KTHLARGESTDATEIF Function
Extracts the ranked Datetime value from the values in a column, where k=1 returns the maximum value, when a specified condition is met. The value for k must be between 1 and 1000, inclusive. Inputs must be Datetime.
KTHLARGESTDATEIF calculations are filtered by a conditional applied to the group.
For purposes of this calculation, two instances of the same value are treated as the same value of k. So, if your dataset contains three rows with column values 2020-02-15, 2020-02-14, and 2020-02-14, then KTHLARGESTDATEIF returns 2020-02-14 for k=2 and 2020-02-14 for k=3.
Input column must be of Datetime type. Other values column are ignored. If a row contains a missing or null value, it is not factored into the calculation.
Nota
When added to a transformation, this function is applied to the current sample. If you change your sample or run the job, the computed values for this function are updated. Transformations that change the number of rows in subsequent recipe steps do not affect the values computed for this step.
To perform a simple kth largest calculation on Datetime values without conditionals, use the KTHLARGESTDATE function. See KTHLARGESTDATE Function.
For a version of this function that applies to non-Datetime values, see KTHLARGESTIF 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
kthlargestdateif(transDate, 2, salesPerson == 'jsmith')
Output: Returns the secondmost recent Date (rank=2) from the transDate column when the salesPerson value is jsmith.
Syntax and Arguments
kthlargestdateif(col_ref, limit, test_expression) [group:group_col_ref] [limit:limit_count]
Argument | Required? | Data Type | Description |
|---|---|---|---|
col_ref | Y | string | Reference to the column you wish to evaluate. |
k_integer | Y | integer | The ranking of the value to extract from the source column |
test_expression | Y | string | Expression that is evaluated. Must resolve to |
For more information on syntax standards, see Language Documentation Syntax Notes.
For more information on the group and limit parameter, see Pivot Transform.
col_ref
Name of the column whose values you wish to use in the calculation. Inputs must be Datetime values.
Usage Notes:
Required? | Data Type | Example Value |
|---|---|---|
Yes | String that corresponds to the name of the column | transactionDate |
k_integer
Integer representing the ranking of the value to extract from the source column.
Nota
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.
test_expression
This parameter contains the expression to evaluate. This expression must resolve to a Boolean (true or false) value.
Usage Notes:
Required? | Data Type | Example Value |
|---|---|---|
Yes | String expression that evaluates to | (LastName == 'Mouse' && FirstName == 'Mickey') |
Examples
Sugerencia
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 |