**NOTE: **Output order of nested values from an aggregation function cannot be determined in advance. The work to generate output values is done in parallel, which results in different ordering of any nested values for each execution run for each running environment.

**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. See Pivot Transform.

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

These aggregate functions are available:

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