Calculated columns

Calculated columns allow you to define custom formulas to generate or transform data using logical, mathematical, statistical, or text-based operations. These formulas are applied after other generators like mockers or AI synthesis, ensuring that all base data is available before the calculation is performed.

When to use

• To clean or reformat data (e.g. trimming, date formatting)

• To apply conditional logic (e.g. gender-based name generation)

• To compute derived values from other columns (e.g. sales + tax)

• To categorize or flag data based on specific criteria

When not to use

• For straightforward mock data generation without dependencies

• When no conditional logic is needed

• If the column is already correctly populated or handled by simpler generators

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Interactive guide: How to apply a calculated column formula

Follow the interactive guide below to apply a calculated column formula.

Syntax and structure

Calculated columns follow a structured expression syntax combining functions, column references, constants, and mockers.

Example formulas

[Total Sales] + ([Total Sales] * [Tax Rate])

IF([Gender] = 'M', MOCK_FIRST_NAME, IF([Gender] = 'F', MOCK_FIRST_NAME_FEMALE, 'nothing'))

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Key syntax rules

• Column reference: [ColumnName] for same-table columns

• Functions: IF(), AND(), DATE(), etc.

• Mockers: Use MOCK_FIRST_NAME, or MOCK_CONSISTENT_FIRST_NAME for consistent mapping

• Operators: +, -, *, /, =, <>, <, >

• Constants: Use strings "text", numbers 100, or dates DATE(2020, 12, 31)

• Avoid: Column names starting with _, which will cause formula errors

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Using mockers in formulas

To insert mock data dynamically within formulas:

• Type MOCK_ and choose from the autosuggest list

• Use names like MOCK_FIRST_NAME, MOCK_COMPANY_EMAIL

• For consistent mapping, use MOCK_CONSISTENT_FIRST_NAME, etc.

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Supported data types

Generator	Supported data types
Calculated Columns	Categorical, Discrete, Continuous

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Calculated columns give you full control over how synthetic values are created or transformed. They’re ideal for applying custom logic while retaining flexibility in the data generation process.