Module data_quality 

Data quality variations for realistic synthetic data.

This module provides tools to introduce realistic data quality issues:

• Missing values (configurable by field)
• Format variations (dates, amounts, identifiers)
• Duplicates (exact and near-duplicates)
• Typos (substitution, transposition, insertion, deletion)
• Encoding issues (character corruption)
• Labels for ML training

These variations make synthetic data more realistic for testing data cleaning, ETL pipelines, and data quality tools.

Structs

DataQualityConfig

Configuration for the data quality injector.

DataQualityConfigBuilder

Builder for DataQualityConfig.

DataQualityInjector

Main data quality injector.

DataQualityStats

Combined statistics for all data quality issues.

DuplicateConfig

Configuration for duplicate generation.

DuplicateDetector

Detects potential duplicates in a dataset.

DuplicateGenerator

Duplicate generator.

DuplicateRecord

A duplicate record with metadata.

DuplicateStats

Statistics for duplicate generation.

FormatVariationConfig

Configuration for format variations.

FormatVariationInjector

Format variation injector.

FormatVariationStats

Statistics for format variations.

Homophones

Common homophones (words that sound alike).

KeyboardLayout

QWERTY keyboard layout for nearby key substitution.

MissingCondition

Condition for MAR missing values.

MissingPattern

Pattern for MNAR missing values.

MissingValueConfig

Configuration for missing values by field.

MissingValueInjector

Missing value injector.

MissingValueStats

Statistics about missing values.

OCRConfusions

OCR-similar characters (often confused in OCR).

QualityIssue

A data quality issue record.

QualityIssueLabel

A label describing a data quality issue for ML training.

QualityLabelSummary

Summary statistics for quality labels.

QualityLabels

Collection of quality issue labels with aggregation methods.

TypoConfig

Configuration for typo generation.

TypoGenerator

Typo generator.

TypoStats

Statistics for typo generation.

Enums

AmountFormat

Amount format variations.

ConditionType

Type of condition for missing values.

DateFormat

Date format variations.

DuplicateType

Type of duplicate.

EncodingIssue

Encoding issue types.

IdentifierFormat

Identifier format variations.

LabeledIssueType

Type of data quality issue.

MissingValue

Represents a missing value placeholder.

MissingValueStrategy

Strategy for missing value injection.

PatternType

Type of pattern for MNAR.

QualityIssueSubtype

Subtype providing more detail about the issue.

QualityIssueType

Type of quality issue.

TextFormat

Text format variations.

TypoType

Type of typo/error.

Traits

Duplicatable

Trait for records that can be duplicated.

Functions

introduce_encoding_issue

Introduces encoding issues.

random_missing_representation

Selects a random missing value representation.