PostgreSQL specific aggregation functions
These functions 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]}
General-purpose aggregation functions
ArrayAgg
-
class
ArrayAgg(expression, distinct=False, filter=None, **extra)[source]
Returns a list of values, including nulls, concatenated into an array.
-
distinct
New in Django 2.0:
An optional boolean argument that determines if array values
will be distinct. Defaults to False.
BitAnd
-
class
BitAnd(expression, filter=None, **extra)[source]
Returns an int of the bitwise AND of all non-null input values, or
None if all values are null.
BitOr
-
class
BitOr(expression, filter=None, **extra)[source]
Returns an int of the bitwise OR of all non-null input values, or
None if all values are null.
BoolAnd
-
class
BoolAnd(expression, filter=None, **extra)[source]
Returns True, if all input values are true, None if all values are
null or if there are no values, otherwise False .
BoolOr
-
class
BoolOr(expression, filter=None, **extra)[source]
Returns True if at least one input value is true, None if all
values are null or if there are no values, otherwise False.
JSONBAgg
-
class
JSONBAgg(expressions, filter=None, **extra)[source]
Returns the input values as a JSON array. Requires PostgreSQL ≥ 9.5.
StringAgg
-
class
StringAgg(expression, delimiter, distinct=False, filter=None)[source]
Returns the input values concatenated into a string, separated by
the delimiter string.
-
delimiter
Required argument. Needs to be a string.
-
distinct
An optional boolean argument that determines if concatenated values
will be distinct. Defaults to False.
Aggregate functions for statistics
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
-
class
Corr(y, x, filter=None)[source]
Returns the correlation coefficient as a float, or None if there
aren’t any matching rows.
CovarPop
-
class
CovarPop(y, x, sample=False, filter=None)[source]
Returns the population covariance as a float, or None if there
aren’t any matching rows.
Has one optional argument:
-
sample
By default CovarPop returns the general population covariance.
However, if sample=True, the return value will be the sample
population covariance.
RegrAvgX
-
class
RegrAvgX(y, x, filter=None)[source]
Returns the average of the independent variable (sum(x)/N) as a
float, or None if there aren’t any matching rows.
RegrAvgY
-
class
RegrAvgY(y, x, filter=None)[source]
Returns the average of the dependent variable (sum(y)/N) as a
float, or None if there aren’t any matching rows.
RegrCount
-
class
RegrCount(y, x, filter=None)[source]
Returns an int of the number of input rows in which both expressions
are not null.
RegrIntercept
-
class
RegrIntercept(y, x, filter=None)[source]
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
-
class
RegrR2(y, x, filter=None)[source]
Returns the square of the correlation coefficient as a float, or
None if there aren’t any matching rows.
RegrSlope
-
class
RegrSlope(y, x, filter=None)[source]
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
-
class
RegrSXX(y, x, filter=None)[source]
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
-
class
RegrSXY(y, x, filter=None)[source]
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
-
class
RegrSYY(y, x, filter=None)[source]
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.
Usage examples
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}