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:
Output: Generates a new column containing the average of all values in the
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:
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 (
NOTE: You cannot use aggregate functions inside of conditionals that evaluate to
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.
These aggregate functions are available:
- ANY Function
- ANYIF Function
- AVERAGE Function
- AVERAGEIF Function
- COUNTA Function
- COUNTAIF Function
- COUNTDISTINCT Function
- COUNTDISTINCTIF Function
- COUNT Function
- COUNTIF Function
- KTHLARGEST Function
- KTHLARGESTIF Function
- KTHLARGESTUNIQUE Function
- LIST Function
- LISTIF Function
- MAX Function
- MAXIF Function
- MIN Function
- MINIF Function
- MODE Function
- MODEIF Function
- STDEV Function
- STDEVIF Function
- SUM Function
- SUMIF Function
- VAR Function
- VARIF Function
- LISTUNIQUE Function
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