deirokay.statements.builtin.unique.Unique

class deirokay.statements.builtin.unique.Unique(*args, **kwargs)[source]

Bases: BaseStatement

Checks for the unicity of rows in a scope.

The only available option is:

at_least_%: The minimum percentage of unique rows.

Examples

In a table containing information about cities of your country, you expect the pair of columns state and city to be unique across all rows. It means that, although some values of state can be repeated, as well as city names, the combination of both columns should be unique. You can declare the following validation item to represent this rule:

{
    "scope": ["state", "city"],
    "statements": [
        {
            "name": "unique"
        }
    ]
}

Methods

`attach_backend`	Generate a subclass that concretizes multibackend backend methods into their intended name.
`get_backend`	Get current active backend for this class.
`profile`	Given a template data table, generate a statement dict from it.
`register_backend_method`	Proxy for register_backend_method to register an existing function as a backend-specific method.
`report`	Receive a DataFrame containing only columns on the scope of validation and returns a report of related metrics that can be used later to declare this Statement as fulfilled or failed.
`result`	Receive the report previously generated and declare this statement as either fulfilled (True) or failed (False).

Attributes

`expected_parameters`	Parameters expected for this statement.
`name`	Statement name when referred in Validation Documents (only valid for Deirokay built-in statements).
`supported_backends`	Backends supported by this resource.

__call__(df: DeirokayDataSource) → dict: Run statement instance.

classmethod __init_subclass__() → None: Validate subclassed statement.

classmethod __post_attach_backend__(): This classmethod can be optionally overwritten to serve as a callback function for when the attach_backend() method is called.

classmethod attach_backend(backend: Backend) → Type[_AnyMultiBackendClass]

Generate a subclass that concretizes multibackend backend methods into their intended name. The methods marked with the given backend will compose the returned class.

Parameters

cls (type) – Class to be subclassed with the given backend.
backend (Backend) – Backend to be selected.

Returns

Subclass of the current class with methods filtered for the given backend.

Return type

Type[MultiBackendMixin]

expected_parameters: List[str] = ['at_least_%']

Parameters expected for this statement.

Type: List[str]

classmethod get_backend() → Backend

Get current active backend for this class.

Returns: The current active backend.
Return type: Backend
Raises: InvalidBackend – Backend not set or not a valid execution class.

name: str = 'unique'

Statement name when referred in Validation Documents (only valid for Deirokay built-in statements).

Type: str

static profile(df: DeirokayDataSource) → Dict[str, Any]

Given a template data table, generate a statement dict from it.

Parameters: df (DataFrame) – The DataFrame to be used as template.
Returns: Statement dict.
Return type: dict
Raises: NotImplementedError – If this method is not implemented by the subclass or the profile generation for this statement was intentionally skipped.

classmethod register_backend_method(alias_for: str, func: Callable[[...], Any], backend: Backend) → None

Proxy for register_backend_method to register an existing function as a backend-specific method.

Parameters

alias_for (str) – The name of the method to be substituted with a backend-specific version.
func (AnyCallable) – Existing function to be registered as a method.
backend (Backend) – Backend for the method.

report(df: DeirokayDataSource) → dict

Receive a DataFrame containing only columns on the scope of validation and returns a report of related metrics that can be used later to declare this Statement as fulfilled or failed.

Parameters: df (DataFrame) – The scoped DataFrame columns to be analysed in this report by this statement.
Returns: A dictionary of useful statistics about the target columns.
Return type: dict

result(report: dict) → bool[source]

Receive the report previously generated and declare this statement as either fulfilled (True) or failed (False).

Parameters: report (dict) – Report generated by report method. Should ideally contain all statistics necessary to evaluate the statement validity.
Returns: Whether or not this statement passed.
Return type: bool

supported_backends: List[Backend] = [<Backend.PANDAS: 'pandas'>, <Backend.DASK: 'dask'>]

Backends supported by this resource.

Type: List[Backend]