Data Manipulation¶
Coalescing, type checking, and data standardization.
Returns the first non-None value in the given list of arguments. If all arguments are None, returns None.
| RETURNS | DESCRIPTION |
|---|---|
any **Example**:: >>> coalesce(None, None, "first non-none", 5) 'first non-none' >>> coalesce(None, None, None) None **Note**: This function is analogous to the SQL COALESCE function and is useful for defaulting values when multiple potential inputs may contain None.
|
The first argument that is not None, or None if all arguments are None. |
standardize_none(data: Any, none_like_values: set = None, evaluate_as_string: bool = False) -> Any
Recursively standardizes mistyped None values to proper None. --> ('', ' ', 'null', 'none', 'nan', 'n/a', 'na', 'undefined', 'missing', 'nil', 'void', 'blank') Handles pandas DataFrames and Series if pandas is available.
If not provided, defaults to common None-like placeholders.
| RETURNS | DESCRIPTION |
|---|---|
Any
|
The standardized data with all mistyped None values replaced with proper None. |
typechecker(data: Union[Dict[str, Any], List[Dict[str, Any]]], expected_types: Dict[str, Union[Type, List[Type]]], none_is_ok: bool = False, errors: str = 'raise', verbose: bool = False) -> bool
Validates that each entry in a dictionary or a list of dictionaries matches the expected type.
Args: data: A dictionary or a list of dictionaries to validate. expected_types: A dictionary mapping parameter names to the expected types. Each type can be a single type or a list of types. none_is_ok: If True, allows None as a valid value. errors: A string that determines the error handling strategy. If set to 'raise', the function will raise a TypeError when a mismatch is found. If set to 'coerce', the function will attempt to coerce the value to the expected type.
Raises: TypeError: If any parameter does not match the expected type and 'errors' is set to 'raise'. ValueError: If any required parameter is missing.
Returns: bool: True if the data is valid, False otherwise.
Usage example: >>> from WrenchCL.Tools import typechecker
>>> data = [
>>> {"name": "John", "age": 30},
>>> {"name": "Jane", "age": "twenty"}
>>> ]
>>> expected_types = {"name": str, "age": int}
>>> try:
>>> result = typechecker(data, expected_types, none_is_ok=False, errors='raise')
>>> print("Data is valid:", result)
>>> except (TypeError, ValueError) as e:
>>> print(f"Validation failed: {e}")
# This will raise a TypeError because the age of "Jane" is not an int