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]}
ArrayAgg
¶BitAnd
¶BitAnd
(expression, filter=None, **extra)¶Returns an int
of the bitwise AND
of all non-null input values, or
None
if all values are null.
BitOr
¶BitOr
(expression, filter=None, **extra)¶Returns an int
of the bitwise OR
of all non-null input values, or
None
if all values are null.
BoolAnd
¶BoolAnd
(expression, filter=None, **extra)¶Returns True
, if all input values are true, None
if all values are
null or if there are no values, otherwise False
.
BoolOr
¶BoolOr
(expression, filter=None, **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
¶JSONBAgg
(expressions, filter=None, **extra)¶Returns the input values as a JSON
array. Requires PostgreSQL ≥ 9.5.
StringAgg
¶StringAgg
(expression, delimiter, distinct=False, filter=None)¶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
.
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
¶Corr
(y, x, filter=None)¶Returns the correlation coefficient as a float
, or None
if there
aren’t any matching rows.
CovarPop
¶CovarPop
(y, x, sample=False, filter=None)¶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
¶RegrAvgX
(y, x, filter=None)¶Returns the average of the independent variable (sum(x)/N
) as a
float
, or None
if there aren’t any matching rows.
RegrAvgY
¶RegrAvgY
(y, x, filter=None)¶Returns the average of the dependent variable (sum(y)/N
) as a
float
, or None
if there aren’t any matching rows.
RegrCount
¶RegrCount
(y, x, filter=None)¶Returns an int
of the number of input rows in which both expressions
are not null.
RegrIntercept
¶RegrIntercept
(y, x, filter=None)¶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
¶RegrR2
(y, x, filter=None)¶Returns the square of the correlation coefficient as a float
, or
None
if there aren’t any matching rows.
RegrSlope
¶RegrSlope
(y, x, filter=None)¶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
¶RegrSXX
(y, x, filter=None)¶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.
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}
Jul 01, 2019