Row Constraints API

NotNull

ydata.constraints.rows.constraint.NotNull

Bases: RowConstraint

Check that values in the specified columns are not null.

Parameters:

Name	Type	Description	Default
columns	str | list[str]	Column(s) that must not contain null / NaN values.	required
name	str | None	Human-readable label shown in engine summaries.	None

Unique

ydata.constraints.rows.constraint.Unique

Bases: RowConstraint

Check that values in the specified columns contain no duplicates.

A row is flagged as violating when its value appears more than once in that column. This is the standard uniqueness / primary-key check.

Parameters:

Name	Type	Description	Default
columns	str | list[str]	Column(s) whose values must be unique.	required
name	str | None	Human-readable label shown in engine summaries.	None

GreaterThan

ydata.constraints.rows.constraint.GreaterThan

Bases: RowConstraint

validate(dataset)

Validate the constraint against a dataset.

Args: dataset (Dataset): Dataset to test

Returns:

Name	Type	Description
pdDataFrame	DataFrame	constraint mask

LowerThan

ydata.constraints.rows.constraint.LowerThan

Bases: RowConstraint

validate(dataset)

Validate the constraint against a dataset.

Args: dataset (Dataset): Dataset to test

Returns:

Name	Type	Description
pdDataFrame	DataFrame	constraint mask

Between

ydata.constraints.rows.constraint.Between

Bases: RowConstraint

validate(dataset)

Validate the constraint against a dataset.

Args: dataset (Dataset): Dataset to test

Returns:

Name	Type	Description
pdDataFrame	DataFrame	constraint mask

Positive

ydata.constraints.rows.constraint.Positive

Bases: GreaterThan

validate(dataset)

Validate the constraint against a dataset.

Parameters:

Name	Type	Description	Default
dataset	Dataset	Dataset to test	required

Returns:

Name	Type	Description
pdDataFrame	DataFrame	constraint mask

NotIncludedIn

ydata.constraints.rows.constraint.NotIncludedIn

Bases: RowConstraint

Check that values in a column are NOT in a specified blocklist.

This is the complement of :class:IncludedIn — rows containing any of the forbidden values are flagged as violations.

Parameters:

Name	Type	Description	Default
column	str	Column on which the constraint is applied.	required
values	list[Any] | Any	Single value or list of values that are not allowed.	required
name	str | None	Human-readable label shown in engine summaries.	None

IncludedIn

ydata.constraints.rows.constraint.IncludedIn

Bases: RowConstraint

validate(dataset)

Validate the constraint against a dataset.

Args: dataset (Dataset): Dataset to test

Returns:

Name	Type	Description
pdDataFrame	DataFrame	constraint mask

StringLength

ydata.constraints.rows.constraint.StringLength

Bases: RowConstraint

Check that string values in the specified columns have an acceptable length.

Length is measured in characters after coercing to str. Both bounds are inclusive. Omit max_length to only enforce a minimum.

Parameters:

Name	Type	Description	Default
columns	str | list[str]	Column(s) to check.	required
min_length	int	Minimum allowed string length (inclusive). Defaults to 0.	0
max_length	int | None	Maximum allowed string length (inclusive). None means no upper bound.	None
name	str | None	Human-readable label shown in engine summaries.	None

Monotonic

ydata.constraints.rows.constraint.Monotonic

Bases: RowConstraint

Check that values in a column are monotonically ordered.

Row i is flagged as violating when it breaks the ordering relative to row i-1. The first row always passes (no predecessor to compare to).

Works with numeric and datetime columns. For strictly increasing/decreasing sequences, equal consecutive values are treated as violations.

Parameters:

Name	Type	Description	Default
columns	str | list[str]	Column(s) to check.	required
increasing	bool	If True (default), values must be non-decreasing. If False, values must be non-increasing.	True
strict	bool	If True, equal consecutive values are violations (strictly monotonic). Defaults to False.	False
name	str | None	Human-readable label shown in engine summaries.	None

Regex

ydata.constraints.rows.constraint.Regex

Bases: RowConstraint

validate(dataset)

Validate the constraint against a dataset.

Args: dataset (Dataset): Dataset to test

Returns:

Name	Type	Description
pdDataFrame	DataFrame	constraint mask

BetweenDates

ydata.constraints.rows.constraint.BetweenDates

Bases: RowConstraint

validate(dataset)

Validate the constraint against a dataset.

Args: dataset (Dataset): Dataset to test

Returns:

Name	Type	Description
pdDataFrame	DataFrame	constraint mask

RelationConstraint

ydata.constraints.rows.constraint.RelationConstraint

Bases: RowConstraint

Validates that each row's key (matching columns) and value columns match a reference. Key columns must be single values; value columns can be scalar or list of allowed values per key in the reference.

validate(dataset)

Validate the constraint against a dataset.

Parameters:

Name	Type	Description	Default
dataset	Dataset	Dataset to test	required

Returns:

Name	Type	Description
pdDataFrame	DataFrame	constraint mask (True = satisfied, False = violated)

CombineConstraints

ydata.constraints.rows.constraint.CombineConstraints

Bases: RowConstraint

validate(dataset)

Validate the constraint against a dataset.

Args: dataset (Dataset): Dataset to test

Returns:

Name	Type	Description
pdDataFrame	DataFrame	constraint mask

RowCustomConstraint

ydata.constraints.rows.constraint.RowCustomConstraint

Bases: RowConstraint

validate(dataset)

Validate the constraint against a dataset.

Parameters:

Name	Type	Description	Default
dataset	Dataset	Dataset to test	required

Returns:

Name	Type	Description
pdDataFrame	DataFrame	constraint mask