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Data validator

DataValidator dataclass

A class to validate data based on a set of validation rules.

Attributes:

Name Type Description
rules list[ValidationRule]

A list of validation rules.

Examples:

ValidationRules = [
    ValidationRule("first_name_check", F_.printable_only("first_name")),
    ValidationRule("last_name_check", F_.printable_only("last_name")),
    ValidationRule("address_check", F_.printable_only("address")),
    ValidationRule("region_check", F_.printable_only("region")),
    ValidationRule("code_check", [F_.two_character_only("code")]),
    ValidationRule("postcode_check", F_.printable_only("postcode")),
]

validator = DataValidator(ValidationRules)

conditions = {
    "first_name_check": F_.printable_only("first_name"),
    "last_name_check": F_.printable_only("last_name"),
    "address_check": F_.printable_only("address"),
    "region_check": F_.printable_only("region"),
    "code_check": [F_.two_character_only("code")],
    "postcode_check": F_.printable_only("postcode"),
}

validator = DataValidator.from_dict(conditions)
Source code in pysparky/data_validator.py 
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@dataclass
class DataValidator:
    """
    A class to validate data based on a set of validation rules.

    Attributes:
        rules (list[ValidationRule]): A list of validation rules.

    Examples:
        ``` py
        ValidationRules = [
            ValidationRule("first_name_check", F_.printable_only("first_name")),
            ValidationRule("last_name_check", F_.printable_only("last_name")),
            ValidationRule("address_check", F_.printable_only("address")),
            ValidationRule("region_check", F_.printable_only("region")),
            ValidationRule("code_check", [F_.two_character_only("code")]),
            ValidationRule("postcode_check", F_.printable_only("postcode")),
        ]

        validator = DataValidator(ValidationRules)

        conditions = {
            "first_name_check": F_.printable_only("first_name"),
            "last_name_check": F_.printable_only("last_name"),
            "address_check": F_.printable_only("address"),
            "region_check": F_.printable_only("region"),
            "code_check": [F_.two_character_only("code")],
            "postcode_check": F_.printable_only("postcode"),
        }

        validator = DataValidator.from_dict(conditions)
        ```
    """

    rules: list[ValidationRule]

    @classmethod
    def from_dict(cls, data: dict[str, Union[list[Column], Column]]) -> "DataValidator":
        """
        Creates a DataValidator instance from a dictionary.

        Args:
            data (dict[str, list[Column] | Column]): A dictionary where keys are rule names and values are lists of conditions or a single condition.

        Returns:
            DataValidator: An instance of DataValidator.

        Examples:
            ``` py
            conditions = {
                "first_name_check": F_.printable_only("first_name"),
                "last_name_check": F_.printable_only("last_name"),
                "address_check": F_.printable_only("address"),
                "region_check": F_.printable_only("region"),
                "code_check": [F_.two_character_only("code")],
                "postcode_check": F_.printable_only("postcode"),
            }


            validator = DataValidator.from_dict(conditions)
            ```

        """
        rules = [ValidationRule(name, conditions) for name, conditions in data.items()]
        return cls(rules=rules)

    @property
    def query_map(self) -> dict[str, Column]:
        """
        Gets a dictionary mapping rule names to their combined conditions.

        Returns:
            dict[str, Column]: A dictionary where keys are rule names
                and values are combined conditions.

        Examples:
            ``` py
            sdf.withColumns(validator.query_map)
            ```

        """
        return {rule.name: rule.combined_condition for rule in self.rules}

    def apply_conditions(self, sdf: DataFrame) -> DataFrame:
        """
        Applies the combined conditions to the Spark DataFrame.

        Args:
            sdf (DataFrame): The Spark DataFrame to which the conditions will be applied.

        Returns:
            DataFrame: The Spark DataFrame with the conditions applied.

        Examples:
            ``` py
            validator.apply_conditions(data_sdf)
            ```

        """
        return sdf.withColumns(self.query_map)

    def filter_invalid(self, sdf: DataFrame) -> DataFrame:
        """
        Filters out invalid rows from the Spark DataFrame based on the rules.

        Args:
            sdf (DataFrame): The Spark DataFrame to be filtered.

        Returns:
            DataFrame: The Spark DataFrame with invalid rows filtered out.

        Examples:
            ``` py
            validator.filter_invalid(data_sdf)
            ```

        """
        return filters(
            self.apply_conditions(sdf),
            [
                (F.col(column_name) == False)  # pylint: disable=singleton-comparison
                for column_name in self.query_map.keys()
            ],
            operator_="or",
        )

    def filter_valid(self, sdf: DataFrame) -> DataFrame:
        """
        Filters out valid rows from the Spark DataFrame based on the rules.

        Args:
            sdf (DataFrame): The Spark DataFrame to be filtered.

        Returns:
            DataFrame: The Spark DataFrame with valid rows filtered out.

        Examples:
            ``` py
            validator.filter_valid(data_sdf).select(data_sdf.columns).show()
            ```

        """
        return filters(
            self.apply_conditions(sdf),
            [
                (F.col(column_name) == True)  # pylint: disable=singleton-comparison
                for column_name in self.query_map.keys()
            ],
            operator_="and",
        )

query_map property

Gets a dictionary mapping rule names to their combined conditions.

Returns:

Type Description
dict[str, Column]

dict[str, Column]: A dictionary where keys are rule names and values are combined conditions.

Examples:

sdf.withColumns(validator.query_map)

apply_conditions(sdf)

Applies the combined conditions to the Spark DataFrame.

Parameters:

Name Type Description Default
sdf DataFrame

The Spark DataFrame to which the conditions will be applied.

 required

Returns:

Name Type Description
DataFrame DataFrame

The Spark DataFrame with the conditions applied.

Examples:

validator.apply_conditions(data_sdf)
Source code in pysparky/data_validator.py 
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def apply_conditions(self, sdf: DataFrame) -> DataFrame:
    """
    Applies the combined conditions to the Spark DataFrame.

    Args:
        sdf (DataFrame): The Spark DataFrame to which the conditions will be applied.

    Returns:
        DataFrame: The Spark DataFrame with the conditions applied.

    Examples:
        ``` py
        validator.apply_conditions(data_sdf)
        ```

    """
    return sdf.withColumns(self.query_map)

filter_invalid(sdf)

Filters out invalid rows from the Spark DataFrame based on the rules.

Parameters:

Name Type Description Default
sdf DataFrame

The Spark DataFrame to be filtered.

 required

Returns:

Name Type Description
DataFrame DataFrame

The Spark DataFrame with invalid rows filtered out.

Examples:

validator.filter_invalid(data_sdf)
Source code in pysparky/data_validator.py 
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def filter_invalid(self, sdf: DataFrame) -> DataFrame:
    """
    Filters out invalid rows from the Spark DataFrame based on the rules.

    Args:
        sdf (DataFrame): The Spark DataFrame to be filtered.

    Returns:
        DataFrame: The Spark DataFrame with invalid rows filtered out.

    Examples:
        ``` py
        validator.filter_invalid(data_sdf)
        ```

    """
    return filters(
        self.apply_conditions(sdf),
        [
            (F.col(column_name) == False)  # pylint: disable=singleton-comparison
            for column_name in self.query_map.keys()
        ],
        operator_="or",
    )

filter_valid(sdf)

Filters out valid rows from the Spark DataFrame based on the rules.

Parameters:

Name Type Description Default
sdf DataFrame

The Spark DataFrame to be filtered.

 required

Returns:

Name Type Description
DataFrame DataFrame

The Spark DataFrame with valid rows filtered out.

Examples:

validator.filter_valid(data_sdf).select(data_sdf.columns).show()
Source code in pysparky/data_validator.py 
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def filter_valid(self, sdf: DataFrame) -> DataFrame:
    """
    Filters out valid rows from the Spark DataFrame based on the rules.

    Args:
        sdf (DataFrame): The Spark DataFrame to be filtered.

    Returns:
        DataFrame: The Spark DataFrame with valid rows filtered out.

    Examples:
        ``` py
        validator.filter_valid(data_sdf).select(data_sdf.columns).show()
        ```

    """
    return filters(
        self.apply_conditions(sdf),
        [
            (F.col(column_name) == True)  # pylint: disable=singleton-comparison
            for column_name in self.query_map.keys()
        ],
        operator_="and",
    )

from_dict(data) classmethod

Creates a DataValidator instance from a dictionary.

Parameters:

Name Type Description Default
data dict[str, list[Column] | Column]

A dictionary where keys are rule names and values are lists of conditions or a single condition.

 required

Returns:

Name Type Description
DataValidator DataValidator

An instance of DataValidator.

Examples:

conditions = {
    "first_name_check": F_.printable_only("first_name"),
    "last_name_check": F_.printable_only("last_name"),
    "address_check": F_.printable_only("address"),
    "region_check": F_.printable_only("region"),
    "code_check": [F_.two_character_only("code")],
    "postcode_check": F_.printable_only("postcode"),
}


validator = DataValidator.from_dict(conditions)
Source code in pysparky/data_validator.py 
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@classmethod
def from_dict(cls, data: dict[str, Union[list[Column], Column]]) -> "DataValidator":
    """
    Creates a DataValidator instance from a dictionary.

    Args:
        data (dict[str, list[Column] | Column]): A dictionary where keys are rule names and values are lists of conditions or a single condition.

    Returns:
        DataValidator: An instance of DataValidator.

    Examples:
        ``` py
        conditions = {
            "first_name_check": F_.printable_only("first_name"),
            "last_name_check": F_.printable_only("last_name"),
            "address_check": F_.printable_only("address"),
            "region_check": F_.printable_only("region"),
            "code_check": [F_.two_character_only("code")],
            "postcode_check": F_.printable_only("postcode"),
        }


        validator = DataValidator.from_dict(conditions)
        ```

    """
    rules = [ValidationRule(name, conditions) for name, conditions in data.items()]
    return cls(rules=rules)

ValidationRule dataclass

A class to represent a validation rule.

Attributes:

Name Type Description
name str

The name of the validation rule.
conditions list[Column]

A list of conditions (Spark Columns) that make up the rule.
combined_condition Column

The combined condition of all the conditions using logical AND. It will generate from conditions

Examples:

ValidationRule("first_name_check", F_.printable_only("first_name")),

Source code in pysparky/data_validator.py 
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@dataclass
class ValidationRule:
    """
    A class to represent a validation rule.

    Attributes:
        name (str): The name of the validation rule.
        conditions (list[Column]): A list of conditions (Spark Columns) that make up the rule.
        combined_condition (Column): The combined condition of all the conditions using logical AND.
            It will generate from conditions

    Examples:
        ValidationRule("first_name_check", F_.printable_only("first_name")),
    """

    name: str
    conditions: list[Column] | Column
    combined_condition: Column = field(init=False)

    def __post_init__(self):
        self.conditions = enabler.ensure_list(self.conditions)
        self.combined_condition = F_.condition_and(*self.conditions)