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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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transform step to reshape the data.

Aggregate across an entire column:

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derive type:single value:

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AVERAGE(Scores)

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

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pivot value:

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AVERAGE(Score) limit: 1

p01NameValuesp01Valueaverage(Score)p02NameMax number of columns to createp02Value1SearchTermPivot columnsOutput: Generates a single-column 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 transformation transform, 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 transformation  transform to change the structure of your data. Example:

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pivot group: StudentId value:

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AVERAGE(Score) limit: 1

p01NameRow labelsp01ValueStudentIdp02NameValuesp02Valueaverage(Score)p03NameMax number of columns to createSearchTermPivot columnsIn 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 true or false.

A pivot Pivot Table transformation  can can include multiple aggregate functions and group columns from the pre-aggregate dataset.

For more information on the transformationtransform, see Pivot Data.

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NOTE: Null values are ignored as inputs to these functions.


 These These aggregate functions are available:

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