These functions are available from the django.contrib.postgres.aggregates
module. They are described in more detail in the PostgreSQL docs.
注解
All functions come without default aliases, so you must explicitly provide one. For example:
>>> SomeModel.objects.aggregate(arr=ArrayAgg('somefield'))
{'arr': [0, 1, 2]}
ArrayAggPArrayAgg(expression, distinct=False, filter=None, ordering=(), **extra)[源代码]PReturns a list of values, including nulls, concatenated into an array.
distinctPAn optional boolean argument that determines if array values
will be distinct. Defaults to False.
orderingPAn optional string of a field name (with an optional "-" prefix
which indicates descending order) or an expression (or a tuple or list
of strings and/or expressions) that specifies the ordering of the
elements in the result list.
举例:
'some_field'
'-some_field'
from django.db.models import F
F('some_field').desc()
BitAndPBitOrPBoolAndPBoolOrPJSONBAggPStringAggPStringAgg(expression, delimiter, distinct=False, filter=None, ordering=())[源代码]PReturns the input values concatenated into a string, separated by
the delimiter string.
delimiterPRequired argument. Needs to be a string.
distinctPAn optional boolean argument that determines if concatenated values
will be distinct. Defaults to False.
orderingPAn optional string of a field name (with an optional "-" prefix
which indicates descending order) or an expression (or a tuple or list
of strings and/or expressions) that specifies the ordering of the
elements in the result string.
Examples are the same as for ArrayAgg.ordering.
y and xPThe 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.
CorrPCovarPopPCovarPop(y, x, sample=False, filter=None)[源代码]PReturns the population covariance as a float, or None if there
aren't any matching rows.
包含一个可选参数:
samplePBy default CovarPop returns the general population covariance.
However, if sample=True, the return value will be the sample
population covariance.
RegrAvgXPRegrAvgYPRegrCountPRegrInterceptPRegrR2PRegrSlopePRegrSXXPRegrSXYPWe 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}
8月 23, 2019