# Django 5.2.4 documentation

# 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.

Note

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)

### `Case`

class Case(*cases, **extra)

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%')]>

Note

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
    )