ANYIF Function
Selects a single non-null value from rows in each group that meet a specific condition.
注記
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 counting of non-nulls without conditionals, use the ANY function. See ANY 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
anyif(custId, donation = 10000)
Output: Returns a single value from custId when the donation value is greater than 10000.
Syntax and Arguments
anyif(col_ref, 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. |
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 parameters, see Pivot Transform.
col_ref
Name of the column whose values you wish to use in the calculation. Column must be a numeric (Integer or Decimal) type.
Usage Notes:
Required? | Data Type | Example Value |
|---|---|---|
Yes | String that corresponds to the name of the column | myValues |
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
ヒント
For additional examples, see Common Tasks.
Example - ANYIF and LISTIF Functions
This example illustrates you to identify and list all values within a group that meet a specified condition.
Functions:
Item | Description |
|---|---|
ANYIF Function | Selects a single non-null value from rows in each group that meet a specific condition. |
LISTIF Function | Returns list of all values in a column for rows that match a specified condition. |
WEEKDAY Function | Derives the numeric value for the day of the week ( |
Source:
The following data identifies sales figures by salespeople for a week:
EmployeeId | Date | Sales |
|---|---|---|
S001 | 1/23/17 | 25 |
S002 | 1/23/17 | 40 |
S003 | 1/23/17 | 48 |
S001 | 1/24/17 | 81 |
S002 | 1/24/17 | 11 |
S003 | 1/24/17 | 25 |
S001 | 1/25/17 | 9 |
S002 | 1/25/17 | 40 |
S003 | 1/25/17 | |
S001 | 1/26/17 | 77 |
S002 | 1/26/17 | 83 |
S003 | 1/26/17 | |
S001 | 1/27/17 | 17 |
S002 | 1/27/17 | 71 |
S003 | 1/27/17 | 29 |
S001 | 1/28/17 | |
S002 | 1/28/17 | |
S003 | 1/28/17 | 14 |
S001 | 1/29/17 | 2 |
S002 | 1/29/17 | 7 |
S003 | 1/29/17 | 99 |
Transformation:
In this example, you are interested in the high performers. A good day in sales is one in which an individual sells more than 80 units. First, you want to identify the day of week:
Transformation Name | |
|---|---|
Parameter: Formula type | Single row formula |
Parameter: Formula | WEEKDAY(Date) |
Parameter: New column name | 'DayOfWeek' |
Values greater than 5 in DayOfWeek are weekend dates. You can use the following to identify if anyone reached this highwater marker during the workweek (non-weekend):
Transformation Name | |
|---|---|
Parameter: Rows labels | EmployeeId,Date |
Parameter: Values | ANYIF(Sales, (Sales > 80 && DayOfWeek < 6)) |
Parameter: Max number of columns to create | 1 |
Before adding the step to the recipe, you take note of the individuals who reached this mark in the anyif_Sales column for special recognition.
Now, you want to find out sales for individuals during the week. You can use the following to filter the data to show only for weekdays:
Transformation Name | |
|---|---|
Parameter: Rows labels | EmployeeId,Date |
Parameter: Values | LISTIF(Sales, 1000, (DayOfWeek < 6)) |
Parameter: Max number of columns to create | 1 |
To clean up, you might select and replace the following values in the listif_Sales column with empty strings:
[" "] []
Results:
EmployeeId | Date | listif_Sales |
|---|---|---|
S001 | 1/23/17 | 25 |
S002 | 1/23/17 | 40 |
S003 | 1/23/17 | 48 |
S001 | 1/24/17 | 81 |
S002 | 1/24/17 | 11 |
S003 | 1/24/17 | 25 |
S001 | 1/25/17 | 40 |
S002 | 1/25/17 | |
S003 | 1/25/17 | 66 |
S001 | 1/26/17 | 77 |
S002 | 1/26/17 | 83 |
S003 | 1/26/17 | |
S001 | 1/27/17 | 17 |
S002 | 1/27/17 | 71 |
S003 | 1/27/17 | 29 |
S001 | 1/28/17 | |
S002 | 1/28/17 | |
S003 | 1/28/17 | |
S001 | 1/29/17 | |
S002 | 1/29/17 | |
S003 | 1/29/17 |