Run checks

Utilities for running Pandas Checks data checks.

SubsetTypes = Union[list, str, int, bool, slice, pd.Index, None] module-attribute

_apply_modifications(data, fn=lambda df: df, subset=None)

Applies user's modifications to a data object.

Parameters:

Name	Type	Description	Default
data	Any	May be any Pandas DataFrame, Series, string, or other variable	required
fn	Callable	An optional lambda function to modify data	lambda df: df
subset	SubsetTypes	Optional column name or names to filter the data to. Applied after fn.	None

Returns:

Type	Description
Any	Modified and optionally subsetted data object. If all arguments are defaults, data is returned unchanged.

_check_data(data, check_fn=lambda df: df, modify_fn=lambda df: df, subset=None, msg=None)

Runs a selected check on a data object

Parameters:

Name	Type	Description	Default
data	Any	A Pandas DataFrame, Series, string, or other variable	required
check_fn	Callable	Function to apply to data for checking. For example if we're running .check.value_counts(), this function would appply the Pandas value_counts() method	lambda df: df
modify_fn	Callable	Optional function to modify data before checking	lambda df: df
subset	SubsetTypes	Optional column name or names to select before running check_fn	None
msg	Union[str, None]	Name to use when displaying check result	None

Returns:

Type	Description
None	None

_display_check(data, name=None)

Renders the result of a Pandas Checks method.

Parameters:

Name	Type	Description	Default
data	Any	The data to display, whether a DataFrame, Series, string, or other printable.	required
name	Union[str, None]	The optional name of the check.	None

Returns:

Type	Description
None	None

get_mode()

Returns whether Pandas Checks is currently running checks and assertions.

Returns:

Type	Description
Dict[str, bool]	A dictionary containing the current settings.