View Source

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.

Tip: 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.

Tip: If you need the location of the matched value within the source, use the FIND function. See FIND Function.

keep row: IN(brand, ['discount','mid','high-end'] )

Output: Keeps all rows in which the value in the brand column is either discount, mid, or high-end.

derive type:single value: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

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.

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.

Required?	Data Type	Example Value
Yes	Array literal	`'Steve'`

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.

Transform:

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:

set col: Color value: NULL() row: 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:

set col: Color value: 'orange' row: 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:

set col: Color value: 'orange' row: (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):

set col: Color value: 'orange' row: (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