IN Function
Returns true if the first parameter is contained in the array of values in the second parameter.
The value to match can be a literal or a reference to a column.
The second parameter must be in array format.
Since the IN function returns a Boolean value, it can be used as a function or a conditional.
Astuce
When you select values in a histogram for a column of String type, the function that identifies the values on which to perform a transform is typically IN.
Astuce
If you need the location of the matched value within the source, use the FIND function. See FIND 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
in(brand, ['discount','mid','high-end'])
Output: Returns true if the value in the brand column is either discount, mid, or high-end.
Syntax and Arguments
in(column_string, values_array)
Argument | Required? | Data Type | Description |
|---|---|---|---|
column_string | Y | string | Name of column or literal to locate in the column specified in the second parameter |
values_array | Y | array literal | Array literal of values to search |
For more information on syntax standards, see Language Documentation Syntax Notes.
column_string
Name of the column or literal to find in the second parameter.
Missing values generate missing string results.
String constants must be quoted (
'Hello, World').
Multiple columns and wildcards are not supported.
Usage Notes:
Required? | Data Type | Example Value |
|---|---|---|
Yes | Column reference or any value | myColumn |
values_array
Array of values to search for the first parameter.
Column references are not supported.
Usage Notes:
Required? | Data Type | Example Value |
|---|---|---|
Yes | Array literal | 'Steve' |
Examples
Astuce
For additional examples, see Common Tasks.
Example - Replace T-shirt color
Source:
You have the following source data on your products:
ProdId | ProductName | Color | Brand |
|---|---|---|---|
P001 | T-shirt | white | discount |
P002 | pants | beige | discount |
P003 | hat | black | discount |
P004 | T-shirt | white | mid |
P005 | pants | black | mid |
P006 | hat | red | mid |
P007 | T-shirt | white | high-end |
P008 | pants | white | high-end |
P009 | hat | blue | high-end |
In the data, you notice an error. For the discount and mid brands, T-shirt color should be orange. You need to fix it.
Transformation:
In the Transformer page, you select the white value from the histogram at the top of the Color column. Among the suggestion cards, select the Set transform. For the first variant, all values are missing. Click Modify. The current transform is the following:
Transformation Name | |
|---|---|
Parameter: Columns | Color |
Parameter: Formula | null() |
Parameter: Group rows by | Color == 'white' |
In the Preview, you can see that this transform matches all white values in the column and replaces them with a null value. Since the replacement value is orange, you can edit the transform so it looks like the following:
Transformation Name | |
|---|---|
Parameter: Columns | Color |
Parameter: Formula | 'orange' |
Parameter: Group rows by | Color == 'white' |
This step looks better. However, it is replacing all instances of white, including those for white pants (P008) and high-end T-shirts (p007), which should not be replaced. To fix, you must add conditions to the row expression. First, add the following, which ensures that the transform only replaces for T-shirts:
Transformation Name | |
|---|---|
Parameter: Columns | Color |
Parameter: Formula | 'orange' |
Parameter: Group rows by | (Color == 'white' && ProductName == 'T-shirt') |
Now, the Preview shows that only T-shirt values are being changed. The transform needs to be further modified to restrict only to the appropriate brands (discount and mid):
Transformation Name | |
|---|---|
Parameter: Columns | Color |
Parameter: Formula | 'orange' |
Parameter: Group rows by | (Color == 'white' && ProductName == 'T-shirt' && IN(Brand, ["discount","mid"])) |
Note
It's possible to specify the brand restriction as (Brand <> 'high-end'). However, if there are other brand values in the full dataset, this restriction fails.
Results:
ProdId | ProductName | Color | Brand |
|---|---|---|---|
P001 | T-shirt | orange | discount |
P002 | pants | beige | discount |
P003 | hat | black | discount |
P004 | T-shirt | orange | mid |
P005 | pants | black | mid |
P006 | hat | red | mid |
P007 | T-shirt | white | high-end |
P008 | pants | white | high-end |
P009 | hat | blue | high-end |