QuerySet API reference

QuerySet API reference

`QuerySet` API reference

This document describes the details of the QuerySet API. It builds on the material presented in the model and database query guides, so you’ll probably want to read and understand those documents before reading this one.

Throughout this reference we’ll use the example blog models presented in the database query guide.

When `QuerySet`s are evaluated

Internally, a QuerySet can be constructed, filtered, sliced, and generally passed around without actually hitting the database. No database activity actually occurs until you do something to evaluate the queryset.

You can evaluate a QuerySet in the following ways:

Iteration. A QuerySet is iterable, and it executes its database query the first time you iterate over it. For example, this will print the headline of all entries in the database:

for e in Entry.objects.all():
    print(e.headline)

Note: Don’t use this if all you want to do is determine if at least one result exists. It’s more efficient to use exists().

Asynchronous iteration. A QuerySet can also be iterated over using async for:

async for e in Entry.objects.all():
    results.append(e)

Both synchronous and asynchronous iterators of QuerySets share the same underlying cache.

Slicing. As explained in Limiting QuerySets, a QuerySet can be sliced, using Python’s array-slicing syntax. Slicing an unevaluated QuerySet usually returns another unevaluated QuerySet, but Django will execute the database query if you use the “step” parameter of slice syntax, and will return a list. Slicing a QuerySet that has been evaluated also returns a list.

Also note that even though slicing an unevaluated QuerySet returns another unevaluated QuerySet, modifying it further (e.g., adding more filters, or modifying ordering) is not allowed, since that does not translate well into SQL and it would not have a clear meaning either.

Pickling/Caching. See the following section for details of what is involved when pickling QuerySets. The important thing for the purposes of this section is that the results are read from the database.
repr(). A QuerySet is evaluated when you call repr() on it. This is for convenience in the Python interactive interpreter, so you can immediately see your results when using the API interactively.
len(). A QuerySet is evaluated when you call len() on it. This, as you might expect, returns the length of the result list.

Note: If you only need to determine the number of records in the set (and don’t need the actual objects), it’s much more efficient to handle a count at the database level using SQL’s SELECT COUNT(*). Django provides a count() method for precisely this reason.

list(). Force evaluation of a QuerySet by calling list() on it. For example:

entry_list = list(Entry.objects.all())

bool(). Testing a QuerySet in a boolean context, such as using bool(), or, and or an if statement, will cause the query to be executed. If there is at least one result, the QuerySet is True, otherwise False. For example:

if Entry.objects.filter(headline="Test"):
    print("There is at least one Entry with the headline Test")

Note: If you only want to determine if at least one result exists (and don’t need the actual objects), it’s more efficient to use exists().

Pickling `QuerySet`s

If you pickle a QuerySet, this will force all the results to be loaded into memory prior to pickling. Pickling is usually used as a precursor to caching and when the cached queryset is reloaded, you want the results to already be present and ready for use (reading from the database can take some time, defeating the purpose of caching). This means that when you unpickle a QuerySet, it contains the results at the moment it was pickled, rather than the results that are currently in the database.

If you only want to pickle the necessary information to recreate the QuerySet from the database at a later time, pickle the query attribute of the QuerySet. You can then recreate the original QuerySet (without any results loaded) using some code like this:

>>> import pickle
>>> query = pickle.loads(s)  # Assuming 's' is the pickled string.
>>> qs = MyModel.objects.all()
>>> qs.query = query  # Restore the original 'query'.

The query attribute is an opaque object. It represents the internals of the query construction and is not part of the public API. However, it is safe (and fully supported) to pickle and unpickle the attribute’s contents as described here.

Restrictions on QuerySet.values_list()

If you recreate QuerySet.values_list() using the pickled query attribute, it will be converted to QuerySet.values():

>>> import pickle
>>> qs = Blog.objects.values_list("id", "name")
>>> qs
<QuerySet [(1, 'Beatles Blog')]>
>>> reloaded_qs = Blog.objects.all()
>>> reloaded_qs.query = pickle.loads(pickle.dumps(qs.query))
>>> reloaded_qs
<QuerySet [{'id': 1, 'name': 'Beatles Blog'}]>

You can’t share pickles between versions

Pickles of QuerySets are only valid for the version of Django that was used to generate them. If you generate a pickle using Django version N, there is no guarantee that pickle will be readable with Django version N+1. Pickles should not be used as part of a long-term archival strategy.

Since pickle compatibility errors can be difficult to diagnose, such as silently corrupted objects, a RuntimeWarning is raised when you try to unpickle a queryset in a Django version that is different than the one in which it was pickled.

`QuerySet` API

Here’s the formal declaration of a QuerySet:

class QuerySet(model=None , query=None , using=None , hints=None)[source]

Usually when you’ll interact with a QuerySet you’ll use it by chaining filters. To make this work, most QuerySet methods return new querysets. These methods are covered in detail later in this section.

The QuerySet class has the following public attributes you can use for introspection:

ordered[source]

True if the QuerySet is ordered — i.e. has an order_by() clause or a default ordering on the model. False otherwise.

db[source]

The database that will be used if this query is executed now.

Note

The query parameter to QuerySet exists so that specialized query subclasses can reconstruct internal query state. The value of the parameter is an opaque representation of that query state and is not part of a public API.

Methods that return new `QuerySet`s

Django provides a range of QuerySet refinement methods that modify either the types of results returned by the QuerySet or the way its SQL query is executed.

Note

These methods do not run database queries, therefore they are safe to run in asynchronous code , and do not have separate asynchronous versions.

`filter()`

filter(args_ , _*kwargs)

Returns a new QuerySet containing objects that match the given lookup parameters.

The lookup parameters (**kwargs) should be in the format described in Field lookups below. Multiple parameters are joined via AND in the underlying SQL statement.

If you need to execute more complex queries (for example, queries with OR statements), you can use Q objects (*args).

`exclude()`

exclude(args_ , _*kwargs)

Returns a new QuerySet containing objects that do not match the given lookup parameters.

The lookup parameters (**kwargs) should be in the format described in Field lookups below. Multiple parameters are joined via AND in the underlying SQL statement, and the whole thing is enclosed in a NOT().

This example excludes all entries whose pub_date is later than 2005-1-3 AND whose headline is “Hello”:

Entry.objects.exclude(pub_date__gt=datetime.date(2005, 1, 3), headline="Hello")

In SQL terms, that evaluates to:

SELECT ...
WHERE NOT (pub_date > '2005-1-3' AND headline = 'Hello')

This example excludes all entries whose pub_date is later than 2005-1-3 OR whose headline is “Hello”:

Entry.objects.exclude(pub_date__gt=datetime.date(2005, 1, 3)).exclude(headline="Hello")

In SQL terms, that evaluates to:

SELECT ...
WHERE NOT pub_date > '2005-1-3'
AND NOT headline = 'Hello'

Note the second example is more restrictive.

If you need to execute more complex queries (for example, queries with OR statements), you can use Q objects (*args).

`annotate()`

annotate(args_ , _*kwargs)

Annotates each object in the QuerySet with the provided list of query expressions. An expression may be a simple value, a reference to a field on the model (or any related models), or an aggregate expression (averages, sums, etc.) that has been computed over the objects that are related to the objects in the QuerySet.

Each argument to annotate() is an annotation that will be added to each object in the QuerySet that is returned.

The aggregation functions that are provided by Django are described in Aggregation Functions below.

Annotations specified using keyword arguments will use the keyword as the alias for the annotation. Anonymous arguments will have an alias generated for them based upon the name of the aggregate function and the model field that is being aggregated. Only aggregate expressions that reference a single field can be anonymous arguments. Everything else must be a keyword argument.

For example, if you were manipulating a list of blogs, you may want to determine how many entries have been made in each blog:

>>> from django.db.models import Count
>>> q = Blog.objects.annotate(Count("entry"))
## The name of the first blog
>>> q[0].name
'Blogasaurus'
## The number of entries on the first blog
>>> q[0].entry__count
42

The Blog model doesn’t define an entry__count attribute by itself, but by using a keyword argument to specify the aggregate function, you can control the name of the annotation:

>>> q = Blog.objects.annotate(number_of_entries=Count("entry"))
## The number of entries on the first blog, using the name provided
>>> q[0].number_of_entries
42

For an in-depth discussion of aggregation, see the topic guide on Aggregation.

`alias()`

alias(args_ , _*kwargs)

Same as annotate(), but instead of annotating objects in the QuerySet, saves the expression for later reuse with other QuerySet methods. This is useful when the result of the expression itself is not needed but it is used for filtering, ordering, or as a part of a complex expression. Not selecting the unused value removes redundant work from the database which should result in better performance.

For example, if you want to find blogs with more than 5 entries, but are not interested in the exact number of entries, you could do this:

>>> from django.db.models import Count
>>> blogs = Blog.objects.alias(entries=Count("entry")).filter(entries__gt=5)

alias() can be used in conjunction with annotate(), exclude(), filter(), order_by(), and update(). To use aliased expression with other methods (e.g. aggregate()), you must promote it to an annotation:

Blog.objects.alias(entries=Count("entry")).annotate(
    entries=F("entries"),
).aggregate(Sum("entries"))

filter() and order_by() can take expressions directly, but expression construction and usage often does not happen in the same place (for example, QuerySet method creates expressions, for later use in views). alias() allows building complex expressions incrementally, possibly spanning multiple methods and modules, refer to the expression parts by their aliases and only use annotate() for the final result.

`order_by()`

order_by(*fields)

By default, results returned by a QuerySet are ordered by the ordering tuple given by the ordering option in the model’s Meta. You can override this on a per-QuerySet basis by using the order_by method.

Example:

Entry.objects.filter(pub_date__year=2005).order_by("-pub_date", "headline")

The result above will be ordered by pub_date descending, then by headline ascending. The negative sign in front of "-pub_date" indicates descending order. Ascending order is implied. To order randomly, use "?", like so:

Entry.objects.order_by("?")

Note: order_by('?') queries may be expensive and slow, depending on the database backend you’re using.

To order by a field in a different model, use the same syntax as when you are querying across model relations. That is, the name of the field, followed by a double underscore (__), followed by the name of the field in the new model, and so on for as many models as you want to join. For example:

Entry.objects.order_by("blog__name", "headline")

If you try to order by a field that is a relation to another model, Django will use the default ordering on the related model, or order by the related model’s primary key if there is no Meta.ordering specified. For example, since the Blog model has no default ordering specified:

Entry.objects.order_by("blog")

…is identical to:

Entry.objects.order_by("blog__id")

If Blog had ordering = ['name'], then the first queryset would be identical to:

Entry.objects.order_by("blog__name")

You can also order by query expressions by calling asc() or desc() on the expression:

Entry.objects.order_by(Coalesce("summary", "headline").desc())

asc() and desc() have arguments (nulls_first and nulls_last) that control how null values are sorted.

Be cautious when ordering by fields in related models if you are also using distinct(). See the note in distinct() for an explanation of how related model ordering can change the expected results.

Note

It is permissible to specify a multi-valued field to order the results by (for example, a ManyToManyField field, or the reverse relation of a ForeignKey field).

Consider this case:

class Event(Model):
    parent = models.ForeignKey(
        "self",
        on_delete=models.CASCADE,
        related_name="children",
    )
    date = models.DateField()

Event.objects.order_by("children__date")

Here, there could potentially be multiple ordering data for each Event; each Event with multiple children will be returned multiple times into the new QuerySet that order_by() creates. In other words, using order_by() on the QuerySet could return more items than you were working on to begin with - which is probably neither expected nor useful.

Thus, take care when using multi-valued field to order the results. If you can be sure that there will only be one ordering piece of data for each of the items you’re ordering, this approach should not present problems. If not, make sure the results are what you expect.

There’s no way to specify whether ordering should be case sensitive. With respect to case-sensitivity, Django will order results however your database backend normally orders them.

You can order by a field converted to lowercase with Lower which will achieve case-consistent ordering:

Entry.objects.order_by(Lower("headline").desc())

If you don’t want any ordering to be applied to a query, not even the default ordering, call order_by() with no parameters.

You can tell if a query is ordered or not by checking the QuerySet.ordered attribute, which will be True if the QuerySet has been ordered in any way.

Each order_by() call will clear any previous ordering. For example, this query will be ordered by pub_date and not headline:

Entry.objects.order_by("headline").order_by("pub_date")

Warning

Ordering is not a free operation. Each field you add to the ordering incurs a cost to your database. Each foreign key you add will implicitly include all of its default orderings as well.

If a query doesn’t have an ordering specified, results are returned from the database in an unspecified order. A particular ordering is guaranteed only when ordering by a set of fields that uniquely identify each object in the results. For example, if a name field isn’t unique, ordering by it won’t guarantee objects with the same name always appear in the same order.

`reverse()`

reverse()

Use the reverse() method to reverse the order in which a queryset’s elements are returned. Calling reverse() a second time restores the ordering back to the normal direction.

To retrieve the “last” five items in a queryset, you could do this:

my_queryset.reverse()[:5]

Note that this is not quite the same as slicing from the end of a sequence in Python. The above example will return the last item first, then the penultimate item and so on. If we had a Python sequence and looked at seq[-5:], we would see the fifth-last item first. Django doesn’t support that mode of access (slicing from the end), because it’s not possible to do it efficiently in SQL.

Also, note that reverse() should generally only be called on a QuerySet which has a defined ordering (e.g., when querying against a model which defines a default ordering, or when using order_by()). If no such ordering is defined for a given QuerySet, calling reverse() on it has no real effect (the ordering was undefined prior to calling reverse(), and will remain undefined afterward).

`distinct()`

distinct(*fields)

Returns a new QuerySet that uses SELECT DISTINCT in its SQL query. This eliminates duplicate rows from the query results.

By default, a QuerySet will not eliminate duplicate rows. In practice, this is rarely a problem, because simple queries such as Blog.objects.all() don’t introduce the possibility of duplicate result rows. However, if your query spans multiple tables, it’s possible to get duplicate results when a QuerySet is evaluated. That’s when you’d use distinct().

Note

Any fields used in an order_by() call are included in the SQL SELECT columns. This can sometimes lead to unexpected results when used in conjunction with distinct(). If you order by fields from a related model, those fields will be added to the selected columns and they may make otherwise duplicate rows appear to be distinct. Since the extra columns don’t appear in the returned results (they are only there to support ordering), it sometimes looks like non-distinct results are being returned.

Similarly, if you use a values() query to restrict the columns selected, the columns used in any order_by() (or default model ordering) will still be involved and may affect uniqueness of the results.

The moral here is that if you are using distinct() be careful about ordering by related models. Similarly, when using distinct() and values() together, be careful when ordering by fields not in the values() call.

On PostgreSQL only, you can pass positional arguments (*fields) in order to specify the names of fields to which the DISTINCT should apply. This translates to a SELECT DISTINCT ON SQL query. Here’s the difference. For a normal distinct() call, the database compares each field in each row when determining which rows are distinct. For a distinct() call with specified field names, the database will only compare the specified field names.

Note

When you specify field names, you must provide an order_by() in the QuerySet, and the fields in order_by() must start with the fields in distinct(), in the same order.

For example, SELECT DISTINCT ON (a) gives you the first row for each value in column a. If you don’t specify an order, you’ll get some arbitrary row.

Examples (those after the first will only work on PostgreSQL):

>>> Author.objects.distinct()
[...]

>>> Entry.objects.order_by("pub_date").distinct("pub_date")
[...]

>>> Entry.objects.order_by("blog").distinct("blog")
[...]

>>> Entry.objects.order_by("author", "pub_date").distinct("author", "pub_date")
[...]

>>> Entry.objects.order_by("blog__name", "mod_date").distinct("blog__name", "mod_date")
[...]

>>> Entry.objects.order_by("author", "pub_date").distinct("author")
[...]

Note

Keep in mind that order_by() uses any default related model ordering that has been defined. You might have to explicitly order by the relation _id or referenced field to make sure the DISTINCT ON expressions match those at the beginning of the ORDER BY clause. For example, if the Blog model defined an ordering by name:

Entry.objects.order_by("blog").distinct("blog")

…wouldn’t work because the query would be ordered by blog__name thus mismatching the DISTINCT ON expression. You’d have to explicitly order by the relation _id field (blog_id in this case) or the referenced one (blog__pk) to make sure both expressions match.

`values()`

values(fields_ , _*expressions)

Returns a QuerySet that returns dictionaries, rather than model instances, when used as an iterable.

Each of those dictionaries represents an object, with the keys corresponding to the attribute names of model objects.

This example compares the dictionaries of values() with the normal model objects:

## This list contains a Blog object.
>>> Blog.objects.filter(name__startswith="Beatles")
<QuerySet [<Blog: Beatles Blog>]>

## This list contains a dictionary.
>>> Blog.objects.filter(name__startswith="Beatles").values()
<QuerySet [{'id': 1, 'name': 'Beatles Blog', 'tagline': 'All the latest Beatles news.'}]>

The values() method takes optional positional arguments, *fields, which specify field names to which the SELECT should be limited. If you specify the fields, each dictionary will contain only the field keys/values for the fields you specify. If you don’t specify the fields, each dictionary will contain a key and value for every field in the database table.

Example:

>>> Blog.objects.values()
<QuerySet [{'id': 1, 'name': 'Beatles Blog', 'tagline': 'All the latest Beatles news.'}]>
>>> Blog.objects.values("id", "name")
<QuerySet [{'id': 1, 'name': 'Beatles Blog'}]>

The values() method also takes optional keyword arguments, **expressions, which are passed through to annotate():

>>> from django.db.models.functions import Lower
>>> Blog.objects.values(lower_name=Lower("name"))
<QuerySet [{'lower_name': 'beatles blog'}]>

You can use built-in and custom lookups in ordering. For example:

>>> from django.db.models import CharField
>>> from django.db.models.functions import Lower
>>> CharField.register_lookup(Lower)
>>> Blog.objects.values("name__lower")
<QuerySet [{'name__lower': 'beatles blog'}]>

An aggregate within a values() clause is applied before other arguments within the same values() clause. If you need to group by another value, add it to an earlier values() clause instead. For example:

>>> from django.db.models import Count
>>> Blog.objects.values("entry__authors", entries=Count("entry"))
<QuerySet [{'entry__authors': 1, 'entries': 20}, {'entry__authors': 1, 'entries': 13}]>
>>> Blog.objects.values("entry__authors").annotate(entries=Count("entry"))
<QuerySet [{'entry__authors': 1, 'entries': 33}]>

A few subtleties that are worth mentioning:

If you have a field called foo that is a ForeignKey, the default values() call will return a dictionary key called foo_id, since this is the name of the hidden model attribute that stores the actual value (the foo attribute refers to the related model). When you are calling values() and passing in field names, you can pass in either foo or foo_id and you will get back the same thing (the dictionary key will match the field name you passed in).

For example:

>>> Entry.objects.values()
<QuerySet [{'blog_id': 1, 'headline': 'First Entry', ...}, ...]>

>>> Entry.objects.values("blog")
<QuerySet [{'blog': 1}, ...]>

>>> Entry.objects.values("blog_id")
<QuerySet [{'blog_id': 1}, ...]>

When using values() together with distinct(), be aware that ordering can affect the results. See the note in distinct() for details.
If you use a values() clause after an extra() call, any fields defined by a select argument in the extra() must be explicitly included in the values() call. Any extra() call made after a values() call will have its extra selected fields ignored.
Calling only() and defer() after values() doesn’t make sense, so doing so will raise a TypeError.
Combining transforms and aggregates requires the use of two annotate() calls, either explicitly or as keyword arguments to values(). As above, if the transform has been registered on the relevant field type the first annotate() can be omitted, thus the following examples are equivalent:

>>> from django.db.models import CharField, Count
>>> from django.db.models.functions import Lower
>>> CharField.register_lookup(Lower)
>>> Blog.objects.values("entry__authors__name__lower").annotate(entries=Count("entry"))
<QuerySet [{'entry__authors__name__lower': 'test author', 'entries': 33}]>
>>> Blog.objects.values(entry__authors__name__lower=Lower("entry__authors__name")).annotate(
...     entries=Count("entry")
... )
<QuerySet [{'entry__authors__name__lower': 'test author', 'entries': 33}]>
>>> Blog.objects.annotate(entry__authors__name__lower=Lower("entry__authors__name")).values(
...     "entry__authors__name__lower"
... ).annotate(entries=Count("entry"))
<QuerySet [{'entry__authors__name__lower': 'test author', 'entries': 33}]>

It is useful when you know you’re only going to need values from a small number of the available fields and you won’t need the functionality of a model instance object. It’s more efficient to select only the fields you need to use.

Finally, note that you can call filter(), order_by(), etc. after the values() call, that means that these two calls are identical:

Blog.objects.values().order_by("id")
Blog.objects.order_by("id").values()

The people who made Django prefer to put all the SQL-affecting methods first, followed (optionally) by any output-affecting methods (such as values()), but it doesn’t really matter. This is your chance to really flaunt your individualism.

You can also refer to fields on related models with reverse relations through OneToOneField, ForeignKey and ManyToManyField attributes:

>>> Blog.objects.values("name", "entry__headline")
<QuerySet [{'name': 'My blog', 'entry__headline': 'An entry'},
     {'name': 'My blog', 'entry__headline': 'Another entry'}, ...]>

Warning

Because ManyToManyField attributes and reverse relations can have multiple related rows, including these can have a multiplier effect on the size of your result set. This will be especially pronounced if you include multiple such fields in your values() query, in which case all possible combinations will be returned.

Special values for JSONField on SQLite

Due to the way the JSON_EXTRACT and JSON_TYPE SQL functions are implemented on SQLite, and lack of the BOOLEAN data type, values() will return True, False, and None instead of "true", "false", and "null" strings for JSONField key transforms.

`values_list()`

values_list(*fields , flat=False , named=False)

This is similar to values() except that instead of returning dictionaries, it returns tuples when iterated over. Each tuple contains the value from the respective field or expression passed into the values_list() call — so the first item is the first field, etc. For example:

>>> Entry.objects.values_list("id", "headline")
<QuerySet [(1, 'First entry'), ...]>
>>> from django.db.models.functions import Lower
>>> Entry.objects.values_list("id", Lower("headline"))
<QuerySet [(1, 'first entry'), ...]>

If you only pass in a single field, you can also pass in the flat parameter. If True, this will mean the returned results are single values, rather than 1-tuples. An example should make the difference clearer:

>>> Entry.objects.values_list("id").order_by("id")
<QuerySet[(1,), (2,), (3,), ...]>

>>> Entry.objects.values_list("id", flat=True).order_by("id")
<QuerySet [1, 2, 3, ...]>

It is an error to pass in flat when there is more than one field.

You can pass named=True to get results as a namedtuple():

>>> Entry.objects.values_list("id", "headline", named=True)
<QuerySet [Row(id=1, headline='First entry'), ...]>

Using a named tuple may make use of the results more readable, at the expense of a small performance penalty for transforming the results into a named tuple.

If you don’t pass any values to values_list(), it will return all the fields in the model, in the order they were declared.

A common need is to get a specific field value of a certain model instance. To achieve that, use values_list() followed by a get() call:

>>> Entry.objects.values_list("headline", flat=True).get(pk=1)
'First entry'

values() and values_list() are both intended as optimizations for a specific use case: retrieving a subset of data without the overhead of creating a model instance. This metaphor falls apart when dealing with many-to-many and other multivalued relations (such as the one-to-many relation of a reverse foreign key) because the “one row, one object” assumption doesn’t hold.

For example, notice the behavior when querying across a ManyToManyField:

>>> Author.objects.values_list("name", "entry__headline")
<QuerySet [('Noam Chomsky', 'Impressions of Gaza'),
 ('George Orwell', 'Why Socialists Do Not Believe in Fun'),
 ('George Orwell', 'In Defence of English Cooking'),
 ('Don Quixote', None)]>

Authors with multiple entries appear multiple times and authors without any entries have None for the entry headline.

Similarly, when querying a reverse foreign key, None appears for entries not having any author:

>>> Entry.objects.values_list("authors")
<QuerySet [('Noam Chomsky',), ('George Orwell',), (None,)]>

Special values for JSONField on SQLite

Due to the way the JSON_EXTRACT and JSON_TYPE SQL functions are implemented on SQLite, and lack of the BOOLEAN data type, values_list() will return True, False, and None instead of "true", "false", and "null" strings for JSONField key transforms.

`dates()`

dates(field , kind , order=’ASC’)

Returns a QuerySet that evaluates to a list of datetime.date objects representing all available dates of a particular kind within the contents of the QuerySet.

field should be the name of a DateField of your model. kind should be either "year", "month", "week", or "day". Each datetime.date object in the result list is “truncated” to the given type.

"year" returns a list of all distinct year values for the field.
"month" returns a list of all distinct year/month values for the field.
"week" returns a list of all distinct year/week values for the field. All dates will be a Monday.
"day" returns a list of all distinct year/month/day values for the field.

order, which defaults to 'ASC', should be either 'ASC' or 'DESC'. This specifies how to order the results.

Examples:

>>> Entry.objects.dates("pub_date", "year")
[datetime.date(2005, 1, 1)]
>>> Entry.objects.dates("pub_date", "month")
[datetime.date(2005, 2, 1), datetime.date(2005, 3, 1)]
>>> Entry.objects.dates("pub_date", "week")
[datetime.date(2005, 2, 14), datetime.date(2005, 3, 14)]
>>> Entry.objects.dates("pub_date", "day")
[datetime.date(2005, 2, 20), datetime.date(2005, 3, 20)]
>>> Entry.objects.dates("pub_date", "day", order="DESC")
[datetime.date(2005, 3, 20), datetime.date(2005, 2, 20)]
>>> Entry.objects.filter(headline__contains="Lennon").dates("pub_date", "day")
[datetime.date(2005, 3, 20)]

`datetimes()`

datetimes(field_name , kind , order=’ASC’ , tzinfo=None)

Returns a QuerySet that evaluates to a list of datetime.datetime objects representing all available dates of a particular kind within the contents of the QuerySet.

field_name should be the name of a DateTimeField of your model.

kind should be either "year", "month", "week", "day", "hour", "minute", or "second". Each datetime.datetime object in the result list is “truncated” to the given type.

order, which defaults to 'ASC', should be either 'ASC' or 'DESC'. This specifies how to order the results.

tzinfo defines the time zone to which datetimes are converted prior to truncation. Indeed, a given datetime has different representations depending on the time zone in use. This parameter must be a datetime.tzinfo object. If it’s None, Django uses the current time zone. It has no effect when USE_TZ is False.

Note

This function performs time zone conversions directly in the database. As a consequence, your database must be able to interpret the value of tzinfo.tzname(None). This translates into the following requirements:

SQLite: no requirements. Conversions are performed in Python.
PostgreSQL: no requirements (see Time Zones).
Oracle: no requirements (see Choosing a Time Zone File).
MySQL: load the time zone tables with mysql_tzinfo_to_sql.

`none()`

none()

Calling none() will create a queryset that never returns any objects and no query will be executed when accessing the results. A qs.none() queryset is an instance of EmptyQuerySet.

Examples:

>>> Entry.objects.none()
<QuerySet []>
>>> from django.db.models.query import EmptyQuerySet
>>> isinstance(Entry.objects.none(), EmptyQuerySet)
True

`all()`

all()

Returns a copy of the current QuerySet (or QuerySet subclass). This can be useful in situations where you might want to pass in either a model manager or a QuerySet and do further filtering on the result. After calling all() on either object, you’ll definitely have a QuerySet to work with.

When a QuerySet is evaluated, it typically caches its results. If the data in the database might have changed since a QuerySet was evaluated, you can get updated results for the same query by calling all() on a previously evaluated QuerySet.

`union()`

union(*other_qs , all=False)

Uses SQL’s UNION operator to combine the results of two or more QuerySets. For example:

>>> qs1.union(qs2, qs3)

The UNION operator selects only distinct values by default. To allow duplicate values, use the all=True argument.

union(), intersection(), and difference() return model instances of the type of the first QuerySet even if the arguments are QuerySets of other models. Passing different models works as long as the SELECT list is the same in all QuerySets (at least the types, the names don’t matter as long as the types are in the same order). In such cases, you must use the column names from the first QuerySet in QuerySet methods applied to the resulting QuerySet. For example:

>>> qs1 = Author.objects.values_list("name")
>>> qs2 = Entry.objects.values_list("headline")
>>> qs1.union(qs2).order_by("name")

In addition, only LIMIT, OFFSET, COUNT(*), ORDER BY, and specifying columns (i.e. slicing, count(), exists(), order_by(), and values()/values_list()) are allowed on the resulting QuerySet. Further, databases place restrictions on what operations are allowed in the combined queries. For example, most databases don’t allow LIMIT or OFFSET in the combined queries.

`intersection()`

intersection(*other_qs)

Uses SQL’s INTERSECT operator to return the shared elements of two or more QuerySets. For example:

>>> qs1.intersection(qs2, qs3)

See union() for some restrictions.

`difference()`

difference(*other_qs)

Uses SQL’s EXCEPT operator to keep only elements present in the QuerySet but not in some other QuerySets. For example:

>>> qs1.difference(qs2, qs3)

See union() for some restrictions.

`select_related()`

select_related(*fields)

Returns a QuerySet that will “follow” foreign-key relationships, selecting additional related-object data when it executes its query. This is a performance booster which results in a single more complex query but means later use of foreign-key relationships won’t require database queries.

The following examples illustrate the difference between plain lookups and select_related() lookups. Here’s standard lookup:

## Hits the database.
e = Entry.objects.get(id=5)

## Hits the database again to get the related Blog object.
b = e.blog

And here’s select_related lookup:

## Hits the database.
e = Entry.objects.select_related("blog").get(id=5)

## Doesn't hit the database, because e.blog has been prepopulated
## in the previous query.
b = e.blog

You can use select_related() with any queryset of objects:

from django.utils import timezone

## Find all the blogs with entries scheduled to be published in the future.
blogs = set()

for e in Entry.objects.filter(pub_date__gt=timezone.now()).select_related("blog"):
    # Without select_related(), this would make a database query for each
    # loop iteration in order to fetch the related blog for each entry.
    blogs.add(e.blog)

The order of filter() and select_related() chaining isn’t important. These querysets are equivalent:

Entry.objects.filter(pub_date__gt=timezone.now()).select_related("blog")
Entry.objects.select_related("blog").filter(pub_date__gt=timezone.now())

You can follow foreign keys in a similar way to querying them. If you have the following models:

from django.db import models

class City(models.Model):
    # ...
    pass

class Person(models.Model):
    # ...
    hometown = models.ForeignKey(
        City,
        on_delete=models.SET_NULL,
        blank=True,
        null=True,
    )

class Book(models.Model):
    # ...
    author = models.ForeignKey(Person, on_delete=models.CASCADE)

… then a call to Book.objects.select_related('author__hometown').get(id=4) will cache the related Person and the related City:

```

Hits the database with joins to the author and hometown tables.

b = Book.objects.select_related("author__hometown").get(id=4) p = b.author # Doesn't hit the database. c = p.hometown # Doesn't hit the database.

（本文超出站点单文档体积上限，已按小节截断）

QuerySet API reference

QuerySet API reference