**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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