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Documentation for comparison_library

The comparison_library contains pre-made comparisons available for use directly as described in this topic guide. However, not every comparison is available for every Splink-compatible SQL backend.

The pre-made Splink comparisons available for each SQL dialect are as given in this table:


DuckDB
Spark
Athena
SQLite
PostgreSql
array_intersect_at_sizes ✓ ✓ ✓ ✓
damerau_levenshtein_at_thresholds ✓ ✓ ✓
datediff_at_thresholds ✓ ✓ ✓ ✓
distance_function_at_thresholds ✓ ✓ ✓ ✓ ✓
distance_in_km_at_thresholds ✓ ✓ ✓ ✓
exact_match ✓ ✓ ✓ ✓ ✓
jaccard_at_thresholds ✓ ✓
jaro_at_thresholds ✓ ✓ ✓
jaro_winkler_at_thresholds ✓ ✓ ✓
levenshtein_at_thresholds ✓ ✓ ✓ ✓ ✓

The detailed API for each of these are outlined below.

Library comparison APIs

Bases: Comparison

Source code in splink/comparison_library.py 
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class ExactMatchBase(Comparison):
    def __init__(
        self,
        col_name,
        regex_extract: str = None,
        valid_string_pattern: str = None,
        set_to_lowercase: bool = False,
        term_frequency_adjustments=False,
        m_probability_exact_match=None,
        m_probability_else=None,
        include_colname_in_charts_label=False,
    ) -> Comparison:
        """A comparison of the data in `col_name` with two levels:

        - Exact match
        - Anything else

        Args:
            col_name (str): The name of the column to compare
            regex_extract (str): Regular expression pattern to evaluate a match on.
            valid_string_pattern (str): regular expression pattern that if not
                matched will result in column being treated as a null.
            set_to_lowercase (bool): If True, sets all entries to lowercase.
            term_frequency_adjustments (bool, optional): If True, term frequency
                adjustments will be made on the exact match level. Defaults to False.
            m_probability_exact_match (float, optional): If provided, overrides the
                default m probability for the exact match level. Defaults to None.
            m_probability_else (float, optional): If provided, overrides the
                default m probability for the 'anything else' level. Defaults to None.
            include_colname_in_charts_label: If true, append col name to label for
                charts.  Defaults to False.

        Examples:
            === ":simple-duckdb: DuckDB"
                Create comparison with exact match level
                ``` python
                import splink.duckdb.comparison_library as cl
                cl.exact_match("first_name")
                ```
                Create comparison with exact match level based on a
                substring of first_name as determined by a regular expression
                ``` python
                import splink.duckdb.comparison_library as cl
                cl.exact_match("first_name", regex_extract="^[A-Z]{1,4}")
                ```
            === ":simple-apachespark: Spark"
                Create comparison with exact match level
                ``` python
                import splink.spark.comparison_library as cl
                cl.exact_match("first_name")
                ```
                Create comparison with exact match level based on a
                substring of first_name as determined by a regular expression
                ``` python
                import splink.spark.comparison_library as cl
                cl.exact_match("first_name", regex_extract="^[A-Z]{1,4}")
                ```
            === ":simple-amazonaws: Athena"
                Create comparison with exact match level
                ``` python
                import splink.athena.comparison_library as cl
                cl.exact_match("first_name")
                ```
                Create comparison with exact match level based on a
                substring of first_name as determined by a regular expression
                ``` python
                import splink.athena.comparison_library as cl
                cl.exact_match("first_name", regex_extract="^[A-Z]{1,4}")
                ```
            === ":simple-sqlite: SQLite"
                Create comparison with exact match level
                ``` python
                import splink.sqlite.comparison_library as cl
                cl.exact_match("first_name")
                ```
            === ":simple-postgresql: PostgreSql"
                Create comparison with exact match level
                ``` python
                import splink.postgres.comparison_library as cl
                cl.exact_match("first_name")
                ```

        Returns:
            Comparison: A comparison for exact match that can be included in the Splink
                settings dictionary
        """

        comparison_dict = {
            "comparison_description": "Exact match vs. anything else",
            "comparison_levels": [
                self._null_level(col_name, valid_string_pattern),
                self._exact_match_level(
                    col_name,
                    regex_extract=regex_extract,
                    set_to_lowercase=set_to_lowercase,
                    term_frequency_adjustments=term_frequency_adjustments,
                    m_probability=m_probability_exact_match,
                    include_colname_in_charts_label=include_colname_in_charts_label,
                ),
                self._else_level(m_probability=m_probability_else),
            ],
        }
        super().__init__(comparison_dict)

__init__(col_name, regex_extract=None, valid_string_pattern=None, set_to_lowercase=False, term_frequency_adjustments=False, m_probability_exact_match=None, m_probability_else=None, include_colname_in_charts_label=False)

A comparison of the data in col_name with two levels:

  • Exact match
  • Anything else

Parameters:

Name Type Description Default
col_name str

The name of the column to compare

 required
regex_extract str

Regular expression pattern to evaluate a match on.

 None
valid_string_pattern str

regular expression pattern that if not matched will result in column being treated as a null.

 None
set_to_lowercase bool

If True, sets all entries to lowercase.

 False
term_frequency_adjustments bool

If True, term frequency adjustments will be made on the exact match level. Defaults to False.

 False
m_probability_exact_match float

If provided, overrides the default m probability for the exact match level. Defaults to None.

 None
m_probability_else float

If provided, overrides the default m probability for the 'anything else' level. Defaults to None.

 None
include_colname_in_charts_label

If true, append col name to label for charts. Defaults to False.

 False

Examples:

Create comparison with exact match level 

import splink.duckdb.comparison_library as cl
cl.exact_match("first_name")
Create comparison with exact match level based on a substring of first_name as determined by a regular expression 
import splink.duckdb.comparison_library as cl
cl.exact_match("first_name", regex_extract="^[A-Z]{1,4}")

Create comparison with exact match level 

import splink.spark.comparison_library as cl
cl.exact_match("first_name")
Create comparison with exact match level based on a substring of first_name as determined by a regular expression 
import splink.spark.comparison_library as cl
cl.exact_match("first_name", regex_extract="^[A-Z]{1,4}")

Create comparison with exact match level 

import splink.athena.comparison_library as cl
cl.exact_match("first_name")
Create comparison with exact match level based on a substring of first_name as determined by a regular expression 
import splink.athena.comparison_library as cl
cl.exact_match("first_name", regex_extract="^[A-Z]{1,4}")

Create comparison with exact match level 

import splink.sqlite.comparison_library as cl
cl.exact_match("first_name")

Create comparison with exact match level 

import splink.postgres.comparison_library as cl
cl.exact_match("first_name")

Returns:

Name Type Description
Comparison Comparison

A comparison for exact match that can be included in the Splink settings dictionary
Source code in splink/comparison_library.py 
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def __init__(
    self,
    col_name,
    regex_extract: str = None,
    valid_string_pattern: str = None,
    set_to_lowercase: bool = False,
    term_frequency_adjustments=False,
    m_probability_exact_match=None,
    m_probability_else=None,
    include_colname_in_charts_label=False,
) -> Comparison:
    """A comparison of the data in `col_name` with two levels:

    - Exact match
    - Anything else

    Args:
        col_name (str): The name of the column to compare
        regex_extract (str): Regular expression pattern to evaluate a match on.
        valid_string_pattern (str): regular expression pattern that if not
            matched will result in column being treated as a null.
        set_to_lowercase (bool): If True, sets all entries to lowercase.
        term_frequency_adjustments (bool, optional): If True, term frequency
            adjustments will be made on the exact match level. Defaults to False.
        m_probability_exact_match (float, optional): If provided, overrides the
            default m probability for the exact match level. Defaults to None.
        m_probability_else (float, optional): If provided, overrides the
            default m probability for the 'anything else' level. Defaults to None.
        include_colname_in_charts_label: If true, append col name to label for
            charts.  Defaults to False.

    Examples:
        === ":simple-duckdb: DuckDB"
            Create comparison with exact match level
            ``` python
            import splink.duckdb.comparison_library as cl
            cl.exact_match("first_name")
            ```
            Create comparison with exact match level based on a
            substring of first_name as determined by a regular expression
            ``` python
            import splink.duckdb.comparison_library as cl
            cl.exact_match("first_name", regex_extract="^[A-Z]{1,4}")
            ```
        === ":simple-apachespark: Spark"
            Create comparison with exact match level
            ``` python
            import splink.spark.comparison_library as cl
            cl.exact_match("first_name")
            ```
            Create comparison with exact match level based on a
            substring of first_name as determined by a regular expression
            ``` python
            import splink.spark.comparison_library as cl
            cl.exact_match("first_name", regex_extract="^[A-Z]{1,4}")
            ```
        === ":simple-amazonaws: Athena"
            Create comparison with exact match level
            ``` python
            import splink.athena.comparison_library as cl
            cl.exact_match("first_name")
            ```
            Create comparison with exact match level based on a
            substring of first_name as determined by a regular expression
            ``` python
            import splink.athena.comparison_library as cl
            cl.exact_match("first_name", regex_extract="^[A-Z]{1,4}")
            ```
        === ":simple-sqlite: SQLite"
            Create comparison with exact match level
            ``` python
            import splink.sqlite.comparison_library as cl
            cl.exact_match("first_name")
            ```
        === ":simple-postgresql: PostgreSql"
            Create comparison with exact match level
            ``` python
            import splink.postgres.comparison_library as cl
            cl.exact_match("first_name")
            ```

    Returns:
        Comparison: A comparison for exact match that can be included in the Splink
            settings dictionary
    """

    comparison_dict = {
        "comparison_description": "Exact match vs. anything else",
        "comparison_levels": [
            self._null_level(col_name, valid_string_pattern),
            self._exact_match_level(
                col_name,
                regex_extract=regex_extract,
                set_to_lowercase=set_to_lowercase,
                term_frequency_adjustments=term_frequency_adjustments,
                m_probability=m_probability_exact_match,
                include_colname_in_charts_label=include_colname_in_charts_label,
            ),
            self._else_level(m_probability=m_probability_else),
        ],
    }
    super().__init__(comparison_dict)

Bases: Comparison

Source code in splink/comparison_library.py 
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class DistanceFunctionAtThresholdsBase(Comparison):
    def __init__(
        self,
        col_name: str,
        distance_function_name: str,
        distance_threshold_or_thresholds: float | list,
        regex_extract: str = None,
        valid_string_pattern: str = None,
        set_to_lowercase: bool = False,
        higher_is_more_similar: bool = True,
        include_exact_match_level=True,
        term_frequency_adjustments=False,
        m_probability_exact_match=None,
        m_probability_or_probabilities_thres: float | list = None,
        m_probability_else=None,
    ) -> Comparison:
        """A comparison of the data in `col_name` with a user-provided distance
        function used to assess middle similarity levels.

        The user-provided distance function must exist in the SQL backend.

        An example of the output with default arguments and setting
        `distance_function_name` to `jaccard` and
        `distance_threshold_or_thresholds = [0.9,0.7]` would be

        - Exact match
        - Jaccard distance <= 0.9
        - Jaccard distance <= 0.7
        - Anything else

        Note: distance_function_at_thresholds() is primarily used in the
        backend to create the out-of-the-box cl.XXX_at_thresholds() functions

        Args:
            col_name (str): The name of the column to compare
            distance_function_name (str): The name of the distance function.
            distance_threshold_or_thresholds (Union[int, list], optional): The
                threshold(s) to use for the middle similarity level(s).
                Defaults to [1, 2].
            regex_extract (str): Regular expression pattern to evaluate a match on.
            valid_string_pattern (str): regular expression pattern that if not
                matched will result in column being treated as a null.
            set_to_lowercase (bool): If True, sets all entries to lowercase.
            higher_is_more_similar (bool): If True, a higher value of the distance
                function indicates a higher similarity (e.g. jaro_winkler).
                If false, a higher value indicates a lower similarity
                (e.g. levenshtein).
            include_exact_match_level (bool, optional): If True, include an exact match
                level. Defaults to True.
            term_frequency_adjustments (bool, optional): If True, apply term frequency
                adjustments to the exact match level. Defaults to False.
            m_probability_exact_match (float, optional): If provided, overrides the
                default m probability for the exact match level. Defaults to None.
            m_probability_or_probabilities_thres (Union[float, list], optional):
                If provided, overrides the default m probabilities
                for the thresholds specified. Defaults to None.
            m_probability_else (float, optional): If provided, overrides the
                default m probability for the 'anything else' level. Defaults to None.

        Examples:
            === ":simple-duckdb: DuckDB"
                Apply the `jaccard` function in a comparison with levels 0.9 and 0.7
                ``` python
                import splink.duckdb.comparison_library as cl
                cl.distance_function_at_thresholds("name",
                                   distance_function_name = 'jaccard',
                                   distance_threshold_or_thresholds = [0.9, 0.7]
                                   )
                ```
                Apply the `jaccard` function in a comparison with levels 0.9 and 0.7 on
                a substring of name column as determined by a regular expression
                ``` python
                import splink.duckdb.comparison_library as cl
                cl.distance_function_at_thresholds("name",
                                   distance_function_name = 'jaccard',
                                   distance_threshold_or_thresholds = [0.9, 0.7],
                                   regex_extract="^[A-Z]{1,4}
                                   )
                ```
            === ":simple-apachespark: Spark"
                Apply the `jaccard` function in a comparison with levels 0.9 and 0.7
                ``` python
                import splink.spark.comparison_library as cl
                cl.distance_function_at_thresholds("name",
                                   distance_function_name = 'jaccard',
                                   distance_threshold_or_thresholds = [0.9, 0.7]
                                   )
                ```
                Apply the `jaccard` function in a comparison with levels 0.9 and 0.7 on
                a substring of name column as determined by a regular expression
                ``` python
                import splink.spark.comparison_library as cl
                cl.distance_function_at_thresholds("name",
                                   distance_function_name = 'jaccard',
                                   distance_threshold_or_thresholds = [0.9, 0.7],
                                   regex_extract="^[A-Z]{1,4}
                                   )
                ```
            === ":simple-amazonaws: Athena"
                Apply the `levenshtein_distance` function in a comparison with
                levels 1 and 2
                ``` python
                import splink.athena.comparison_library as cl
                cl.distance_function_at_thresholds("name",
                                   distance_function_name = 'levenshtein_distance',
                                   distance_threshold_or_thresholds = [1, 2],
                                   higher_is_more_similar = False
                                   )
                ```
                Apply the `jaccard` function in a comparison with levels 0.9 and 0.7 on
                a substring of name column as determined by a regular expression
                ``` python
                import splink.athena.comparison_library as cl
                cl.distance_function_at_thresholds("name",
                                   distance_function_name = 'jaccard',
                                   distance_threshold_or_thresholds = [0.9, 0.7],
                                   regex_extract="^[A-Z]{1,4}
                                   )
                ```
            === ":simple-sqlite: SQLite"
                Apply the `levenshtein` function in a comparison with
                levels 1 and 2
                ``` python
                import splink.sqlite.comparison_library as cl
                cl.distance_function_at_thresholds("name",
                                   distance_function_name = 'levenshtein',
                                   distance_threshold_or_thresholds = [1, 2],
                                   higher_is_more_similar = False
                                   )
                ```
            === ":simple-postgresql: PostgreSql"
                Apply the `levenshtein` function in a comparison with
                levels 1 and 2
                ``` python
                import splink.postgres.comparison_library as cl
                cl.distance_function_at_thresholds("name",
                                   distance_function_name = 'levenshtein',
                                   distance_threshold_or_thresholds = [1, 2],
                                   higher_is_more_similar = False
                                   )
                ```
                ```

        Returns:
            Comparison: A comparison for a chosen distance function similarity that
                can be included in the Splink settings dictionary.
        """
        # Validate user inputs

        distance_threshold_or_thresholds = ensure_is_iterable(
            distance_threshold_or_thresholds
        )

        comparison_at_thresholds_error_logger(
            distance_function_name, distance_threshold_or_thresholds
        )

        comparison_levels = []
        comparison_levels.append(self._null_level(col_name, valid_string_pattern))
        if include_exact_match_level:
            level = self._exact_match_level(
                col_name,
                term_frequency_adjustments=term_frequency_adjustments,
                regex_extract=regex_extract,
                set_to_lowercase=set_to_lowercase,
                m_probability=m_probability_exact_match,
            )
            comparison_levels.append(level)

        threshold_comparison_levels = distance_threshold_comparison_levels(
            self,
            col_name,
            distance_function_name,
            distance_threshold_or_thresholds,
            regex_extract,
            set_to_lowercase,
            m_probability_or_probabilities_thres,
        )
        comparison_levels = comparison_levels + threshold_comparison_levels

        comparison_levels.append(
            self._else_level(m_probability=m_probability_else),
        )

        # Construct comparison description
        comparison_desc = ""
        if include_exact_match_level:
            comparison_desc += "Exact match vs. "

        threshold_desc = distance_threshold_description(
            col_name, distance_function_name, distance_threshold_or_thresholds
        )
        comparison_desc += threshold_desc

        comparison_desc += "anything else"

        comparison_dict = {
            "comparison_description": comparison_desc,
            "comparison_levels": comparison_levels,
        }
        super().__init__(comparison_dict)

    @property
    def _is_distance_subclass(self):
        return False

__init__(col_name, distance_function_name, distance_threshold_or_thresholds, regex_extract=None, valid_string_pattern=None, set_to_lowercase=False, higher_is_more_similar=True, include_exact_match_level=True, term_frequency_adjustments=False, m_probability_exact_match=None, m_probability_or_probabilities_thres=None, m_probability_else=None)

A comparison of the data in col_name with a user-provided distance function used to assess middle similarity levels.

The user-provided distance function must exist in the SQL backend.

An example of the output with default arguments and setting distance_function_name to jaccard and distance_threshold_or_thresholds = [0.9,0.7] would be

  • Exact match
  • Jaccard distance <= 0.9
  • Jaccard distance <= 0.7
  • Anything else

Note: distance_function_at_thresholds() is primarily used in the backend to create the out-of-the-box cl.XXX_at_thresholds() functions

Parameters:

Name Type Description Default
col_name str

The name of the column to compare

 required
distance_function_name str

The name of the distance function.

 required
distance_threshold_or_thresholds Union[int, list]

The threshold(s) to use for the middle similarity level(s). Defaults to [1, 2].

 required
regex_extract str

Regular expression pattern to evaluate a match on.

 None
valid_string_pattern str

regular expression pattern that if not matched will result in column being treated as a null.

 None
set_to_lowercase bool

If True, sets all entries to lowercase.

 False
higher_is_more_similar bool

If True, a higher value of the distance function indicates a higher similarity (e.g. jaro_winkler). If false, a higher value indicates a lower similarity (e.g. levenshtein).

 True
include_exact_match_level bool

If True, include an exact match level. Defaults to True.

 True
term_frequency_adjustments bool

If True, apply term frequency adjustments to the exact match level. Defaults to False.

 False
m_probability_exact_match float

If provided, overrides the default m probability for the exact match level. Defaults to None.

 None
m_probability_or_probabilities_thres Union[float, list]

If provided, overrides the default m probabilities for the thresholds specified. Defaults to None.

 None
m_probability_else float

If provided, overrides the default m probability for the 'anything else' level. Defaults to None.

 None

Examples:

Apply the jaccard function in a comparison with levels 0.9 and 0.7 

import splink.duckdb.comparison_library as cl
cl.distance_function_at_thresholds("name",
                   distance_function_name = 'jaccard',
                   distance_threshold_or_thresholds = [0.9, 0.7]
                   )
Apply the jaccard function in a comparison with levels 0.9 and 0.7 on a substring of name column as determined by a regular expression 
import splink.duckdb.comparison_library as cl
cl.distance_function_at_thresholds("name",
                   distance_function_name = 'jaccard',
                   distance_threshold_or_thresholds = [0.9, 0.7],
                   regex_extract="^[A-Z]{1,4}
                   )

Apply the jaccard function in a comparison with levels 0.9 and 0.7 

import splink.spark.comparison_library as cl
cl.distance_function_at_thresholds("name",
                   distance_function_name = 'jaccard',
                   distance_threshold_or_thresholds = [0.9, 0.7]
                   )
Apply the jaccard function in a comparison with levels 0.9 and 0.7 on a substring of name column as determined by a regular expression 
import splink.spark.comparison_library as cl
cl.distance_function_at_thresholds("name",
                   distance_function_name = 'jaccard',
                   distance_threshold_or_thresholds = [0.9, 0.7],
                   regex_extract="^[A-Z]{1,4}
                   )

Apply the levenshtein_distance function in a comparison with levels 1 and 2 

import splink.athena.comparison_library as cl
cl.distance_function_at_thresholds("name",
                   distance_function_name = 'levenshtein_distance',
                   distance_threshold_or_thresholds = [1, 2],
                   higher_is_more_similar = False
                   )
Apply the jaccard function in a comparison with levels 0.9 and 0.7 on a substring of name column as determined by a regular expression 
import splink.athena.comparison_library as cl
cl.distance_function_at_thresholds("name",
                   distance_function_name = 'jaccard',
                   distance_threshold_or_thresholds = [0.9, 0.7],
                   regex_extract="^[A-Z]{1,4}
                   )

Apply the levenshtein function in a comparison with levels 1 and 2 

import splink.sqlite.comparison_library as cl
cl.distance_function_at_thresholds("name",
                   distance_function_name = 'levenshtein',
                   distance_threshold_or_thresholds = [1, 2],
                   higher_is_more_similar = False
                   )

Apply the levenshtein function in a comparison with levels 1 and 2 

import splink.postgres.comparison_library as cl
cl.distance_function_at_thresholds("name",
                   distance_function_name = 'levenshtein',
                   distance_threshold_or_thresholds = [1, 2],
                   higher_is_more_similar = False
                   )
```

Returns:

Name Type Description
Comparison Comparison

A comparison for a chosen distance function similarity that can be included in the Splink settings dictionary.
Source code in splink/comparison_library.py 
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def __init__(
    self,
    col_name: str,
    distance_function_name: str,
    distance_threshold_or_thresholds: float | list,
    regex_extract: str = None,
    valid_string_pattern: str = None,
    set_to_lowercase: bool = False,
    higher_is_more_similar: bool = True,
    include_exact_match_level=True,
    term_frequency_adjustments=False,
    m_probability_exact_match=None,
    m_probability_or_probabilities_thres: float | list = None,
    m_probability_else=None,
) -> Comparison:
    """A comparison of the data in `col_name` with a user-provided distance
    function used to assess middle similarity levels.

    The user-provided distance function must exist in the SQL backend.

    An example of the output with default arguments and setting
    `distance_function_name` to `jaccard` and
    `distance_threshold_or_thresholds = [0.9,0.7]` would be

    - Exact match
    - Jaccard distance <= 0.9
    - Jaccard distance <= 0.7
    - Anything else

    Note: distance_function_at_thresholds() is primarily used in the
    backend to create the out-of-the-box cl.XXX_at_thresholds() functions

    Args:
        col_name (str): The name of the column to compare
        distance_function_name (str): The name of the distance function.
        distance_threshold_or_thresholds (Union[int, list], optional): The
            threshold(s) to use for the middle similarity level(s).
            Defaults to [1, 2].
        regex_extract (str): Regular expression pattern to evaluate a match on.
        valid_string_pattern (str): regular expression pattern that if not
            matched will result in column being treated as a null.
        set_to_lowercase (bool): If True, sets all entries to lowercase.
        higher_is_more_similar (bool): If True, a higher value of the distance
            function indicates a higher similarity (e.g. jaro_winkler).
            If false, a higher value indicates a lower similarity
            (e.g. levenshtein).
        include_exact_match_level (bool, optional): If True, include an exact match
            level. Defaults to True.
        term_frequency_adjustments (bool, optional): If True, apply term frequency
            adjustments to the exact match level. Defaults to False.
        m_probability_exact_match (float, optional): If provided, overrides the
            default m probability for the exact match level. Defaults to None.
        m_probability_or_probabilities_thres (Union[float, list], optional):
            If provided, overrides the default m probabilities
            for the thresholds specified. Defaults to None.
        m_probability_else (float, optional): If provided, overrides the
            default m probability for the 'anything else' level. Defaults to None.

    Examples:
        === ":simple-duckdb: DuckDB"
            Apply the `jaccard` function in a comparison with levels 0.9 and 0.7
            ``` python
            import splink.duckdb.comparison_library as cl
            cl.distance_function_at_thresholds("name",
                               distance_function_name = 'jaccard',
                               distance_threshold_or_thresholds = [0.9, 0.7]
                               )
            ```
            Apply the `jaccard` function in a comparison with levels 0.9 and 0.7 on
            a substring of name column as determined by a regular expression
            ``` python
            import splink.duckdb.comparison_library as cl
            cl.distance_function_at_thresholds("name",
                               distance_function_name = 'jaccard',
                               distance_threshold_or_thresholds = [0.9, 0.7],
                               regex_extract="^[A-Z]{1,4}
                               )
            ```
        === ":simple-apachespark: Spark"
            Apply the `jaccard` function in a comparison with levels 0.9 and 0.7
            ``` python
            import splink.spark.comparison_library as cl
            cl.distance_function_at_thresholds("name",
                               distance_function_name = 'jaccard',
                               distance_threshold_or_thresholds = [0.9, 0.7]
                               )
            ```
            Apply the `jaccard` function in a comparison with levels 0.9 and 0.7 on
            a substring of name column as determined by a regular expression
            ``` python
            import splink.spark.comparison_library as cl
            cl.distance_function_at_thresholds("name",
                               distance_function_name = 'jaccard',
                               distance_threshold_or_thresholds = [0.9, 0.7],
                               regex_extract="^[A-Z]{1,4}
                               )
            ```
        === ":simple-amazonaws: Athena"
            Apply the `levenshtein_distance` function in a comparison with
            levels 1 and 2
            ``` python
            import splink.athena.comparison_library as cl
            cl.distance_function_at_thresholds("name",
                               distance_function_name = 'levenshtein_distance',
                               distance_threshold_or_thresholds = [1, 2],
                               higher_is_more_similar = False
                               )
            ```
            Apply the `jaccard` function in a comparison with levels 0.9 and 0.7 on
            a substring of name column as determined by a regular expression
            ``` python
            import splink.athena.comparison_library as cl
            cl.distance_function_at_thresholds("name",
                               distance_function_name = 'jaccard',
                               distance_threshold_or_thresholds = [0.9, 0.7],
                               regex_extract="^[A-Z]{1,4}
                               )
            ```
        === ":simple-sqlite: SQLite"
            Apply the `levenshtein` function in a comparison with
            levels 1 and 2
            ``` python
            import splink.sqlite.comparison_library as cl
            cl.distance_function_at_thresholds("name",
                               distance_function_name = 'levenshtein',
                               distance_threshold_or_thresholds = [1, 2],
                               higher_is_more_similar = False
                               )
            ```
        === ":simple-postgresql: PostgreSql"
            Apply the `levenshtein` function in a comparison with
            levels 1 and 2
            ``` python
            import splink.postgres.comparison_library as cl
            cl.distance_function_at_thresholds("name",
                               distance_function_name = 'levenshtein',
                               distance_threshold_or_thresholds = [1, 2],
                               higher_is_more_similar = False
                               )
            ```
            ```

    Returns:
        Comparison: A comparison for a chosen distance function similarity that
            can be included in the Splink settings dictionary.
    """
    # Validate user inputs

    distance_threshold_or_thresholds = ensure_is_iterable(
        distance_threshold_or_thresholds
    )

    comparison_at_thresholds_error_logger(
        distance_function_name, distance_threshold_or_thresholds
    )

    comparison_levels = []
    comparison_levels.append(self._null_level(col_name, valid_string_pattern))
    if include_exact_match_level:
        level = self._exact_match_level(
            col_name,
            term_frequency_adjustments=term_frequency_adjustments,
            regex_extract=regex_extract,
            set_to_lowercase=set_to_lowercase,
            m_probability=m_probability_exact_match,
        )
        comparison_levels.append(level)

    threshold_comparison_levels = distance_threshold_comparison_levels(
        self,
        col_name,
        distance_function_name,
        distance_threshold_or_thresholds,
        regex_extract,
        set_to_lowercase,
        m_probability_or_probabilities_thres,
    )
    comparison_levels = comparison_levels + threshold_comparison_levels

    comparison_levels.append(
        self._else_level(m_probability=m_probability_else),
    )

    # Construct comparison description
    comparison_desc = ""
    if include_exact_match_level:
        comparison_desc += "Exact match vs. "

    threshold_desc = distance_threshold_description(
        col_name, distance_function_name, distance_threshold_or_thresholds
    )
    comparison_desc += threshold_desc

    comparison_desc += "anything else"

    comparison_dict = {
        "comparison_description": comparison_desc,
        "comparison_levels": comparison_levels,
    }
    super().__init__(comparison_dict)

Bases: DistanceFunctionAtThresholdsBase

Source code in splink/comparison_library.py 
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class LevenshteinAtThresholdsBase(DistanceFunctionAtThresholdsBase):
    def __init__(
        self,
        col_name: str,
        distance_threshold_or_thresholds: int | list = [1, 2],
        regex_extract: str = None,
        valid_string_pattern: str = None,
        set_to_lowercase: bool = False,
        include_exact_match_level=True,
        term_frequency_adjustments=False,
        m_probability_exact_match=None,
        m_probability_or_probabilities_lev: float | list = None,
        m_probability_else=None,
    ) -> Comparison:
        """A comparison of the data in `col_name` with the levenshtein distance used to
        assess middle similarity levels.

        An example of the output with default arguments and setting
        `distance_threshold_or_thresholds = [1,2]` would be

        - Exact match
        - Levenshtein distance <= 1
        - Levenshtein distance <= 2
        - Anything else

        Args:
            col_name (str): The name of the column to compare
            distance_threshold_or_thresholds (Union[int, list], optional): The
                threshold(s) to use for the middle similarity level(s).
                Defaults to [1, 2].
            regex_extract (str): Regular expression pattern to evaluate a match on.
            valid_string_pattern (str): regular expression pattern that if not
                matched will result in column being treated as a null.
            include_exact_match_level (bool, optional): If True, include an exact match
                level. Defaults to True.
            term_frequency_adjustments (bool, optional): If True, apply term frequency
                adjustments to the exact match level. Defaults to False.
            m_probability_exact_match (float, optional): If provided, overrides the
                default m probability for the exact match level. Defaults to None.
            m_probability_or_probabilities_lev (Union[float, list], optional):
                If provided, overrides the default m probabilities
                for the thresholds specified for given function. Defaults to None.
            m_probability_else (float, optional): If provided, overrides the
                default m probability for the 'anything else' level. Defaults to None.

        Examples:
            === ":simple-duckdb: DuckDB"
                Create comparison with levenshtein match levels with distance <=1
                and <=2
                ``` python
                import splink.duckdb.comparison_library as cl
                cl.levenshtein_at_thresholds("first_name", [1,2])
                ```
                Create comparison with levenshtein match levels with distance <=1
                and <=2
                on a substring of name column as determined by a regular expression
                ``` python
                import splink.duckdb.comparison_library as cl
                cl.levenshtein_at_thresholds("first_name", [1,2], regex_extract="^A|B")
                ```
            === ":simple-apachespark: Spark"
                Create comparison with levenshtein match levels with distance <=1
                and <=2
                ``` python
                import splink.spark.comparison_library as cl
                cl.levenshtein_at_thresholds("first_name", [1,2])
                ```
                Create comparison with levenshtein match levels with distance <=1
                and <=2
                on a substring of name column as determined by a regular expression
                ``` python
                import splink.spark.comparison_library as cl
                cl.levenshtein_at_thresholds("first_name", [1,2], regex_extract="^A|B")
                ```
            === ":simple-amazonaws: Athena"
                Create comparison with levenshtein match levels with distance <=1
                and <=2
                ``` python
                import splink.athena.comparison_library as cl
                cl.levenshtein_at_thresholds("first_name", [1,2])
                ```
                Create comparison with levenshtein match levels with distance <=1
                and <=2
                on a substring of name column as determined by a regular expression
                ``` python
                import splink.athena.comparison_library as cl
                cl.levenshtein_at_thresholds("first_name", [1,2], regex_extract="^A|B")
                ```
            === ":simple-sqlite: SQLite"
                Create comparison with levenshtein match levels with distance <=1
                and <=2
                ``` python
                import splink.athena.comparison_library as cl
                cl.levenshtein_at_thresholds("first_name", [1,2])
                ```
            === ":simple-postgresql: PostgreSql"
                Create comparison with levenshtein match levels with distance <=1
                and <=2
                ``` python
                import splink.postgres.comparison_library as cl
                cl.levenshtein_at_thresholds("first_name", [1,2])
                ```
                ```

        Returns:
            Comparison: A comparison for Levenshtein similarity that can be included
                in the Splink settings dictionary.
        """

        super().__init__(
            col_name,
            distance_function_name=self._levenshtein_name,
            distance_threshold_or_thresholds=distance_threshold_or_thresholds,
            regex_extract=regex_extract,
            valid_string_pattern=valid_string_pattern,
            set_to_lowercase=set_to_lowercase,
            higher_is_more_similar=False,
            include_exact_match_level=include_exact_match_level,
            term_frequency_adjustments=term_frequency_adjustments,
            m_probability_exact_match=m_probability_exact_match,
            m_probability_or_probabilities_thres=m_probability_or_probabilities_lev,
            m_probability_else=m_probability_else,
        )

    @property
    def _is_distance_subclass(self):
        return True

__init__(col_name, distance_threshold_or_thresholds=[1, 2], regex_extract=None, valid_string_pattern=None, set_to_lowercase=False, include_exact_match_level=True, term_frequency_adjustments=False, m_probability_exact_match=None, m_probability_or_probabilities_lev=None, m_probability_else=None)

A comparison of the data in col_name with the levenshtein distance used to assess middle similarity levels.

An example of the output with default arguments and setting distance_threshold_or_thresholds = [1,2] would be

  • Exact match
  • Levenshtein distance <= 1
  • Levenshtein distance <= 2
  • Anything else

Parameters:

Name Type Description Default
col_name str

The name of the column to compare

 required
distance_threshold_or_thresholds Union[int, list]

The threshold(s) to use for the middle similarity level(s). Defaults to [1, 2].

 [1, 2]
regex_extract str

Regular expression pattern to evaluate a match on.

 None
valid_string_pattern str

regular expression pattern that if not matched will result in column being treated as a null.

 None
include_exact_match_level bool

If True, include an exact match level. Defaults to True.

 True
term_frequency_adjustments bool

If True, apply term frequency adjustments to the exact match level. Defaults to False.

 False
m_probability_exact_match float

If provided, overrides the default m probability for the exact match level. Defaults to None.

 None
m_probability_or_probabilities_lev Union[float, list]

If provided, overrides the default m probabilities for the thresholds specified for given function. Defaults to None.

 None
m_probability_else float

If provided, overrides the default m probability for the 'anything else' level. Defaults to None.

 None

Examples:

Create comparison with levenshtein match levels with distance <=1 and <=2 

import splink.duckdb.comparison_library as cl
cl.levenshtein_at_thresholds("first_name", [1,2])
Create comparison with levenshtein match levels with distance <=1 and <=2 on a substring of name column as determined by a regular expression 
import splink.duckdb.comparison_library as cl
cl.levenshtein_at_thresholds("first_name", [1,2], regex_extract="^A|B")

Create comparison with levenshtein match levels with distance <=1 and <=2 

import splink.spark.comparison_library as cl
cl.levenshtein_at_thresholds("first_name", [1,2])
Create comparison with levenshtein match levels with distance <=1 and <=2 on a substring of name column as determined by a regular expression 
import splink.spark.comparison_library as cl
cl.levenshtein_at_thresholds("first_name", [1,2], regex_extract="^A|B")

Create comparison with levenshtein match levels with distance <=1 and <=2 

import splink.athena.comparison_library as cl
cl.levenshtein_at_thresholds("first_name", [1,2])
Create comparison with levenshtein match levels with distance <=1 and <=2 on a substring of name column as determined by a regular expression 
import splink.athena.comparison_library as cl
cl.levenshtein_at_thresholds("first_name", [1,2], regex_extract="^A|B")

Create comparison with levenshtein match levels with distance <=1 and <=2 

import splink.athena.comparison_library as cl
cl.levenshtein_at_thresholds("first_name", [1,2])

Create comparison with levenshtein match levels with distance <=1 and <=2 

import splink.postgres.comparison_library as cl
cl.levenshtein_at_thresholds("first_name", [1,2])
```

Returns:

Name Type Description
Comparison Comparison

A comparison for Levenshtein similarity that can be included in the Splink settings dictionary.
Source code in splink/comparison_library.py 
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def __init__(
    self,
    col_name: str,
    distance_threshold_or_thresholds: int | list = [1, 2],
    regex_extract: str = None,
    valid_string_pattern: str = None,
    set_to_lowercase: bool = False,
    include_exact_match_level=True,
    term_frequency_adjustments=False,
    m_probability_exact_match=None,
    m_probability_or_probabilities_lev: float | list = None,
    m_probability_else=None,
) -> Comparison:
    """A comparison of the data in `col_name` with the levenshtein distance used to
    assess middle similarity levels.

    An example of the output with default arguments and setting
    `distance_threshold_or_thresholds = [1,2]` would be

    - Exact match
    - Levenshtein distance <= 1
    - Levenshtein distance <= 2
    - Anything else

    Args:
        col_name (str): The name of the column to compare
        distance_threshold_or_thresholds (Union[int, list], optional): The
            threshold(s) to use for the middle similarity level(s).
            Defaults to [1, 2].
        regex_extract (str): Regular expression pattern to evaluate a match on.
        valid_string_pattern (str): regular expression pattern that if not
            matched will result in column being treated as a null.
        include_exact_match_level (bool, optional): If True, include an exact match
            level. Defaults to True.
        term_frequency_adjustments (bool, optional): If True, apply term frequency
            adjustments to the exact match level. Defaults to False.
        m_probability_exact_match (float, optional): If provided, overrides the
            default m probability for the exact match level. Defaults to None.
        m_probability_or_probabilities_lev (Union[float, list], optional):
            If provided, overrides the default m probabilities
            for the thresholds specified for given function. Defaults to None.
        m_probability_else (float, optional): If provided, overrides the
            default m probability for the 'anything else' level. Defaults to None.

    Examples:
        === ":simple-duckdb: DuckDB"
            Create comparison with levenshtein match levels with distance <=1
            and <=2
            ``` python
            import splink.duckdb.comparison_library as cl
            cl.levenshtein_at_thresholds("first_name", [1,2])
            ```
            Create comparison with levenshtein match levels with distance <=1
            and <=2
            on a substring of name column as determined by a regular expression
            ``` python
            import splink.duckdb.comparison_library as cl
            cl.levenshtein_at_thresholds("first_name", [1,2], regex_extract="^A|B")
            ```
        === ":simple-apachespark: Spark"
            Create comparison with levenshtein match levels with distance <=1
            and <=2
            ``` python
            import splink.spark.comparison_library as cl
            cl.levenshtein_at_thresholds("first_name", [1,2])
            ```
            Create comparison with levenshtein match levels with distance <=1
            and <=2
            on a substring of name column as determined by a regular expression
            ``` python
            import splink.spark.comparison_library as cl
            cl.levenshtein_at_thresholds("first_name", [1,2], regex_extract="^A|B")
            ```
        === ":simple-amazonaws: Athena"
            Create comparison with levenshtein match levels with distance <=1
            and <=2
            ``` python
            import splink.athena.comparison_library as cl
            cl.levenshtein_at_thresholds("first_name", [1,2])
            ```
            Create comparison with levenshtein match levels with distance <=1
            and <=2
            on a substring of name column as determined by a regular expression
            ``` python
            import splink.athena.comparison_library as cl
            cl.levenshtein_at_thresholds("first_name", [1,2], regex_extract="^A|B")
            ```
        === ":simple-sqlite: SQLite"
            Create comparison with levenshtein match levels with distance <=1
            and <=2
            ``` python
            import splink.athena.comparison_library as cl
            cl.levenshtein_at_thresholds("first_name", [1,2])
            ```
        === ":simple-postgresql: PostgreSql"
            Create comparison with levenshtein match levels with distance <=1
            and <=2
            ``` python
            import splink.postgres.comparison_library as cl
            cl.levenshtein_at_thresholds("first_name", [1,2])
            ```
            ```

    Returns:
        Comparison: A comparison for Levenshtein similarity that can be included
            in the Splink settings dictionary.
    """

    super().__init__(
        col_name,
        distance_function_name=self._levenshtein_name,
        distance_threshold_or_thresholds=distance_threshold_or_thresholds,
        regex_extract=regex_extract,
        valid_string_pattern=valid_string_pattern,
        set_to_lowercase=set_to_lowercase,
        higher_is_more_similar=False,
        include_exact_match_level=include_exact_match_level,
        term_frequency_adjustments=term_frequency_adjustments,
        m_probability_exact_match=m_probability_exact_match,
        m_probability_or_probabilities_thres=m_probability_or_probabilities_lev,
        m_probability_else=m_probability_else,
    )

Bases: DistanceFunctionAtThresholdsBase

Source code in splink/comparison_library.py 
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class DamerauLevenshteinAtThresholdsBase(DistanceFunctionAtThresholdsBase):
    def __init__(
        self,
        col_name: str,
        distance_threshold_or_thresholds: int | list = 1,
        regex_extract: str = None,
        valid_string_pattern: str = None,
        set_to_lowercase: bool = False,
        include_exact_match_level=True,
        term_frequency_adjustments=False,
        m_probability_exact_match=None,
        m_probability_or_probabilities_dl: float | list = None,
        m_probability_else=None,
    ) -> Comparison:
        """A comparison of the data in `col_name` with the damerau-levenshtein distance
        used to assess middle similarity levels.

        An example of the output with default arguments and setting
        `distance_threshold_or_thresholds = [1]` would be

        - Exact match
        - Damerau-Levenshtein distance <= 1
        - Anything else

        Args:
            col_name (str): The name of the column to compare
            distance_threshold_or_thresholds (Union[int, list], optional): The
                threshold(s) to use for the middle similarity level(s).
                Defaults to 1.
            regex_extract (str): Regular expression pattern to evaluate a match on.
            valid_string_pattern (str): regular expression pattern that if not
                matched will result in column being treated as a null.
            include_exact_match_level (bool, optional): If True, include an exact match
                level. Defaults to True.
            term_frequency_adjustments (bool, optional): If True, apply term frequency
                adjustments to the exact match level. Defaults to False.
            m_probability_exact_match (_type_, optional): If provided, overrides the
                default m probability for the exact match level. Defaults to None.
            m_probability_or_probabilities_dl (Union[float, list], optional):
                _description_. If provided, overrides the default m probabilities
                for the thresholds specified for given function. Defaults to None.
            m_probability_else (_type_, optional): If provided, overrides the
                default m probability for the 'anything else' level. Defaults to None.
        Examples:
            === ":simple-duckdb: DuckDB"
                Create comparison with damerau-levenshtein match levels with
                distance <= 1, 2
                ``` python
                import splink.duckdb.comparison_library as cl
                cl.damerau_levenshtein_at_thresholds("first_name", [1,2])
                ```
                Create comparison with damerau-levenshtein match levels with
                distance <= 1
                on a substring of name column as determined by a regular expression
                ``` python
                import splink.duckdb.comparison_library as cl
                cl.damerau_levenshtein_at_thresholds("first_name",
                                                     [1,2],
                                                     regex_extract="^A|B")
                ```
            === ":simple-apachespark: Spark"
                Create comparison with damerau-levenshtein match levels with
                distance <= 1, 2
                ``` python
                import splink.spark.comparison_library as cl
                cl.damerau_levenshtein_at_thresholds("first_name", [1,2])
                ```
                Create comparison with damerau-evenshtein match levels with
                distance <= 1, 2
                on a substring of name column as determined by a regular expression
                ``` python
                import splink.spark.comparison_library as cl
                cl.damerau_levenshtein_at_thresholds("first_name",
                                                     [1,2],
                                                     regex_extract="^A|B")
                ```
            === ":simple-sqlite: SQLite"
                Create comparison with damerau-levenshtein match levels with
                distance <= 1, 2
                ``` python
                import splink.sqlite.comparison_library as cl
                cl.damerau_levenshtein_at_thresholds("first_name", [1,2])
                ```

        Returns:
            Comparison: A comparison for Damerau-Levenshtein similarity that can be
            included in the Splink settings dictionary.
        """

        super().__init__(
            col_name,
            distance_function_name=self._levenshtein_name,
            distance_threshold_or_thresholds=distance_threshold_or_thresholds,
            regex_extract=regex_extract,
            valid_string_pattern=valid_string_pattern,
            set_to_lowercase=set_to_lowercase,
            higher_is_more_similar=False,
            include_exact_match_level=include_exact_match_level,
            term_frequency_adjustments=term_frequency_adjustments,
            m_probability_exact_match=m_probability_exact_match,
            m_probability_or_probabilities_thres=m_probability_or_probabilities_dl,
            m_probability_else=m_probability_else,
        )

    @property
    def _is_distance_subclass(self):
        return True

__init__(col_name, distance_threshold_or_thresholds=1, regex_extract=None, valid_string_pattern=None, set_to_lowercase=False, include_exact_match_level=True, term_frequency_adjustments=False, m_probability_exact_match=None, m_probability_or_probabilities_dl=None, m_probability_else=None)

A comparison of the data in col_name with the damerau-levenshtein distance used to assess middle similarity levels.

An example of the output with default arguments and setting distance_threshold_or_thresholds = [1] would be

  • Exact match
  • Damerau-Levenshtein distance <= 1
  • Anything else

Parameters:

Name Type Description Default
col_name str

The name of the column to compare

 required
distance_threshold_or_thresholds Union[int, list]

The threshold(s) to use for the middle similarity level(s). Defaults to 1.

 1
regex_extract str

Regular expression pattern to evaluate a match on.

 None
valid_string_pattern str

regular expression pattern that if not matched will result in column being treated as a null.

 None
include_exact_match_level bool

If True, include an exact match level. Defaults to True.

 True
term_frequency_adjustments bool

If True, apply term frequency adjustments to the exact match level. Defaults to False.

 False
m_probability_exact_match _type_

If provided, overrides the default m probability for the exact match level. Defaults to None.

 None
m_probability_or_probabilities_dl Union[float, list]

description. If provided, overrides the default m probabilities for the thresholds specified for given function. Defaults to None.

 None
m_probability_else _type_

If provided, overrides the default m probability for the 'anything else' level. Defaults to None.

 None

Returns:

Name Type Description
Comparison Comparison

A comparison for Damerau-Levenshtein similarity that can be
Comparison

included in the Splink settings dictionary.
Source code in splink/comparison_library.py 
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def __init__(
    self,
    col_name: str,
    distance_threshold_or_thresholds: int | list = 1,
    regex_extract: str = None,
    valid_string_pattern: str = None,
    set_to_lowercase: bool = False,
    include_exact_match_level=True,
    term_frequency_adjustments=False,
    m_probability_exact_match=None,
    m_probability_or_probabilities_dl: float | list = None,
    m_probability_else=None,
) -> Comparison:
    """A comparison of the data in `col_name` with the damerau-levenshtein distance
    used to assess middle similarity levels.

    An example of the output with default arguments and setting
    `distance_threshold_or_thresholds = [1]` would be

    - Exact match
    - Damerau-Levenshtein distance <= 1
    - Anything else

    Args:
        col_name (str): The name of the column to compare
        distance_threshold_or_thresholds (Union[int, list], optional): The
            threshold(s) to use for the middle similarity level(s).
            Defaults to 1.
        regex_extract (str): Regular expression pattern to evaluate a match on.
        valid_string_pattern (str): regular expression pattern that if not
            matched will result in column being treated as a null.
        include_exact_match_level (bool, optional): If True, include an exact match
            level. Defaults to True.
        term_frequency_adjustments (bool, optional): If True, apply term frequency
            adjustments to the exact match level. Defaults to False.
        m_probability_exact_match (_type_, optional): If provided, overrides the
            default m probability for the exact match level. Defaults to None.
        m_probability_or_probabilities_dl (Union[float, list], optional):
            _description_. If provided, overrides the default m probabilities
            for the thresholds specified for given function. Defaults to None.
        m_probability_else (_type_, optional): If provided, overrides the
            default m probability for the 'anything else' level. Defaults to None.
    Examples:
        === ":simple-duckdb: DuckDB"
            Create comparison with damerau-levenshtein match levels with
            distance <= 1, 2
            ``` python
            import splink.duckdb.comparison_library as cl
            cl.damerau_levenshtein_at_thresholds("first_name", [1,2])
            ```
            Create comparison with damerau-levenshtein match levels with
            distance <= 1
            on a substring of name column as determined by a regular expression
            ``` python
            import splink.duckdb.comparison_library as cl
            cl.damerau_levenshtein_at_thresholds("first_name",
                                                 [1,2],
                                                 regex_extract="^A|B")
            ```
        === ":simple-apachespark: Spark"
            Create comparison with damerau-levenshtein match levels with
            distance <= 1, 2
            ``` python
            import splink.spark.comparison_library as cl
            cl.damerau_levenshtein_at_thresholds("first_name", [1,2])
            ```
            Create comparison with damerau-evenshtein match levels with
            distance <= 1, 2
            on a substring of name column as determined by a regular expression
            ``` python
            import splink.spark.comparison_library as cl
            cl.damerau_levenshtein_at_thresholds("first_name",
                                                 [1,2],
                                                 regex_extract="^A|B")
            ```
        === ":simple-sqlite: SQLite"
            Create comparison with damerau-levenshtein match levels with
            distance <= 1, 2
            ``` python
            import splink.sqlite.comparison_library as cl
            cl.damerau_levenshtein_at_thresholds("first_name", [1,2])
            ```

    Returns:
        Comparison: A comparison for Damerau-Levenshtein similarity that can be
        included in the Splink settings dictionary.
    """

    super().__init__(
        col_name,
        distance_function_name=self._levenshtein_name,
        distance_threshold_or_thresholds=distance_threshold_or_thresholds,
        regex_extract=regex_extract,
        valid_string_pattern=valid_string_pattern,
        set_to_lowercase=set_to_lowercase,
        higher_is_more_similar=False,
        include_exact_match_level=include_exact_match_level,
        term_frequency_adjustments=term_frequency_adjustments,
        m_probability_exact_match=m_probability_exact_match,
        m_probability_or_probabilities_thres=m_probability_or_probabilities_dl,
        m_probability_else=m_probability_else,
    )

Bases: DistanceFunctionAtThresholdsBase

Source code in splink/comparison_library.py 
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class JaccardAtThresholdsBase(DistanceFunctionAtThresholdsBase):
    def __init__(
        self,
        col_name: str,
        distance_threshold_or_thresholds: int | list = [0.9, 0.7],
        regex_extract: str = None,
        valid_string_pattern: str = None,
        set_to_lowercase: bool = False,
        include_exact_match_level=True,
        term_frequency_adjustments=False,
        m_probability_exact_match=None,
        m_probability_or_probabilities_jac: float | list = None,
        m_probability_else=None,
    ) -> Comparison:
        """A comparison of the data in `col_name` with the jaccard distance used to
        assess middle similarity levels.

        An example of the output with default arguments and setting
        `distance_threshold_or_thresholds = [0.9,0.7]` would be

        - Exact match
        - Jaccard distance <= 0.9
        - Jaccard distance <= 0.7
        - Anything else

        Args:
            col_name (str): The name of the column to compare
            distance_threshold_or_thresholds (Union[int, list], optional): The
                threshold(s) to use for the middle similarity level(s).
                Defaults to [0.9, 0.7].
            regex_extract (str): Regular expression pattern to evaluate a match on.
            valid_string_pattern (str): regular expression pattern that if not
                matched will result in column being treated as a null.
            include_exact_match_level (bool, optional): If True, include an exact match
                level. Defaults to True.
            term_frequency_adjustments (bool, optional): If True, apply term frequency
                adjustments to the exact match level. Defaults to False.
            m_probability_exact_match (float, optional): If provided, overrides the
                default m probability for the exact match level. Defaults to None.
            m_probability_or_probabilities_jac (Union[float, list], optional):
                If provided, overrides the default m probabilities
                for the thresholds specified for given function. Defaults to None.
            m_probability_else (float, optional): If provided, overrides the
                default m probability for the 'anything else' level. Defaults to None.

        Examples:
            === ":simple-duckdb: DuckDB"
                Create comparison with jaccard match levels with similarity score >=0.9
                and >=0.7
                ``` python
                import splink.duckdb.comparison_library as cl
                cl.jaccard_at_thresholds("first_name", [1,2])
                ```
                Create comparison with jaccard match levels with similarity score >=0.9
                and >=0.7 on a substring of name column as determined by a regular
                expression
                ``` python
                import splink.duckdb.comparison_library as cl
                cl.jaccard_at_thresholds("first_name", [1,2], regex_extract="^A|B")
                ```
            === ":simple-apachespark: Spark"
                Create comparison with jaccard match levels with similarity score >=0.9
                and >=0.7
                ``` python
                import splink.spark.comparison_library as cl
                cl.jaccard_at_thresholds("first_name", [1,2])
                ```
                Create comparison with jaccard match levels with similarity score >=0.9
                and >=0.7 on a substring of name column as determined by a regular
                expression
                ``` python
                import splink.spark.comparison_library as cl
                cl.jaccard_at_thresholds("first_name", [1,2], regex_extract="^A|B")
                ```

        Returns:
            Comparison: A comparison for Jaccard similarity that can be included
                in the Splink settings dictionary.
        """

        super().__init__(
            col_name,
            distance_function_name=self._jaccard_name,
            distance_threshold_or_thresholds=distance_threshold_or_thresholds,
            regex_extract=regex_extract,
            valid_string_pattern=valid_string_pattern,
            set_to_lowercase=set_to_lowercase,
            higher_is_more_similar=True,
            include_exact_match_level=include_exact_match_level,
            term_frequency_adjustments=term_frequency_adjustments,
            m_probability_exact_match=m_probability_exact_match,
            m_probability_or_probabilities_thres=m_probability_or_probabilities_jac,
            m_probability_else=m_probability_else,
        )

    @property
    def _is_distance_subclass(self):
        return True

__init__(col_name, distance_threshold_or_thresholds=[0.9, 0.7], regex_extract=None, valid_string_pattern=None, set_to_lowercase=False, include_exact_match_level=True, term_frequency_adjustments=False, m_probability_exact_match=None, m_probability_or_probabilities_jac=None, m_probability_else=None)

A comparison of the data in col_name with the jaccard distance used to assess middle similarity levels.

An example of the output with default arguments and setting distance_threshold_or_thresholds = [0.9,0.7] would be

  • Exact match
  • Jaccard distance <= 0.9
  • Jaccard distance <= 0.7
  • Anything else

Parameters:

Name Type Description Default
col_name str

The name of the column to compare

 required
distance_threshold_or_thresholds Union[int, list]

The threshold(s) to use for the middle similarity level(s). Defaults to [0.9, 0.7].

 [0.9, 0.7]
regex_extract str

Regular expression pattern to evaluate a match on.

 None
valid_string_pattern str

regular expression pattern that if not matched will result in column being treated as a null.

 None
include_exact_match_level bool

If True, include an exact match level. Defaults to True.

 True
term_frequency_adjustments bool

If True, apply term frequency adjustments to the exact match level. Defaults to False.

 False
m_probability_exact_match float

If provided, overrides the default m probability for the exact match level. Defaults to None.

 None
m_probability_or_probabilities_jac Union[float, list]

If provided, overrides the default m probabilities for the thresholds specified for given function. Defaults to None.

 None
m_probability_else float

If provided, overrides the default m probability for the 'anything else' level. Defaults to None.

 None

Examples:

Create comparison with jaccard match levels with similarity score >=0.9 and >=0.7 

import splink.duckdb.comparison_library as cl
cl.jaccard_at_thresholds("first_name", [1,2])
Create comparison with jaccard match levels with similarity score >=0.9 and >=0.7 on a substring of name column as determined by a regular expression 
import splink.duckdb.comparison_library as cl
cl.jaccard_at_thresholds("first_name", [1,2], regex_extract="^A|B")

Create comparison with jaccard match levels with similarity score >=0.9 and >=0.7 

import splink.spark.comparison_library as cl
cl.jaccard_at_thresholds("first_name", [1,2])
Create comparison with jaccard match levels with similarity score >=0.9 and >=0.7 on a substring of name column as determined by a regular expression 
import splink.spark.comparison_library as cl
cl.jaccard_at_thresholds("first_name", [1,2], regex_extract="^A|B")

Returns:

Name Type Description
Comparison Comparison

A comparison for Jaccard similarity that can be included in the Splink settings dictionary.
Source code in splink/comparison_library.py 
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def __init__(
    self,
    col_name: str,
    distance_threshold_or_thresholds: int | list = [0.9, 0.7],
    regex_extract: str = None,
    valid_string_pattern: str = None,
    set_to_lowercase: bool = False,
    include_exact_match_level=True,
    term_frequency_adjustments=False,
    m_probability_exact_match=None,
    m_probability_or_probabilities_jac: float | list = None,
    m_probability_else=None,
) -> Comparison:
    """A comparison of the data in `col_name` with the jaccard distance used to
    assess middle similarity levels.

    An example of the output with default arguments and setting
    `distance_threshold_or_thresholds = [0.9,0.7]` would be

    - Exact match
    - Jaccard distance <= 0.9
    - Jaccard distance <= 0.7
    - Anything else

    Args:
        col_name (str): The name of the column to compare
        distance_threshold_or_thresholds (Union[int, list], optional): The
            threshold(s) to use for the middle similarity level(s).
            Defaults to [0.9, 0.7].
        regex_extract (str): Regular expression pattern to evaluate a match on.
        valid_string_pattern (str): regular expression pattern that if not
            matched will result in column being treated as a null.
        include_exact_match_level (bool, optional): If True, include an exact match
            level. Defaults to True.
        term_frequency_adjustments (bool, optional): If True, apply term frequency
            adjustments to the exact match level. Defaults to False.
        m_probability_exact_match (float, optional): If provided, overrides the
            default m probability for the exact match level. Defaults to None.
        m_probability_or_probabilities_jac (Union[float, list], optional):
            If provided, overrides the default m probabilities
            for the thresholds specified for given function. Defaults to None.
        m_probability_else (float, optional): If provided, overrides the
            default m probability for the 'anything else' level. Defaults to None.

    Examples:
        === ":simple-duckdb: DuckDB"
            Create comparison with jaccard match levels with similarity score >=0.9
            and >=0.7
            ``` python
            import splink.duckdb.comparison_library as cl
            cl.jaccard_at_thresholds("first_name", [1,2])
            ```
            Create comparison with jaccard match levels with similarity score >=0.9
            and >=0.7 on a substring of name column as determined by a regular
            expression
            ``` python
            import splink.duckdb.comparison_library as cl
            cl.jaccard_at_thresholds("first_name", [1,2], regex_extract="^A|B")
            ```
        === ":simple-apachespark: Spark"
            Create comparison with jaccard match levels with similarity score >=0.9
            and >=0.7
            ``` python
            import splink.spark.comparison_library as cl
            cl.jaccard_at_thresholds("first_name", [1,2])
            ```
            Create comparison with jaccard match levels with similarity score >=0.9
            and >=0.7 on a substring of name column as determined by a regular
            expression
            ``` python
            import splink.spark.comparison_library as cl
            cl.jaccard_at_thresholds("first_name", [1,2], regex_extract="^A|B")
            ```

    Returns:
        Comparison: A comparison for Jaccard similarity that can be included
            in the Splink settings dictionary.
    """

    super().__init__(
        col_name,
        distance_function_name=self._jaccard_name,
        distance_threshold_or_thresholds=distance_threshold_or_thresholds,
        regex_extract=regex_extract,
        valid_string_pattern=valid_string_pattern,
        set_to_lowercase=set_to_lowercase,
        higher_is_more_similar=True,
        include_exact_match_level=include_exact_match_level,
        term_frequency_adjustments=term_frequency_adjustments,
        m_probability_exact_match=m_probability_exact_match,
        m_probability_or_probabilities_thres=m_probability_or_probabilities_jac,
        m_probability_else=m_probability_else,
    )

Bases: DistanceFunctionAtThresholdsBase

Source code in splink/comparison_library.py 
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class JaroAtThresholdsBase(DistanceFunctionAtThresholdsBase):
    def __init__(
        self,
        col_name: str,
        distance_threshold_or_thresholds: int | list = [0.9, 0.7],
        regex_extract: str = None,
        valid_string_pattern: str = None,
        set_to_lowercase: bool = False,
        include_exact_match_level=True,
        term_frequency_adjustments=False,
        m_probability_exact_match=None,
        m_probability_or_probabilities_jar: float | list = None,
        m_probability_else=None,
    ) -> Comparison:
        """A comparison of the data in `col_name` with the jaro distance used to
        assess middle similarity levels.

        An example of the output with default arguments and setting
        `distance_threshold_or_thresholds = [0.9, 0.7]` would be

        - Exact match
        - Jaro distance <= 0.9
        - Jaro distance <= 0.7
        - Anything else

        Args:
            col_name (str): The name of the column to compare
            distance_threshold_or_thresholds (Union[int, list], optional): The
                threshold(s) to use for the middle similarity level(s).
                Defaults to [0.9, 0.7].
            regex_extract (str): Regular expression pattern to evaluate a match on.
            valid_string_pattern (str): regular expression pattern that if not
                matched will result in column being treated as a null.
            include_exact_match_level (bool, optional): If True, include an exact match
                level. Defaults to True.
            term_frequency_adjustments (bool, optional): If True, apply term frequency
                adjustments to the exact match level. Defaults to False.
            m_probability_exact_match (float, optional): If provided, overrides the
                default m probability for the exact match level. Defaults to None.
            m_probability_or_probabilities_jar (Union[float, list], optional):
                If provided, overrides the default m probabilities
                for the thresholds specified for given function. Defaults to None.
            m_probability_else (float, optional): If provided, overrides the
                default m probability for the 'anything else' level. Defaults to None.

        Examples:
            === ":simple-duckdb: DuckDB"
                Create comparison with jaro match levels with similarity score >=0.9
                and >=0.7
                ``` python
                import splink.duckdb.comparison_library as cl
                cl.jaro_at_thresholds("first_name", [0.9, 0.7])
                ```
                Create comparison with jaro match levels with similarity score >=0.9
                and >=0.7 on a substring of name column as determined by a regular
                expression
                ``` python
                import splink.duckdb.comparison_library as cl
                cl.jaro_at_thresholds("first_name", [0.9, 0.7], regex_extract="^[A-Z]")
                ```
            === ":simple-apachespark: Spark"
                Create comparison with jaro match levels with similarity score >=0.9
                and >=0.7
                ``` python
                import splink.spark.comparison_library as cl
                cl.jaro_at_thresholds("first_name", [0.9, 0.7])
                ```
                Create comparison with jaro match levels with similarity score >=0.9
                and >=0.7 on a substring of name column as determined by a regular
                expression
                ``` python
                import splink.spark.comparison_library as cl
                cl.jaro_at_thresholds("first_name", [0.9, 0.7], regex_extract="^[A-Z]")
                ```
            === ":simple-sqlite: SQLite"
                Create comparison with jaro match levels with similarity score >=0.9
                and >=0.7
                ``` python
                import splink.sqlite.comparison_library as cl
                cl.jaro_at_thresholds("first_name", [0.9, 0.7])
                ```

        Returns:
            Comparison:
        """

        super().__init__(
            col_name,
            distance_function_name=self._jaro_name,
            distance_threshold_or_thresholds=distance_threshold_or_thresholds,
            regex_extract=regex_extract,
            valid_string_pattern=valid_string_pattern,
            set_to_lowercase=set_to_lowercase,
            higher_is_more_similar=True,
            include_exact_match_level=include_exact_match_level,
            term_frequency_adjustments=term_frequency_adjustments,
            m_probability_exact_match=m_probability_exact_match,
            m_probability_or_probabilities_thres=m_probability_or_probabilities_jar,
            m_probability_else=m_probability_else,
        )

    @property
    def _is_distance_subclass(self):
        return True

__init__(col_name, distance_threshold_or_thresholds=[0.9, 0.7], regex_extract=None, valid_string_pattern=None, set_to_lowercase=False, include_exact_match_level=True, term_frequency_adjustments=False, m_probability_exact_match=None, m_probability_or_probabilities_jar=None, m_probability_else=None)

A comparison of the data in col_name with the jaro distance used to assess middle similarity levels.

An example of the output with default arguments and setting distance_threshold_or_thresholds = [0.9, 0.7] would be

  • Exact match
  • Jaro distance <= 0.9
  • Jaro distance <= 0.7
  • Anything else

Parameters:

Name Type Description Default
col_name str

The name of the column to compare

 required
distance_threshold_or_thresholds Union[int, list]

The threshold(s) to use for the middle similarity level(s). Defaults to [0.9, 0.7].

 [0.9, 0.7]
regex_extract str

Regular expression pattern to evaluate a match on.

 None
valid_string_pattern str

regular expression pattern that if not matched will result in column being treated as a null.

 None
include_exact_match_level bool

If True, include an exact match level. Defaults to True.

 True
term_frequency_adjustments bool

If True, apply term frequency adjustments to the exact match level. Defaults to False.

 False
m_probability_exact_match float

If provided, overrides the default m probability for the exact match level. Defaults to None.

 None
m_probability_or_probabilities_jar Union[float, list]

If provided, overrides the default m probabilities for the thresholds specified for given function. Defaults to None.

 None
m_probability_else float

If provided, overrides the default m probability for the 'anything else' level. Defaults to None.

 None

Examples:

Create comparison with jaro match levels with similarity score >=0.9 and >=0.7 

import splink.duckdb.comparison_library as cl
cl.jaro_at_thresholds("first_name", [0.9, 0.7])
Create comparison with jaro match levels with similarity score >=0.9 and >=0.7 on a substring of name column as determined by a regular expression 
import splink.duckdb.comparison_library as cl
cl.jaro_at_thresholds("first_name", [0.9, 0.7], regex_extract="^[A-Z]")

Create comparison with jaro match levels with similarity score >=0.9 and >=0.7 

import splink.spark.comparison_library as cl
cl.jaro_at_thresholds("first_name", [0.9, 0.7])
Create comparison with jaro match levels with similarity score >=0.9 and >=0.7 on a substring of name column as determined by a regular expression 
import splink.spark.comparison_library as cl
cl.jaro_at_thresholds("first_name", [0.9, 0.7], regex_extract="^[A-Z]")

Create comparison with jaro match levels with similarity score >=0.9 and >=0.7 

import splink.sqlite.comparison_library as cl
cl.jaro_at_thresholds("first_name", [0.9, 0.7])

Returns:

Name Type Description
Comparison Comparison
Source code in splink/comparison_library.py 
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def __init__(
    self,
    col_name: str,
    distance_threshold_or_thresholds: int | list = [0.9, 0.7],
    regex_extract: str = None,
    valid_string_pattern: str = None,
    set_to_lowercase: bool = False,
    include_exact_match_level=True,
    term_frequency_adjustments=False,
    m_probability_exact_match=None,
    m_probability_or_probabilities_jar: float | list = None,
    m_probability_else=None,
) -> Comparison:
    """A comparison of the data in `col_name` with the jaro distance used to
    assess middle similarity levels.

    An example of the output with default arguments and setting
    `distance_threshold_or_thresholds = [0.9, 0.7]` would be

    - Exact match
    - Jaro distance <= 0.9
    - Jaro distance <= 0.7
    - Anything else

    Args:
        col_name (str): The name of the column to compare
        distance_threshold_or_thresholds (Union[int, list], optional): The
            threshold(s) to use for the middle similarity level(s).
            Defaults to [0.9, 0.7].
        regex_extract (str): Regular expression pattern to evaluate a match on.
        valid_string_pattern (str): regular expression pattern that if not
            matched will result in column being treated as a null.
        include_exact_match_level (bool, optional): If True, include an exact match
            level. Defaults to True.
        term_frequency_adjustments (bool, optional): If True, apply term frequency
            adjustments to the exact match level. Defaults to False.
        m_probability_exact_match (float, optional): If provided, overrides the
            default m probability for the exact match level. Defaults to None.
        m_probability_or_probabilities_jar (Union[float, list], optional):
            If provided, overrides the default m probabilities
            for the thresholds specified for given function. Defaults to None.
        m_probability_else (float, optional): If provided, overrides the
            default m probability for the 'anything else' level. Defaults to None.

    Examples:
        === ":simple-duckdb: DuckDB"
            Create comparison with jaro match levels with similarity score >=0.9
            and >=0.7
            ``` python
            import splink.duckdb.comparison_library as cl
            cl.jaro_at_thresholds("first_name", [0.9, 0.7])
            ```
            Create comparison with jaro match levels with similarity score >=0.9
            and >=0.7 on a substring of name column as determined by a regular
            expression
            ``` python
            import splink.duckdb.comparison_library as cl
            cl.jaro_at_thresholds("first_name", [0.9, 0.7], regex_extract="^[A-Z]")
            ```
        === ":simple-apachespark: Spark"
            Create comparison with jaro match levels with similarity score >=0.9
            and >=0.7
            ``` python
            import splink.spark.comparison_library as cl
            cl.jaro_at_thresholds("first_name", [0.9, 0.7])
            ```
            Create comparison with jaro match levels with similarity score >=0.9
            and >=0.7 on a substring of name column as determined by a regular
            expression
            ``` python
            import splink.spark.comparison_library as cl
            cl.jaro_at_thresholds("first_name", [0.9, 0.7], regex_extract="^[A-Z]")
            ```
        === ":simple-sqlite: SQLite"
            Create comparison with jaro match levels with similarity score >=0.9
            and >=0.7
            ``` python
            import splink.sqlite.comparison_library as cl
            cl.jaro_at_thresholds("first_name", [0.9, 0.7])
            ```

    Returns:
        Comparison:
    """

    super().__init__(
        col_name,
        distance_function_name=self._jaro_name,
        distance_threshold_or_thresholds=distance_threshold_or_thresholds,
        regex_extract=regex_extract,
        valid_string_pattern=valid_string_pattern,
        set_to_lowercase=set_to_lowercase,
        higher_is_more_similar=True,
        include_exact_match_level=include_exact_match_level,
        term_frequency_adjustments=term_frequency_adjustments,
        m_probability_exact_match=m_probability_exact_match,
        m_probability_or_probabilities_thres=m_probability_or_probabilities_jar,
        m_probability_else=m_probability_else,
    )

Bases: DistanceFunctionAtThresholdsBase

Source code in splink/comparison_library.py 
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class JaroWinklerAtThresholdsBase(DistanceFunctionAtThresholdsBase):
    def __init__(
        self,
        col_name: str,
        distance_threshold_or_thresholds: int | list = [0.9, 0.7],
        regex_extract: str = None,
        valid_string_pattern: str = None,
        set_to_lowercase: bool = False,
        include_exact_match_level=True,
        term_frequency_adjustments=False,
        m_probability_exact_match=None,
        m_probability_or_probabilities_jw: float | list = None,
        m_probability_else=None,
    ) -> Comparison:
        """A comparison of the data in `col_name` with the jaro_winkler distance used to
        assess middle similarity levels.

        An example of the output with default arguments and setting
        `distance_threshold_or_thresholds = [0.9, 0.7]` would be

        - Exact match
        - Jaro-Winkler distance <= 0.9
        - Jaro-Winkler distance <= 0.7
        - Anything else

        Args:
            col_name (str): The name of the column to compare
            distance_threshold_or_thresholds (Union[int, list], optional): The
                threshold(s) to use for the middle similarity level(s).
                Defaults to [0.9, 0.7].
            regex_extract (str): Regular expression pattern to evaluate a match on.
            valid_string_pattern (str): regular expression pattern that if not
                matched will result in column being treated as a null.
            include_exact_match_level (bool, optional): If True, include an exact match
                level. Defaults to True.
            term_frequency_adjustments (bool, optional): If True, apply term frequency
                adjustments to the exact match level. Defaults to False.
            m_probability_exact_match (float, optional): If provided, overrides the
                default m probability for the exact match level. Defaults to None.
            m_probability_or_probabilities_jw (Union[float, list], optional):
                If provided, overrides the default m probabilities
                for the thresholds specified for given function. Defaults to None.
            m_probability_else (float, optional): If provided, overrides the
                default m probability for the 'anything else' level. Defaults to None.


        Examples:
            === ":simple-duckdb: DuckDB"
                Create comparison with jaro_winkler match levels with similarity
                score >= 0.9 and >=0.7
                ``` python
                import splink.duckdb.comparison_library as cl
                cl.jaro_winkler_at_thresholds("first_name", [0.9, 0.7])
                ```
                Create comparison with jaro_winkler match levels with similarity
                score =>0.9 and >=0.7 on a substring of name column as determined by
                a regular expression
                ``` python
                import splink.duckdb.comparison_library as cl
                cl.jaro_winkler_at_thresholds("first_name",
                                              [0.9, 0.7],
                                              regex_extract="^[A-Z]"
                                              )
                ```
            === ":simple-apachespark: Spark"
                Create comparison with jaro_winkler match levels with similarity
                score >=0.9 and >=0.7
                ``` python
                import splink.spark.comparison_library as cl
                cl.jaro_winkler_at_thresholds("first_name", [0.9, 0.7])
                ```
                Create comparison with jaro_winkler match levels with similarity
                score >=0.9 and >=0.7 on a substring of name column as determined
                by a regular expression
                ``` python
                import splink.spark.comparison_library as cl
                cl.jaro_winkler_at_thresholds("first_name",
                                              [0.9, 0.7],
                                              regex_extract="^[A-Z]"
                                              )
                ```
            === ":simple-sqlite: SQLite"
                Create comparison with jaro_winkler match levels with similarity
                score >=0.9 and >=0.7
                ``` python
                import splink.sqlite.comparison_library as cl
                cl.jaro_winkler_at_thresholds("first_name", [0.9, 0.7])
                ```

        Returns:
            Comparison: A comparison for Jaro Winkler similarity that can be included
                in the Splink settings dictionary.
        """

        super().__init__(
            col_name,
            distance_function_name=self._jaro_winkler_name,
            distance_threshold_or_thresholds=distance_threshold_or_thresholds,
            regex_extract=regex_extract,
            valid_string_pattern=valid_string_pattern,
            set_to_lowercase=set_to_lowercase,
            higher_is_more_similar=True,
            include_exact_match_level=include_exact_match_level,
            term_frequency_adjustments=term_frequency_adjustments,
            m_probability_exact_match=m_probability_exact_match,
            m_probability_or_probabilities_thres=m_probability_or_probabilities_jw,
            m_probability_else=m_probability_else,
        )

    @property
    def _is_distance_subclass(self):
        return True

__init__(col_name, distance_threshold_or_thresholds=[0.9, 0.7], regex_extract=None, valid_string_pattern=None, set_to_lowercase=False, include_exact_match_level=True, term_frequency_adjustments=False, m_probability_exact_match=None, m_probability_or_probabilities_jw=None, m_probability_else=None)

A comparison of the data in col_name with the jaro_winkler distance used to assess middle similarity levels.

An example of the output with default arguments and setting distance_threshold_or_thresholds = [0.9, 0.7] would be

  • Exact match
  • Jaro-Winkler distance <= 0.9
  • Jaro-Winkler distance <= 0.7
  • Anything else

Parameters:

Name Type Description Default
col_name str

The name of the column to compare

 required
distance_threshold_or_thresholds Union[int, list]

The threshold(s) to use for the middle similarity level(s). Defaults to [0.9, 0.7].

 [0.9, 0.7]
regex_extract str

Regular expression pattern to evaluate a match on.

 None
valid_string_pattern str

regular expression pattern that if not matched will result in column being treated as a null.

 None
include_exact_match_level bool

If True, include an exact match level. Defaults to True.

 True
term_frequency_adjustments bool

If True, apply term frequency adjustments to the exact match level. Defaults to False.

 False
m_probability_exact_match float

If provided, overrides the default m probability for the exact match level. Defaults to None.

 None
m_probability_or_probabilities_jw Union[float, list]

If provided, overrides the default m probabilities for the thresholds specified for given function. Defaults to None.

 None
m_probability_else float

If provided, overrides the default m probability for the 'anything else' level. Defaults to None.

 None

Examples:

Create comparison with jaro_winkler match levels with similarity score >= 0.9 and >=0.7 

import splink.duckdb.comparison_library as cl
cl.jaro_winkler_at_thresholds("first_name", [0.9, 0.7])
Create comparison with jaro_winkler match levels with similarity score =>0.9 and >=0.7 on a substring of name column as determined by a regular expression 
import splink.duckdb.comparison_library as cl
cl.jaro_winkler_at_thresholds("first_name",
                              [0.9, 0.7],
                              regex_extract="^[A-Z]"
                              )

Create comparison with jaro_winkler match levels with similarity score >=0.9 and >=0.7 

import splink.spark.comparison_library as cl
cl.jaro_winkler_at_thresholds("first_name", [0.9, 0.7])
Create comparison with jaro_winkler match levels with similarity score >=0.9 and >=0.7 on a substring of name column as determined by a regular expression 
import splink.spark.comparison_library as cl
cl.jaro_winkler_at_thresholds("first_name",
                              [0.9, 0.7],
                              regex_extract="^[A-Z]"
                              )

Create comparison with jaro_winkler match levels with similarity score >=0.9 and >=0.7 

import splink.sqlite.comparison_library as cl
cl.jaro_winkler_at_thresholds("first_name", [0.9, 0.7])

Returns:

Name Type Description
Comparison Comparison

A comparison for Jaro Winkler similarity that can be included in the Splink settings dictionary.
Source code in splink/comparison_library.py 
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def __init__(
    self,
    col_name: str,
    distance_threshold_or_thresholds: int | list = [0.9, 0.7],
    regex_extract: str = None,
    valid_string_pattern: str = None,
    set_to_lowercase: bool = False,
    include_exact_match_level=True,
    term_frequency_adjustments=False,
    m_probability_exact_match=None,
    m_probability_or_probabilities_jw: float | list = None,
    m_probability_else=None,
) -> Comparison:
    """A comparison of the data in `col_name` with the jaro_winkler distance used to
    assess middle similarity levels.

    An example of the output with default arguments and setting
    `distance_threshold_or_thresholds = [0.9, 0.7]` would be

    - Exact match
    - Jaro-Winkler distance <= 0.9
    - Jaro-Winkler distance <= 0.7
    - Anything else

    Args:
        col_name (str): The name of the column to compare
        distance_threshold_or_thresholds (Union[int, list], optional): The
            threshold(s) to use for the middle similarity level(s).
            Defaults to [0.9, 0.7].
        regex_extract (str): Regular expression pattern to evaluate a match on.
        valid_string_pattern (str): regular expression pattern that if not
            matched will result in column being treated as a null.
        include_exact_match_level (bool, optional): If True, include an exact match
            level. Defaults to True.
        term_frequency_adjustments (bool, optional): If True, apply term frequency
            adjustments to the exact match level. Defaults to False.
        m_probability_exact_match (float, optional): If provided, overrides the
            default m probability for the exact match level. Defaults to None.
        m_probability_or_probabilities_jw (Union[float, list], optional):
            If provided, overrides the default m probabilities
            for the thresholds specified for given function. Defaults to None.
        m_probability_else (float, optional): If provided, overrides the
            default m probability for the 'anything else' level. Defaults to None.


    Examples:
        === ":simple-duckdb: DuckDB"
            Create comparison with jaro_winkler match levels with similarity
            score >= 0.9 and >=0.7
            ``` python
            import splink.duckdb.comparison_library as cl
            cl.jaro_winkler_at_thresholds("first_name", [0.9, 0.7])
            ```
            Create comparison with jaro_winkler match levels with similarity
            score =>0.9 and >=0.7 on a substring of name column as determined by
            a regular expression
            ``` python
            import splink.duckdb.comparison_library as cl
            cl.jaro_winkler_at_thresholds("first_name",
                                          [0.9, 0.7],
                                          regex_extract="^[A-Z]"
                                          )
            ```
        === ":simple-apachespark: Spark"
            Create comparison with jaro_winkler match levels with similarity
            score >=0.9 and >=0.7
            ``` python
            import splink.spark.comparison_library as cl
            cl.jaro_winkler_at_thresholds("first_name", [0.9, 0.7])
            ```
            Create comparison with jaro_winkler match levels with similarity
            score >=0.9 and >=0.7 on a substring of name column as determined
            by a regular expression
            ``` python
            import splink.spark.comparison_library as cl
            cl.jaro_winkler_at_thresholds("first_name",
                                          [0.9, 0.7],
                                          regex_extract="^[A-Z]"
                                          )
            ```
        === ":simple-sqlite: SQLite"
            Create comparison with jaro_winkler match levels with similarity
            score >=0.9 and >=0.7
            ``` python
            import splink.sqlite.comparison_library as cl
            cl.jaro_winkler_at_thresholds("first_name", [0.9, 0.7])
            ```

    Returns:
        Comparison: A comparison for Jaro Winkler similarity that can be included
            in the Splink settings dictionary.
    """

    super().__init__(
        col_name,
        distance_function_name=self._jaro_winkler_name,
        distance_threshold_or_thresholds=distance_threshold_or_thresholds,
        regex_extract=regex_extract,
        valid_string_pattern=valid_string_pattern,
        set_to_lowercase=set_to_lowercase,
        higher_is_more_similar=True,
        include_exact_match_level=include_exact_match_level,
        term_frequency_adjustments=term_frequency_adjustments,
        m_probability_exact_match=m_probability_exact_match,
        m_probability_or_probabilities_thres=m_probability_or_probabilities_jw,
        m_probability_else=m_probability_else,
    )

Bases: Comparison

Source code in splink/comparison_library.py 
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class ArrayIntersectAtSizesBase(Comparison):
    def __init__(
        self,
        col_name: str,
        size_or_sizes: int | list = [1],
        m_probability_or_probabilities_sizes: float | list = None,
        m_probability_else=None,
    ) -> Comparison:
        """A comparison of the data in array column `col_name` with various
        intersection sizes to assess similarity levels.

        An example of the output with default arguments and setting
        `size_or_sizes = [3, 1]` would be

        - Intersection has at least 3 elements
        - Intersection has at least 1 element (i.e. 1 or 2)
        - Anything else (i.e. empty intersection)

        Args:
            col_name (str): The name of the column to compare
            size_or_sizes (Union[int, list], optional): The size(s) of intersection
                to use for the non-'else' similarity level(s). Should be in
                descending order. Defaults to [1].
            m_probability_or_probabilities_sizes (Union[float, list], optional):
                If provided, overrides the default m probabilities
                for the sizes specified. Defaults to None.
            m_probability_else (float, optional): If provided, overrides the
                default m probability for the 'anything else' level. Defaults to None.

        Examples:
            === ":simple-duckdb: DuckDB"
                ``` python
                import splink.duckdb.comparison_library as cl
                cl.array_intersect_at_sizes("first_name", [3, 1])
                ```
            === ":simple-apachespark: Spark"
                ``` python
                import splink.spark.comparison_library as cl
                cl.array_intersect_at_sizes("first_name", [3, 1])
                ```
            === ":simple-amazonaws: Athena"
                ``` python
                import splink.athena.comparison_library as cl
                cl.array_intersect_at_sizes("first_name", [3, 1])
                ```
            === ":simple-postgresql: PostgreSql"
                ``` python
                import splink.postgres.comparison_library as cl
                cl.array_intersect_at_sizes("first_name", [3, 1])
                ```

        Returns:
            Comparison: A comparison for the intersection of arrays that can be included
                in the Splink settings dictionary.
        """

        sizes = ensure_is_iterable(size_or_sizes)
        if len(sizes) == 0:
            raise ValueError(
                "`size_or_sizes` must have at least one element, so that Comparison "
                "has more than just an 'else' level"
            )
        if any(size <= 0 for size in sizes):
            raise ValueError("All entries of `size_or_sizes` must be postive")

        if m_probability_or_probabilities_sizes is None:
            m_probability_or_probabilities_sizes = [None] * len(sizes)
        m_probabilities = ensure_is_iterable(m_probability_or_probabilities_sizes)

        comparison_levels = []
        comparison_levels.append(self._null_level(col_name))

        for size_intersect, m_prob in zip(sizes, m_probabilities):
            level = self._array_intersect_level(
                col_name, m_probability=m_prob, min_intersection=size_intersect
            )
            comparison_levels.append(level)

        comparison_levels.append(
            self._else_level(m_probability=m_probability_else),
        )

        comparison_desc = ""

        size_desc = ", ".join([str(s) for s in sizes])
        plural = "" if len(sizes) == 1 else "s"
        comparison_desc += (
            f"Array intersection at minimum size{plural} {size_desc} vs. "
        )
        comparison_desc += "anything else"

        comparison_dict = {
            "comparison_description": comparison_desc,
            "comparison_levels": comparison_levels,
        }
        super().__init__(comparison_dict)

    @property
    def _array_intersect_level(self):
        raise NotImplementedError("Intersect level not defined on base class")

__init__(col_name, size_or_sizes=[1], m_probability_or_probabilities_sizes=None, m_probability_else=None)

A comparison of the data in array column col_name with various intersection sizes to assess similarity levels.

An example of the output with default arguments and setting size_or_sizes = [3, 1] would be

  • Intersection has at least 3 elements
  • Intersection has at least 1 element (i.e. 1 or 2)
  • Anything else (i.e. empty intersection)

Parameters:

Name Type Description Default
col_name str

The name of the column to compare

 required
size_or_sizes Union[int, list]

The size(s) of intersection to use for the non-'else' similarity level(s). Should be in descending order. Defaults to [1].

 [1]
m_probability_or_probabilities_sizes Union[float, list]

If provided, overrides the default m probabilities for the sizes specified. Defaults to None.

 None
m_probability_else float

If provided, overrides the default m probability for the 'anything else' level. Defaults to None.

 None

Examples:

import splink.duckdb.comparison_library as cl
cl.array_intersect_at_sizes("first_name", [3, 1])

import splink.spark.comparison_library as cl
cl.array_intersect_at_sizes("first_name", [3, 1])

import splink.athena.comparison_library as cl
cl.array_intersect_at_sizes("first_name", [3, 1])

import splink.postgres.comparison_library as cl
cl.array_intersect_at_sizes("first_name", [3, 1])

Returns:

Name Type Description
Comparison Comparison

A comparison for the intersection of arrays that can be included in the Splink settings dictionary.
Source code in splink/comparison_library.py 
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def __init__(
    self,
    col_name: str,
    size_or_sizes: int | list = [1],
    m_probability_or_probabilities_sizes: float | list = None,
    m_probability_else=None,
) -> Comparison:
    """A comparison of the data in array column `col_name` with various
    intersection sizes to assess similarity levels.

    An example of the output with default arguments and setting
    `size_or_sizes = [3, 1]` would be

    - Intersection has at least 3 elements
    - Intersection has at least 1 element (i.e. 1 or 2)
    - Anything else (i.e. empty intersection)

    Args:
        col_name (str): The name of the column to compare
        size_or_sizes (Union[int, list], optional): The size(s) of intersection
            to use for the non-'else' similarity level(s). Should be in
            descending order. Defaults to [1].
        m_probability_or_probabilities_sizes (Union[float, list], optional):
            If provided, overrides the default m probabilities
            for the sizes specified. Defaults to None.
        m_probability_else (float, optional): If provided, overrides the
            default m probability for the 'anything else' level. Defaults to None.

    Examples:
        === ":simple-duckdb: DuckDB"
            ``` python
            import splink.duckdb.comparison_library as cl
            cl.array_intersect_at_sizes("first_name", [3, 1])
            ```
        === ":simple-apachespark: Spark"
            ``` python
            import splink.spark.comparison_library as cl
            cl.array_intersect_at_sizes("first_name", [3, 1])
            ```
        === ":simple-amazonaws: Athena"
            ``` python
            import splink.athena.comparison_library as cl
            cl.array_intersect_at_sizes("first_name", [3, 1])
            ```
        === ":simple-postgresql: PostgreSql"
            ``` python
            import splink.postgres.comparison_library as cl
            cl.array_intersect_at_sizes("first_name", [3, 1])
            ```

    Returns:
        Comparison: A comparison for the intersection of arrays that can be included
            in the Splink settings dictionary.
    """

    sizes = ensure_is_iterable(size_or_sizes)
    if len(sizes) == 0:
        raise ValueError(
            "`size_or_sizes` must have at least one element, so that Comparison "
            "has more than just an 'else' level"
        )
    if any(size <= 0 for size in sizes):
        raise ValueError("All entries of `size_or_sizes` must be postive")

    if m_probability_or_probabilities_sizes is None:
        m_probability_or_probabilities_sizes = [None] * len(sizes)
    m_probabilities = ensure_is_iterable(m_probability_or_probabilities_sizes)

    comparison_levels = []
    comparison_levels.append(self._null_level(col_name))

    for size_intersect, m_prob in zip(sizes, m_probabilities):
        level = self._array_intersect_level(
            col_name, m_probability=m_prob, min_intersection=size_intersect
        )
        comparison_levels.append(level)

    comparison_levels.append(
        self._else_level(m_probability=m_probability_else),
    )

    comparison_desc = ""

    size_desc = ", ".join([str(s) for s in sizes])
    plural = "" if len(sizes) == 1 else "s"
    comparison_desc += (
        f"Array intersection at minimum size{plural} {size_desc} vs. "
    )
    comparison_desc += "anything else"

    comparison_dict = {
        "comparison_description": comparison_desc,
        "comparison_levels": comparison_levels,
    }
    super().__init__(comparison_dict)

Bases: Comparison

Source code in splink/comparison_library.py 
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class DatediffAtThresholdsBase(Comparison):
    def __init__(
        self,
        col_name: str,
        date_thresholds: int | list = [1],
        date_metrics: str | list = ["year"],
        cast_strings_to_date=False,
        date_format: str = None,
        invalid_dates_as_null: bool = False,
        include_exact_match_level=True,
        term_frequency_adjustments=False,
        m_probability_exact_match=None,
        m_probability_or_probabilities_dat: float | list = None,
        m_probability_else=None,
    ) -> Comparison:
        """A comparison of the data in the date column `col_name` with various
        date thresholds and metrics to assess similarity levels.

        An example of the output with default arguments and settings
        `date_thresholds = [1]` and `date_metrics = ['day']` would be
        - The two input dates are within 1 day of one another
        - Anything else (i.e. all other dates lie outside this range)

        `date_thresholds` and `date_metrics` should be used in conjunction
        with one another.
        For example, `date_thresholds = [10, 12, 15]` with
        `date_metrics = ['day', 'month', 'year']` would result in the following checks:

        - The two dates are within 10 days of one another
        - The two dates are within 12 months of one another
        - And the two dates are within 15 years of one another

        Args:
            col_name (str): The name of the date column to compare.
            date_thresholds (Union[int, list], optional): The size(s) of given date
                thresholds, to assess whether two dates fall within a given time
                interval.
                These values can be any integer value and should be used in tandem with
                `date_metrics`.
            date_metrics (Union[str, list], optional): The unit of time you wish your
                `date_thresholds` to be measured against.
                Metrics should be one of `day`, `month` or `year`.
            cast_strings_to_date (bool, optional): Set to True to
                enable date-casting when input dates are strings. Also adjust
                date_format if date-strings are not in (yyyy-mm-dd) format.
                Defaults to False.
            date_format(str, optional): Format of input dates if date-strings
                are given. Must be consistent across record pairs. If None
                (the default), downstream functions for each backend assign
                date_format to ISO 8601 format (yyyy-mm-dd).
                Set to "yyyy-MM-dd" for Spark and "%Y-%m-%d" for DuckDB and Athena
                when invalid_dates_as_null=True
            invalid_dates_as_null (bool, optional): assign any dates that do not adhere
                to date_format to the null level. Defaults to False.
            include_exact_match_level (bool, optional): If True, include an exact match
                level. Defaults to True.
            term_frequency_adjustments (bool, optional): If True, apply term frequency
                adjustments to the exact match level. Defaults to False.
            m_probability_exact_match (_type_, optional): If provided, overrides the
                default m probability for the exact match level. Defaults to None.
            m_probability_or_probabilities_dat (Union[float, list], optional):
                If provided, overrides the default m probabilities
                for the sizes specified. Defaults to None.
            m_probability_else (_type_, optional): If provided, overrides the
                default m probability for the 'anything else' level. Defaults to None.

        Examples:
            === ":simple-duckdb: DuckDB"
                Date Difference comparison at thresholds 10 days, 12 months and 15 years
                ``` python
                import splink.duckdb.comparison_library as cl
                cl.datediff_at_thresholds("date",
                                            date_thresholds = [10, 12, 15],
                                            date_metrics = ['day', 'month', 'year']
                                            )
                ```

                Datediff comparison with date-casting and unspecified date_format
                (default = %Y-%m-%d)
                ``` python
                import splink.duckdb.comparison_library as cl
                cl.datediff_at_thresholds("date",
                                            date_thresholds=[1,5],
                                            date_metrics = ["day", "year"],
                                            cast_strings_to_date=True
                                            )
                ```
                Datediff comparison with date-casting and specified (non-default)
                date_format
                ``` python
                import splink.duckdb.comparison_library as cl
                cl.datediff_at_thresholds("date",
                                            date_thresholds=[1,5],
                                            date_metrics = ["day", "year"],
                                            cast_strings_to_date=True,
                                            date_format='%d/%m/%Y'
                                            )
                ```
                Datediff comparison with date-casting and invalid dates set to null
                ```py
                import splink.duckdb.comparison_library as cl
                cl.datediff_at_thresholds("date",
                                            date_thresholds=[1,5],
                                            date_metrics = ["day", "year"],
                                            cast_strings_to_date=True,
                                            invalid_dates_as_null=True
                                            )
                ```
            === ":simple-apachespark: Spark"
                Date Difference comparison at thresholds 10 days, 12 months and 15 years
                ``` python
                import splink.spark.comparison_library as cl
                cl.datediff_at_thresholds("date",
                                            date_thresholds = [10, 12, 15],
                                            date_metrics = ['day', 'month', 'year']
                                            )
                ```

                Datediff comparison with date-casting and unspecified date_format
                (default = %Y-%m-%d)
                ``` python
                    import splink.spark.comparison_library as cl
                    cl.datediff_at_thresholds("date",
                                                date_thresholds=[1,5],
                                                date_metrics = ["day", "year"],
                                                cast_strings_to_date=True
                                                )
                ```

                Datediff comparison with date-casting and specified (non-default)
                date_format
                ``` python
                import splink.spark.comparison_library as cl
                cl.datediff_at_thresholds("date",
                                            date_thresholds=[1,5],
                                            date_metrics = ["day", "year"],
                                            cast_strings_to_date=True,
                                            date_format='%d/%m/%Y'
                                            )
                ```
                 Datediff comparison with date-casting and invalid dates set to null
                ```py
                import splink.spark.comparison_library as cl
                cl.datediff_at_thresholds("date",
                                            date_thresholds=[1,5],
                                            date_metrics = ["day", "year"],
                                            cast_strings_to_date=True,
                                            invalid_dates_as_null=True
                                            )
                ```
            === "":simple-amazonaws: Athena"
                Date Difference comparison at thresholds 10 days, 12 months and 15 years
                ``` python
                import splink.athena.comparison_library as cl
                cl.datediff_at_thresholds("date",
                                            date_thresholds = [10, 12, 15],
                                            date_metrics = ['day', 'month', 'year']
                                            )
                ```

                Datediff comparison with date-casting and unspecified date_format
                (default = %Y-%m-%d)
                ``` python
                    import splink.athena.comparison_library as cl
                    cl.datediff_at_thresholds("date",
                                                date_thresholds=[1,5],
                                                date_metrics = ["day", "year"],
                                                cast_strings_to_date=True
                                                )
                ```

                Datediff comparison with date-casting and specified (non-default)
                date_format
                ``` python
                import splink.athena.comparison_library as cl
                cl.datediff_at_thresholds("date",
                                            date_thresholds=[1,5],
                                            date_metrics = ["day", "year"],
                                            cast_strings_to_date=True,
                                            date_format='%d/%m/%Y'
                                            )
                ```
                 Datediff comparison with date-casting and invalid dates set to null
                ```py
                import splink.athena.comparison_library as cl
                cl.datediff_at_thresholds("date",
                                            date_thresholds=[1,5],
                                            date_metrics = ["day", "year"],
                                            cast_strings_to_date=True,
                                            invalid_dates_as_null=True
                                            )
                ```
            === ":simple-postgresql: PostgreSql"
                Date Difference comparison at thresholds 10 days, 12 months and 15 years
                ``` python
                import splink.postgres.comparison_library as cl
                cl.datediff_at_thresholds("date",
                                            date_thresholds = [10, 12, 15],
                                            date_metrics = ['day', 'month', 'year']
                                            )
                ```

                Datediff comparison with date-casting and unspecified date_format
                (default = yyyy-MM-dd)
                ``` python
                    import splink.postgres.comparison_library as cl
                    cl.datediff_at_thresholds("date",
                                                date_thresholds=[1,5],
                                                date_metrics = ["day", "year"],
                                                cast_strings_to_date=True
                                                )
                ```

                Datediff comparison with date-casting and specified (non-default)
                date_format
                ``` python
                import splink.postgres.comparison_library as cl
                cl.datediff_at_thresholds("date",
                                            date_thresholds=[1,5],
                                            date_metrics = ["day", "year"],
                                            cast_strings_to_date=True,
                                            date_format='dd/MM/yyyy'
                                            )
                ```

        Returns:
            Comparison: A comparison for Datediff that can be included in the Splink
                settings dictionary.
        """

        thresholds = ensure_is_iterable(date_thresholds)
        metrics = ensure_is_iterable(date_metrics)

        # Validate user inputs
        comparison_at_thresholds_error_logger("datediff", date_thresholds)
        datediff_error_logger(thresholds, metrics)

        if m_probability_or_probabilities_dat is None:
            m_probability_or_probabilities_dat = [None] * len(thresholds)
        m_probabilities = ensure_is_iterable(m_probability_or_probabilities_dat)

        comparison_levels = []
        comparison_levels.append(
            self._null_level(
                col_name,
                invalid_dates_as_null=invalid_dates_as_null,
                valid_string_pattern=date_format,
            )
        )

        if include_exact_match_level:
            level = self._exact_match_level(
                col_name,
                term_frequency_adjustments=term_frequency_adjustments,
                m_probability=m_probability_exact_match,
            )
            comparison_levels.append(level)

        for date_thres, date_metr, m_prob in zip(thresholds, metrics, m_probabilities):
            level = self._datediff_level(
                col_name,
                date_threshold=date_thres,
                date_metric=date_metr,
                m_probability=m_prob,
                cast_strings_to_date=cast_strings_to_date,
                date_format=date_format,
            )
            comparison_levels.append(level)

        comparison_levels.append(
            self._else_level(m_probability=m_probability_else),
        )

        comparison_desc = ""
        if include_exact_match_level:
            comparison_desc += "Exact match vs. "

        thres_desc = ", ".join(
            [f"{m.title()}(s): {v}" for m, v in zip(metrics, thresholds)]
        )
        plural = "" if len(thresholds) == 1 else "s"
        comparison_desc += (
            f"Dates within the following threshold{plural} {thres_desc} vs. "
        )
        comparison_desc += "anything else"

        comparison_dict = {
            "comparison_description": comparison_desc,
            "comparison_levels": comparison_levels,
        }
        super().__init__(comparison_dict)

    @property
    def _datediff_level(self):
        raise NotImplementedError("Datediff level not defined on base class")

__init__(col_name, date_thresholds=[1], date_metrics=['year'], cast_strings_to_date=False, date_format=None, invalid_dates_as_null=False, include_exact_match_level=True, term_frequency_adjustments=False, m_probability_exact_match=None, m_probability_or_probabilities_dat=None, m_probability_else=None)

A comparison of the data in the date column col_name with various date thresholds and metrics to assess similarity levels.

An example of the output with default arguments and settings date_thresholds = [1] and date_metrics = ['day'] would be - The two input dates are within 1 day of one another - Anything else (i.e. all other dates lie outside this range)

date_thresholds and date_metrics should be used in conjunction with one another. For example, date_thresholds = [10, 12, 15] with date_metrics = ['day', 'month', 'year'] would result in the following checks:

  • The two dates are within 10 days of one another
  • The two dates are within 12 months of one another
  • And the two dates are within 15 years of one another

Parameters:

Name Type Description Default
col_name str

The name of the date column to compare.

 required
date_thresholds Union[int, list]

The size(s) of given date thresholds, to assess whether two dates fall within a given time interval. These values can be any integer value and should be used in tandem with date_metrics.

 [1]
date_metrics Union[str, list]

The unit of time you wish your date_thresholds to be measured against. Metrics should be one of day, month or year.

 ['year']
cast_strings_to_date bool

Set to True to enable date-casting when input dates are strings. Also adjust date_format if date-strings are not in (yyyy-mm-dd) format. Defaults to False.

 False
date_format(str, optional

Format of input dates if date-strings are given. Must be consistent across record pairs. If None (the default), downstream functions for each backend assign date_format to ISO 8601 format (yyyy-mm-dd). Set to "yyyy-MM-dd" for Spark and "%Y-%m-%d" for DuckDB and Athena when invalid_dates_as_null=True

 required
invalid_dates_as_null bool

assign any dates that do not adhere to date_format to the null level. Defaults to False.

 False
include_exact_match_level bool

If True, include an exact match level. Defaults to True.

 True
term_frequency_adjustments bool

If True, apply term frequency adjustments to the exact match level. Defaults to False.

 False
m_probability_exact_match _type_

If provided, overrides the default m probability for the exact match level. Defaults to None.

 None
m_probability_or_probabilities_dat Union[float, list]

If provided, overrides the default m probabilities for the sizes specified. Defaults to None.

 None
m_probability_else _type_

If provided, overrides the default m probability for the 'anything else' level. Defaults to None.

 None

Examples:

Date Difference comparison at thresholds 10 days, 12 months and 15 years 

import splink.duckdb.comparison_library as cl
cl.datediff_at_thresholds("date",
                            date_thresholds = [10, 12, 15],
                            date_metrics = ['day', 'month', 'year']
                            )

Datediff comparison with date-casting and unspecified date_format (default = %Y-%m-%d) 

import splink.duckdb.comparison_library as cl
cl.datediff_at_thresholds("date",
                            date_thresholds=[1,5],
                            date_metrics = ["day", "year"],
                            cast_strings_to_date=True
                            )
Datediff comparison with date-casting and specified (non-default) date_format 
import splink.duckdb.comparison_library as cl
cl.datediff_at_thresholds("date",
                            date_thresholds=[1,5],
                            date_metrics = ["day", "year"],
                            cast_strings_to_date=True,
                            date_format='%d/%m/%Y'
                            )
Datediff comparison with date-casting and invalid dates set to null 
import splink.duckdb.comparison_library as cl
cl.datediff_at_thresholds("date",
                            date_thresholds=[1,5],
                            date_metrics = ["day", "year"],
                            cast_strings_to_date=True,
                            invalid_dates_as_null=True
                            )

Date Difference comparison at thresholds 10 days, 12 months and 15 years 

import splink.spark.comparison_library as cl
cl.datediff_at_thresholds("date",
                            date_thresholds = [10, 12, 15],
                            date_metrics = ['day', 'month', 'year']
                            )

Datediff comparison with date-casting and unspecified date_format (default = %Y-%m-%d) 

    import splink.spark.comparison_library as cl
    cl.datediff_at_thresholds("date",
                                date_thresholds=[1,5],
                                date_metrics = ["day", "year"],
                                cast_strings_to_date=True
                                )

Datediff comparison with date-casting and specified (non-default) date_format 

import splink.spark.comparison_library as cl
cl.datediff_at_thresholds("date",
                            date_thresholds=[1,5],
                            date_metrics = ["day", "year"],
                            cast_strings_to_date=True,
                            date_format='%d/%m/%Y'
                            )
Datediff comparison with date-casting and invalid dates set to null 
import splink.spark.comparison_library as cl
cl.datediff_at_thresholds("date",
                            date_thresholds=[1,5],
                            date_metrics = ["day", "year"],
                            cast_strings_to_date=True,
                            invalid_dates_as_null=True
                            )

Date Difference comparison at thresholds 10 days, 12 months and 15 years 

import splink.athena.comparison_library as cl
cl.datediff_at_thresholds("date",
                            date_thresholds = [10, 12, 15],
                            date_metrics = ['day', 'month', 'year']
                            )

Datediff comparison with date-casting and unspecified date_format (default = %Y-%m-%d) 

    import splink.athena.comparison_library as cl
    cl.datediff_at_thresholds("date",
                                date_thresholds=[1,5],
                                date_metrics = ["day", "year"],
                                cast_strings_to_date=True
                                )

Datediff comparison with date-casting and specified (non-default) date_format 

import splink.athena.comparison_library as cl
cl.datediff_at_thresholds("date",
                            date_thresholds=[1,5],
                            date_metrics = ["day", "year"],
                            cast_strings_to_date=True,
                            date_format='%d/%m/%Y'
                            )
Datediff comparison with date-casting and invalid dates set to null 
import splink.athena.comparison_library as cl
cl.datediff_at_thresholds("date",
                            date_thresholds=[1,5],
                            date_metrics = ["day", "year"],
                            cast_strings_to_date=True,
                            invalid_dates_as_null=True
                            )

Date Difference comparison at thresholds 10 days, 12 months and 15 years 

import splink.postgres.comparison_library as cl
cl.datediff_at_thresholds("date",
                            date_thresholds = [10, 12, 15],
                            date_metrics = ['day', 'month', 'year']
                            )

Datediff comparison with date-casting and unspecified date_format (default = yyyy-MM-dd) 

    import splink.postgres.comparison_library as cl
    cl.datediff_at_thresholds("date",
                                date_thresholds=[1,5],
                                date_metrics = ["day", "year"],
                                cast_strings_to_date=True
                                )

Datediff comparison with date-casting and specified (non-default) date_format 

import splink.postgres.comparison_library as cl
cl.datediff_at_thresholds("date",
                            date_thresholds=[1,5],
                            date_metrics = ["day", "year"],
                            cast_strings_to_date=True,
                            date_format='dd/MM/yyyy'
                            )

Returns:

Name Type Description
Comparison Comparison

A comparison for Datediff that can be included in the Splink settings dictionary.
Source code in splink/comparison_library.py 
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def __init__(
    self,
    col_name: str,
    date_thresholds: int | list = [1],
    date_metrics: str | list = ["year"],
    cast_strings_to_date=False,
    date_format: str = None,
    invalid_dates_as_null: bool = False,
    include_exact_match_level=True,
    term_frequency_adjustments=False,
    m_probability_exact_match=None,
    m_probability_or_probabilities_dat: float | list = None,
    m_probability_else=None,
) -> Comparison:
    """A comparison of the data in the date column `col_name` with various
    date thresholds and metrics to assess similarity levels.

    An example of the output with default arguments and settings
    `date_thresholds = [1]` and `date_metrics = ['day']` would be
    - The two input dates are within 1 day of one another
    - Anything else (i.e. all other dates lie outside this range)

    `date_thresholds` and `date_metrics` should be used in conjunction
    with one another.
    For example, `date_thresholds = [10, 12, 15]` with
    `date_metrics = ['day', 'month', 'year']` would result in the following checks:

    - The two dates are within 10 days of one another
    - The two dates are within 12 months of one another
    - And the two dates are within 15 years of one another

    Args:
        col_name (str): The name of the date column to compare.
        date_thresholds (Union[int, list], optional): The size(s) of given date
            thresholds, to assess whether two dates fall within a given time
            interval.
            These values can be any integer value and should be used in tandem with
            `date_metrics`.
        date_metrics (Union[str, list], optional): The unit of time you wish your
            `date_thresholds` to be measured against.
            Metrics should be one of `day`, `month` or `year`.
        cast_strings_to_date (bool, optional): Set to True to
            enable date-casting when input dates are strings. Also adjust
            date_format if date-strings are not in (yyyy-mm-dd) format.
            Defaults to False.
        date_format(str, optional): Format of input dates if date-strings
            are given. Must be consistent across record pairs. If None
            (the default), downstream functions for each backend assign
            date_format to ISO 8601 format (yyyy-mm-dd).
            Set to "yyyy-MM-dd" for Spark and "%Y-%m-%d" for DuckDB and Athena
            when invalid_dates_as_null=True
        invalid_dates_as_null (bool, optional): assign any dates that do not adhere
            to date_format to the null level. Defaults to False.
        include_exact_match_level (bool, optional): If True, include an exact match
            level. Defaults to True.
        term_frequency_adjustments (bool, optional): If True, apply term frequency
            adjustments to the exact match level. Defaults to False.
        m_probability_exact_match (_type_, optional): If provided, overrides the
            default m probability for the exact match level. Defaults to None.
        m_probability_or_probabilities_dat (Union[float, list], optional):
            If provided, overrides the default m probabilities
            for the sizes specified. Defaults to None.
        m_probability_else (_type_, optional): If provided, overrides the
            default m probability for the 'anything else' level. Defaults to None.

    Examples:
        === ":simple-duckdb: DuckDB"
            Date Difference comparison at thresholds 10 days, 12 months and 15 years
            ``` python
            import splink.duckdb.comparison_library as cl
            cl.datediff_at_thresholds("date",
                                        date_thresholds = [10, 12, 15],
                                        date_metrics = ['day', 'month', 'year']
                                        )
            ```

            Datediff comparison with date-casting and unspecified date_format
            (default = %Y-%m-%d)
            ``` python
            import splink.duckdb.comparison_library as cl
            cl.datediff_at_thresholds("date",
                                        date_thresholds=[1,5],
                                        date_metrics = ["day", "year"],
                                        cast_strings_to_date=True
                                        )
            ```
            Datediff comparison with date-casting and specified (non-default)
            date_format
            ``` python
            import splink.duckdb.comparison_library as cl
            cl.datediff_at_thresholds("date",
                                        date_thresholds=[1,5],
                                        date_metrics = ["day", "year"],
                                        cast_strings_to_date=True,
                                        date_format='%d/%m/%Y'
                                        )
            ```
            Datediff comparison with date-casting and invalid dates set to null
            ```py
            import splink.duckdb.comparison_library as cl
            cl.datediff_at_thresholds("date",
                                        date_thresholds=[1,5],
                                        date_metrics = ["day", "year"],
                                        cast_strings_to_date=True,
                                        invalid_dates_as_null=True
                                        )
            ```
        === ":simple-apachespark: Spark"
            Date Difference comparison at thresholds 10 days, 12 months and 15 years
            ``` python
            import splink.spark.comparison_library as cl
            cl.datediff_at_thresholds("date",
                                        date_thresholds = [10, 12, 15],
                                        date_metrics = ['day', 'month', 'year']
                                        )
            ```

            Datediff comparison with date-casting and unspecified date_format
            (default = %Y-%m-%d)
            ``` python
                import splink.spark.comparison_library as cl
                cl.datediff_at_thresholds("date",
                                            date_thresholds=[1,5],
                                            date_metrics = ["day", "year"],
                                            cast_strings_to_date=True
                                            )
            ```

            Datediff comparison with date-casting and specified (non-default)
            date_format
            ``` python
            import splink.spark.comparison_library as cl
            cl.datediff_at_thresholds("date",
                                        date_thresholds=[1,5],
                                        date_metrics = ["day", "year"],
                                        cast_strings_to_date=True,
                                        date_format='%d/%m/%Y'
                                        )
            ```
             Datediff comparison with date-casting and invalid dates set to null
            ```py
            import splink.spark.comparison_library as cl
            cl.datediff_at_thresholds("date",
                                        date_thresholds=[1,5],
                                        date_metrics = ["day", "year"],
                                        cast_strings_to_date=True,
                                        invalid_dates_as_null=True
                                        )
            ```
        === "":simple-amazonaws: Athena"
            Date Difference comparison at thresholds 10 days, 12 months and 15 years
            ``` python
            import splink.athena.comparison_library as cl
            cl.datediff_at_thresholds("date",
                                        date_thresholds = [10, 12, 15],
                                        date_metrics = ['day', 'month', 'year']
                                        )
            ```

            Datediff comparison with date-casting and unspecified date_format
            (default = %Y-%m-%d)
            ``` python
                import splink.athena.comparison_library as cl
                cl.datediff_at_thresholds("date",
                                            date_thresholds=[1,5],
                                            date_metrics = ["day", "year"],
                                            cast_strings_to_date=True
                                            )
            ```

            Datediff comparison with date-casting and specified (non-default)
            date_format
            ``` python
            import splink.athena.comparison_library as cl
            cl.datediff_at_thresholds("date",
                                        date_thresholds=[1,5],
                                        date_metrics = ["day", "year"],
                                        cast_strings_to_date=True,
                                        date_format='%d/%m/%Y'
                                        )
            ```
             Datediff comparison with date-casting and invalid dates set to null
            ```py
            import splink.athena.comparison_library as cl
            cl.datediff_at_thresholds("date",
                                        date_thresholds=[1,5],
                                        date_metrics = ["day", "year"],
                                        cast_strings_to_date=True,
                                        invalid_dates_as_null=True
                                        )
            ```
        === ":simple-postgresql: PostgreSql"
            Date Difference comparison at thresholds 10 days, 12 months and 15 years
            ``` python
            import splink.postgres.comparison_library as cl
            cl.datediff_at_thresholds("date",
                                        date_thresholds = [10, 12, 15],
                                        date_metrics = ['day', 'month', 'year']
                                        )
            ```

            Datediff comparison with date-casting and unspecified date_format
            (default = yyyy-MM-dd)
            ``` python
                import splink.postgres.comparison_library as cl
                cl.datediff_at_thresholds("date",
                                            date_thresholds=[1,5],
                                            date_metrics = ["day", "year"],
                                            cast_strings_to_date=True
                                            )
            ```

            Datediff comparison with date-casting and specified (non-default)
            date_format
            ``` python
            import splink.postgres.comparison_library as cl
            cl.datediff_at_thresholds("date",
                                        date_thresholds=[1,5],
                                        date_metrics = ["day", "year"],
                                        cast_strings_to_date=True,
                                        date_format='dd/MM/yyyy'
                                        )
            ```

    Returns:
        Comparison: A comparison for Datediff that can be included in the Splink
            settings dictionary.
    """

    thresholds = ensure_is_iterable(date_thresholds)
    metrics = ensure_is_iterable(date_metrics)

    # Validate user inputs
    comparison_at_thresholds_error_logger("datediff", date_thresholds)
    datediff_error_logger(thresholds, metrics)

    if m_probability_or_probabilities_dat is None:
        m_probability_or_probabilities_dat = [None] * len(thresholds)
    m_probabilities = ensure_is_iterable(m_probability_or_probabilities_dat)

    comparison_levels = []
    comparison_levels.append(
        self._null_level(
            col_name,
            invalid_dates_as_null=invalid_dates_as_null,
            valid_string_pattern=date_format,
        )
    )

    if include_exact_match_level:
        level = self._exact_match_level(
            col_name,
            term_frequency_adjustments=term_frequency_adjustments,
            m_probability=m_probability_exact_match,
        )
        comparison_levels.append(level)

    for date_thres, date_metr, m_prob in zip(thresholds, metrics, m_probabilities):
        level = self._datediff_level(
            col_name,
            date_threshold=date_thres,
            date_metric=date_metr,
            m_probability=m_prob,
            cast_strings_to_date=cast_strings_to_date,
            date_format=date_format,
        )
        comparison_levels.append(level)

    comparison_levels.append(
        self._else_level(m_probability=m_probability_else),
    )

    comparison_desc = ""
    if include_exact_match_level:
        comparison_desc += "Exact match vs. "

    thres_desc = ", ".join(
        [f"{m.title()}(s): {v}" for m, v in zip(metrics, thresholds)]
    )
    plural = "" if len(thresholds) == 1 else "s"
    comparison_desc += (
        f"Dates within the following threshold{plural} {thres_desc} vs. "
    )
    comparison_desc += "anything else"

    comparison_dict = {
        "comparison_description": comparison_desc,
        "comparison_levels": comparison_levels,
    }
    super().__init__(comparison_dict)

Bases: Comparison

Source code in splink/comparison_library.py 
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class DistanceInKMAtThresholdsBase(Comparison):
    def __init__(
        self,
        lat_col: str,
        long_col: str,
        km_thresholds: int | list = [0.1, 1],
        include_exact_match_level=False,
        m_probability_exact_match=None,
        m_probability_or_probabilities_km: float | list = None,
        m_probability_else=None,
    ) -> Comparison:
        """A comparison of the coordinates defined in 'lat_col' and
        'long col' giving the haversine distance between them in km.

        An example of the output with default arguments and settings
        `km_thresholds = [1]` would be

        - The two coordinates within 1 km of one another
        - Anything else (i.e.  the distance between all coordinate lie outside
        this range)

        Args:
            col_name (str): The name of the date column to compare.
            lat_col (str): The name of the column containing the lattitude of the
                coordinates.
            long_col (str): The name of the column containing the longitude of the
                coordinates.
            km_thresholds (Union[int, list], optional): The size(s) of given date
                thresholds, to assess whether two coordinates fall within a given
                distance.
            include_exact_match_level (bool, optional): If True, include an exact match
                level. Defaults to True.
            m_probability_exact_match (float, optional): If provided, overrides the
                default m probability for the exact match level. Defaults to None.
            m_probability_or_probabilities_km (Union[float, list], optional):
                If provided, overrides the default m probabilities
                for the sizes specified. Defaults to None.
            m_probability_else (float, optional): If provided, overrides the
                default m probability for the 'anything else' level. Defaults to None.

        Examples:
            === ":simple-duckdb: DuckDB"
                ``` python
                import splink.duckdb.comparison_library as cl
                cl.distance_in_km_at_thresholds("lat_col",
                                           "long_col",
                                           km_thresholds = [0.1, 1, 10]
                                        )
                ```
            === ":simple-apachespark: Spark"
                ``` python
                import splink.spark.comparison_library as cl
                cl.distance_in_km_at_thresholds("lat_col",
                                           "long_col",
                                           km_thresholds = [0.1, 1, 10]
                                        )
                ```
            === ":simple-amazonaws: Athena"
                ``` python
                import splink.athena.comparison_library as cl
                cl.distance_in_km_at_thresholds("lat_col",
                                           "long_col",
                                           km_thresholds = [0.1, 1, 10]
                                        )
                ```
            === ":simple-postgresql: PostgreSql"
                ``` python
                import splink.postgres.comparison_library as cl
                cl.distance_in_km_at_thresholds("lat_col",
                                           "long_col",
                                           km_thresholds = [0.1, 1, 10]
                                        )
                ```

        Returns:
            Comparison: A comparison for Distance in KM that can be included in the
                Splink settings dictionary.
        """

        thresholds = ensure_is_iterable(km_thresholds)

        if m_probability_or_probabilities_km is None:
            m_probability_or_probabilities_km = [None] * len(thresholds)
        m_probabilities = ensure_is_iterable(m_probability_or_probabilities_km)

        comparison_levels = []

        null_level = {
            "sql_condition": f"({lat_col}_l IS NULL OR {lat_col}_r IS NULL) \n"
            f"OR ({long_col}_l IS NULL OR {long_col}_r IS NULL)",
            "label_for_charts": "Null",
            "is_null_level": True,
        }
        comparison_levels.append(null_level)

        if include_exact_match_level:
            label_suffix = f" {lat_col}, {long_col}"
            level = {
                "sql_condition": f"({lat_col}_l = {lat_col}_r) \n"
                f"AND ({long_col}_l = {long_col}_r)",
                "label_for_charts": f"Exact match{label_suffix}",
            }

            if m_probability_exact_match:
                level["m_probability"] = m_probability_exact_match

            comparison_levels.append(level)

        for km_thres, m_prob in zip(km_thresholds, m_probabilities):
            level = self._distance_in_km_level(
                lat_col,
                long_col,
                km_threshold=km_thres,
                m_probability=m_prob,
            )
            comparison_levels.append(level)

        comparison_levels.append(
            self._else_level(m_probability=m_probability_else),
        )

        comparison_desc = ""
        if include_exact_match_level:
            comparison_desc += "Exact match vs. "

        thres_desc = ", ".join([f"Km threshold(s): {thres}" for thres in thresholds])
        plural = "" if len(thresholds) == 1 else "s"
        comparison_desc += (
            f"Km distance within the following threshold{plural} {thres_desc} vs. "
        )
        comparison_desc += "anything else"

        comparison_dict = {
            "comparison_description": comparison_desc,
            "comparison_levels": comparison_levels,
        }
        super().__init__(comparison_dict)

__init__(lat_col, long_col, km_thresholds=[0.1, 1], include_exact_match_level=False, m_probability_exact_match=None, m_probability_or_probabilities_km=None, m_probability_else=None)

A comparison of the coordinates defined in 'lat_col' and 'long col' giving the haversine distance between them in km.

An example of the output with default arguments and settings km_thresholds = [1] would be

  • The two coordinates within 1 km of one another
  • Anything else (i.e. the distance between all coordinate lie outside this range)

Parameters:

Name Type Description Default
col_name str

The name of the date column to compare.

 required
lat_col str

The name of the column containing the lattitude of the coordinates.

 required
long_col str

The name of the column containing the longitude of the coordinates.

 required
km_thresholds Union[int, list]

The size(s) of given date thresholds, to assess whether two coordinates fall within a given distance.

 [0.1, 1]
include_exact_match_level bool

If True, include an exact match level. Defaults to True.

 False
m_probability_exact_match float

If provided, overrides the default m probability for the exact match level. Defaults to None.

 None
m_probability_or_probabilities_km Union[float, list]

If provided, overrides the default m probabilities for the sizes specified. Defaults to None.

 None
m_probability_else float

If provided, overrides the default m probability for the 'anything else' level. Defaults to None.

 None

Examples:

import splink.duckdb.comparison_library as cl
cl.distance_in_km_at_thresholds("lat_col",
                           "long_col",
                           km_thresholds = [0.1, 1, 10]
                        )

import splink.spark.comparison_library as cl
cl.distance_in_km_at_thresholds("lat_col",
                           "long_col",
                           km_thresholds = [0.1, 1, 10]
                        )

import splink.athena.comparison_library as cl
cl.distance_in_km_at_thresholds("lat_col",
                           "long_col",
                           km_thresholds = [0.1, 1, 10]
                        )

import splink.postgres.comparison_library as cl
cl.distance_in_km_at_thresholds("lat_col",
                           "long_col",
                           km_thresholds = [0.1, 1, 10]
                        )

Returns:

Name Type Description
Comparison Comparison

A comparison for Distance in KM that can be included in the Splink settings dictionary.
Source code in splink/comparison_library.py 
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def __init__(
    self,
    lat_col: str,
    long_col: str,
    km_thresholds: int | list = [0.1, 1],
    include_exact_match_level=False,
    m_probability_exact_match=None,
    m_probability_or_probabilities_km: float | list = None,
    m_probability_else=None,
) -> Comparison:
    """A comparison of the coordinates defined in 'lat_col' and
    'long col' giving the haversine distance between them in km.

    An example of the output with default arguments and settings
    `km_thresholds = [1]` would be

    - The two coordinates within 1 km of one another
    - Anything else (i.e.  the distance between all coordinate lie outside
    this range)

    Args:
        col_name (str): The name of the date column to compare.
        lat_col (str): The name of the column containing the lattitude of the
            coordinates.
        long_col (str): The name of the column containing the longitude of the
            coordinates.
        km_thresholds (Union[int, list], optional): The size(s) of given date
            thresholds, to assess whether two coordinates fall within a given
            distance.
        include_exact_match_level (bool, optional): If True, include an exact match
            level. Defaults to True.
        m_probability_exact_match (float, optional): If provided, overrides the
            default m probability for the exact match level. Defaults to None.
        m_probability_or_probabilities_km (Union[float, list], optional):
            If provided, overrides the default m probabilities
            for the sizes specified. Defaults to None.
        m_probability_else (float, optional): If provided, overrides the
            default m probability for the 'anything else' level. Defaults to None.

    Examples:
        === ":simple-duckdb: DuckDB"
            ``` python
            import splink.duckdb.comparison_library as cl
            cl.distance_in_km_at_thresholds("lat_col",
                                       "long_col",
                                       km_thresholds = [0.1, 1, 10]
                                    )
            ```
        === ":simple-apachespark: Spark"
            ``` python
            import splink.spark.comparison_library as cl
            cl.distance_in_km_at_thresholds("lat_col",
                                       "long_col",
                                       km_thresholds = [0.1, 1, 10]
                                    )
            ```
        === ":simple-amazonaws: Athena"
            ``` python
            import splink.athena.comparison_library as cl
            cl.distance_in_km_at_thresholds("lat_col",
                                       "long_col",
                                       km_thresholds = [0.1, 1, 10]
                                    )
            ```
        === ":simple-postgresql: PostgreSql"
            ``` python
            import splink.postgres.comparison_library as cl
            cl.distance_in_km_at_thresholds("lat_col",
                                       "long_col",
                                       km_thresholds = [0.1, 1, 10]
                                    )
            ```

    Returns:
        Comparison: A comparison for Distance in KM that can be included in the
            Splink settings dictionary.
    """

    thresholds = ensure_is_iterable(km_thresholds)

    if m_probability_or_probabilities_km is None:
        m_probability_or_probabilities_km = [None] * len(thresholds)
    m_probabilities = ensure_is_iterable(m_probability_or_probabilities_km)

    comparison_levels = []

    null_level = {
        "sql_condition": f"({lat_col}_l IS NULL OR {lat_col}_r IS NULL) \n"
        f"OR ({long_col}_l IS NULL OR {long_col}_r IS NULL)",
        "label_for_charts": "Null",
        "is_null_level": True,
    }
    comparison_levels.append(null_level)

    if include_exact_match_level:
        label_suffix = f" {lat_col}, {long_col}"
        level = {
            "sql_condition": f"({lat_col}_l = {lat_col}_r) \n"
            f"AND ({long_col}_l = {long_col}_r)",
            "label_for_charts": f"Exact match{label_suffix}",
        }

        if m_probability_exact_match:
            level["m_probability"] = m_probability_exact_match

        comparison_levels.append(level)

    for km_thres, m_prob in zip(km_thresholds, m_probabilities):
        level = self._distance_in_km_level(
            lat_col,
            long_col,
            km_threshold=km_thres,
            m_probability=m_prob,
        )
        comparison_levels.append(level)

    comparison_levels.append(
        self._else_level(m_probability=m_probability_else),
    )

    comparison_desc = ""
    if include_exact_match_level:
        comparison_desc += "Exact match vs. "

    thres_desc = ", ".join([f"Km threshold(s): {thres}" for thres in thresholds])
    plural = "" if len(thresholds) == 1 else "s"
    comparison_desc += (
        f"Km distance within the following threshold{plural} {thres_desc} vs. "
    )
    comparison_desc += "anything else"

    comparison_dict = {
        "comparison_description": comparison_desc,
        "comparison_levels": comparison_levels,
    }
    super().__init__(comparison_dict)
Last update: 2023-09-29
Created: 2023-09-29