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Aggregate functions perform a computation against a set of values to generate a single result. For example, you could use an aggregate function to compute the average (mean) order over a period of time. Aggregations can be applied as standard functions or used as part of a 
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transformation step to reshape the data. 
Aggregate across an entire column:
d 

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Output: Generates a new column containing the average of all values in the Scores
column.
d 

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Output: Generates a singlecolumn table with a single value, which contains the average of all values in the Scores
column. The limit defines the maximum number of columns that can be generated.
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NOTE: When aggregate functions are applied as part of a pivot transformtransformation, they typically involve multiple parameters as part of an operation to reshape the dataset. See below. 
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Aggregate functions can be used with the the pivot transform transformation to change the structure of your data. Example:
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In the above instance, the resulting dataset contains two columns:
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NOTE: You cannot use aggregate functions inside of conditionals that evaluate to 
A pivot transform can transformation can include multiple aggregate functions and group columns from the preaggregate dataset.
For more information on the transform, see Pivot Transform.
These See Pivot Transform.
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NOTE: Null values are ignored as inputs to these functions. 
These aggregate functions are available:
D children  

