Aggregate Functions
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([AwsDataCatalog].[sampledb].Customers.Id).label("CustomCount"), [AwsDataCatalog].[sampledb].Customers.Name).group_by([AwsDataCatalog].[sampledb].Customers.Name) for instance in rs: print("Count: ", instance.CustomCount) print("Name: ", instance.Name) print("---------")
You can also execute COUNT using the session object's execute() method:
rs = session.execute([AwsDataCatalog].[sampledb].Customers_table.select().with_only_columns([func.count([AwsDataCatalog].[sampledb].Customers_table.c.Id).label("CustomCount"), [AwsDataCatalog].[sampledb].Customers_table.c.Name])group_by([AwsDataCatalog].[sampledb].Customers_table.c.Name)) 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([AwsDataCatalog].[sampledb].Customers.AnnualRevenue).label("CustomSum"), [AwsDataCatalog].[sampledb].Customers.Name).group_by([AwsDataCatalog].[sampledb].Customers.Name) for instance in rs: print("Sum: ", instance.CustomSum) print("Name: ", instance.Name) print("---------")
You can also invoke SUM using the session object's execute() method.
rs = session.execute([AwsDataCatalog].[sampledb].Customers_table.select().with_only_columns([func.sum([AwsDataCatalog].[sampledb].Customers_table.c.AnnualRevenue).label("CustomSum"), [AwsDataCatalog].[sampledb].Customers_table.c.Name]).group_by([AwsDataCatalog].[sampledb].Customers_table.c.Name)) 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([AwsDataCatalog].[sampledb].Customers.AnnualRevenue).label("CustomAvg"), [AwsDataCatalog].[sampledb].Customers.Name).group_by([AwsDataCatalog].[sampledb].Customers.Name) for instance in rs: print("Avg: ", instance.CustomAvg) print("Name: ", instance.Name) print("---------")
You can also use the session object's execute() method to invoke AVG:
rs = session.execute([AwsDataCatalog].[sampledb].Customers_table.select().with_only_columns([func.avg([AwsDataCatalog].[sampledb].Customers_table.c.AnnualRevenue).label("CustomAvg"), [AwsDataCatalog].[sampledb].Customers_table.c.Name]).group_by([AwsDataCatalog].[sampledb].Customers_table.c.Name)) 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([AwsDataCatalog].[sampledb].Customers.AnnualRevenue).label("CustomMax"), func.min([AwsDataCatalog].[sampledb].Customers.AnnualRevenue).label("CustomMin"), [AwsDataCatalog].[sampledb].Customers.Name).group_by([AwsDataCatalog].[sampledb].Customers.Name) for instance in rs: print("Max: ", instance.CustomMax) print("Min: ", instance.CustomMin) print("Name: ", instance.Name) print("---------")
You can also use the session object's execute() method to invoke MAX and MIN:
rs = session.execute([AwsDataCatalog].[sampledb].Customers_table.select().with_only_columns([func.max([AwsDataCatalog].[sampledb].Customers_table.c.AnnualRevenue).label("CustomMax"), func.min([AwsDataCatalog].[sampledb].Customers_table.c.AnnualRevenue).label("CustomMin"), [AwsDataCatalog].[sampledb].Customers_table.c.Name]).group_by([AwsDataCatalog].[sampledb].Customers_table.c.Name)) for instance in rs: