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228 lines
6.1 KiB
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228 lines
6.1 KiB
Plaintext
=========================================
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PostgreSQL specific aggregation functions
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=========================================
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.. module:: django.contrib.postgres.aggregates
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:synopsis: PostgreSQL specific aggregation functions
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These functions are described in more detail in the `PostgreSQL docs
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<https://www.postgresql.org/docs/current/static/functions-aggregate.html>`_.
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.. note::
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All functions come without default aliases, so you must explicitly provide
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one. For example::
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>>> SomeModel.objects.aggregate(arr=ArrayAgg('somefield'))
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{'arr': [0, 1, 2]}
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General-purpose aggregation functions
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=====================================
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``ArrayAgg``
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------------
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.. class:: ArrayAgg(expression, distinct=False, filter=None, **extra)
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Returns a list of values, including nulls, concatenated into an array.
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.. attribute:: distinct
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An optional boolean argument that determines if array values
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will be distinct. Defaults to ``False``.
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``BitAnd``
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----------
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.. class:: BitAnd(expression, filter=None, **extra)
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Returns an ``int`` of the bitwise ``AND`` of all non-null input values, or
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``None`` if all values are null.
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``BitOr``
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---------
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.. class:: BitOr(expression, filter=None, **extra)
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Returns an ``int`` of the bitwise ``OR`` of all non-null input values, or
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``None`` if all values are null.
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``BoolAnd``
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-----------
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.. class:: BoolAnd(expression, filter=None, **extra)
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Returns ``True``, if all input values are true, ``None`` if all values are
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null or if there are no values, otherwise ``False`` .
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``BoolOr``
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----------
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.. class:: BoolOr(expression, filter=None, **extra)
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Returns ``True`` if at least one input value is true, ``None`` if all
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values are null or if there are no values, otherwise ``False``.
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``JSONBAgg``
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------------
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.. class:: JSONBAgg(expressions, filter=None, **extra)
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Returns the input values as a ``JSON`` array. Requires PostgreSQL ≥ 9.5.
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``StringAgg``
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-------------
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.. class:: StringAgg(expression, delimiter, distinct=False, filter=None)
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Returns the input values concatenated into a string, separated by
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the ``delimiter`` string.
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.. attribute:: delimiter
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Required argument. Needs to be a string.
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.. attribute:: distinct
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An optional boolean argument that determines if concatenated values
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will be distinct. Defaults to ``False``.
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Aggregate functions for statistics
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==================================
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``y`` and ``x``
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---------------
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The arguments ``y`` and ``x`` for all these functions can be the name of a
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field or an expression returning a numeric data. Both are required.
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``Corr``
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--------
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.. class:: Corr(y, x, filter=None)
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Returns the correlation coefficient as a ``float``, or ``None`` if there
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aren't any matching rows.
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``CovarPop``
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------------
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.. class:: CovarPop(y, x, sample=False, filter=None)
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Returns the population covariance as a ``float``, or ``None`` if there
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aren't any matching rows.
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Has one optional argument:
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.. attribute:: sample
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By default ``CovarPop`` returns the general population covariance.
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However, if ``sample=True``, the return value will be the sample
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population covariance.
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``RegrAvgX``
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------------
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.. class:: RegrAvgX(y, x, filter=None)
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Returns the average of the independent variable (``sum(x)/N``) as a
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``float``, or ``None`` if there aren't any matching rows.
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``RegrAvgY``
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------------
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.. class:: RegrAvgY(y, x, filter=None)
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Returns the average of the dependent variable (``sum(y)/N``) as a
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``float``, or ``None`` if there aren't any matching rows.
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``RegrCount``
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-------------
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.. class:: RegrCount(y, x, filter=None)
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Returns an ``int`` of the number of input rows in which both expressions
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are not null.
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``RegrIntercept``
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-----------------
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.. class:: RegrIntercept(y, x, filter=None)
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Returns the y-intercept of the least-squares-fit linear equation determined
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by the ``(x, y)`` pairs as a ``float``, or ``None`` if there aren't any
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matching rows.
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``RegrR2``
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----------
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.. class:: RegrR2(y, x, filter=None)
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Returns the square of the correlation coefficient as a ``float``, or
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``None`` if there aren't any matching rows.
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``RegrSlope``
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-------------
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.. class:: RegrSlope(y, x, filter=None)
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Returns the slope of the least-squares-fit linear equation determined
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by the ``(x, y)`` pairs as a ``float``, or ``None`` if there aren't any
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matching rows.
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``RegrSXX``
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-----------
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.. class:: RegrSXX(y, x, filter=None)
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Returns ``sum(x^2) - sum(x)^2/N`` ("sum of squares" of the independent
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variable) as a ``float``, or ``None`` if there aren't any matching rows.
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``RegrSXY``
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-----------
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.. class:: RegrSXY(y, x, filter=None)
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Returns ``sum(x*y) - sum(x) * sum(y)/N`` ("sum of products" of independent
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times dependent variable) as a ``float``, or ``None`` if there aren't any
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matching rows.
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``RegrSYY``
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-----------
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.. class:: RegrSYY(y, x, filter=None)
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Returns ``sum(y^2) - sum(y)^2/N`` ("sum of squares" of the dependent
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variable) as a ``float``, or ``None`` if there aren't any matching rows.
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Usage examples
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==============
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We will use this example table::
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| FIELD1 | FIELD2 | FIELD3 |
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|--------|--------|--------|
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| foo | 1 | 13 |
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| bar | 2 | (null) |
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| test | 3 | 13 |
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Here's some examples of some of the general-purpose aggregation functions::
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>>> TestModel.objects.aggregate(result=StringAgg('field1', delimiter=';'))
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{'result': 'foo;bar;test'}
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>>> TestModel.objects.aggregate(result=ArrayAgg('field2'))
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{'result': [1, 2, 3]}
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>>> TestModel.objects.aggregate(result=ArrayAgg('field1'))
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{'result': ['foo', 'bar', 'test']}
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The next example shows the usage of statistical aggregate functions. The
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underlying math will be not described (you can read about this, for example, at
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`wikipedia <https://en.wikipedia.org/wiki/Regression_analysis>`_)::
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>>> TestModel.objects.aggregate(count=RegrCount(y='field3', x='field2'))
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{'count': 2}
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>>> TestModel.objects.aggregate(avgx=RegrAvgX(y='field3', x='field2'),
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... avgy=RegrAvgY(y='field3', x='field2'))
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{'avgx': 2, 'avgy': 13}
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