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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}
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