# Tabular Constraints¶

## FixedCombinations¶

 FixedCombinations(column_names) Ensure that the combinations across multiple columns are fixed. FixedCombinations.fit(table_data) Fit Constraint class to data. FixedCombinations.transform(table_data) Perform necessary transformations needed by constraint. FixedCombinations.fit_transform(table_data) Fit this Constraint to the data and then transform it. FixedCombinations.reverse_transform(table_data) Handle logic around reverse transforming constraints. FixedCombinations.is_valid(table_data) Say whether the column values are within the original combinations. FixedCombinations.filter_valid(table_data) Get only the rows that are valid. FixedCombinations.from_dict(constraint_dict) Build a Constraint object from a dict. Return a dict representation of this Constraint.

## Inequality¶

 Inequality(low_column_name, high_column_name) Ensure that the high_column_name column is greater than the low_column_name one. Inequality.fit(table_data) Fit Constraint class to data. Inequality.transform(table_data) Perform necessary transformations needed by constraint. Inequality.fit_transform(table_data) Fit this Constraint to the data and then transform it. Inequality.reverse_transform(table_data) Handle logic around reverse transforming constraints. Inequality.is_valid(table_data) Check whether high is greater than low in each row. Inequality.filter_valid(table_data) Get only the rows that are valid. Inequality.from_dict(constraint_dict) Build a Constraint object from a dict. Return a dict representation of this Constraint.

## ScalarInequality¶

 ScalarInequality(column_name, relation, value) Ensure an inequality between the column_name column and a scalar value. ScalarInequality.fit(table_data) Fit Constraint class to data. ScalarInequality.transform(table_data) Perform necessary transformations needed by constraint. ScalarInequality.fit_transform(table_data) Fit this Constraint to the data and then transform it. ScalarInequality.reverse_transform(table_data) Handle logic around reverse transforming constraints. ScalarInequality.is_valid(table_data) Say whether high is greater than low in each row. ScalarInequality.filter_valid(table_data) Get only the rows that are valid. ScalarInequality.from_dict(constraint_dict) Build a Constraint object from a dict. Return a dict representation of this Constraint.

## Positive¶

 Positive(column_name[, strict]) Ensure the column_name column is greater than zero. Positive.fit(table_data) Fit Constraint class to data. Positive.transform(table_data) Perform necessary transformations needed by constraint. Positive.fit_transform(table_data) Fit this Constraint to the data and then transform it. Positive.reverse_transform(table_data) Handle logic around reverse transforming constraints. Positive.is_valid(table_data) Say whether high is greater than low in each row. Positive.filter_valid(table_data) Get only the rows that are valid. Positive.from_dict(constraint_dict) Build a Constraint object from a dict. Return a dict representation of this Constraint.

## Negative¶

 Negative(column_name[, strict]) Ensure that the given columns are always negative. Negative.fit(table_data) Fit Constraint class to data. Negative.transform(table_data) Perform necessary transformations needed by constraint. Negative.fit_transform(table_data) Fit this Constraint to the data and then transform it. Negative.reverse_transform(table_data) Handle logic around reverse transforming constraints. Negative.is_valid(table_data) Say whether high is greater than low in each row. Negative.filter_valid(table_data) Get only the rows that are valid. Negative.from_dict(constraint_dict) Build a Constraint object from a dict. Return a dict representation of this Constraint.

## Range¶

 Range(low_column_name, middle_column_name, …) Ensure that the middle_column_name is between low and high columns. Range.fit(table_data) Fit Constraint class to data. Range.transform(table_data) Perform necessary transformations needed by constraint. Range.fit_transform(table_data) Fit this Constraint to the data and then transform it. Range.reverse_transform(table_data) Handle logic around reverse transforming constraints. Range.is_valid(table_data) Say whether the constraint_column is between the low and high values. Range.filter_valid(table_data) Get only the rows that are valid. Range.from_dict(constraint_dict) Build a Constraint object from a dict. Return a dict representation of this Constraint.

## ScalarRange¶

 ScalarRange(column_name, low_value, high_value) Ensure that the column_name is between the range of low and high. ScalarRange.fit(table_data) Fit Constraint class to data. ScalarRange.transform(table_data) Perform necessary transformations needed by constraint. ScalarRange.fit_transform(table_data) Fit this Constraint to the data and then transform it. ScalarRange.reverse_transform(table_data) Handle logic around reverse transforming constraints. ScalarRange.is_valid(table_data) Say whether the column_name is between the low and high values. ScalarRange.filter_valid(table_data) Get only the rows that are valid. ScalarRange.from_dict(constraint_dict) Build a Constraint object from a dict. Return a dict representation of this Constraint.

## OneHotEncoding¶

 OneHotEncoding(column_names) Ensure the appropriate columns are one hot encoded. OneHotEncoding.fit(table_data) Fit Constraint class to data. OneHotEncoding.transform(table_data) Perform necessary transformations needed by constraint. OneHotEncoding.fit_transform(table_data) Fit this Constraint to the data and then transform it. OneHotEncoding.reverse_transform(table_data) Handle logic around reverse transforming constraints. OneHotEncoding.is_valid(table_data) Check whether the data satisfies the one-hot constraint. OneHotEncoding.filter_valid(table_data) Get only the rows that are valid. OneHotEncoding.from_dict(constraint_dict) Build a Constraint object from a dict. Return a dict representation of this Constraint.

## Unique¶

 Unique(column_names) Ensure that each value for a specified column/group of columns is unique. Unique.fit(table_data) Fit Constraint class to data. Unique.transform(table_data) Perform necessary transformations needed by constraint. Unique.fit_transform(table_data) Fit this Constraint to the data and then transform it. Unique.reverse_transform(table_data) Handle logic around reverse transforming constraints. Unique.is_valid(table_data) Get indices of first instance of unique rows. Unique.filter_valid(table_data) Get only the rows that are valid. Unique.from_dict(constraint_dict) Build a Constraint object from a dict. Return a dict representation of this Constraint.