KTHLARGESTUNIQUEDATE Function
Extracts the ranked unique Datetime value from the values in a column, where k=1 returns the maximum value. The value for k must be between 1 and 1000, inclusive. Inputs must be Datetime.
For purposes of this calculation, two instances of the same value are treated as the same value of k. if your dataset contains three rows with column values 2020-02-15, 2020-02-14, 2020-02-14, and 2020-02-14, then KTHLARGESTDATE returns 2020-02-14 for k=2 and 2020-02-13 for k=3.
For a non-unique version of this function, see KTHLARGESTDATE Function.
When used in apivottransform, the function is computed for each instance of the value specified in thegroupparameter. See Pivot Transform.
Input column must be Datetime type. Other values column are ignored. If a row contains a missing or null value, it is not factored into the calculation.
For a version of this function that applies to non-Datetime values, see KTHLARGESTUNIQUE 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
kthlargestuniquedate(myDate, 3)
Output: Returns the third highest unique value from the myDate column.
Syntax and Arguments
kthlargestuniquedate(function_col_ref, k_integer) [ group:group_col_ref] [limit:limit_count]
Argument | Required? | Data Type | Description |
|---|---|---|---|
function_col_ref | Y | string | Name of column to which to apply the function |
k_integer | Y | integer (positive) | The ranking of the unique value to extract from the source column |
For more information on the group and limit parameters, see Pivot Transform.
For more information on syntax standards, see Language Documentation Syntax Notes.
function_col_ref
Name of the column the values of which you want to calculate the mean. Inputs must be Datetime values.
Literal values are not supported as inputs.
Multiple columns and wildcards are not supported.
Usage Notes:
Required? | Data Type | Example Value |
|---|---|---|
Yes | String (column reference) | transactionDate |
k_integer
Integer representing the ranking of the unique value to extract from the source column. Duplicate values are treated as a single value for purposes of this function's calculation.
Note
The value for k must be an integer between 1 and 1,000 inclusive.
k=1represents the maximum value in the column.If k is greater than or equal to the number of values in the column, the minimum value is returned.
Missing and null values are not factored into the ranking of
k.
Usage Notes:
Required? | Data Type | Example Value |
|---|---|---|
Yes | Integer (positive) | 4 |
Examples
Astuce
For additional examples, see Common Tasks.
Example - KTHLARGESTDATE functions
This example illustrates how you can apply conditionals to calculate minimum, maximum, and most common date values.
Functions:
Item | Description |
|---|---|
KTHLARGESTDATE Function | Extracts the ranked Datetime value from the values in a column, where |
KTHLARGESTUNIQUEDATE Function | Extracts the ranked unique Datetime value from the values in a column, where |
KTHLARGESTDATEIF Function | Extracts the ranked Datetime value from the values in a column, where |
KTHLARGESTUNIQUEDATEIF Function | Extracts the ranked unique Datetime value from the values in a column, where |
Source:
Here is some example transaction data:
Date | Product | Units | UnitCost | OrderValue |
|---|---|---|---|---|
3/28/2020 | ProductA | 4 | 10.00 | 40.00 |
3/8/2020 | ProductB | 4 | 20.00 | 80.00 |
3/12/2020 | ProductC | 2 | 30.00 | 60.00 |
3/23/2020 | ProductA | 1 | 10.00 | 10.00 |
3/20/2020 | ProductB | 2 | 20.00 | 40.00 |
3/12/2020 | ProductC | 9 | 30.00 | 270.00 |
3/28/2020 | ProductA | 5 | 10.00 | 50.00 |
3/23/2020 | ProductB | 8 | 20.00 | 160.00 |
3/16/2020 | ProductC | 9 | 30.00 | 270.00 |
3/8/2020 | ProductA | 5 | 10.00 | 50.00 |
3/10/2020 | ProductB | 3 | 20.00 | 60.00 |
3/13/2020 | ProductC | 1 | 30.00 | 30.00 |
3/12/2020 | ProductA | 7 | 10.00 | 70.00 |
3/10/2020 | ProductB | 7 | 20.00 | 140.00 |
3/24/2020 | ProductC | 9 | 30.00 | 270.00 |
3/15/2020 | ProductA | 8 | 10.00 | 80.00 |
3/10/2020 | ProductB | 5 | 20.00 | 100.00 |
3/10/2020 | ProductC | 4 | 30.00 | 120.00 |
Transformation:
The following transformation computes the third highest date in the Date column:
Transformation Name | |
|---|---|
Parameter: Formula type | Single row formula |
Parameter: Formula | kthlargestdate(Date, 3) |
Parameter: New column name | 'kthlargestdate' |
This transformation computes the third highest unique value in the Date column:
Transformation Name | |
|---|---|
Parameter: Formula type | Single row formula |
Parameter: Formula | kthlargestuniquedate(Date, 3) |
Parameter: New column name | 'kthlargestuniquedate' |
Following transformation calculates the 3rd highest date value when the OrderValue > 200:
Transformation Name | |
|---|---|
Parameter: Formula type | Single row formula |
Parameter: Formula | kthlargestdateif(Date, 3, OrderValue > 200) |
Parameter: New column name | 'kthlargestdateif' |
Following transformation calculates the 3rd highest unique date value when the OrderValue > 200:
Transformation Name | |
|---|---|
Parameter: Formula type | Single row formula |
Parameter: Formula | kthlargestuniquedateif(Date, 3, OrderValue > 200) |
Parameter: New column name | 'kthlargestuniquedateif' |
Results:
Date | Product | Units | UnitCost | OrderValue | kthlargestdate | kthlargestuniquedate | kthlargestdateif | kthlargestuniquedateif |
|---|---|---|---|---|---|---|---|---|
3/28/2020 | ProductA | 4 | 10.00 | 40.00 | 03-24-2020 | 03-23-2020 | 03-23-2020 | 03-23-2020 |
3/8/2020 | ProductB | 4 | 20.00 | 80.00 | 03-24-2020 | 03-23-2020 | 03-23-2020 | 03-23-2020 |
3/12/2020 | ProductC | 2 | 30.00 | 60.00 | 03-24-2020 | 03-23-2020 | 03-23-2020 | 03-23-2020 |
3/23/2020 | ProductA | 1 | 10.00 | 10.00 | 03-24-2020 | 03-23-2020 | 03-23-2020 | 03-23-2020 |
3/20/2020 | ProductB | 2 | 20.00 | 40.00 | 03-24-2020 | 03-23-2020 | 03-23-2020 | 03-23-2020 |
3/12/2020 | ProductC | 9 | 30.00 | 270.00 | 03-24-2020 | 03-23-2020 | 03-23-2020 | 03-23-2020 |
3/28/2020 | ProductA | 5 | 10.00 | 50.00 | 03-24-2020 | 03-23-2020 | 03-23-2020 | 03-23-2020 |
3/23/2020 | ProductB | 8 | 20.00 | 160.00 | 03-24-2020 | 03-23-2020 | 03-23-2020 | 03-23-2020 |
3/16/2020 | ProductC | 9 | 30.00 | 270.00 | 03-24-2020 | 03-23-2020 | 03-23-2020 | 03-23-2020 |
3/8/2020 | ProductA | 5 | 10.00 | 50.00 | 03-24-2020 | 03-23-2020 | 03-23-2020 | 03-23-2020 |
3/10/2020 | ProductB | 3 | 20.00 | 60.00 | 03-24-2020 | 03-23-2020 | 03-23-2020 | 03-23-2020 |
3/13/2020 | ProductC | 1 | 30.00 | 30.00 | 03-24-2020 | 03-23-2020 | 03-23-2020 | 03-23-2020 |
3/12/2020 | ProductA | 7 | 10.00 | 70.00 | 03-24-2020 | 03-23-2020 | 03-23-2020 | 03-23-2020 |
3/10/2020 | ProductB | 7 | 20.00 | 140.00 | 03-24-2020 | 03-23-2020 | 03-23-2020 | 03-23-2020 |
3/24/2020 | ProductC | 9 | 30.00 | 270.00 | 03-24-2020 | 03-23-2020 | 03-23-2020 | 03-23-2020 |
3/15/2020 | ProductA | 8 | 10.00 | 80.00 | 03-24-2020 | 03-23-2020 | 03-23-2020 | 03-23-2020 |
3/10/2020 | ProductB | 5 | 20.00 | 100.00 | 03-24-2020 | 03-23-2020 | 03-23-2020 | 03-23-2020 |
3/10/2020 | ProductC | 4 | 30.00 | 120.00 | 03-24-2020 | 03-23-2020 | 03-23-2020 | 03-23-2020 |