Certain aggregate functions can also be used within SQLAlchemy by using the func module.

To import this module, execute:

from sqlalchemy.sql import func

Once func is imported, the following aggregate functions are available:

COUNT

The following example counts the number of records in a set of groups using the session object's query() method.

rs = session.query(func.count([CData].[Sample].Customers._id).label("CustomCount"), [CData].[Sample].Customers.City).group_by([CData].[Sample].Customers.City)
for instance in rs:
	print("Count: ", instance.CustomCount)
	print("City: ", instance.City)
	print("---------")

You can also execute COUNT using the session object's execute() method:

rs = session.execute([CData].[Sample].Customers_table.select().with_only_columns([func.count([CData].[Sample].Customers_table.c._id).label("CustomCount"), [CData].[Sample].Customers_table.c.City])group_by([CData].[Sample].Customers_table.c.City))
for instance in rs:

SUM

This example calculates the cumulative amount of a numeric column in a set of groups.

rs = session.query(func.sum([CData].[Sample].Customers.Balance).label("CustomSum"), [CData].[Sample].Customers.City).group_by([CData].[Sample].Customers.City)
for instance in rs:
	print("Sum: ", instance.CustomSum)
	print("City: ", instance.City)
	print("---------")

You can also invoke SUM using the session object's execute() method.

rs = session.execute([CData].[Sample].Customers_table.select().with_only_columns([func.sum([CData].[Sample].Customers_table.c.Balance).label("CustomSum"), [CData].[Sample].Customers_table.c.City]).group_by([CData].[Sample].Customers_table.c.City))
for instance in rs:

AVG

This example uses the session object's query() method to calculate the average amount of a numeric column in a set of groups:

rs = session.query(func.avg([CData].[Sample].Customers.Balance).label("CustomAvg"), [CData].[Sample].Customers.City).group_by([CData].[Sample].Customers.City)
for instance in rs:
	print("Avg: ", instance.CustomAvg)
	print("City: ", instance.City)
	print("---------")

You can also use the session object's execute() method to invoke AVG:

rs = session.execute([CData].[Sample].Customers_table.select().with_only_columns([func.avg([CData].[Sample].Customers_table.c.Balance).label("CustomAvg"), [CData].[Sample].Customers_table.c.City]).group_by([CData].[Sample].Customers_table.c.City))
for instance in rs:

MAX and MIN

This example finds the maximum value and minimum value of a numeric column in a set of groups.

rs = session.query(func.max([CData].[Sample].Customers.Balance).label("CustomMax"), func.min([CData].[Sample].Customers.Balance).label("CustomMin"), [CData].[Sample].Customers.City).group_by([CData].[Sample].Customers.City)
for instance in rs:
	print("Max: ", instance.CustomMax)
	print("Min: ", instance.CustomMin)
	print("City: ", instance.City)
	print("---------")

You can also use the session object's execute() method to invoke MAX and MIN:

rs = session.execute([CData].[Sample].Customers_table.select().with_only_columns([func.max([CData].[Sample].Customers_table.c.Balance).label("CustomMax"), func.min([CData].[Sample].Customers_table.c.Balance).label("CustomMin"), [CData].[Sample].Customers_table.c.City]).group_by([CData].[Sample].Customers_table.c.City))
for instance in rs: