These functions are available from the django.contrib.postgres.aggregates
module. They are described in more detail in the PostgreSQL docs.
Note
All functions come without default aliases, so you must explicitly provide one. For example:
>>> SomeModel.objects.aggregate(arr=ArrayAgg('somefield'))
{'arr': [0, 1, 2]}
ArrayAgg¶Returns a list of values, including nulls, concatenated into an array.
An optional boolean argument that determines if array values
will be distinct. Defaults to False.
An optional string of a field name (with an optional "-" prefix
which indicates descending order) or an expression (or a tuple or list
of strings and/or expressions) that specifies the ordering of the
elements in the result list.
Examples:
'some_field'
'-some_field'
from django.db.models import F
F('some_field').desc()
BitAnd¶Returns an int of the bitwise AND of all non-null input values, or
None if all values are null.
BitOr¶Returns an int of the bitwise OR of all non-null input values, or
None if all values are null.
BoolAnd¶Returns True, if all input values are true, None if all values are
null or if there are no values, otherwise False .
Usage example:
class Comment(models.Model):
body = models.TextField()
published = models.BooleanField()
rank = models.IntegerField()
>>> from django.db.models import Q
>>> from django.contrib.postgres.aggregates import BoolAnd
>>> Comment.objects.aggregate(booland=BoolAnd('published'))
{'booland': False}
>>> Comment.objects.aggregate(booland=BoolAnd(Q(rank__lt=100)))
{'booland': True}
BoolOr¶Returns True if at least one input value is true, None if all
values are null or if there are no values, otherwise False.
Usage example:
class Comment(models.Model):
body = models.TextField()
published = models.BooleanField()
rank = models.IntegerField()
>>> from django.db.models import Q
>>> from django.contrib.postgres.aggregates import BoolOr
>>> Comment.objects.aggregate(boolor=BoolOr('published'))
{'boolor': True}
>>> Comment.objects.aggregate(boolor=BoolOr(Q(rank__gt=2)))
{'boolor': False}
JSONBAgg¶Returns the input values as a JSON array.
An optional boolean argument that determines if array values will be
distinct. Defaults to False.
An optional string of a field name (with an optional "-" prefix
which indicates descending order) or an expression (or a tuple or list
of strings and/or expressions) that specifies the ordering of the
elements in the result list.
Examples are the same as for ArrayAgg.ordering.
StringAgg¶Returns the input values concatenated into a string, separated by
the delimiter string.
Required argument. Needs to be a string.
An optional boolean argument that determines if concatenated values
will be distinct. Defaults to False.
An optional string of a field name (with an optional "-" prefix
which indicates descending order) or an expression (or a tuple or list
of strings and/or expressions) that specifies the ordering of the
elements in the result string.
Examples are the same as for ArrayAgg.ordering.
y and x¶The arguments y and x for all these functions can be the name of a
field or an expression returning a numeric data. Both are required.
Corr¶Returns the correlation coefficient as a float, or None if there
aren’t any matching rows.
CovarPop¶Returns the population covariance as a float, or None if there
aren’t any matching rows.
Has one optional argument:
By default CovarPop returns the general population covariance.
However, if sample=True, the return value will be the sample
population covariance.
RegrAvgX¶Returns the average of the independent variable (sum(x)/N) as a
float, or None if there aren’t any matching rows.
RegrAvgY¶Returns the average of the dependent variable (sum(y)/N) as a
float, or None if there aren’t any matching rows.
RegrCount¶Returns an int of the number of input rows in which both expressions
are not null.
RegrIntercept¶Returns the y-intercept of the least-squares-fit linear equation determined
by the (x, y) pairs as a float, or None if there aren’t any
matching rows.
RegrR2¶Returns the square of the correlation coefficient as a float, or
None if there aren’t any matching rows.
RegrSlope¶Returns the slope of the least-squares-fit linear equation determined
by the (x, y) pairs as a float, or None if there aren’t any
matching rows.
RegrSXX¶Returns sum(x^2) - sum(x)^2/N (“sum of squares” of the independent
variable) as a float, or None if there aren’t any matching rows.
RegrSXY¶Returns sum(x*y) - sum(x) * sum(y)/N (“sum of products” of independent
times dependent variable) as a float, or None if there aren’t any
matching rows.
RegrSYY¶Returns sum(y^2) - sum(y)^2/N (“sum of squares” of the dependent
variable) as a float, or None if there aren’t any matching rows.
We will use this example table:
| FIELD1 | FIELD2 | FIELD3 |
|--------|--------|--------|
| foo | 1 | 13 |
| bar | 2 | (null) |
| test | 3 | 13 |
Here’s some examples of some of the general-purpose aggregation functions:
>>> TestModel.objects.aggregate(result=StringAgg('field1', delimiter=';'))
{'result': 'foo;bar;test'}
>>> TestModel.objects.aggregate(result=ArrayAgg('field2'))
{'result': [1, 2, 3]}
>>> TestModel.objects.aggregate(result=ArrayAgg('field1'))
{'result': ['foo', 'bar', 'test']}
The next example shows the usage of statistical aggregate functions. The underlying math will be not described (you can read about this, for example, at wikipedia):
>>> TestModel.objects.aggregate(count=RegrCount(y='field3', x='field2'))
{'count': 2}
>>> TestModel.objects.aggregate(avgx=RegrAvgX(y='field3', x='field2'),
... avgy=RegrAvgY(y='field3', x='field2'))
{'avgx': 2, 'avgy': 13}
Dec 25, 2023