mutcleaner.utils.type_converter

Type conversion utilities for pandas DataFrames.

This module provides flexible and efficient data type conversion capabilities with support for pandas, numpy, and Python built-in types.

Functions

convert_data_types(dataset, type_conversions)	Convert data types for specified columns with enhanced type support.
convert_data_types_batch(dataset, ...[, ...])	Batch conversion version for large datasets.
convert_to_boolean(series, errors)	Intelligent boolean conversion handling various boolean representations.
get_conversion_function(target_type, ...)	Get appropriate conversion function for target type.
normalize_type_conversions(type_conversions, ...)	Normalize type conversion mapping to standardized format.

mutcleaner.utils.type_converter.convert_data_types(dataset, type_conversions, handle_errors='coerce', optimize_memory=True)

Convert data types for specified columns with enhanced type support.

Parameters:

• dataset (DataFrame) – Input dataset with columns to be converted

• type_conversions (Dict[str, Union[str, Type, dtype]]) – Type conversion mapping in format {column_name: target_type} Supported formats: - String types: ‘float’, ‘int’, ‘str’, ‘category’, ‘bool’, ‘datetime’ - Numpy types: np.float32, np.float64, np.int32, np.int64, etc. - Pandas types: ‘Int64’, ‘Float64’, ‘string’, ‘boolean’ - Python types: float, int, str, bool

• handle_errors (str) – Error handling strategy: ‘raise’, ‘coerce’, or ‘ignore’

• optimize_memory (bool) – Whether to automatically optimize memory usage

Return type:

DataFrame

Returns:

Dataset with converted data types

mutcleaner.utils.type_converter.convert_data_types_batch(dataset, type_conversions, handle_errors='coerce', optimize_memory=True, chunk_size=10000)

Batch conversion version for large datasets.

Parameters:

• dataset (DataFrame) – Input dataset

• type_conversions (Dict[str, Union[str, Type, dtype]]) – Type conversion mapping

• handle_errors (str) – Error handling strategy

• optimize_memory (bool) – Whether to optimize memory usage

• chunk_size (int) – Chunk size for processing large datasets

Return type:

DataFrame

Returns:

Dataset with converted data types

mutcleaner.utils.type_converter.convert_to_boolean(series, errors)

Intelligent boolean conversion handling various boolean representations.

Parameters:

• series (Series) – Input series to convert

• errors (str) – Error handling strategy

Return type:

Series

Returns:

Boolean series

mutcleaner.utils.type_converter.get_conversion_function(target_type, optimize_memory)

Get appropriate conversion function for target type.

Parameters:

• target_type (Union[str, Type, dtype]) – Target data type

• optimize_memory (bool) – Whether to use memory-optimized types

Return type:

Tuple[str, Callable]

Returns:

Type name and conversion function

mutcleaner.utils.type_converter.normalize_type_conversions(type_conversions, optimize_memory)

Normalize type conversion mapping to standardized format.

Return type:

Dict[str, Tuple[str, Callable]]

Returns:

Mapping of {column: (type_name, conversion_function)}