---
title: "PostgreSQL specific aggregation functions"
version: 1.11
locale: es
source: https://docs.djangoproject.com/es/1.11/ref/contrib/postgres/aggregates/
canonical: https://djangodocs.dev/es/1.11/ref/contrib/postgres/aggregates/
---
# PostgreSQL specific aggregation functions

These functions are described in more detail in the [PostgreSQL docs](https://www.postgresql.org/docs/current/static/functions-aggregate.html).

> **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, **extra)`

Returns a list of values, including nulls, concatenated into an array.

### `BitAnd`

#### `class BitAnd(expression, **extra)`

Returns an `int` of the bitwise `AND` of all non-null input values, or
`None` if all values are null.

### `BitOr`

#### `class BitOr(expression, **extra)`

Returns an `int` of the bitwise `OR` of all non-null input values, or
`None` if all values are null.

### `BoolAnd`

#### `class BoolAnd(expression, **extra)`

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, **extra)`

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, **extra)`

> **New in Django 1.11**

Returns the input values as a `JSON` array. Requires PostgreSQL ≥ 9.5.

### `StringAgg`

#### `class StringAgg(expression, delimiter, distinct=False)`

Returns the input values concatenated into a string, separated by
the `delimiter` string.

#### `delimiter`

Required argument. Needs to be a string.

#### `distinct`

> **New in Django 1.11**

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)`

Returns the correlation coefficient as a `float`, or `None` if there
aren’t any matching rows.

### `CovarPop`

#### `class CovarPop(y, x, sample=False)`

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)`

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)`

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)`

Returns an `int` of the number of input rows in which both expressions
are not null.

### `RegrIntercept`

#### `class RegrIntercept(y, x)`

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)`

Returns the square of the correlation coefficient as a `float`, or
`None` if there aren’t any matching rows.

### `RegrSlope`

#### `class RegrSlope(y, x)`

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)`

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)`

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)`

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](https://en.wikipedia.org/wiki/Regression_analysis)):

```
>>> 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}
```