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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:
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Output: Generates a new column containing the average of all values in the Scores column.
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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.
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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:
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In the above instance, the resulting dataset contains two columns:
studentId- one row for each distinct student ID valueaverage_Scores- average score by each student (studentId)
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NOTE: You cannot use aggregate functions inside of conditionals that evaluate to |
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.
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NOTE: Null values are ignored as inputs to these functions. |
These aggregate functions are available:
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