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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 transformation step to reshape the data.

Aggregate across an entire column:

Transformation Name New formula
Parameter: Formula type Single row formula
Parameter: Formula average(Scores)

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

Transformation Name Pivot columns
Parameter: Values average(Score)
Parameter: Max number of columns to create 1

Output: 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.

NOTE: When aggregate functions are applied as part of a pivot transformation, they typically involve multiple parameters as part of an operation to reshape the dataset. See below.

Aggregate across groups of values within a column:

Aggregate functions can be used with the pivot transformation to change the structure of your data. Example:

Transformation Name Pivot columns
Parameter: Row labels StudentId
Parameter: Values average(Score)
Parameter: Max number of columns to create 1

In the above instance, the resulting dataset contains two columns:

  • studentId - one row for each distinct student ID value
  • average_Scores - average score by each student (studentId)

NOTE: You cannot use aggregate functions inside of conditionals that evaluate to true or false.

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

For more information on the transformation, see Pivot Data.

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

 These aggregate functions are available:

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