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 transform step to reshape the data.

**Aggregate across an entire column:**

derive type:single value:AVERAGE(Scores)

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

column.

pivot value: AVERAGE(Score) limit: 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`

transform, 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`

transform to change the structure of your data. Example:

pivot group: StudentId value: AVERAGE(Score) limit: 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 Table transformation can include multiple aggregate functions and group columns from the pre-aggregate dataset.

For more information on the transform, see Pivot Data.

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

These aggregate functions are available:

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