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 Weblog models presented in the database query guide.
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
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 (partially or fully) also returns a list.
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: Don’t use len()
on QuerySet
s if all you want to do is
determine the number of records in the set. It’s much more efficient to
handle a count at the database level, using SQL’s SELECT COUNT(*)
,
and Django provides a count()
method for precisely this reason. See
count()
below.
list(). Force evaluation of a QuerySet
by calling list()
on
it. For example:
entry_list = list(Entry.objects.all())
Be warned, though, that this could have a large memory overhead, because
Django will load each element of the list into memory. In contrast,
iterating over a QuerySet
will take advantage of your database to
load data and instantiate objects only as you need them.
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: Don’t use this if all you want to do is determine if at least one
result exists, and don’t need the actual objects. It’s more efficient to
use exists()
(see below).
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.
Though you usually won’t create one manually — you’ll go through a
Manager
— here’s the formal declaration of a
QuerySet
:
QuerySet
([model=None, query=None, using=None])¶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 two public attributes you can use for
introspection:
ordered
¶True
if the QuerySet
is ordered — i.e. has an
order_by()
clause or a default ordering on the model.
False
otherwise.
db
¶The database that will be used if this query is executed now.
Note
The query
parameter to QuerySet
exists so that specialized
query subclasses such as
GeoQuerySet
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.
To put it simply: if you need to ask, you don’t need to use it.
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.
filter
(**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.
exclude
(**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.
annotate
(*args, **kwargs)¶Annotates each object in the QuerySet
with the provided list of
aggregate values (averages, sums, etc) that have 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.
For example, if you were manipulating a list of blogs, you may want to determine how many entries have been made in each blog:
>>> 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.
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:
Entry.objects.order_by('blog')
...is identical to:
Entry.objects.order_by('blog__id')
...since the Blog
model has no default ordering specified.
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.
It is permissible to specify a multi-valued field to order the results by (for
example, a ManyToManyField
field). Normally
this won’t be a sensible thing to do and it’s really an advanced usage
feature. However, if you know that your queryset’s filtering or available data
implies that there will only be one ordering piece of data for each of the main
items you are selecting, the ordering may well be exactly what you want to do.
Use ordering on multi-valued fields with care and 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.
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.
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
([*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.
As of Django 1.4, 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
This ability to specify field names is only available in PostgreSQL.
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:
>>> 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')
[...]
values
(*fields)¶Returns a ValuesQuerySet
— a QuerySet
subclass that returns
dictionaries when used as an iterable, rather than model-instance objects.
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')
[<Blog: Beatles Blog>]
# This list contains a dictionary.
>>> Blog.objects.filter(name__startswith='Beatles').values()
[{'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()
[{'id': 1, 'name': 'Beatles Blog', 'tagline': 'All the latest Beatles news.'}],
>>> Blog.objects.values('id', 'name')
[{'id': 1, 'name': 'Beatles Blog'}]
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()
[{'blog_id': 1, 'headline': u'First Entry', ...}, ...]
>>> Entry.objects.values('blog')
[{'blog': 1}, ...]
>>> Entry.objects.values('blog_id')
[{'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.
A ValuesQuerySet
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 a ValuesQuerySet
is a subclass of QuerySet
, so it has all
methods of QuerySet
. You can call filter()
on it, or order_by()
, or
whatever. Yes, that means 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.
The values()
method previously did not return anything for
ManyToManyField
attributes and would raise an error
if you tried to pass this type of field to it.
This restriction has been lifted, and you can now also refer to fields on
related models with reverse relations through OneToOneField
, ForeignKey
and ManyToManyField
attributes:
Blog.objects.values('name', 'entry__headline')
[{'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.
values_list
(*fields)¶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 passed into the values_list()
call — so the first item is
the first field, etc. For example:
>>> Entry.objects.values_list('id', 'headline')
[(1, u'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 one-tuples. An example should make the difference clearer:
>>> Entry.objects.values_list('id').order_by('id')
[(1,), (2,), (3,), ...]
>>> Entry.objects.values_list('id', flat=True).order_by('id')
[1, 2, 3, ...]
It is an error to pass in flat
when there is more than one field.
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.
dates
(field, kind, order='ASC')¶Returns a DateQuerySet
— 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
should be the name of a DateField
or DateTimeField
of your
model.
kind
should be either "year"
, "month"
or "day"
. Each
datetime.datetime
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."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.datetime(2005, 1, 1)]
>>> Entry.objects.dates('pub_date', 'month')
[datetime.datetime(2005, 2, 1), datetime.datetime(2005, 3, 1)]
>>> Entry.objects.dates('pub_date', 'day')
[datetime.datetime(2005, 2, 20), datetime.datetime(2005, 3, 20)]
>>> Entry.objects.dates('pub_date', 'day', order='DESC')
[datetime.datetime(2005, 3, 20), datetime.datetime(2005, 2, 20)]
>>> Entry.objects.filter(headline__contains='Lennon').dates('pub_date', 'day')
[datetime.datetime(2005, 3, 20)]
Warning
When time zone support is enabled, Django uses UTC in the database connection, which means the aggregation is performed in UTC. This is a known limitation of the current implementation.
none
()¶Returns an EmptyQuerySet
— a QuerySet
subclass that always evaluates to
an empty list. This can be used in cases where you know that you should return
an empty result set and your caller is expecting a QuerySet
object (instead
of returning an empty list, for example.)
Examples:
>>> Entry.objects.none()
[]
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.
extra
(select=None, where=None, params=None, tables=None, order_by=None, select_params=None)¶Sometimes, the Django query syntax by itself can’t easily express a complex
WHERE
clause. For these edge cases, Django provides the extra()
QuerySet
modifier — a hook for injecting specific clauses into the SQL
generated by a QuerySet
.
By definition, these extra lookups may not be portable to different database engines (because you’re explicitly writing SQL code) and violate the DRY principle, so you should avoid them if possible.
Specify one or more of params
, select
, where
or tables
. None
of the arguments is required, but you should use at least one of them.
select
The select
argument lets you put extra fields in the SELECT
clause. It should be a dictionary mapping attribute names to SQL
clauses to use to calculate that attribute.
Example:
Entry.objects.extra(select={'is_recent': "pub_date > '2006-01-01'"})
As a result, each Entry
object will have an extra attribute,
is_recent
, a boolean representing whether the entry’s pub_date
is greater than Jan. 1, 2006.
Django inserts the given SQL snippet directly into the SELECT
statement, so the resulting SQL of the above example would be something
like:
SELECT blog_entry.*, (pub_date > '2006-01-01') AS is_recent
FROM blog_entry;
The next example is more advanced; it does a subquery to give each
resulting Blog
object an entry_count
attribute, an integer count
of associated Entry
objects:
Blog.objects.extra(
select={
'entry_count': 'SELECT COUNT(*) FROM blog_entry WHERE blog_entry.blog_id = blog_blog.id'
},
)
In this particular case, we’re exploiting the fact that the query will
already contain the blog_blog
table in its FROM
clause.
The resulting SQL of the above example would be:
SELECT blog_blog.*, (SELECT COUNT(*) FROM blog_entry WHERE blog_entry.blog_id = blog_blog.id) AS entry_count
FROM blog_blog;
Note that the parentheses required by most database engines around
subqueries are not required in Django’s select
clauses. Also note
that some database backends, such as some MySQL versions, don’t support
subqueries.
In some rare cases, you might wish to pass parameters to the SQL
fragments in extra(select=...)
. For this purpose, use the
select_params
parameter. Since select_params
is a sequence and
the select
attribute is a dictionary, some care is required so that
the parameters are matched up correctly with the extra select pieces.
In this situation, you should use a
django.utils.datastructures.SortedDict
for the select
value, not just a normal Python dictionary.
This will work, for example:
Blog.objects.extra(
select=SortedDict([('a', '%s'), ('b', '%s')]),
select_params=('one', 'two'))
The only thing to be careful about when using select parameters in
extra()
is to avoid using the substring "%%s"
(that’s two
percent characters before the s
) in the select strings. Django’s
tracking of parameters looks for %s
and an escaped %
character
like this isn’t detected. That will lead to incorrect results.
where
/ tables
You can define explicit SQL WHERE
clauses — perhaps to perform
non-explicit joins — by using where
. You can manually add tables to
the SQL FROM
clause by using tables
.
where
and tables
both take a list of strings. All where
parameters are “AND”ed to any other search criteria.
Example:
Entry.objects.extra(where=['id IN (3, 4, 5, 20)'])
...translates (roughly) into the following SQL:
SELECT * FROM blog_entry WHERE id IN (3, 4, 5, 20);
Be careful when using the tables
parameter if you’re specifying
tables that are already used in the query. When you add extra tables
via the tables
parameter, Django assumes you want that table
included an extra time, if it is already included. That creates a
problem, since the table name will then be given an alias. If a table
appears multiple times in an SQL statement, the second and subsequent
occurrences must use aliases so the database can tell them apart. If
you’re referring to the extra table you added in the extra where
parameter this is going to cause errors.
Normally you’ll only be adding extra tables that don’t already appear
in the query. However, if the case outlined above does occur, there are
a few solutions. First, see if you can get by without including the
extra table and use the one already in the query. If that isn’t
possible, put your extra()
call at the front of the queryset
construction so that your table is the first use of that table.
Finally, if all else fails, look at the query produced and rewrite your
where
addition to use the alias given to your extra table. The
alias will be the same each time you construct the queryset in the same
way, so you can rely upon the alias name to not change.
order_by
If you need to order the resulting queryset using some of the new
fields or tables you have included via extra()
use the order_by
parameter to extra()
and pass in a sequence of strings. These
strings should either be model fields (as in the normal
order_by()
method on querysets), of the form
table_name.column_name
or an alias for a column that you specified
in the select
parameter to extra()
.
For example:
q = Entry.objects.extra(select={'is_recent': "pub_date > '2006-01-01'"})
q = q.extra(order_by = ['-is_recent'])
This would sort all the items for which is_recent
is true to the
front of the result set (True
sorts before False
in a
descending ordering).
This shows, by the way, that you can make multiple calls to extra()
and it will behave as you expect (adding new constraints each time).
params
The where
parameter described above may use standard Python
database string placeholders — '%s'
to indicate parameters the
database engine should automatically quote. The params
argument is
a list of any extra parameters to be substituted.
Example:
Entry.objects.extra(where=['headline=%s'], params=['Lennon'])
Always use params
instead of embedding values directly into
where
because params
will ensure values are quoted correctly
according to your particular backend. For example, quotes will be
escaped correctly.
Bad:
Entry.objects.extra(where=["headline='Lennon'"])
Good:
Entry.objects.extra(where=['headline=%s'], params=['Lennon'])
defer
(*fields)¶In some complex data-modeling situations, your models might contain a lot of fields, some of which could contain a lot of data (for example, text fields), or require expensive processing to convert them to Python objects. If you are using the results of a queryset in some situation where you know you don’t know if you need those particular fields when you initially fetch the data, you can tell Django not to retrieve them from the database.
This is done by passing the names of the fields to not load to defer()
:
Entry.objects.defer("headline", "body")
A queryset that has deferred fields will still return model instances. Each deferred field will be retrieved from the database if you access that field (one at a time, not all the deferred fields at once).
You can make multiple calls to defer()
. Each call adds new fields to the
deferred set:
# Defers both the body and headline fields.
Entry.objects.defer("body").filter(rating=5).defer("headline")
The order in which fields are added to the deferred set does not matter.
Calling defer()
with a field name that has already been deferred is
harmless (the field will still be deferred).
You can defer loading of fields in related models (if the related models are
loading via select_related()
) by using the standard double-underscore
notation to separate related fields:
Blog.objects.select_related().defer("entry__headline", "entry__body")
If you want to clear the set of deferred fields, pass None
as a parameter
to defer()
:
# Load all fields immediately.
my_queryset.defer(None)
Some fields in a model won’t be deferred, even if you ask for them. You can
never defer the loading of the primary key. If you are using
select_related()
to retrieve related models, you shouldn’t defer the
loading of the field that connects from the primary model to the related one
(at the moment, that doesn’t raise an error, but it will eventually).
Note
The defer()
method (and its cousin, only()
, below) are only for
advanced use-cases. They provide an optimization for when you have analyzed
your queries closely and understand exactly what information you need and
have measured that the difference between returning the fields you need and
the full set of fields for the model will be significant.
Even if you think you are in the advanced use-case situation, only use
defer() when you cannot, at queryset load time, determine if you will need
the extra fields or not. If you are frequently loading and using a
particular subset of your data, the best choice you can make is to
normalize your models and put the non-loaded data into a separate model
(and database table). If the columns must stay in the one table for some
reason, create a model with Meta.managed = False
(see the
managed attribute
documentation)
containing just the fields you normally need to load and use that where you
might otherwise call defer()
. This makes your code more explicit to the
reader, is slightly faster and consumes a little less memory in the Python
process.
only
(*fields)¶The only()
method is more or less the opposite of defer()
. You call
it with the fields that should not be deferred when retrieving a model. If
you have a model where almost all the fields need to be deferred, using
only()
to specify the complementary set of fields can result in simpler
code.
Suppose you have a model with fields name
, age
and biography
. The
following two querysets are the same, in terms of deferred fields:
Person.objects.defer("age", "biography")
Person.objects.only("name")
Whenever you call only()
it replaces the set of fields to load
immediately. The method’s name is mnemonic: only those fields are loaded
immediately; the remainder are deferred. Thus, successive calls to only()
result in only the final fields being considered:
# This will defer all fields except the headline.
Entry.objects.only("body", "rating").only("headline")
Since defer()
acts incrementally (adding fields to the deferred list), you
can combine calls to only()
and defer()
and things will behave
logically:
# Final result is that everything except "headline" is deferred.
Entry.objects.only("headline", "body").defer("body")
# Final result loads headline and body immediately (only() replaces any
# existing set of fields).
Entry.objects.defer("body").only("headline", "body")
All of the cautions in the note for the defer()
documentation apply to
only()
as well. Use it cautiously and only after exhausting your other
options.
using
(alias)¶This method is for controlling which database the QuerySet
will be
evaluated against if you are using more than one database. The only argument
this method takes is the alias of a database, as defined in
DATABASES
.
For example:
# queries the database with the 'default' alias.
>>> Entry.objects.all()
# queries the database with the 'backup' alias
>>> Entry.objects.using('backup')
select_for_update
(nowait=False)¶Returns a queryset that will lock rows until the end of the transaction,
generating a SELECT ... FOR UPDATE
SQL statement on supported databases.
For example:
entries = Entry.objects.select_for_update().filter(author=request.user)
All matched entries will be locked until the end of the transaction block, meaning that other transactions will be prevented from changing or acquiring locks on them.
Usually, if another transaction has already acquired a lock on one of the
selected rows, the query will block until the lock is released. If this is
not the behavior you want, call select_for_update(nowait=True)
. This will
make the call non-blocking. If a conflicting lock is already acquired by
another transaction, DatabaseError
will be raised when the
queryset is evaluated.
Note that using select_for_update()
will cause the current transaction to be
considered dirty, if under transaction management. This is to ensure that
Django issues a COMMIT
or ROLLBACK
, releasing any locks held by the
SELECT FOR UPDATE
.
Currently, the postgresql_psycopg2
, oracle
, and mysql
database
backends support select_for_update()
. However, MySQL has no support for the
nowait
argument. Obviously, users of external third-party backends should
check with their backend’s documentation for specifics in those cases.
Passing nowait=True
to select_for_update
using database backends that
do not support nowait
, such as MySQL, will cause a
DatabaseError
to be raised. This is in order to prevent code
unexpectedly blocking.
Using select_for_update
on backends which do not support
SELECT ... FOR UPDATE
(such as SQLite) will have no effect.
The following QuerySet
methods evaluate the QuerySet
and return
something other than a QuerySet
.
These methods do not use a cache (see Caching and QuerySets). Rather, they query the database each time they’re called.
get
(**kwargs)¶Returns the object matching the given lookup parameters, which should be in the format described in Field lookups.
get()
raises MultipleObjectsReturned
if more
than one object was found. The
MultipleObjectsReturned
exception is an
attribute of the model class.
get()
raises a DoesNotExist
exception if an
object wasn’t found for the given parameters. This exception is also an
attribute of the model class. Example:
Entry.objects.get(id='foo') # raises Entry.DoesNotExist
The DoesNotExist
exception inherits from
django.core.exceptions.ObjectDoesNotExist
, so you can target multiple
DoesNotExist
exceptions. Example:
from django.core.exceptions import ObjectDoesNotExist
try:
e = Entry.objects.get(id=3)
b = Blog.objects.get(id=1)
except ObjectDoesNotExist:
print "Either the entry or blog doesn't exist."
create
(**kwargs)¶A convenience method for creating an object and saving it all in one step. Thus:
p = Person.objects.create(first_name="Bruce", last_name="Springsteen")
and:
p = Person(first_name="Bruce", last_name="Springsteen")
p.save(force_insert=True)
are equivalent.
The force_insert parameter is documented
elsewhere, but all it means is that a new object will always be created.
Normally you won’t need to worry about this. However, if your model contains a
manual primary key value that you set and if that value already exists in the
database, a call to create()
will fail with an
IntegrityError
since primary keys must be unique. Be
prepared to handle the exception if you are using manual primary keys.
get_or_create
(**kwargs)¶A convenience method for looking up an object with the given kwargs, creating one if necessary.
Returns a tuple of (object, created)
, where object
is the retrieved or
created object and created
is a boolean specifying whether a new object was
created.
This is meant as a shortcut to boilerplatish code and is mostly useful for data-import scripts. For example:
try:
obj = Person.objects.get(first_name='John', last_name='Lennon')
except Person.DoesNotExist:
obj = Person(first_name='John', last_name='Lennon', birthday=date(1940, 10, 9))
obj.save()
This pattern gets quite unwieldy as the number of fields in a model goes up.
The above example can be rewritten using get_or_create()
like so:
obj, created = Person.objects.get_or_create(first_name='John', last_name='Lennon',
defaults={'birthday': date(1940, 10, 9)})
Any keyword arguments passed to get_or_create()
— except an optional one
called defaults
— will be used in a get()
call. If an object is
found, get_or_create()
returns a tuple of that object and False
. If an
object is not found, get_or_create()
will instantiate and save a new
object, returning a tuple of the new object and True
. The new object will
be created roughly according to this algorithm:
defaults = kwargs.pop('defaults', {})
params = dict([(k, v) for k, v in kwargs.items() if '__' not in k])
params.update(defaults)
obj = self.model(**params)
obj.save()
In English, that means start with any non-'defaults'
keyword argument that
doesn’t contain a double underscore (which would indicate a non-exact lookup).
Then add the contents of defaults
, overriding any keys if necessary, and
use the result as the keyword arguments to the model class. As hinted at
above, this is a simplification of the algorithm that is used, but it contains
all the pertinent details. The internal implementation has some more
error-checking than this and handles some extra edge-conditions; if you’re
interested, read the code.
If you have a field named defaults
and want to use it as an exact lookup in
get_or_create()
, just use 'defaults__exact'
, like so:
Foo.objects.get_or_create(defaults__exact='bar', defaults={'defaults': 'baz'})
The get_or_create()
method has similar error behavior to create()
when you’re using manually specified primary keys. If an object needs to be
created and the key already exists in the database, an
IntegrityError
will be raised.
Finally, a word on using get_or_create()
in Django views. As mentioned
earlier, get_or_create()
is mostly useful in scripts that need to parse
data and create new records if existing ones aren’t available. But if you need
to use get_or_create()
in a view, please make sure to use it only in
POST
requests unless you have a good reason not to. GET
requests
shouldn’t have any effect on data; use POST
whenever a request to a page
has a side effect on your data. For more, see Safe methods in the HTTP spec.
bulk_create
(objs)¶This method inserts the provided list of objects into the database in an efficient manner (generally only 1 query, no matter how many objects there are):
>>> Entry.objects.bulk_create([
... Entry(headline="Django 1.0 Released"),
... Entry(headline="Django 1.1 Announced"),
... Entry(headline="Breaking: Django is awesome")
... ])
This has a number of caveats though:
save()
method will not be called, and the pre_save
and
post_save
signals will not be sent.AutoField
it
does not retrieve and set the primary key attribute, as save()
does.Limits of SQLite
SQLite sets a limit on the number of parameters per SQL statement. The maximum is defined by the SQLITE_MAX_VARIABLE_NUMBER compilation option, which defaults to 999. For instance, if your model has 8 fields (including the primary key), you cannot create more than 999 // 8 = 124 instances at a time. If you exceed this limit, you’ll get an exception:
django.db.utils.DatabaseError: too many SQL variables
If your application’s performance requirements exceed SQLite’s limits, you should switch to another database engine, such as PostgreSQL.
count
()¶Returns an integer representing the number of objects in the database matching
the QuerySet
. The count()
method never raises exceptions.
Example:
# Returns the total number of entries in the database.
Entry.objects.count()
# Returns the number of entries whose headline contains 'Lennon'
Entry.objects.filter(headline__contains='Lennon').count()
A count()
call performs a SELECT COUNT(*)
behind the scenes, so you
should always use count()
rather than loading all of the record into Python
objects and calling len()
on the result (unless you need to load the
objects into memory anyway, in which case len()
will be faster).
Depending on which database you’re using (e.g. PostgreSQL vs. MySQL),
count()
may return a long integer instead of a normal Python integer. This
is an underlying implementation quirk that shouldn’t pose any real-world
problems.
in_bulk
(id_list)¶Takes a list of primary-key values and returns a dictionary mapping each primary-key value to an instance of the object with the given ID.
Example:
>>> Blog.objects.in_bulk([1])
{1: <Blog: Beatles Blog>}
>>> Blog.objects.in_bulk([1, 2])
{1: <Blog: Beatles Blog>, 2: <Blog: Cheddar Talk>}
>>> Blog.objects.in_bulk([])
{}
If you pass in_bulk()
an empty list, you’ll get an empty dictionary.
iterator
()¶Evaluates the QuerySet
(by performing the query) and returns an iterator
(see PEP 234) over the results. A QuerySet
typically caches its results
internally so that repeated evaluations do not result in additional queries. In
contrast, iterator()
will read results directly, without doing any caching
at the QuerySet
level (internally, the default iterator calls iterator()
and caches the return value). For a QuerySet
which returns a large number of
objects that you only need to access once, this can results in better
performance and a significant reduction in memory.
Note that using iterator()
on a QuerySet
which has already been
evaluated will force it to evaluate again, repeating the query.
Also, use of iterator()
causes previous prefetch_related()
calls to be
ignored since these two optimizations do not make sense together.
latest
(field_name=None)¶Returns the latest object in the table, by date, using the field_name
provided as the date field.
This example returns the latest Entry
in the table, according to the
pub_date
field:
Entry.objects.latest('pub_date')
If your model’s Meta specifies
get_latest_by
, you can leave off the
field_name
argument to latest()
. Django will use the field specified
in get_latest_by
by default.
Like get()
, latest()
raises
DoesNotExist
if there is no object with the given
parameters.
Note latest()
exists purely for convenience and readability.
aggregate
(*args, **kwargs)¶Returns a dictionary of aggregate values (averages, sums, etc) calculated over
the QuerySet
. Each argument to aggregate()
specifies a value that will
be included in the dictionary that is returned.
The aggregation functions that are provided by Django are described in Aggregation Functions below.
Aggregates specified using keyword arguments will use the keyword as the name for the annotation. Anonymous arguments will have a name generated for them based upon the name of the aggregate function and the model field that is being aggregated.
For example, when you are working with blog entries, you may want to know the number of authors that have contributed blog entries:
>>> q = Blog.objects.aggregate(Count('entry'))
{'entry__count': 16}
By using a keyword argument to specify the aggregate function, you can control the name of the aggregation value that is returned:
>>> q = Blog.objects.aggregate(number_of_entries=Count('entry'))
{'number_of_entries': 16}
For an in-depth discussion of aggregation, see the topic guide on Aggregation.
exists
()¶Returns True
if the QuerySet
contains any results, and False
if not. This tries to perform the query in the simplest and fastest way
possible, but it does execute nearly the same query. This means that calling
QuerySet.exists()
is faster than bool(some_query_set)
, but not by
a large degree. If some_query_set
has not yet been evaluated, but you know
that it will be at some point, then using some_query_set.exists()
will do
more overall work (one query for the existence check plus an extra one to later
retrieve the results) than simply using bool(some_query_set)
, which
retrieves the results and then checks if any were returned.
update
(**kwargs)¶Performs an SQL update query for the specified fields, and returns the number of rows affected.
For example, to turn comments off for all blog entries published in 2010, you could do this:
>>> Entry.objects.filter(pub_date__year=2010).update(comments_on=False)
(This assumes your Entry
model has fields pub_date
and comments_on
.)
You can update multiple fields — there’s no limit on how many. For example,
here we update the comments_on
and headline
fields:
>>> Entry.objects.filter(pub_date__year=2010).update(comments_on=False, headline='This is old')
The update()
method is applied instantly, and the only restriction on the
QuerySet
that is updated is that it can only update columns in the
model’s main table, not on related models. You can’t do this, for example:
>>> Entry.objects.update(blog__name='foo') # Won't work!
Filtering based on related fields is still possible, though:
>>> Entry.objects.filter(blog__id=1).update(comments_on=True)
You cannot call update()
on a QuerySet
that has had a slice taken
or can otherwise no longer be filtered.
The update()
method returns the number of affected rows:
>>> Entry.objects.filter(id=64).update(comments_on=True)
1
>>> Entry.objects.filter(slug='nonexistent-slug').update(comments_on=True)
0
>>> Entry.objects.filter(pub_date__year=2010).update(comments_on=False)
132
If you’re just updating a record and don’t need to do anything with the model
object, the most efficient approach is to call update()
, rather than
loading the model object into memory. For example, instead of doing this:
e = Entry.objects.get(id=10)
e.comments_on = False
e.save()
...do this:
Entry.objects.filter(id=10).update(comments_on=False)
Using update()
also prevents a race condition wherein something might
change in your database in the short period of time between loading the object
and calling save()
.
Finally, realize that update()
does an update at the SQL level and, thus,
does not call any save()
methods on your models, nor does it emit the
pre_save
or
post_save
signals (which are a consequence of
calling Model.save()
). If you want to
update a bunch of records for a model that has a custom
save()`()
method, loop over them and call
save()
, like this:
for e in Entry.objects.filter(pub_date__year=2010):
e.comments_on = False
e.save()
delete
()¶Performs an SQL delete query on all rows in the QuerySet
. The
delete()
is applied instantly. You cannot call delete()
on a
QuerySet
that has had a slice taken or can otherwise no longer be
filtered.
For example, to delete all the entries in a particular blog:
>>> b = Blog.objects.get(pk=1)
# Delete all the entries belonging to this Blog.
>>> Entry.objects.filter(blog=b).delete()
By default, Django’s ForeignKey
emulates the SQL
constraint ON DELETE CASCADE
— in other words, any objects with foreign
keys pointing at the objects to be deleted will be deleted along with them.
For example:
blogs = Blog.objects.all()
# This will delete all Blogs and all of their Entry objects.
blogs.delete()
on_delete
argument to the
ForeignKey
.The delete()
method does a bulk delete and does not call any delete()
methods on your models. It does, however, emit the
pre_delete
and
post_delete
signals for all deleted objects
(including cascaded deletions).
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()
.
For an introduction, see models and database queries documentation.
Exact match. If the value provided for comparison is None
, it will be
interpreted as an SQL NULL
(see isnull
for more details).
Examples:
Entry.objects.get(id__exact=14)
Entry.objects.get(id__exact=None)
SQL equivalents:
SELECT ... WHERE id = 14;
SELECT ... WHERE id IS NULL;
MySQL comparisons
In MySQL, a database table’s “collation” setting determines whether
exact
comparisons are case-sensitive. This is a database setting, not
a Django setting. It’s possible to configure your MySQL tables to use
case-sensitive comparisons, but some trade-offs are involved. For more
information about this, see the collation section
in the databases documentation.
Case-insensitive exact match.
Example:
Blog.objects.get(name__iexact='beatles blog')
SQL equivalent:
SELECT ... WHERE name ILIKE 'beatles blog';
Note this will match 'Beatles Blog'
, 'beatles blog'
, 'BeAtLes
BLoG'
, etc.
SQLite users
When using the SQLite backend and Unicode (non-ASCII) strings, bear in mind the database note about string comparisons. SQLite does not do case-insensitive matching for Unicode strings.
Case-sensitive containment test.
Example:
Entry.objects.get(headline__contains='Lennon')
SQL equivalent:
SELECT ... WHERE headline LIKE '%Lennon%';
Note this will match the headline 'Lennon honored today'
but not 'lennon
honored today'
.
SQLite users
SQLite doesn’t support case-sensitive LIKE
statements; contains
acts like icontains
for SQLite. See the database note for more information.
Case-insensitive containment test.
Example:
Entry.objects.get(headline__icontains='Lennon')
SQL equivalent:
SELECT ... WHERE headline ILIKE '%Lennon%';
SQLite users
When using the SQLite backend and Unicode (non-ASCII) strings, bear in mind the database note about string comparisons.
In a given list.
Example:
Entry.objects.filter(id__in=[1, 3, 4])
SQL equivalent:
SELECT ... WHERE id IN (1, 3, 4);
You can also use a queryset to dynamically evaluate the list of values instead of providing a list of literal values:
inner_qs = Blog.objects.filter(name__contains='Cheddar')
entries = Entry.objects.filter(blog__in=inner_qs)
This queryset will be evaluated as subselect statement:
SELECT ... WHERE blog.id IN (SELECT id FROM ... WHERE NAME LIKE '%Cheddar%')
The above code fragment could also be written as follows:
inner_q = Blog.objects.filter(name__contains='Cheddar').values('pk').query
entries = Entry.objects.filter(blog__in=inner_q)
Warning
This query
attribute should be considered an opaque internal attribute.
It’s fine to use it like above, but its API may change between Django
versions.
This second form is a bit less readable and unnatural to write, since it
accesses the internal query
attribute and requires a ValuesQuerySet
.
If your code doesn’t require compatibility with Django 1.0, use the first
form, passing in a queryset directly.
If you pass in a ValuesQuerySet
or ValuesListQuerySet
(the result of
calling values()
or values_list()
on a queryset) as the value to an
__in
lookup, you need to ensure you are only extracting one field in the
result. For example, this will work (filtering on the blog names):
inner_qs = Blog.objects.filter(name__contains='Ch').values('name')
entries = Entry.objects.filter(blog__name__in=inner_qs)
This example will raise an exception, since the inner query is trying to extract two field values, where only one is expected:
# Bad code! Will raise a TypeError.
inner_qs = Blog.objects.filter(name__contains='Ch').values('name', 'id')
entries = Entry.objects.filter(blog__name__in=inner_qs)
Performance considerations
Be cautious about using nested queries and understand your database server’s performance characteristics (if in doubt, benchmark!). Some database backends, most notably MySQL, don’t optimize nested queries very well. It is more efficient, in those cases, to extract a list of values and then pass that into the second query. That is, execute two queries instead of one:
values = Blog.objects.filter(
name__contains='Cheddar').values_list('pk', flat=True)
entries = Entry.objects.filter(blog__in=list(values))
Note the list()
call around the Blog QuerySet
to force execution of
the first query. Without it, a nested query would be executed, because
QuerySets are lazy.
Greater than or equal to.
Less than.
Less than or equal to.
Case-sensitive starts-with.
Example:
Entry.objects.filter(headline__startswith='Will')
SQL equivalent:
SELECT ... WHERE headline LIKE 'Will%';
SQLite doesn’t support case-sensitive LIKE
statements; startswith
acts
like istartswith
for SQLite.
Case-insensitive starts-with.
Example:
Entry.objects.filter(headline__istartswith='will')
SQL equivalent:
SELECT ... WHERE headline ILIKE 'Will%';
SQLite users
When using the SQLite backend and Unicode (non-ASCII) strings, bear in mind the database note about string comparisons.
Case-sensitive ends-with.
Example:
Entry.objects.filter(headline__endswith='cats')
SQL equivalent:
SELECT ... WHERE headline LIKE '%cats';
SQLite users
SQLite doesn’t support case-sensitive LIKE
statements; endswith
acts like iendswith
for SQLite. Refer to the database note documentation for more.
Case-insensitive ends-with.
Example:
Entry.objects.filter(headline__iendswith='will')
SQL equivalent:
SELECT ... WHERE headline ILIKE '%will'
SQLite users
When using the SQLite backend and Unicode (non-ASCII) strings, bear in mind the database note about string comparisons.
Range test (inclusive).
Example:
start_date = datetime.date(2005, 1, 1)
end_date = datetime.date(2005, 3, 31)
Entry.objects.filter(pub_date__range=(start_date, end_date))
SQL equivalent:
SELECT ... WHERE pub_date BETWEEN '2005-01-01' and '2005-03-31';
You can use range
anywhere you can use BETWEEN
in SQL — for dates,
numbers and even characters.
For date/datetime fields, exact year match. Takes a four-digit year.
Example:
Entry.objects.filter(pub_date__year=2005)
SQL equivalent:
SELECT ... WHERE pub_date BETWEEN '2005-01-01' AND '2005-12-31 23:59:59.999999';
(The exact SQL syntax varies for each database engine.)
For date and datetime fields, an exact month match. Takes an integer 1 (January) through 12 (December).
Example:
Entry.objects.filter(pub_date__month=12)
SQL equivalent:
SELECT ... WHERE EXTRACT('month' FROM pub_date) = '12';
(The exact SQL syntax varies for each database engine.)
For date and datetime fields, an exact day match.
Example:
Entry.objects.filter(pub_date__day=3)
SQL equivalent:
SELECT ... WHERE EXTRACT('day' FROM pub_date) = '3';
(The exact SQL syntax varies for each database engine.)
Note this will match any record with a pub_date on the third day of the month, such as January 3, July 3, etc.
For date and datetime fields, a ‘day of the week’ match.
Takes an integer value representing the day of week from 1 (Sunday) to 7 (Saturday).
Example:
Entry.objects.filter(pub_date__week_day=2)
(No equivalent SQL code fragment is included for this lookup because implementation of the relevant query varies among different database engines.)
Note this will match any record with a pub_date
that falls on a Monday (day
2 of the week), regardless of the month or year in which it occurs. Week days
are indexed with day 1 being Sunday and day 7 being Saturday.
Warning
When time zone support is enabled, Django
uses UTC in the database connection, which means the year
, month
,
day
and week_day
lookups are performed in UTC. This is a known
limitation of the current implementation.
Takes either True
or False
, which correspond to SQL queries of
IS NULL
and IS NOT NULL
, respectively.
Example:
Entry.objects.filter(pub_date__isnull=True)
SQL equivalent:
SELECT ... WHERE pub_date IS NULL;
A boolean full-text search, taking advantage of full-text indexing. This is
like contains
but is significantly faster due to full-text indexing.
Example:
Entry.objects.filter(headline__search="+Django -jazz Python")
SQL equivalent:
SELECT ... WHERE MATCH(tablename, headline) AGAINST (+Django -jazz Python IN BOOLEAN MODE);
Note this is only available in MySQL and requires direct manipulation of the database to add the full-text index. By default Django uses BOOLEAN MODE for full text searches. See the MySQL documentation for additional details.
Case-sensitive regular expression match.
The regular expression syntax is that of the database backend in use.
In the case of SQLite, which has no built in regular expression support,
this feature is provided by a (Python) user-defined REGEXP function, and
the regular expression syntax is therefore that of Python’s re
module.
Example:
Entry.objects.get(title__regex=r'^(An?|The) +')
SQL equivalents:
SELECT ... WHERE title REGEXP BINARY '^(An?|The) +'; -- MySQL
SELECT ... WHERE REGEXP_LIKE(title, '^(an?|the) +', 'c'); -- Oracle
SELECT ... WHERE title ~ '^(An?|The) +'; -- PostgreSQL
SELECT ... WHERE title REGEXP '^(An?|The) +'; -- SQLite
Using raw strings (e.g., r'foo'
instead of 'foo'
) for passing in the
regular expression syntax is recommended.
Case-insensitive regular expression match.
Example:
Entry.objects.get(title__iregex=r'^(an?|the) +')
SQL equivalents:
SELECT ... WHERE title REGEXP '^(an?|the) +'; -- MySQL
SELECT ... WHERE REGEXP_LIKE(title, '^(an?|the) +', 'i'); -- Oracle
SELECT ... WHERE title ~* '^(an?|the) +'; -- PostgreSQL
SELECT ... WHERE title REGEXP '(?i)^(an?|the) +'; -- SQLite
Django provides the following aggregation functions in the
django.db.models
module. For details on how to use these
aggregate functions, see
the topic guide on aggregation.
Avg
(field)¶Returns the mean value of the given field, which must be numeric.
<field>__avg
float
Count
(field, distinct=False)¶Returns the number of objects that are related through the provided field.
<field>__count
int
Has one optional argument:
distinct
¶If distinct=True
, the count will only include unique instances.
This is the SQL equivalent of COUNT(DISTINCT <field>)
. The default
value is False
.
Max
(field)¶Returns the maximum value of the given field.
<field>__max
Min
(field)¶Returns the minimum value of the given field.
<field>__min
StdDev
(field, sample=False)¶Returns the standard deviation of the data in the provided field.
<field>__stddev
float
Has one optional argument:
sample
¶By default, StdDev
returns the population standard deviation. However,
if sample=True
, the return value will be the sample standard deviation.
SQLite
SQLite doesn’t provide StdDev
out of the box. An implementation
is available as an extension module for SQLite. Consult the SQlite
documentation for instructions on obtaining and installing this
extension.
Sum
(field)¶Computes the sum of all values of the given field.
<field>__sum
Variance
(field, sample=False)¶Returns the variance of the data in the provided field.
<field>__variance
float
Has one optional argument:
sample
¶By default, Variance
returns the population variance. However,
if sample=True
, the return value will be the sample variance.
SQLite
SQLite doesn’t provide Variance
out of the box. An implementation
is available as an extension module for SQLite. Consult the SQlite
documentation for instructions on obtaining and installing this
extension.
Jul 07, 2017