The simplest way to retrieve objects from a table is to get all of them. To do this, use the all() method on a Manager:

>>> all_entries = Entry.objects.all()

The all() method returns a QuerySet of all the objects in the database.

The QuerySet returned by all() describes all objects in the database table. Usually, though, you'll need to select only a subset of the complete set of objects.

To create such a subset, you refine the initial QuerySet, adding filter conditions. The two most common ways to refine a QuerySet are:

The lookup parameters (**kwargs in the above function definitions) should be in the format described in Field lookups later in this chapter.

>>> Entry.objects.filter(
...     headline__startswith='What'
... ).exclude(
...     pub_date__gte=datetime.date.today()
... ).filter(pub_date__gte=datetime(2005, 1, 30)
... )

Each time you refine a QuerySet, you get a brand-new QuerySet that is in no way bound to the previous QuerySet. Each refinement creates a separate and distinct QuerySet that can be stored, used, and reused.

Example:

>>> q1 = Entry.objects.filter(headline__startswith="What")
>>> q2 = q1.exclude(pub_date__gte=datetime.date.today())
>>> q3 = q1.filter(pub_date__gte=datetime.date.today())

These three QuerySets are separate. The first is a base QuerySet containing all entries that contain a headline starting with What. The second is a subset of the first, with an additional criterion that excludes records whose pub_date is today or in the future. The third is a subset of the first, with an additional criterion that selects only the records whose pub_date is today or in the future. The initial QuerySet (q1) is unaffected by the refinement process.

>>> q = Entry.objects.filter(headline__startswith="What")
>>> q = q.filter(pub_date__lte=datetime.date.today())
>>> q = q.exclude(body_text__icontains="food")
>>> print(q)

filter() will always give you a QuerySet, even if only a single object matches the query-in this case, it will be a QuerySet containing a single element.

If you know there is only one object that matches your query, you can use the get() method on a Manager which returns the object directly:

>>> one_entry = Entry.objects.get(pk=1)

You can use any query expression with get(), just like with filter()-again, see Field lookups in the next section of this chapter.

Note that there is a difference between using get(), and using filter() with a slice of [0]. If there are no results that match the query, get() will raise a DoesNotExist exception. This exception is an attribute of the model class that the query is being performed on-so in the code above, if there is no Entry object with a primary key of 1, Django will raise Entry.DoesNotExist.

Similarly, Django will complain if more than one item matches the get() query. In this case, it will raise MultipleObjectsReturned, which again is an attribute of the model class itself.

Most of the time you'll use all(), get(), filter(), and exclude() when you need to look up objects from the database. However, that's far from all there is; see the QuerySet API Reference at https://docs.djangoproject.com/en/1.8/ref/models/querysets/, for a complete list of all the various QuerySet methods.

Use a subset of Python's array-slicing syntax to limit your QuerySet to a certain number of results. This is the equivalent of SQL's LIMIT and OFFSET clauses.

For example, this returns the first 5 objects (LIMIT 5):

>>> Entry.objects.all()[:5]

This returns the sixth through tenth objects (OFFSET 5 LIMIT 5):

>>> Entry.objects.all()[5:10]

Negative indexing (that is, Entry.objects.all()[-1]) is not supported.

Generally, slicing a QuerySet returns a new QuerySet-it doesn't evaluate the query. An exception is if you use the step parameter of Python slice syntax. For example, this would actually execute the query in order to return a list of every second object of the first 10:

>>> Entry.objects.all()[:10:2]

To retrieve a single object rather than a list (for example, SELECT foo FROM bar LIMIT 1), use a simple index instead of a slice.

For example, this returns the first Entry in the database, after ordering entries alphabetically by headline:

>>> Entry.objects.order_by('headline')[0]

This is roughly equivalent to:

>>> Entry.objects.order_by('headline')[0:1].get()

Note, however, that the first of these will raise IndexError while the second will raise DoesNotExist if no objects match the given criteria. See get() for more details.

Field lookups are how you specify the meat of an SQL WHERE clause. They're specified as keyword arguments to the QuerySet methods filter(), exclude(), and get(). Basic lookups keyword arguments take the form field__lookuptype=value. (That's a double-underscore). For example:

>>> Entry.objects.filter(pub_date__lte='2006-01-01')

translates (roughly) into the following SQL:

SELECT * FROM blog_entry WHERE pub_date <= '2006-01-01';

The field specified in a lookup has to be the name of a model field. There's one exception though, in case of a ForeignKey you can specify the field name suffixed with _id. In this case, the value parameter is expected to contain the raw value of the foreign model's primary key. For example:

>>> Entry.objects.filter(blog_id=4)

If you pass an invalid keyword argument, a lookup function will raise TypeError.

The complete list of field lookups are:

A complete reference, including examples for each field lookup can be found in the field lookup reference at https://docs.djangoproject.com/en/1.8/ref/models/querysets/#field-lookups.

Django offers a powerful and intuitive way to follow relationships in lookups, taking care of the SQL JOINs for you automatically, behind the scenes. To span a relationship, just use the field name of related fields across models, separated by double underscores, until you get to the field you want.

This example retrieves all Entry objects with a Blog whose name is 'Beatles Blog':

>>> Entry.objects.filter(blog__name='Beatles Blog')

This spanning can be as deep as you'd like.

It works backwards, too. To refer to a reverse relationship, just use the lowercase name of the model.

This example retrieves all Blog objects which have at least one Entry whose headline contains 'Lennon':

>>> Blog.objects.filter(entry__headline__contains='Lennon')

If you are filtering across multiple relationships and one of the intermediate models doesn't have a value that meets the filter condition, Django will treat it as if there is an empty (all values are NULL), but valid, object there. All this means is that no error will be raised. For example, in this filter:

Blog.objects.filter(entry__authors__name='Lennon') 

(if there was a related Author model), if there was no author associated with an entry, it would be treated as if there was also no name attached, rather than raising an error because of the missing author. Usually this is exactly what you want to have happen. The only case where it might be confusing is if you are using isnull. Thus:

Blog.objects.filter(entry__authors__name__isnull=True) 

will return Blog objects that have an empty name on the author and also those which have an empty author on the entry. If you don't want those latter objects, you could write:

Blog.objects.filter(entry__authors__isnull=False, 
        entry__authors__name__isnull=True) 

Blog.objects.filter(entry__headline__contains='Lennon',
        entry__pub_date__year=2008) 

Blog.objects.filter(entry__headline__contains='Lennon').filter(
        entry__pub_date__year=2008) 

In the examples given so far, we have constructed filters that compare the value of a model field with a constant. But what if you want to compare the value of a model field with another field on the same model?

Django provides F expressions to allow such comparisons. Instances of F() act as a reference to a model field within a query. These references can then be used in query filters to compare the values of two different fields on the same model instance.

For example, to find a list of all blog entries that have had more comments than pingbacks, we construct an F() object to reference the pingback count, and use that F() object in the query:

>>> from django.db.models import F
>>> Entry.objects.filter(n_comments__gt=F('n_pingbacks'))

Django supports the use of addition, subtraction, multiplication, division, modulo, and power arithmetic with F() objects, both with constants and with other F() objects. To find all the blog entries with more than twice as many comments as pingbacks, we modify the query:

>>> Entry.objects.filter(n_comments__gt=F('n_pingbacks') * 2)

To find all the entries where the rating of the entry is less than the sum of the pingback count and comment count, we would issue the query:

>>> Entry.objects.filter(rating__lt=F('n_comments') + F('n_pingbacks'))

You can also use the double underscore notation to span relationships in an F() object. An F() object with a double underscore will introduce any joins needed to access the related object.

For example, to retrieve all the entries where the author's name is the same as the blog name, we could issue the query:

>>> Entry.objects.filter(authors__name=F('blog__name'))

For date and date/time fields, you can add or subtract a timedelta object. The following would return all entries that were modified more than 3 days after they were published:

>>> from datetime import timedelta
>>> Entry.objects.filter(mod_date__gt=F('pub_date') + timedelta(days=3))

The F() objects support bitwise operations by .bitand() and .bitor(), for example:

>>> F('somefield').bitand(16)

For convenience, Django provides a pk lookup shortcut, which stands for primary key.

In the example Blog model, the primary key is the id field, so these three statements are equivalent:

>>> Blog.objects.get(id__exact=14) # Explicit form
>>> Blog.objects.get(id=14) # __exact is implied
>>> Blog.objects.get(pk=14) # pk implies id__exact

The use of pk isn't limited to __exact queries-any query term can be combined with pk to perform a query on the primary key of a model:

# Get blogs entries with id 1, 4 and 7
>>> Blog.objects.filter(pk__in=[1,4,7])
# Get all blog entries with id > 14
>>> Blog.objects.filter(pk__gt=14)

pk lookups also work across joins. For example, these three statements are equivalent:

>>> Entry.objects.filter(blog__id__exact=3) # Explicit form
>>> Entry.objects.filter(blog__id=3)        # __exact is implied
>>> Entry.objects.filter(blog__pk=3)        # __pk implies __id__exact

The field lookups that equate to LIKE SQL statements (iexact, contains, icontains, startswith, istartswith, endswith, and iendswith) will automatically escape the two special characters used in LIKE statements-the percent sign and the underscore. (In a LIKE statement, the percent sign signifies a multiple-character wildcard and the underscore signifies a single-character wildcard.)

This means things should work intuitively, so the abstraction doesn't leak. For example, to retrieve all the entries that contain a percent sign, just use the percent sign as any other character:

>>> Entry.objects.filter(headline__contains='%')

Django takes care of the quoting for you; the resulting SQL will look something like this:

SELECT ... WHERE headline LIKE '%\%%';

Same goes for underscores. Both percentage signs and underscores are handled for you transparently.

Each QuerySet contains a cache to minimize database access. Understanding how it works will allow you to write the most efficient code.

In a newly created QuerySet, the cache is empty. The first time a QuerySet is evaluated-and, hence, a database query happens-Django saves the query results in the QuerySet class' cache and returns the results that have been explicitly requested (for example, the next element, if the QuerySet is being iterated over). Subsequent evaluations of the QuerySet reuse the cached results.

Keep this caching behavior in mind, because it may bite you if you don't use your QuerySet correctly. For example, the following will create two QuerySet, evaluate them, and throw them away:

>>> print([e.headline for e in Entry.objects.all()])
>>> print([e.pub_date for e in Entry.objects.all()])

That means the same database query will be executed twice, effectively doubling your database load. Also, there's a possibility the two lists may not include the same database records, because an Entry may have been added or deleted in the split second between the two requests.

To avoid this problem, simply save the QuerySet and reuse it:

>>> queryset = Entry.objects.all()
>>> print([p.headline for p in queryset]) # Evaluate the query set.
>>> print([p.pub_date for p in queryset]) # Re-use the cache from the evaluation.

>>> queryset = Entry.objects.all()
>>> print queryset[5] # Queries the database
>>> print queryset[5] # Queries the database again

>>> queryset = Entry.objects.all()
>>> [entry for entry in queryset] # Queries the database
>>> print queryset[5] # Uses cache
>>> print queryset[5] # Uses cache

>>> [entry for entry in queryset]
>>> bool(queryset)
>>> entry in queryset
>>> list(queryset)