django1/docs/ref/contrib/postgres/aggregates.txt

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=========================================
PostgreSQL specific aggregation functions
=========================================
.. module:: django.contrib.postgres.aggregates
:synopsis: PostgreSQL specific aggregation functions
.. versionadded:: 1.9
These functions are described in more detail in the `PostgreSQL docs
<http://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``.
``StringAgg``
-------------
.. class:: StringAgg(expression, delimiter)
Returns the input values concatenated into a string, separated by
the ``delimiter`` string.
.. attribute:: delimiter
Required argument. Needs to be a string.
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:
.. attribute:: 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 independent 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}