conditional queries.
Conditional update¶
Let’s say we want to change the account_type for our clients to match
their registration dates. We can do this using a conditional expression and the
update() method:
>>> a_month_ago = date.today() - timedelta(days=30)
>>> a_year_ago = date.today() - timedelta(days=365)
>>> # Update the account_type for each Client from the registration date
>>> Client.objects.update(
...     account_type=Case(
...         When(registered_on__lte=a_year_ago, then=Value(Client.PLATINUM)),
...         When(registered_on__lte=a_month_ago, then=Value(Client.GOLD)),
...         default=Value(Client.REGULAR),
...     ),
... )
>>> Client.objects.values_list("name", "account_type")
<QuerySet [('Jane Doe', 'G'), ('James Smith', 'R'), ('Jack Black', 'P')]>
Conditional aggregation¶
What if we want to find out how many clients there are for each
account_type? We can use the filter argument of aggregate
functions to achieve this:
>>> # Create some more Clients first so we can have something to count
>>> Client.objects.create(
...     name="Jean Grey", account_type=Client.REGULAR, registered_on=date.today()
... )
>>> Client.objects.create(
...     name="James Bond", account_type=Client.PLATINUM, registered_on=date.today()
... )
>>> Client.objects.create(
...     name="Jane Porter", account_type=Client.PLATINUM, registered_on=date.today()
... )
>>> # Get counts for each value of account_type
>>> from django.db.models import Count
>>> Client.objects.aggregate(
...     regular=Count("pk", filter=Q(account_type=Client.REGULAR)),
...     gold=Count("pk", filter=Q(account_type=Client.GOLD)),
...     platinum=Count("pk", filter=Q(account_type=Client.PLATINUM)),
... )
{'regular': 2, 'gold': 1, 'platinum': 3}
This aggregate produces a query with the SQL 2003 FILTER WHERE syntax
on databases that support it:
SELECT count('id') FILTER (WHERE account_type=1) as regular,
       count('id') FILTER (WHERE account_type=2) as gold,
       count('id') FILTER (WHERE account_type=3) as platinum
FROM clients;
On other databases, this is emulated using a CASE statement:
SELECT count(CASE WHEN account_type=1 THEN id ELSE null) as regular,
       count(CASE WHEN account_type=2 THEN id ELSE null) as gold,
       count(CASE WHEN account_type=3 THEN id ELSE null) as platinum
FROM clients;
The two SQL statements are functionally equivalent but the more explicit
FILTER may perform better.
Conditional filter¶
When a conditional expression returns a boolean value, it is possible to use it
directly in filters. This means that it will not be added to the SELECT
columns, but you can still use it to filter results:
>>> non_unique_account_type = (
...     Client.objects.filter(
...         account_type=OuterRef("account_type"),
...     )
...     .exclude(pk=OuterRef("pk"))
...     .values("pk")
... )
>>> Client.objects.filter(~Exists(non_unique_account_type))
In SQL terms, that evaluates to:
SELECT ...
FROM client c0
WHERE NOT EXISTS (
  SELECT c1.id
  FROM client c1
  WHERE c1.account_type = c0.account_type AND NOT c1.id = c0.id
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