Conditional expressions let you use if … elif …\nelse logic within filters, annotations, aggregations, and updates. A\nconditional expression evaluates a series of conditions for each row of a\ntable and returns the matching result expression. Conditional expressions can\nalso be combined and nested like other expressions.
Nós estaremos usando o seguinte modelo nos exemplos subsequentes:
\nfrom django.db import models\n\nclass Client(models.Model):\n REGULAR = 'R'\n GOLD = 'G'\n PLATINUM = 'P'\n ACCOUNT_TYPE_CHOICES = (\n (REGULAR, 'Regular'),\n (GOLD, 'Gold'),\n (PLATINUM, 'Platinum'),\n )\n name = models.CharField(max_length=50)\n registered_on = models.DateField()\n account_type = models.CharField(\n max_length=1,\n choices=ACCOUNT_TYPE_CHOICES,\n default=REGULAR,\n )\nWhenLink para este cabeçalho#O objeto When() é usado para encapsular uma condição e seu resultado para uso em uma expressão condicional. Usar o objecto When() é similar ao uso do método filter(). A condição pode ser especificada usando field lookups ou objetos Q. O resultado é retornado usando a palavra-chave then.
Alguns exemplos:
\n>>> from django.db.models import F, Q, When\n>>> # String arguments refer to fields; the following two examples are equivalent:\n>>> When(account_type=Client.GOLD, then='name')\n>>> When(account_type=Client.GOLD, then=F('name'))\n>>> # You can use field lookups in the condition\n>>> from datetime import date\n>>> When(registered_on__gt=date(2014, 1, 1),\n... registered_on__lt=date(2015, 1, 1),\n... then='account_type')\n>>> # Complex conditions can be created using Q objects\n>>> When(Q(name__startswith="John") | Q(name__startswith="Paul"),\n... then='name')\nTenha em mente que cada um desses valores pode ser uma expressão.
\n\nCaseLink para este cabeçalho#A Case() expression is like the if … elif …\nelse statement in Python. Each condition in the provided\nWhen() objects is evaluated in order, until one evaluates to a\ntruthful value. The result expression from the matching When() object\nis returned.
Um exemplo simples:
\n>>>\n>>> from datetime import date, timedelta\n>>> from django.db.models import Case, CharField, Value, When\n>>> Client.objects.create(\n... name='Jane Doe',\n... account_type=Client.REGULAR,\n... registered_on=date.today() - timedelta(days=36))\n>>> Client.objects.create(\n... name='James Smith',\n... account_type=Client.GOLD,\n... registered_on=date.today() - timedelta(days=5))\n>>> Client.objects.create(\n... name='Jack Black',\n... account_type=Client.PLATINUM,\n... registered_on=date.today() - timedelta(days=10 * 365))\n>>> # Get the discount for each Client based on the account type\n>>> Client.objects.annotate(\n... discount=Case(\n... When(account_type=Client.GOLD, then=Value('5%')),\n... When(account_type=Client.PLATINUM, then=Value('10%')),\n... default=Value('0%'),\n... output_field=CharField(),\n... ),\n... ).values_list('name', 'discount')\n<QuerySet [('Jane Doe', '0%'), ('James Smith', '5%'), ('Jack Black', '10%')]>\nCase() accepts any number of When() objects as individual arguments.\nOther options are provided using keyword arguments. If none of the conditions\nevaluate to TRUE, then the expression given with the default keyword\nargument is returned. If a default argument isn’t provided, None is\nused.
If we wanted to change our previous query to get the discount based on how long\nthe Client has been with us, we could do so using lookups:
>>> a_month_ago = date.today() - timedelta(days=30)\n>>> a_year_ago = date.today() - timedelta(days=365)\n>>> # Get the discount for each Client based on the registration date\n>>> Client.objects.annotate(\n... discount=Case(\n... When(registered_on__lte=a_year_ago, then=Value('10%')),\n... When(registered_on__lte=a_month_ago, then=Value('5%')),\n... default=Value('0%'),\n... output_field=CharField(),\n... )\n... ).values_list('name', 'discount')\n<QuerySet [('Jane Doe', '5%'), ('James Smith', '0%'), ('Jack Black', '10%')]>\nCase() also works in a filter() clause. For example, to find gold\nclients that registered more than a month ago and platinum clients that\nregistered more than a year ago:
>>> a_month_ago = date.today() - timedelta(days=30)\n>>> a_year_ago = date.today() - timedelta(days=365)\n>>> Client.objects.filter(\n... registered_on__lte=Case(\n... When(account_type=Client.GOLD, then=a_month_ago),\n... When(account_type=Client.PLATINUM, then=a_year_ago),\n... ),\n... ).values_list('name', 'account_type')\n<QuerySet [('Jack Black', 'P')]>\nExpressões condicionais podem ser usadas em anotações, agregações, consultas e atualizações. Eles também podem ser combinados e aninhados com outras expressões. Isso permite que você faça consultas condicionais poderosas.
\nDigamos que queremos mudar o account_type dos nossos clientes que correspondam às datas de registro. Podemos fazer isso usando uma expressão condicional e o método update():
>>> a_month_ago = date.today() - timedelta(days=30)\n>>> a_year_ago = date.today() - timedelta(days=365)\n>>> # Update the account_type for each Client from the registration date\n>>> Client.objects.update(\n... account_type=Case(\n... When(registered_on__lte=a_year_ago,\n... then=Value(Client.PLATINUM)),\n... When(registered_on__lte=a_month_ago,\n... then=Value(Client.GOLD)),\n... default=Value(Client.REGULAR)\n... ),\n... )\n>>> Client.objects.values_list('name', 'account_type')\n<QuerySet [('Jane Doe', 'G'), ('James Smith', 'R'), ('Jack Black', 'P')]>\nWhat if we want to find out how many clients there are for each\naccount_type? We can use the filter argument of aggregate\nfunctions to achieve this:
>>> # Create some more Clients first so we can have something to count\n>>> Client.objects.create(\n... name='Jean Grey',\n... account_type=Client.REGULAR,\n... registered_on=date.today())\n>>> Client.objects.create(\n... name='James Bond',\n... account_type=Client.PLATINUM,\n... registered_on=date.today())\n>>> Client.objects.create(\n... name='Jane Porter',\n... account_type=Client.PLATINUM,\n... registered_on=date.today())\n>>> # Get counts for each value of account_type\n>>> from django.db.models import Count\n>>> Client.objects.aggregate(\n... regular=Count('pk', filter=Q(account_type=Client.REGULAR)),\n... gold=Count('pk', filter=Q(account_type=Client.GOLD)),\n... platinum=Count('pk', filter=Q(account_type=Client.PLATINUM)),\n... )\n{'regular': 2, 'gold': 1, 'platinum': 3}\nThis aggregate produces a query with the SQL 2003 FILTER WHERE syntax\non databases that support it:
SELECT count('id') FILTER (WHERE account_type=1) as regular,\n count('id') FILTER (WHERE account_type=2) as gold,\n count('id') FILTER (WHERE account_type=3) as platinum\nFROM clients;\nOn other databases, this is emulated using a CASE statement:
SELECT count(CASE WHEN account_type=1 THEN id ELSE null) as regular,\n count(CASE WHEN account_type=2 THEN id ELSE null) as gold,\n count(CASE WHEN account_type=3 THEN id ELSE null) as platinum\nFROM clients;\nThe two SQL statements are functionally equivalent but the more explicit\nFILTER may perform better.