##### Page tree

Contents:

Scheduled Maintenance: docs.trifacta.com will be offline for maintenance at 3:00pm UTC on Thursday March 30 for about 15 minutes.

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.
• 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 order of those records to which this function is applied.

• This function works with the following transforms:
• This function assigns ranking of the next value of a set of ties as a single increment more. For more discrete ranking, see RANK 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

denserank() order:Times group:Racer

Output: Returns the dense ranking of `Times` values, grouped by the `Racer` column.

## Syntax and Arguments

denserank() 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

• Page:
• Page:

• Page:
• Page:
• Page:
• Page:
• Page:
• Page:
• Page:
• Page:
• Page:
• Page: