# ScalarInequality

**Compatibility:** A single numerical or datetime column

The `ScalarInequality`

constraint enforces that all values in a column are greater or less than a fixed (scalar) value. That is, it enforces a lower or upper bound to the synthetic data.

Some models already learn the min and max values of every column in the real dataset and enforce the bounds in the synthetic dataset. For such models, you do not need to add this constraint.

## Parameters

(required) `column_name`

: The name of the column that must follow the constraint

(required) `relation`

: The inequality relation between the column name and the value

| The column is greater than the value |

| The column is greater than or equal to the value |

| The column is less than the value |

| The column is less than or equal to the value |

(required) `value`

: The value that the column should be compared against

| A numerical value |

| A string representing a datetime value |

## Example

Define your constraint using the parameters and then add it to a synthesizer.

## FAQs

**Shortcuts Available.** If you want to enforce a lower bound of 0, use the Positive constraint. For an upper bound of 0, use the Negative constraint. If you want to enforce both upper and lower bounds, use the ScalarRange constraint.

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