Skip to content

Aggregation functions

Currently 6 aggregation functions are supported.

  • count(distinct: bool = True) -> int
  • exists() -> bool
  • sum(columns) -> Any
  • avg(columns) -> Any
  • min(columns) -> Any

  • max(columns) -> Any

  • QuerysetProxy

    • QuerysetProxy.count(distinct=True) method
    • QuerysetProxy.exists() method
    • QuerysetProxy.sum(columns) method
    • QuerysetProxy.avg(columns) method
    • QuerysetProxy.min(column) method
    • QuerysetProxy.max(columns) method

count

count(distinct: bool = True) -> int

Returns number of rows matching the given criteria (i.e. applied with filter and exclude). If distinct is True (the default), this will return the number of primary rows selected. If False, the count will be the total number of rows returned (including extra rows for one-to-many or many-to-many left select_related table joins). False is the legacy (buggy) behavior for workflows that depend on it.

Python
 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
class Book(ormar.Model):
    ormar_config = ormar.OrmarConfig(
        database=DatabaseConnection(DATABASE_URL),
        metadata=sqlalchemy.MetaData(),
        tablename="book"
    )

    id: int = ormar.Integer(primary_key=True)
    title: str = ormar.String(max_length=200)
    author: str = ormar.String(max_length=100)
    genre: str = ormar.String(
        max_length=100,
        default="Fiction",
        choices=["Fiction", "Adventure", "Historic", "Fantasy"],
    )
Python
1
2
# returns count of rows in db for Books model
no_of_books = await Book.objects.count()

exists

exists() -> bool

Returns a bool value to confirm if there are rows matching the given criteria (applied with filter and exclude)

Python
 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
class Book(ormar.Model):
    ormar_config = ormar.OrmarConfig(
        database=DatabaseConnection(DATABASE_URL),
        metadata=sqlalchemy.MetaData(),
        tablename="book"
    )

    id: int = ormar.Integer(primary_key=True)
    title: str = ormar.String(max_length=200)
    author: str = ormar.String(max_length=100)
    genre: str = ormar.String(
        max_length=100,
        default="Fiction",
        choices=["Fiction", "Adventure", "Historic", "Fantasy"],
    )
Python
1
2
# returns a boolean value if given row exists
has_sample = await Book.objects.filter(title='Sample').exists()

sum

sum(columns) -> Any

Returns sum value of columns for rows matching the given criteria (applied with filter and exclude if set before).

You can pass one or many column names including related columns.

As of now each column passed is aggregated separately (so sum(col1+col2) is not possible, you can have sum(col1, col2) and later add 2 returned sums in python)

You cannot sum non numeric columns.

If you aggregate on one column, the single value is directly returned as a result If you aggregate on multiple columns a dictionary with column: result pairs is returned

Given models like follows

Python
1

A sample usage might look like following

Python
 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
author = await Author(name="Author 1").save()
await Book(title="Book 1", year=1920, ranking=3, author=author).save()
await Book(title="Book 2", year=1930, ranking=1, author=author).save()
await Book(title="Book 3", year=1923, ranking=5, author=author).save()

assert await Book.objects.sum("year") == 5773
result = await Book.objects.sum(["year", "ranking"])
assert result == dict(year=5773, ranking=9)

try:
    # cannot sum string column
    await Book.objects.sum("title")
except ormar.QueryDefinitionError:
    pass

assert await Author.objects.select_related("books").sum("books__year") == 5773
result = await Author.objects.select_related("books").sum(
    ["books__year", "books__ranking"]
)
assert result == dict(books__year=5773, books__ranking=9)

assert (
    await Author.objects.select_related("books")
    .filter(books__year__lt=1925)
    .sum("books__year")
    == 3843
)

avg

avg(columns) -> Any

Returns avg value of columns for rows matching the given criteria (applied with filter and exclude if set before).

You can pass one or many column names including related columns.

As of now each column passed is aggregated separately (so sum(col1+col2) is not possible, you can have sum(col1, col2) and later add 2 returned sums in python)

You cannot avg non numeric columns.

If you aggregate on one column, the single value is directly returned as a result If you aggregate on multiple columns a dictionary with column: result pairs is returned

Python
1

A sample usage might look like following

Python
 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
author = await Author(name="Author 1").save()
await Book(title="Book 1", year=1920, ranking=3, author=author).save()
await Book(title="Book 2", year=1930, ranking=1, author=author).save()
await Book(title="Book 3", year=1923, ranking=5, author=author).save()

assert round(float(await Book.objects.avg("year")), 2) == 1924.33
result = await Book.objects.avg(["year", "ranking"])
assert round(float(result.get("year")), 2) == 1924.33
assert result.get("ranking") == 3.0

try:
    # cannot avg string column
    await Book.objects.avg("title")
except ormar.QueryDefinitionError:
    pass

result = await Author.objects.select_related("books").avg("books__year")
assert round(float(result), 2) == 1924.33
result = await Author.objects.select_related("books").avg(
    ["books__year", "books__ranking"]
)
assert round(float(result.get("books__year")), 2) == 1924.33
assert result.get("books__ranking") == 3.0

assert (
    await Author.objects.select_related("books")
    .filter(books__year__lt=1925)
    .avg("books__year")
    == 1921.5
)

min

min(columns) -> Any

Returns min value of columns for rows matching the given criteria (applied with filter and exclude if set before).

You can pass one or many column names including related columns.

As of now each column passed is aggregated separately (so sum(col1+col2) is not possible, you can have sum(col1, col2) and later add 2 returned sums in python)

If you aggregate on one column, the single value is directly returned as a result If you aggregate on multiple columns a dictionary with column: result pairs is returned

Python
1

A sample usage might look like following

Python
 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
author = await Author(name="Author 1").save()
await Book(title="Book 1", year=1920, ranking=3, author=author).save()
await Book(title="Book 2", year=1930, ranking=1, author=author).save()
await Book(title="Book 3", year=1923, ranking=5, author=author).save()

assert await Book.objects.min("year") == 1920
result = await Book.objects.min(["year", "ranking"])
assert result == dict(year=1920, ranking=1)

assert await Book.objects.min("title") == "Book 1"

assert await Author.objects.select_related("books").min("books__year") == 1920
result = await Author.objects.select_related("books").min(
    ["books__year", "books__ranking"]
)
assert result == dict(books__year=1920, books__ranking=1)

assert (
    await Author.objects.select_related("books")
    .filter(books__year__gt=1925)
    .min("books__year")
    == 1930
)

max

max(columns) -> Any

Returns max value of columns for rows matching the given criteria (applied with filter and exclude if set before).

Returns min value of columns for rows matching the given criteria (applied with filter and exclude if set before).

You can pass one or many column names including related columns.

As of now each column passed is aggregated separately (so sum(col1+col2) is not possible, you can have sum(col1, col2) and later add 2 returned sums in python)

If you aggregate on one column, the single value is directly returned as a result If you aggregate on multiple columns a dictionary with column: result pairs is returned

Python
1

A sample usage might look like following

Python
 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
author = await Author(name="Author 1").save()
await Book(title="Book 1", year=1920, ranking=3, author=author).save()
await Book(title="Book 2", year=1930, ranking=1, author=author).save()
await Book(title="Book 3", year=1923, ranking=5, author=author).save()

assert await Book.objects.max("year") == 1930
result = await Book.objects.max(["year", "ranking"])
assert result == dict(year=1930, ranking=5)

assert await Book.objects.max("title") == "Book 3"

assert await Author.objects.select_related("books").max("books__year") == 1930
result = await Author.objects.select_related("books").max(
    ["books__year", "books__ranking"]
)
assert result == dict(books__year=1930, books__ranking=5)

assert (
    await Author.objects.select_related("books")
    .filter(books__year__lt=1925)
    .max("books__year")
    == 1923
)

QuerysetProxy methods

When access directly the related ManyToMany field as well as ReverseForeignKey returns the list of related models.

But at the same time it exposes a subset of QuerySet API, so you can filter, create, select related etc related models directly from parent model.

count

Works exactly the same as count function above but allows you to select columns from related objects from other side of the relation.

Tip

To read more about QuerysetProxy visit querysetproxy section

exists

Works exactly the same as exists function above but allows you to select columns from related objects from other side of the relation.

sum

Works exactly the same as sum function above but allows you to sum columns from related objects from other side of the relation.

avg

Works exactly the same as avg function above but allows you to average columns from related objects from other side of the relation.

min

Works exactly the same as min function above but allows you to select minimum of columns from related objects from other side of the relation.

max

Works exactly the same as max function above but allows you to select maximum of columns from related objects from other side of the relation.

Tip

To read more about QuerysetProxy visit querysetproxy section