PostgreSQL specific aggregation functions
These functions are available from the django.contrib.postgres.aggregates
module. They are described in more detail in the PostgreSQL docs.
Σημείωση
All functions come without default aliases, so you must explicitly provide
one. For example:
>>> SomeModel.objects.aggregate(arr=ArrayAgg('somefield'))
{'arr': [0, 1, 2]}
Common aggregate options
All aggregates have the filter keyword
argument.
General-purpose aggregation functions
ArrayAgg
-
class
ArrayAgg
(expression, distinct=False, filter=None, **extra)[πηγή]
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)[πηγή]
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)[πηγή]
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)[πηγή]
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)[πηγή]
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)[πηγή]
Returns the input values as a JSON
array. Requires PostgreSQL ≥ 9.5.
StringAgg
-
class
StringAgg
(expression, delimiter, distinct=False, filter=None)[πηγή]
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)[πηγή]
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)[πηγή]
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)[πηγή]
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)[πηγή]
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)[πηγή]
Returns an int
of the number of input rows in which both expressions
are not null.
RegrIntercept
-
class
RegrIntercept
(y, x, filter=None)[πηγή]
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)[πηγή]
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)[πηγή]
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)[πηγή]
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)[πηγή]
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)[πηγή]
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}