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Trifacta Dataprep

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If you licensed Dataprep by Trifacta before Oct. 14, 2020, you are using the Dataprep by Trifacta Legacy product edition. On October 14, 2022, this product edition will be decommissioned by Google and will be no longer available for use. Current customers of this product edition are encouraged to transition to one of the product editions hosted by Trifacta. See Product Editions.

Computes the rank of an ordered set of value within groups. Tie values are assigned the same rank, and the next ranking is incremented by the number of tie values.
• Rank values start at `1` and increment.

• Ranking order varies depending on the data type of the source data.

• You must use the `group` and `order` parameters to define the groups of records and the ordering column to which this function is applied.

• This function works with the following transforms:
• This function assigns ranking values to match the total number of rows in a group. If needed, tie values can be assigned the same rank. For more information, see DENSERANK Function.

Wrangle vs. SQL: This function is part of Wrangle , a proprietary data transformation language. Wrangle is not SQL. For more information, see Wrangle Language.

## Basic Usage

rank()

Output: Generates the new column, which contains the ranking of `mySales`, grouped by the `Salesman` column.

## Syntax and Arguments

rank() order: order_col group: group_col

For more information on the `order` and `group` parameters, see Window Transform.

### Example - Rank Functions

This example demonstrates you to generate a ranked order of values.

Functions:

ItemDescription
RANK Function Computes the rank of an ordered set of value within groups. Tie values are assigned the same rank, and the next ranking is incremented by the number of tie values.
DENSERANK Function Computes the rank of an ordered set of value within groups. Tie values are assigned the same rank, and the next ranking is incremented by 1.

Source:

The following dataset contains lap times for three racers in a four-lap race. Note that for some racers, there are tie values for lap times.

RunnerLapTime
Dave172.2
Dave273.31
Dave372.2
Dave470.85
Mark171.73
Mark271.73
Mark372.99
Mark470.63
Tom174.43
Tom270.71
Tom371.02
Tom472.98

Transformation:

You can apply the `RANK()` function to the `Time` column, grouped by individual runner:

Transformation Name `Window` `RANK()` `Runner` `Time`

You can use the `DENSERANK()` function on the same column, grouping by runner:

Transformation Name `Window` `DENSERANK()` `Runner` `Time`

Results:

After renaming the columns, you have the following output:

RunnerLapTimeRankRank-Dense
Mark470.6311
Mark171.7322
Mark271.7322
Mark372.9943
Tom270.7111
Tom371.0222
Tom472.9833
Tom174.4344
Dave470.8511
Dave172.222
Dave372.222
Dave273.3143

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