Filter on falsy values

Description

The Filter on Falsy Values activity evaluates a specific column for values that are considered “falsy” and applies the configured action to the matching rows.
Falsy values typically represent the absence of meaningful data and include:

• false
• 0
• NaN
• "" (empty string)
• undefined

This is useful in workflows that require cleansing, flagging, or isolating incomplete or semantically empty data.

Use case:
Use this activity before transformations or aggregations to clean data rows that might result in logical inconsistencies or misleading results due to falsy entries.

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Input

Type	Description
Data	Input dataset containing the column to test

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Output

Type	Description
Transformed Data	Dataset with rows filtered or flagged as per logic

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Configuration Fields

Field Name	Required	Description
Column	Yes	Specifies the column to evaluate for falsy values.
Falsy Values	Yes	Select one or more falsy values to match. Supported values: false, 0, NaN, "" (empty string), undefined.
Actions	Yes	Action to apply when a match is found: Keep Matching Rows – retain only rows with falsy values Remove Matching Rows – exclude rows with falsy values Flag Rows – add a column flagging matching rows (1 = match, 0 = no match)
Flag Rows Column Name	Conditional	Name of the flag column to be created. Required only when action is Flag Rows.

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Sample Input

column_name	other_column
1	data
0	more data
NaN	test
	example
2	content

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Sample Configuration 1

Field	Value
Column	column_name
Falsy Values	0, NaN
Action	Flag Rows
Flag Rows Column Name	falsy_flag

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Sample Output 1 (Action: Flag Rows)

column_name	other_column	falsy_flag
1	data	0
0	more data	1
NaN	test	1
	example	0
2	content	0

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Sample Configuration 2

Field	Value
Column	column_name
Falsy Values	0, NaN, ""
Action	Remove Matching Rows

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Sample Output 2 (Action: Remove Matching Rows)

column_name	other_column
1	data
2	content

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You can chain this with Filter on Bad Meaning or Fill Columns for improved data quality and resilience.