Skip to content

Documentation for comparison_level_library

The comparison_level_library contains pre-made comparison levels available for use to construct custom comparisons as described in this topic guide. However, not every comparison level is available for every Splink-compatible SQL backend.

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


DuckDB
Spark
Athena
SQLite
PostgreSql
array_intersect_level ✓ ✓ ✓ ✓
columns_reversed_level ✓ ✓ ✓ ✓ ✓
damerau_levenshtein_level ✓ ✓ ✓
datediff_level ✓ ✓ ✓ ✓
distance_function_level ✓ ✓ ✓ ✓ ✓
distance_in_km_level ✓ ✓ ✓ ✓
else_level ✓ ✓ ✓ ✓ ✓
exact_match_level ✓ ✓ ✓ ✓ ✓
jaccard_level ✓ ✓
jaro_level ✓ ✓ ✓
jaro_winkler_level ✓ ✓ ✓
levenshtein_level ✓ ✓ ✓ ✓ ✓
null_level ✓ ✓ ✓ ✓ ✓
percentage_difference_level ✓ ✓ ✓ ✓ ✓

The detailed API for each of these are outlined below.

Library comparison level APIs

Bases: ComparisonLevel

Source code in splink/comparison_level_library.py 
 10
 11
 12
 13
 14
 15
 16
 17
 18
 19
 20
 21
 22
 23
 24
 25
 26
 27
 28
 29
 30
 31
 32
 33
 34
 35
 36
 37
 38
 39
 40
 41
 42
 43
 44
 45
 46
 47
 48
 49
 50
 51
 52
 53
 54
 55
 56
 57
 58
 59
 60
 61
 62
 63
 64
 65
 66
 67
 68
 69
 70
 71
 72
 73
 74
 75
 76
 77
 78
 79
 80
 81
 82
 83
 84
 85
 86
 87
 88
 89
 90
 91
 92
 93
 94
 95
 96
 97
 98
 99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
class NullLevelBase(ComparisonLevel):
    def __init__(
        self,
        col_name,
        valid_string_pattern: str = None,
        invalid_dates_as_null: bool = False,
        valid_string_regex: str = None,
    ) -> ComparisonLevel:
        """Represents comparisons level where one or both sides of the comparison
        contains null values so the similarity cannot be evaluated.
        Assumed to have a partial match weight of zero (null effect
        on overall match weight)
        Args:
            col_name (str): Input column name
            valid_string_pattern (str): pattern (regex or otherwise) that if not
                matched will result in column being treated as a null.
            invalid_dates_as_null (bool): If True, set all invalid dates to null.
                The "correct" format of a date is set by valid_string_pattern.
                Defaults to false.

        Examples:
            === ":simple-duckdb: DuckDB"
                Simple null comparison level
                ``` python
                import splink.duckdb.comparison_level_library as cll
                cll.null_level("name")
                ```
                Null comparison level including strings that do not match
                a given regex pattern
                ``` python
                import splink.duckdb.comparison_level_library as cll
                cll.null_level("name", valid_string_pattern="^[A-Z]{1,7}$")
                ```
            === ":simple-apachespark: Spark"
                Simple null level
                ``` python
                import splink.spark.comparison_level_library as cll
                cll.null_level("name")
                ```
                Null comparison level including strings that do not match
                a given regex pattern
                ``` python
                import splink.spark.comparison_level_library as cll
                cll.null_level("name", valid_string_pattern="^[A-Z]{1,7}$")
                ```
            === ":simple-amazonaws: Athena"
                Simple null level
                ``` python
                import splink.athena.comparison_level_library as cll
                cll.null_level("name")
                ```
                Null comparison level including strings that do not match
                a given regex pattern
                ``` python
                import splink.athena.comparison_level_library as cll
                cll.null_level("name", valid_string_pattern="^[A-Z]{1,7}$")
                ```
            === ":simple-sqlite: SQLite"
                Simple null level
                ``` python
                import splink.sqlite.comparison_level_library as cll
                cll.null_level("name")
                ```
            === ":simple-postgresql: PostgreSql"
                Simple null level
                ``` python
                import splink.postgres.comparison_level_library as cll
                cll.null_level("name")
                ```
        Returns:
            ComparisonLevel: Comparison level for null entries
        """

        # TODO: Remove this compatibility code in a future release once we drop
        # support for "valid_string_regex". Deprecation warning added in 3.9.6
        if valid_string_pattern is not None and valid_string_regex is not None:
            # user supplied both
            raise TypeError("Just use valid_string_pattern")
        elif valid_string_pattern is not None:
            # user is doing it correctly
            pass
        elif valid_string_regex is not None:
            # user is using deprecated argument
            warnings.warn(
                "valid_string_regex is deprecated; use valid_string_pattern",
                DeprecationWarning,
                stacklevel=2,
            )
            valid_string_pattern = valid_string_regex

        col = InputColumn(col_name, sql_dialect=self._sql_dialect)
        col_name_l, col_name_r = col.name_l, col.name_r

        if invalid_dates_as_null:
            # See https://github.com/moj-analytical-services/splink/pull/1939
            col_name_l = self._valid_date_function(col_name_l, valid_string_pattern)
            col_name_r = self._valid_date_function(col_name_r, valid_string_pattern)
            sql = f"""{col_name_l} IS NULL OR {col_name_r} IS NULL"""
        elif valid_string_pattern:
            col_name_l = self._regex_extract_function(col_name_l, valid_string_pattern)
            col_name_r = self._regex_extract_function(col_name_r, valid_string_pattern)
            sql = f"""{col_name_l} IS NULL OR {col_name_r} IS NULL OR
                      {col_name_l} = '' OR {col_name_r} = '' """
        else:
            sql = f"{col_name_l} IS NULL OR {col_name_r} IS NULL"

        level_dict = {
            "sql_condition": sql,
            "label_for_charts": "Null",
            "is_null_level": True,
        }
        super().__init__(level_dict, sql_dialect=self._sql_dialect)

__init__(col_name, valid_string_pattern=None, invalid_dates_as_null=False, valid_string_regex=None)

Represents comparisons level where one or both sides of the comparison contains null values so the similarity cannot be evaluated. Assumed to have a partial match weight of zero (null effect on overall match weight) Args: col_name (str): Input column name valid_string_pattern (str): pattern (regex or otherwise) that if not matched will result in column being treated as a null. invalid_dates_as_null (bool): If True, set all invalid dates to null. The "correct" format of a date is set by valid_string_pattern. Defaults to false.

Examples:

Simple null comparison level 

import splink.duckdb.comparison_level_library as cll
cll.null_level("name")
Null comparison level including strings that do not match a given regex pattern 
import splink.duckdb.comparison_level_library as cll
cll.null_level("name", valid_string_pattern="^[A-Z]{1,7}$")

Simple null level 

import splink.spark.comparison_level_library as cll
cll.null_level("name")
Null comparison level including strings that do not match a given regex pattern 
import splink.spark.comparison_level_library as cll
cll.null_level("name", valid_string_pattern="^[A-Z]{1,7}$")

Simple null level 

import splink.athena.comparison_level_library as cll
cll.null_level("name")
Null comparison level including strings that do not match a given regex pattern 
import splink.athena.comparison_level_library as cll
cll.null_level("name", valid_string_pattern="^[A-Z]{1,7}$")

Simple null level 

import splink.sqlite.comparison_level_library as cll
cll.null_level("name")

Simple null level 

import splink.postgres.comparison_level_library as cll
cll.null_level("name")
Source code in splink/comparison_level_library.py 
 11
 12
 13
 14
 15
 16
 17
 18
 19
 20
 21
 22
 23
 24
 25
 26
 27
 28
 29
 30
 31
 32
 33
 34
 35
 36
 37
 38
 39
 40
 41
 42
 43
 44
 45
 46
 47
 48
 49
 50
 51
 52
 53
 54
 55
 56
 57
 58
 59
 60
 61
 62
 63
 64
 65
 66
 67
 68
 69
 70
 71
 72
 73
 74
 75
 76
 77
 78
 79
 80
 81
 82
 83
 84
 85
 86
 87
 88
 89
 90
 91
 92
 93
 94
 95
 96
 97
 98
 99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
def __init__(
    self,
    col_name,
    valid_string_pattern: str = None,
    invalid_dates_as_null: bool = False,
    valid_string_regex: str = None,
) -> ComparisonLevel:
    """Represents comparisons level where one or both sides of the comparison
    contains null values so the similarity cannot be evaluated.
    Assumed to have a partial match weight of zero (null effect
    on overall match weight)
    Args:
        col_name (str): Input column name
        valid_string_pattern (str): pattern (regex or otherwise) that if not
            matched will result in column being treated as a null.
        invalid_dates_as_null (bool): If True, set all invalid dates to null.
            The "correct" format of a date is set by valid_string_pattern.
            Defaults to false.

    Examples:
        === ":simple-duckdb: DuckDB"
            Simple null comparison level
            ``` python
            import splink.duckdb.comparison_level_library as cll
            cll.null_level("name")
            ```
            Null comparison level including strings that do not match
            a given regex pattern
            ``` python
            import splink.duckdb.comparison_level_library as cll
            cll.null_level("name", valid_string_pattern="^[A-Z]{1,7}$")
            ```
        === ":simple-apachespark: Spark"
            Simple null level
            ``` python
            import splink.spark.comparison_level_library as cll
            cll.null_level("name")
            ```
            Null comparison level including strings that do not match
            a given regex pattern
            ``` python
            import splink.spark.comparison_level_library as cll
            cll.null_level("name", valid_string_pattern="^[A-Z]{1,7}$")
            ```
        === ":simple-amazonaws: Athena"
            Simple null level
            ``` python
            import splink.athena.comparison_level_library as cll
            cll.null_level("name")
            ```
            Null comparison level including strings that do not match
            a given regex pattern
            ``` python
            import splink.athena.comparison_level_library as cll
            cll.null_level("name", valid_string_pattern="^[A-Z]{1,7}$")
            ```
        === ":simple-sqlite: SQLite"
            Simple null level
            ``` python
            import splink.sqlite.comparison_level_library as cll
            cll.null_level("name")
            ```
        === ":simple-postgresql: PostgreSql"
            Simple null level
            ``` python
            import splink.postgres.comparison_level_library as cll
            cll.null_level("name")
            ```
    Returns:
        ComparisonLevel: Comparison level for null entries
    """

    # TODO: Remove this compatibility code in a future release once we drop
    # support for "valid_string_regex". Deprecation warning added in 3.9.6
    if valid_string_pattern is not None and valid_string_regex is not None:
        # user supplied both
        raise TypeError("Just use valid_string_pattern")
    elif valid_string_pattern is not None:
        # user is doing it correctly
        pass
    elif valid_string_regex is not None:
        # user is using deprecated argument
        warnings.warn(
            "valid_string_regex is deprecated; use valid_string_pattern",
            DeprecationWarning,
            stacklevel=2,
        )
        valid_string_pattern = valid_string_regex

    col = InputColumn(col_name, sql_dialect=self._sql_dialect)
    col_name_l, col_name_r = col.name_l, col.name_r

    if invalid_dates_as_null:
        # See https://github.com/moj-analytical-services/splink/pull/1939
        col_name_l = self._valid_date_function(col_name_l, valid_string_pattern)
        col_name_r = self._valid_date_function(col_name_r, valid_string_pattern)
        sql = f"""{col_name_l} IS NULL OR {col_name_r} IS NULL"""
    elif valid_string_pattern:
        col_name_l = self._regex_extract_function(col_name_l, valid_string_pattern)
        col_name_r = self._regex_extract_function(col_name_r, valid_string_pattern)
        sql = f"""{col_name_l} IS NULL OR {col_name_r} IS NULL OR
                  {col_name_l} = '' OR {col_name_r} = '' """
    else:
        sql = f"{col_name_l} IS NULL OR {col_name_r} IS NULL"

    level_dict = {
        "sql_condition": sql,
        "label_for_charts": "Null",
        "is_null_level": True,
    }
    super().__init__(level_dict, sql_dialect=self._sql_dialect)

Bases: ComparisonLevel

Source code in splink/comparison_level_library.py 
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
class ExactMatchLevelBase(ComparisonLevel):
    def __init__(
        self,
        col_name,
        regex_extract: str = None,
        set_to_lowercase: bool = False,
        m_probability=None,
        term_frequency_adjustments=False,
        include_colname_in_charts_label=False,
        manual_col_name_for_charts_label=None,
    ) -> ComparisonLevel:
        """Represents a comparison level where there is an exact match,

        Args:
            col_name (str): Input column name
            regex_extract (str): Regular expression pattern to evaluate a match on.
            set_to_lowercase (bool): If True, sets all entries to lowercase.
            m_probability (float, optional): Starting value for m probability
                Defaults to None.
            term_frequency_adjustments (bool, optional): If True, apply term frequency
                adjustments to the exact match level. Defaults to False.
            include_colname_in_charts_label (bool, optional): If True, include col_name
                in chart labels (e.g. linker.match_weights_chart())
            manual_col_name_for_charts_label (str, optional): string to include as
                 column name in chart label. Acts as a manual overwrite of the
                 colname when include_colname_in_charts_label is True.
                include_colname_in_charts_label=True
        Examples:
            === ":simple-duckdb: DuckDB"
                Simple Exact match level
                ``` python
                import splink.duckdb.comparison_level_library as cll
                cll.exact_match_level("name")
                ```
                Exact match level with term-frequency adjustments
                ``` python
                import splink.duckdb.comparison_level_library as cll
                cll.exact_match_level("name", term_frequency_adjustments=True)
                ```
                Exact match level on a substring of col_name as
                 determined by a regular expression
                ``` python
                import splink.duckdb.comparison_level_library as cll
                cll.exact_match_level("name", regex_extract="^[A-Z]{1,4}")
                ```
            === ":simple-apachespark: Spark"
                Simple Exact match level
                ``` python
                import splink.spark.comparison_level_library as cll
                cll.exact_match_level("name")
                ```
                Exact match level with term-frequency adjustments
                ``` python
                import splink.spark.comparison_level_library as cll
                cll.exact_match_level("name", term_frequency_adjustments=True)
                ```
                Exact match level on a substring of col_name as
                 determined by a regular expression
                ``` python
                import splink.spark.comparison_level_library as cll
                cll.exact_match_level("name", regex_extract="^[A-Z]{1,4}")
                ```
            === ":simple-amazonaws: Athena"
                Simple Exact match level
                ``` python
                import splink.athena.comparison_level_library as cll
                cll.exact_match_level("name")
                ```
                Exact match level with term-frequency adjustments
                ``` python
                import splink.athena.comparison_level_library as cll
                cll.exact_match_level("name", term_frequency_adjustments=True)
                ```
                Exact match level on a substring of col_name as
                 determined by a regular expression
                ``` python
                import splink.athena.comparison_level_library as cll
                cll.exact_match_level("name", regex_extract="^[A-Z]{1,4}")
                ```
            === ":simple-sqlite: SQLite"
                Simple Exact match level
                ``` python
                import splink.sqlite.comparison_level_library as cll
                cll.exact_match_level("name")
                ```
                Exact match level with term-frequency adjustments
                ``` python
                import splink.sqlite.comparison_level_library as cll
                cll.exact_match_level("name", term_frequency_adjustments=True)
            === ":simple-postgresql: PostgreSql"
                Simple Exact match level
                ``` python
                import splink.postgres.comparison_level_library as cll
                cll.exact_match_level("name")
                ```
                Exact match level with term-frequency adjustments
                ``` python
                import splink.postgres.comparison_level_library as cll
                cll.exact_match_level("name", term_frequency_adjustments=True)
                ```
        """
        col = InputColumn(col_name, sql_dialect=self._sql_dialect)

        if include_colname_in_charts_label:
            label_suffix = f" {col_name}"
        elif manual_col_name_for_charts_label:
            label_suffix = f" {manual_col_name_for_charts_label}"
        else:
            label_suffix = ""

        col_name_l, col_name_r = col.name_l, col.name_r

        if set_to_lowercase:
            col_name_l = f"lower({col_name_l})"
            col_name_r = f"lower({col_name_r})"

        if regex_extract:
            col_name_l = self._regex_extract_function(col_name_l, regex_extract)
            col_name_r = self._regex_extract_function(col_name_r, regex_extract)

        sql_cond = f"{col_name_l} = {col_name_r}"
        level_dict = {
            "sql_condition": sql_cond,
            "label_for_charts": f"Exact match{label_suffix}",
        }
        if m_probability:
            level_dict["m_probability"] = m_probability
        if term_frequency_adjustments:
            level_dict["tf_adjustment_column"] = col_name

        super().__init__(level_dict, sql_dialect=self._sql_dialect)

__init__(col_name, regex_extract=None, set_to_lowercase=False, m_probability=None, term_frequency_adjustments=False, include_colname_in_charts_label=False, manual_col_name_for_charts_label=None)

Represents a comparison level where there is an exact match,

Parameters:

Name Type Description Default
col_name str

Input column name

 required
regex_extract str

Regular expression pattern to evaluate a match on.

 None
set_to_lowercase bool

If True, sets all entries to lowercase.

 False
m_probability float

Starting value for m probability Defaults to None.

 None
term_frequency_adjustments bool

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

 False
include_colname_in_charts_label bool

If True, include col_name in chart labels (e.g. linker.match_weights_chart())

 False
manual_col_name_for_charts_label str

string to include as column name in chart label. Acts as a manual overwrite of the colname when include_colname_in_charts_label is True. include_colname_in_charts_label=True

 None
Source code in splink/comparison_level_library.py 
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
def __init__(
    self,
    col_name,
    regex_extract: str = None,
    set_to_lowercase: bool = False,
    m_probability=None,
    term_frequency_adjustments=False,
    include_colname_in_charts_label=False,
    manual_col_name_for_charts_label=None,
) -> ComparisonLevel:
    """Represents a comparison level where there is an exact match,

    Args:
        col_name (str): Input column name
        regex_extract (str): Regular expression pattern to evaluate a match on.
        set_to_lowercase (bool): If True, sets all entries to lowercase.
        m_probability (float, optional): Starting value for m probability
            Defaults to None.
        term_frequency_adjustments (bool, optional): If True, apply term frequency
            adjustments to the exact match level. Defaults to False.
        include_colname_in_charts_label (bool, optional): If True, include col_name
            in chart labels (e.g. linker.match_weights_chart())
        manual_col_name_for_charts_label (str, optional): string to include as
             column name in chart label. Acts as a manual overwrite of the
             colname when include_colname_in_charts_label is True.
            include_colname_in_charts_label=True
    Examples:
        === ":simple-duckdb: DuckDB"
            Simple Exact match level
            ``` python
            import splink.duckdb.comparison_level_library as cll
            cll.exact_match_level("name")
            ```
            Exact match level with term-frequency adjustments
            ``` python
            import splink.duckdb.comparison_level_library as cll
            cll.exact_match_level("name", term_frequency_adjustments=True)
            ```
            Exact match level on a substring of col_name as
             determined by a regular expression
            ``` python
            import splink.duckdb.comparison_level_library as cll
            cll.exact_match_level("name", regex_extract="^[A-Z]{1,4}")
            ```
        === ":simple-apachespark: Spark"
            Simple Exact match level
            ``` python
            import splink.spark.comparison_level_library as cll
            cll.exact_match_level("name")
            ```
            Exact match level with term-frequency adjustments
            ``` python
            import splink.spark.comparison_level_library as cll
            cll.exact_match_level("name", term_frequency_adjustments=True)
            ```
            Exact match level on a substring of col_name as
             determined by a regular expression
            ``` python
            import splink.spark.comparison_level_library as cll
            cll.exact_match_level("name", regex_extract="^[A-Z]{1,4}")
            ```
        === ":simple-amazonaws: Athena"
            Simple Exact match level
            ``` python
            import splink.athena.comparison_level_library as cll
            cll.exact_match_level("name")
            ```
            Exact match level with term-frequency adjustments
            ``` python
            import splink.athena.comparison_level_library as cll
            cll.exact_match_level("name", term_frequency_adjustments=True)
            ```
            Exact match level on a substring of col_name as
             determined by a regular expression
            ``` python
            import splink.athena.comparison_level_library as cll
            cll.exact_match_level("name", regex_extract="^[A-Z]{1,4}")
            ```
        === ":simple-sqlite: SQLite"
            Simple Exact match level
            ``` python
            import splink.sqlite.comparison_level_library as cll
            cll.exact_match_level("name")
            ```
            Exact match level with term-frequency adjustments
            ``` python
            import splink.sqlite.comparison_level_library as cll
            cll.exact_match_level("name", term_frequency_adjustments=True)
        === ":simple-postgresql: PostgreSql"
            Simple Exact match level
            ``` python
            import splink.postgres.comparison_level_library as cll
            cll.exact_match_level("name")
            ```
            Exact match level with term-frequency adjustments
            ``` python
            import splink.postgres.comparison_level_library as cll
            cll.exact_match_level("name", term_frequency_adjustments=True)
            ```
    """
    col = InputColumn(col_name, sql_dialect=self._sql_dialect)

    if include_colname_in_charts_label:
        label_suffix = f" {col_name}"
    elif manual_col_name_for_charts_label:
        label_suffix = f" {manual_col_name_for_charts_label}"
    else:
        label_suffix = ""

    col_name_l, col_name_r = col.name_l, col.name_r

    if set_to_lowercase:
        col_name_l = f"lower({col_name_l})"
        col_name_r = f"lower({col_name_r})"

    if regex_extract:
        col_name_l = self._regex_extract_function(col_name_l, regex_extract)
        col_name_r = self._regex_extract_function(col_name_r, regex_extract)

    sql_cond = f"{col_name_l} = {col_name_r}"
    level_dict = {
        "sql_condition": sql_cond,
        "label_for_charts": f"Exact match{label_suffix}",
    }
    if m_probability:
        level_dict["m_probability"] = m_probability
    if term_frequency_adjustments:
        level_dict["tf_adjustment_column"] = col_name

    super().__init__(level_dict, sql_dialect=self._sql_dialect)

Bases: ComparisonLevel

Source code in splink/comparison_level_library.py 
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
class ElseLevelBase(ComparisonLevel):
    def __init__(
        self,
        m_probability=None,
    ) -> ComparisonLevel:
        """Represents a comparison level for all cases which have not been
        considered by preceding comparison levels,

        Examples:
            === ":simple-duckdb: DuckDB"
                ``` python
                import splink.duckdb.comparison_level_library as cll
                cll.else_level()
                ```
            === ":simple-apachespark: Spark"
                ``` python
                import splink.spark.comparison_level_library as cll
                cll.else_level()
                ```
            === ":simple-amazonaws: Athena"
                ``` python
                import splink.athena.comparison_level_library as cll
                cll.else_level()
                ```
            === ":simple-sqlite: SQLite"
                ``` python
                import splink.sqlite.comparison_level_library as cll
                cll.else_level()
            === ":simple-postgresql: PostgreSql"
                ``` python
                import splink.postgres.comparison_level_library as cll
                cll.else_level()
                ```
        """
        if isinstance(m_probability, str):
            raise ValueError(
                "You provided a string for the value of m probability when it should "
                "be numeric.  Perhaps you passed a column name.  Note that you do "
                "not need to pass a column name into the else level."
            )
        level_dict = {
            "sql_condition": "ELSE",
            "label_for_charts": "All other comparisons",
        }
        if m_probability:
            level_dict["m_probability"] = m_probability
        super().__init__(level_dict)

__init__(m_probability=None)

Represents a comparison level for all cases which have not been considered by preceding comparison levels,

Examples:

import splink.duckdb.comparison_level_library as cll
cll.else_level()

import splink.spark.comparison_level_library as cll
cll.else_level()

import splink.athena.comparison_level_library as cll
cll.else_level()

``` python import splink.sqlite.comparison_level_library as cll cll.else_level()

import splink.postgres.comparison_level_library as cll
cll.else_level()
Source code in splink/comparison_level_library.py 
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
def __init__(
    self,
    m_probability=None,
) -> ComparisonLevel:
    """Represents a comparison level for all cases which have not been
    considered by preceding comparison levels,

    Examples:
        === ":simple-duckdb: DuckDB"
            ``` python
            import splink.duckdb.comparison_level_library as cll
            cll.else_level()
            ```
        === ":simple-apachespark: Spark"
            ``` python
            import splink.spark.comparison_level_library as cll
            cll.else_level()
            ```
        === ":simple-amazonaws: Athena"
            ``` python
            import splink.athena.comparison_level_library as cll
            cll.else_level()
            ```
        === ":simple-sqlite: SQLite"
            ``` python
            import splink.sqlite.comparison_level_library as cll
            cll.else_level()
        === ":simple-postgresql: PostgreSql"
            ``` python
            import splink.postgres.comparison_level_library as cll
            cll.else_level()
            ```
    """
    if isinstance(m_probability, str):
        raise ValueError(
            "You provided a string for the value of m probability when it should "
            "be numeric.  Perhaps you passed a column name.  Note that you do "
            "not need to pass a column name into the else level."
        )
    level_dict = {
        "sql_condition": "ELSE",
        "label_for_charts": "All other comparisons",
    }
    if m_probability:
        level_dict["m_probability"] = m_probability
    super().__init__(level_dict)

Bases: ComparisonLevel

Source code in splink/comparison_level_library.py 
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
class DistanceFunctionLevelBase(ComparisonLevel):
    def __init__(
        self,
        col_name: str,
        distance_function_name: str,
        distance_threshold: int | float,
        regex_extract: str = None,
        set_to_lowercase=False,
        higher_is_more_similar: bool = True,
        include_colname_in_charts_label=False,
        manual_col_name_for_charts_label=None,
        m_probability=None,
    ) -> ComparisonLevel:
        """Represents a comparison level using a user-provided distance function,
        where the similarity

        Args:
            col_name (str): Input column name
            distance_function_name (str): The name of the distance function
            distance_threshold (Union[int, float]): The threshold to use to assess
                similarity
            regex_extract (str): Regular expression pattern to evaluate a match on.
            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_colname_in_charts_label (bool, optional): If True, includes
                col_name in charts label.
            manual_col_name_for_charts_label (str, optional): string to include as
                 column name in chart label. Acts as a manual overwrite of the
                 colname when include_colname_in_charts_label is True.
            m_probability (float, optional): Starting value for m probability
                Defaults to None.

        Examples:
            === ":simple-duckdb: DuckDB"
                Apply the `levenshtein` function to a comparison level
                ``` python
                import splink.duckdb.comparison_level_library as cll
                cll.distance_function_level("name",
                                            "levenshtein",
                                            2,
                                            False)
                ```
            === ":simple-apachespark: Spark"
                Apply the `levenshtein` function to a comparison level
                ``` python
                import splink.spark.comparison_level_library as cll
                cll.distance_function_level("name",
                                            "levenshtein",
                                            2,
                                            False)
                ```
            === ":simple-amazonaws: Athena"
                Apply the `levenshtein_distance` function to a comparison level
                ``` python
                import splink.athena.comparison_level_library as cll
                cll.distance_function_level("name",
                                            "levenshtein_distance",
                                            2,
                                            False)
                ```
            === ":simple-sqlite: SQLite"
                Apply the `levenshtein` function to a comparison level
                ``` python
                import splink.sqlite.comparison_level_library as cll
                cll.distance_function_level("name",
                                            "levenshtein",
                                            2,
                                            False)
                ```
            === ":simple-postgresql: PostgreSql"
                Apply the `levenshtein` function to a comparison level
                ``` python
                import splink.postgres.comparison_level_library as cll
                cll.distance_function_level("name",
                                            "levenshtein",
                                            2,
                                            False)
                ```

        Returns:
            ComparisonLevel: A comparison level for a given distance function
        """
        col = InputColumn(col_name, sql_dialect=self._sql_dialect)

        if higher_is_more_similar:
            operator = ">="
        else:
            operator = "<="

        col_name_l, col_name_r = col.name_l, col.name_r

        if set_to_lowercase:
            col_name_l = f"lower({col_name_l})"
            col_name_r = f"lower({col_name_r})"

        if regex_extract:
            col_name_l = self._regex_extract_function(col_name_l, regex_extract)
            col_name_r = self._regex_extract_function(col_name_r, regex_extract)

        sql_cond = (
            f"{distance_function_name}({col_name_l}, {col_name_r}) "
            f"{operator} {distance_threshold}"
        )

        if include_colname_in_charts_label:
            if manual_col_name_for_charts_label:
                col_name = manual_col_name_for_charts_label

            label_suffix = f" {col_name}"
        else:
            label_suffix = ""

        chart_label = (
            f"{distance_function_name.capitalize()}{label_suffix} {operator} "
            f"{distance_threshold}"
        )

        level_dict = {
            "sql_condition": sql_cond,
            "label_for_charts": chart_label,
        }
        if m_probability:
            level_dict["m_probability"] = m_probability

        super().__init__(level_dict, sql_dialect=self._sql_dialect)

    @property
    def _distance_level(self):
        raise NotImplementedError("Distance function not supported in this dialect")

__init__(col_name, distance_function_name, distance_threshold, regex_extract=None, set_to_lowercase=False, higher_is_more_similar=True, include_colname_in_charts_label=False, manual_col_name_for_charts_label=None, m_probability=None)

Represents a comparison level using a user-provided distance function, where the similarity

Parameters:

Name Type Description Default
col_name str

Input column name

 required
distance_function_name str

The name of the distance function

 required
distance_threshold Union[int, float]

The threshold to use to assess similarity

 required
regex_extract str

Regular expression pattern to evaluate a match on.

 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_colname_in_charts_label bool

If True, includes col_name in charts label.

 False
manual_col_name_for_charts_label str

string to include as column name in chart label. Acts as a manual overwrite of the colname when include_colname_in_charts_label is True.

 None
m_probability float

Starting value for m probability Defaults to None.

 None

Examples:

Apply the levenshtein function to a comparison level 

import splink.duckdb.comparison_level_library as cll
cll.distance_function_level("name",
                            "levenshtein",
                            2,
                            False)

Apply the levenshtein function to a comparison level 

import splink.spark.comparison_level_library as cll
cll.distance_function_level("name",
                            "levenshtein",
                            2,
                            False)

Apply the levenshtein_distance function to a comparison level 

import splink.athena.comparison_level_library as cll
cll.distance_function_level("name",
                            "levenshtein_distance",
                            2,
                            False)

Apply the levenshtein function to a comparison level 

import splink.sqlite.comparison_level_library as cll
cll.distance_function_level("name",
                            "levenshtein",
                            2,
                            False)

Apply the levenshtein function to a comparison level 

import splink.postgres.comparison_level_library as cll
cll.distance_function_level("name",
                            "levenshtein",
                            2,
                            False)

Returns:

Name Type Description
ComparisonLevel ComparisonLevel

A comparison level for a given distance function
Source code in splink/comparison_level_library.py 
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
def __init__(
    self,
    col_name: str,
    distance_function_name: str,
    distance_threshold: int | float,
    regex_extract: str = None,
    set_to_lowercase=False,
    higher_is_more_similar: bool = True,
    include_colname_in_charts_label=False,
    manual_col_name_for_charts_label=None,
    m_probability=None,
) -> ComparisonLevel:
    """Represents a comparison level using a user-provided distance function,
    where the similarity

    Args:
        col_name (str): Input column name
        distance_function_name (str): The name of the distance function
        distance_threshold (Union[int, float]): The threshold to use to assess
            similarity
        regex_extract (str): Regular expression pattern to evaluate a match on.
        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_colname_in_charts_label (bool, optional): If True, includes
            col_name in charts label.
        manual_col_name_for_charts_label (str, optional): string to include as
             column name in chart label. Acts as a manual overwrite of the
             colname when include_colname_in_charts_label is True.
        m_probability (float, optional): Starting value for m probability
            Defaults to None.

    Examples:
        === ":simple-duckdb: DuckDB"
            Apply the `levenshtein` function to a comparison level
            ``` python
            import splink.duckdb.comparison_level_library as cll
            cll.distance_function_level("name",
                                        "levenshtein",
                                        2,
                                        False)
            ```
        === ":simple-apachespark: Spark"
            Apply the `levenshtein` function to a comparison level
            ``` python
            import splink.spark.comparison_level_library as cll
            cll.distance_function_level("name",
                                        "levenshtein",
                                        2,
                                        False)
            ```
        === ":simple-amazonaws: Athena"
            Apply the `levenshtein_distance` function to a comparison level
            ``` python
            import splink.athena.comparison_level_library as cll
            cll.distance_function_level("name",
                                        "levenshtein_distance",
                                        2,
                                        False)
            ```
        === ":simple-sqlite: SQLite"
            Apply the `levenshtein` function to a comparison level
            ``` python
            import splink.sqlite.comparison_level_library as cll
            cll.distance_function_level("name",
                                        "levenshtein",
                                        2,
                                        False)
            ```
        === ":simple-postgresql: PostgreSql"
            Apply the `levenshtein` function to a comparison level
            ``` python
            import splink.postgres.comparison_level_library as cll
            cll.distance_function_level("name",
                                        "levenshtein",
                                        2,
                                        False)
            ```

    Returns:
        ComparisonLevel: A comparison level for a given distance function
    """
    col = InputColumn(col_name, sql_dialect=self._sql_dialect)

    if higher_is_more_similar:
        operator = ">="
    else:
        operator = "<="

    col_name_l, col_name_r = col.name_l, col.name_r

    if set_to_lowercase:
        col_name_l = f"lower({col_name_l})"
        col_name_r = f"lower({col_name_r})"

    if regex_extract:
        col_name_l = self._regex_extract_function(col_name_l, regex_extract)
        col_name_r = self._regex_extract_function(col_name_r, regex_extract)

    sql_cond = (
        f"{distance_function_name}({col_name_l}, {col_name_r}) "
        f"{operator} {distance_threshold}"
    )

    if include_colname_in_charts_label:
        if manual_col_name_for_charts_label:
            col_name = manual_col_name_for_charts_label

        label_suffix = f" {col_name}"
    else:
        label_suffix = ""

    chart_label = (
        f"{distance_function_name.capitalize()}{label_suffix} {operator} "
        f"{distance_threshold}"
    )

    level_dict = {
        "sql_condition": sql_cond,
        "label_for_charts": chart_label,
    }
    if m_probability:
        level_dict["m_probability"] = m_probability

    super().__init__(level_dict, sql_dialect=self._sql_dialect)

Bases: DistanceFunctionLevelBase

Source code in splink/comparison_level_library.py 
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
class LevenshteinLevelBase(DistanceFunctionLevelBase):
    def __init__(
        self,
        col_name: str,
        distance_threshold: int,
        regex_extract: str = None,
        set_to_lowercase=False,
        include_colname_in_charts_label=False,
        manual_col_name_for_charts_label=None,
        m_probability=None,
    ) -> ComparisonLevel:
        """Represents a comparison level using a levenshtein distance function,

        Args:
            col_name (str): Input column name
            distance_threshold (Union[int, float]): The threshold to use to assess
                similarity
            regex_extract (str): Regular expression pattern to evaluate a match on.
            set_to_lowercase (bool): If True, sets all entries to lowercase.
            include_colname_in_charts_label (bool, optional): If True, includes
                col_name in charts label
            manual_col_name_for_charts_label (str, optional): string to include as
                 column name in chart label. Acts as a manual overwrite of the
                 colname when include_colname_in_charts_label is True.
            m_probability (float, optional): Starting value for m probability.
                Defaults to None.

        Examples:
            === ":simple-duckdb: DuckDB"
                Comparison level with levenshtein distance score less than (or equal
                 to) 1
                ``` python
                import splink.duckdb.comparison_level_library as cll
                cll.levenshtein_level("name", 1)
                ```

                Comparison level with levenshtein distance score less than (or equal
                 to) 1 on a subtring of name column as determined by a regular
                expression.
                ```python
                import splink.duckdb.comparison_level_library as cll
                cll.levenshtein_level("name", 1, regex_extract="^[A-Z]{1,4}")
                ```
            === ":simple-apachespark: Spark"
                Comparison level with levenshtein distance score less than (or equal
                 to) 1
                ``` python
                import splink.spark.comparison_level_library as cll
                cll.levenshtein_level("name", 1)
                ```

                Comparison level with levenshtein distance score less than (or equal
                 to) 1 on a subtring of name column as determined by a regular
                expression.
                ```python
                import splink.spark.comparison_level_library as cll
                cll.levenshtein_level("name", 1, regex_extract="^[A-Z]{1,4}")
                ```
            === ":simple-amazonaws: Athena"
                Comparison level with levenshtein distance score less than (or equal
                 to) 1
                ``` python
                import splink.athena.comparison_level_library as cll
                cll.levenshtein_level("name", 1)
                ```

                Comparison level with levenshtein distance score less than (or equal
                 to) 1 on a subtring of name column as determined by a regular
                expression.
                ```python
                import splink.athena.comparison_level_library as cll
                cll.levenshtein_level("name", 1, regex_extract="^[A-Z]{1,4}")
                ```
            === ":simple-sqlite: SQLite"
                Comparison level with levenshtein distance score less than (or equal
                 to) 1
                ``` python
                import splink.sqlite.comparison_level_library as cll
                cll.levenshtein_level("name", 1)
                ```
            === ":simple-postgresql: PostgreSql"
                Comparison level with levenshtein distance score less than (or equal
                 to) 1
                ``` python
                import splink.postgres.comparison_level_library as cll
                cll.levenshtein_level("name", 1)
                ```

        Returns:
            ComparisonLevel: A comparison level that evaluates the
                levenshtein similarity
        """
        super().__init__(
            col_name,
            distance_function_name=self._levenshtein_name,
            distance_threshold=distance_threshold,
            regex_extract=regex_extract,
            set_to_lowercase=set_to_lowercase,
            higher_is_more_similar=False,
            include_colname_in_charts_label=include_colname_in_charts_label,
            m_probability=m_probability,
        )

__init__(col_name, distance_threshold, regex_extract=None, set_to_lowercase=False, include_colname_in_charts_label=False, manual_col_name_for_charts_label=None, m_probability=None)

Represents a comparison level using a levenshtein distance function,

Parameters:

Name Type Description Default
col_name str

Input column name

 required
distance_threshold Union[int, float]

The threshold to use to assess similarity

 required
regex_extract str

Regular expression pattern to evaluate a match on.

 None
set_to_lowercase bool

If True, sets all entries to lowercase.

 False
include_colname_in_charts_label bool

If True, includes col_name in charts label

 False
manual_col_name_for_charts_label str

string to include as column name in chart label. Acts as a manual overwrite of the colname when include_colname_in_charts_label is True.

 None
m_probability float

Starting value for m probability. Defaults to None.

 None

Examples:

Comparison level with levenshtein distance score less than (or equal to) 1 

import splink.duckdb.comparison_level_library as cll
cll.levenshtein_level("name", 1)

Comparison level with levenshtein distance score less than (or equal to) 1 on a subtring of name column as determined by a regular expression. 

import splink.duckdb.comparison_level_library as cll
cll.levenshtein_level("name", 1, regex_extract="^[A-Z]{1,4}")

Comparison level with levenshtein distance score less than (or equal to) 1 

import splink.spark.comparison_level_library as cll
cll.levenshtein_level("name", 1)

Comparison level with levenshtein distance score less than (or equal to) 1 on a subtring of name column as determined by a regular expression. 

import splink.spark.comparison_level_library as cll
cll.levenshtein_level("name", 1, regex_extract="^[A-Z]{1,4}")

Comparison level with levenshtein distance score less than (or equal to) 1 

import splink.athena.comparison_level_library as cll
cll.levenshtein_level("name", 1)

Comparison level with levenshtein distance score less than (or equal to) 1 on a subtring of name column as determined by a regular expression. 

import splink.athena.comparison_level_library as cll
cll.levenshtein_level("name", 1, regex_extract="^[A-Z]{1,4}")

Comparison level with levenshtein distance score less than (or equal to) 1 

import splink.sqlite.comparison_level_library as cll
cll.levenshtein_level("name", 1)

Comparison level with levenshtein distance score less than (or equal to) 1 

import splink.postgres.comparison_level_library as cll
cll.levenshtein_level("name", 1)

Returns:

Name Type Description
ComparisonLevel ComparisonLevel

A comparison level that evaluates the levenshtein similarity
Source code in splink/comparison_level_library.py 
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
def __init__(
    self,
    col_name: str,
    distance_threshold: int,
    regex_extract: str = None,
    set_to_lowercase=False,
    include_colname_in_charts_label=False,
    manual_col_name_for_charts_label=None,
    m_probability=None,
) -> ComparisonLevel:
    """Represents a comparison level using a levenshtein distance function,

    Args:
        col_name (str): Input column name
        distance_threshold (Union[int, float]): The threshold to use to assess
            similarity
        regex_extract (str): Regular expression pattern to evaluate a match on.
        set_to_lowercase (bool): If True, sets all entries to lowercase.
        include_colname_in_charts_label (bool, optional): If True, includes
            col_name in charts label
        manual_col_name_for_charts_label (str, optional): string to include as
             column name in chart label. Acts as a manual overwrite of the
             colname when include_colname_in_charts_label is True.
        m_probability (float, optional): Starting value for m probability.
            Defaults to None.

    Examples:
        === ":simple-duckdb: DuckDB"
            Comparison level with levenshtein distance score less than (or equal
             to) 1
            ``` python
            import splink.duckdb.comparison_level_library as cll
            cll.levenshtein_level("name", 1)
            ```

            Comparison level with levenshtein distance score less than (or equal
             to) 1 on a subtring of name column as determined by a regular
            expression.
            ```python
            import splink.duckdb.comparison_level_library as cll
            cll.levenshtein_level("name", 1, regex_extract="^[A-Z]{1,4}")
            ```
        === ":simple-apachespark: Spark"
            Comparison level with levenshtein distance score less than (or equal
             to) 1
            ``` python
            import splink.spark.comparison_level_library as cll
            cll.levenshtein_level("name", 1)
            ```

            Comparison level with levenshtein distance score less than (or equal
             to) 1 on a subtring of name column as determined by a regular
            expression.
            ```python
            import splink.spark.comparison_level_library as cll
            cll.levenshtein_level("name", 1, regex_extract="^[A-Z]{1,4}")
            ```
        === ":simple-amazonaws: Athena"
            Comparison level with levenshtein distance score less than (or equal
             to) 1
            ``` python
            import splink.athena.comparison_level_library as cll
            cll.levenshtein_level("name", 1)
            ```

            Comparison level with levenshtein distance score less than (or equal
             to) 1 on a subtring of name column as determined by a regular
            expression.
            ```python
            import splink.athena.comparison_level_library as cll
            cll.levenshtein_level("name", 1, regex_extract="^[A-Z]{1,4}")
            ```
        === ":simple-sqlite: SQLite"
            Comparison level with levenshtein distance score less than (or equal
             to) 1
            ``` python
            import splink.sqlite.comparison_level_library as cll
            cll.levenshtein_level("name", 1)
            ```
        === ":simple-postgresql: PostgreSql"
            Comparison level with levenshtein distance score less than (or equal
             to) 1
            ``` python
            import splink.postgres.comparison_level_library as cll
            cll.levenshtein_level("name", 1)
            ```

    Returns:
        ComparisonLevel: A comparison level that evaluates the
            levenshtein similarity
    """
    super().__init__(
        col_name,
        distance_function_name=self._levenshtein_name,
        distance_threshold=distance_threshold,
        regex_extract=regex_extract,
        set_to_lowercase=set_to_lowercase,
        higher_is_more_similar=False,
        include_colname_in_charts_label=include_colname_in_charts_label,
        m_probability=m_probability,
    )

Bases: DistanceFunctionLevelBase

Source code in splink/comparison_level_library.py 
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
class DamerauLevenshteinLevelBase(DistanceFunctionLevelBase):
    def __init__(
        self,
        col_name: str,
        distance_threshold: int,
        regex_extract: str = None,
        set_to_lowercase=False,
        include_colname_in_charts_label=False,
        manual_col_name_for_charts_label=None,
        m_probability=None,
    ) -> ComparisonLevel:
        """Represents a comparison level using a damerau-levenshtein distance
        function,

        Args:
            col_name (str): Input column name
            distance_threshold (Union[int, float]): The threshold to use to assess
                similarity
            regex_extract (str): Regular expression pattern to evaluate a match on.
            set_to_lowercase (bool): If True, sets all entries to lowercase.
            include_colname_in_charts_label (bool, optional): If True, includes
                col_name in charts label
            manual_col_name_for_charts_label (str, optional): string to include as
                 column name in chart label. Acts as a manual overwrite of the
                 colname when include_colname_in_charts_label is True.
            m_probability (float, optional): Starting value for m probability.
                Defaults to None.

        Examples:
            === ":simple-duckdb: DuckDB"
                Comparison level with damerau-levenshtein distance score less than
                (or equal to) 1
                ``` python
                import splink.duckdb.comparison_level_library as cll
                cll.damerau_levenshtein_level("name", 1)
                ```

                Comparison level with damerau-levenshtein distance score less than
                (or equal to) 1 on a subtring of name column as determined by a regular
                expression.
                ```python
                import splink.duckdb.comparison_level_library as cll
                cll.damerau_levenshtein_level("name", 1, regex_extract="^[A-Z]{1,4}")
                ```
            === ":simple-apachespark: Spark"
                Comparison level with damerau-levenshtein distance score less than
                (or equal to) 1
                ``` python
                import splink.spark.comparison_level_library as cll
                cll.damerau_levenshtein_level("name", 1)
                ```

                Comparison level with damerau-levenshtein distance score less than
                (or equal to) 1 on a subtring of name column as determined by a regular
                expression.
                ```python
                import splink.spark.comparison_level_library as cll
                cll.damerau_levenshtein_level("name", 1, regex_extract="^[A-Z]{1,4}")
                ```
            === ":simple-sqlite: SQLite"
                Comparison level with damerau-levenshtein distance score less than
                (or equal to) 1
                ``` python
                import splink.sqlite.comparison_level_library as cll
                cll.damerau_levenshtein_level("name", 1)
                ```

        Returns:
            ComparisonLevel: A comparison level that evaluates the
                Damerau-Levenshtein similarity
        """
        super().__init__(
            col_name,
            distance_function_name=self._damerau_levenshtein_name,
            distance_threshold=distance_threshold,
            regex_extract=regex_extract,
            set_to_lowercase=set_to_lowercase,
            higher_is_more_similar=False,
            include_colname_in_charts_label=include_colname_in_charts_label,
            m_probability=m_probability,
        )

__init__(col_name, distance_threshold, regex_extract=None, set_to_lowercase=False, include_colname_in_charts_label=False, manual_col_name_for_charts_label=None, m_probability=None)

Represents a comparison level using a damerau-levenshtein distance function,

Parameters:

Name Type Description Default
col_name str

Input column name

 required
distance_threshold Union[int, float]

The threshold to use to assess similarity

 required
regex_extract str

Regular expression pattern to evaluate a match on.

 None
set_to_lowercase bool

If True, sets all entries to lowercase.

 False
include_colname_in_charts_label bool

If True, includes col_name in charts label

 False
manual_col_name_for_charts_label str

string to include as column name in chart label. Acts as a manual overwrite of the colname when include_colname_in_charts_label is True.

 None
m_probability float

Starting value for m probability. Defaults to None.

 None

Examples:

Comparison level with damerau-levenshtein distance score less than (or equal to) 1 

import splink.duckdb.comparison_level_library as cll
cll.damerau_levenshtein_level("name", 1)

Comparison level with damerau-levenshtein distance score less than (or equal to) 1 on a subtring of name column as determined by a regular expression. 

import splink.duckdb.comparison_level_library as cll
cll.damerau_levenshtein_level("name", 1, regex_extract="^[A-Z]{1,4}")

Comparison level with damerau-levenshtein distance score less than (or equal to) 1 

import splink.spark.comparison_level_library as cll
cll.damerau_levenshtein_level("name", 1)

Comparison level with damerau-levenshtein distance score less than (or equal to) 1 on a subtring of name column as determined by a regular expression. 

import splink.spark.comparison_level_library as cll
cll.damerau_levenshtein_level("name", 1, regex_extract="^[A-Z]{1,4}")

Comparison level with damerau-levenshtein distance score less than (or equal to) 1 

import splink.sqlite.comparison_level_library as cll
cll.damerau_levenshtein_level("name", 1)

Returns:

Name Type Description
ComparisonLevel ComparisonLevel

A comparison level that evaluates the Damerau-Levenshtein similarity
Source code in splink/comparison_level_library.py 
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
def __init__(
    self,
    col_name: str,
    distance_threshold: int,
    regex_extract: str = None,
    set_to_lowercase=False,
    include_colname_in_charts_label=False,
    manual_col_name_for_charts_label=None,
    m_probability=None,
) -> ComparisonLevel:
    """Represents a comparison level using a damerau-levenshtein distance
    function,

    Args:
        col_name (str): Input column name
        distance_threshold (Union[int, float]): The threshold to use to assess
            similarity
        regex_extract (str): Regular expression pattern to evaluate a match on.
        set_to_lowercase (bool): If True, sets all entries to lowercase.
        include_colname_in_charts_label (bool, optional): If True, includes
            col_name in charts label
        manual_col_name_for_charts_label (str, optional): string to include as
             column name in chart label. Acts as a manual overwrite of the
             colname when include_colname_in_charts_label is True.
        m_probability (float, optional): Starting value for m probability.
            Defaults to None.

    Examples:
        === ":simple-duckdb: DuckDB"
            Comparison level with damerau-levenshtein distance score less than
            (or equal to) 1
            ``` python
            import splink.duckdb.comparison_level_library as cll
            cll.damerau_levenshtein_level("name", 1)
            ```

            Comparison level with damerau-levenshtein distance score less than
            (or equal to) 1 on a subtring of name column as determined by a regular
            expression.
            ```python
            import splink.duckdb.comparison_level_library as cll
            cll.damerau_levenshtein_level("name", 1, regex_extract="^[A-Z]{1,4}")
            ```
        === ":simple-apachespark: Spark"
            Comparison level with damerau-levenshtein distance score less than
            (or equal to) 1
            ``` python
            import splink.spark.comparison_level_library as cll
            cll.damerau_levenshtein_level("name", 1)
            ```

            Comparison level with damerau-levenshtein distance score less than
            (or equal to) 1 on a subtring of name column as determined by a regular
            expression.
            ```python
            import splink.spark.comparison_level_library as cll
            cll.damerau_levenshtein_level("name", 1, regex_extract="^[A-Z]{1,4}")
            ```
        === ":simple-sqlite: SQLite"
            Comparison level with damerau-levenshtein distance score less than
            (or equal to) 1
            ``` python
            import splink.sqlite.comparison_level_library as cll
            cll.damerau_levenshtein_level("name", 1)
            ```

    Returns:
        ComparisonLevel: A comparison level that evaluates the
            Damerau-Levenshtein similarity
    """
    super().__init__(
        col_name,
        distance_function_name=self._damerau_levenshtein_name,
        distance_threshold=distance_threshold,
        regex_extract=regex_extract,
        set_to_lowercase=set_to_lowercase,
        higher_is_more_similar=False,
        include_colname_in_charts_label=include_colname_in_charts_label,
        m_probability=m_probability,
    )

Bases: DistanceFunctionLevelBase

Source code in splink/comparison_level_library.py 
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
class JaroLevelBase(DistanceFunctionLevelBase):
    def __init__(
        self,
        col_name: str,
        distance_threshold: float,
        regex_extract: str = None,
        set_to_lowercase=False,
        include_colname_in_charts_label=False,
        manual_col_name_for_charts_label=None,
        m_probability=None,
    ):
        """Represents a comparison using the jaro distance function

        Args:
            col_name (str): Input column name
            distance_threshold (Union[int, float]): The threshold to use to assess
                similarity
            regex_extract (str): Regular expression pattern to evaluate a match on.
            set_to_lowercase (bool): If True, sets all entries to lowercase.
            include_colname_in_charts_label (bool, optional): If True, includes
                col_name in charts label
            manual_col_name_for_charts_label (str, optional): string to include as
                 column name in chart label. Acts as a manual overwrite of the
                 colname when include_colname_in_charts_label is True.
            m_probability (float, optional): Starting value for m probability.
                Defaults to None.

        Examples:
            === ":simple-duckdb: DuckDB"
                Comparison level with jaro score greater than 0.9
                ``` python
                import splink.duckdb.comparison_level_library as cll
                cll.jaro_level("name", 0.9)
                ```
                Comparison level with a jaro score greater than 0.9 on a substring
                of name column as determined by a regular expression.

                ```python
                import splink.duckdb.comparison_level_library as cll
                cll.jaro_level("name", 0.9, regex_extract="^[A-Z]{1,4}")
                ```
            === ":simple-apachespark: Spark"
                Comparison level with jaro score greater than 0.9
                ``` python
                import splink.spark.comparison_level_library as cll
                cll.jaro_level("name", 0.9)
                ```
                Comparison level with a jaro score greater than 0.9 on a substring
                of name column as determined by a regular expression.

                ```python
                import splink.spark.comparison_level_library as cll
                cll.jaro_level("name", 0.9, regex_extract="^[A-Z]{1,4}")
                ```
            === ":simple-sqlite: SQLite"
                Comparison level with jaro score greater than 0.9
                ``` python
                import splink.sqlite.comparison_level_library as cll
                cll.jaro_level("name", 0.9)
                ```

        Returns:
            ComparisonLevel: A comparison level that evaluates the
                jaro similarity
        """

        super().__init__(
            col_name,
            self._jaro_name,
            distance_threshold=distance_threshold,
            regex_extract=regex_extract,
            set_to_lowercase=set_to_lowercase,
            higher_is_more_similar=True,
            include_colname_in_charts_label=include_colname_in_charts_label,
            m_probability=m_probability,
        )

__init__(col_name, distance_threshold, regex_extract=None, set_to_lowercase=False, include_colname_in_charts_label=False, manual_col_name_for_charts_label=None, m_probability=None)

Represents a comparison using the jaro distance function

Parameters:

Name Type Description Default
col_name str

Input column name

 required
distance_threshold Union[int, float]

The threshold to use to assess similarity

 required
regex_extract str

Regular expression pattern to evaluate a match on.

 None
set_to_lowercase bool

If True, sets all entries to lowercase.

 False
include_colname_in_charts_label bool

If True, includes col_name in charts label

 False
manual_col_name_for_charts_label str

string to include as column name in chart label. Acts as a manual overwrite of the colname when include_colname_in_charts_label is True.

 None
m_probability float

Starting value for m probability. Defaults to None.

 None

Examples:

Comparison level with jaro score greater than 0.9 

import splink.duckdb.comparison_level_library as cll
cll.jaro_level("name", 0.9)
Comparison level with a jaro score greater than 0.9 on a substring of name column as determined by a regular expression. 
import splink.duckdb.comparison_level_library as cll
cll.jaro_level("name", 0.9, regex_extract="^[A-Z]{1,4}")

Comparison level with jaro score greater than 0.9 

import splink.spark.comparison_level_library as cll
cll.jaro_level("name", 0.9)
Comparison level with a jaro score greater than 0.9 on a substring of name column as determined by a regular expression. 
import splink.spark.comparison_level_library as cll
cll.jaro_level("name", 0.9, regex_extract="^[A-Z]{1,4}")

Comparison level with jaro score greater than 0.9 

import splink.sqlite.comparison_level_library as cll
cll.jaro_level("name", 0.9)

Returns:

Name Type Description
ComparisonLevel

A comparison level that evaluates the jaro similarity
Source code in splink/comparison_level_library.py 
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
def __init__(
    self,
    col_name: str,
    distance_threshold: float,
    regex_extract: str = None,
    set_to_lowercase=False,
    include_colname_in_charts_label=False,
    manual_col_name_for_charts_label=None,
    m_probability=None,
):
    """Represents a comparison using the jaro distance function

    Args:
        col_name (str): Input column name
        distance_threshold (Union[int, float]): The threshold to use to assess
            similarity
        regex_extract (str): Regular expression pattern to evaluate a match on.
        set_to_lowercase (bool): If True, sets all entries to lowercase.
        include_colname_in_charts_label (bool, optional): If True, includes
            col_name in charts label
        manual_col_name_for_charts_label (str, optional): string to include as
             column name in chart label. Acts as a manual overwrite of the
             colname when include_colname_in_charts_label is True.
        m_probability (float, optional): Starting value for m probability.
            Defaults to None.

    Examples:
        === ":simple-duckdb: DuckDB"
            Comparison level with jaro score greater than 0.9
            ``` python
            import splink.duckdb.comparison_level_library as cll
            cll.jaro_level("name", 0.9)
            ```
            Comparison level with a jaro score greater than 0.9 on a substring
            of name column as determined by a regular expression.

            ```python
            import splink.duckdb.comparison_level_library as cll
            cll.jaro_level("name", 0.9, regex_extract="^[A-Z]{1,4}")
            ```
        === ":simple-apachespark: Spark"
            Comparison level with jaro score greater than 0.9
            ``` python
            import splink.spark.comparison_level_library as cll
            cll.jaro_level("name", 0.9)
            ```
            Comparison level with a jaro score greater than 0.9 on a substring
            of name column as determined by a regular expression.

            ```python
            import splink.spark.comparison_level_library as cll
            cll.jaro_level("name", 0.9, regex_extract="^[A-Z]{1,4}")
            ```
        === ":simple-sqlite: SQLite"
            Comparison level with jaro score greater than 0.9
            ``` python
            import splink.sqlite.comparison_level_library as cll
            cll.jaro_level("name", 0.9)
            ```

    Returns:
        ComparisonLevel: A comparison level that evaluates the
            jaro similarity
    """

    super().__init__(
        col_name,
        self._jaro_name,
        distance_threshold=distance_threshold,
        regex_extract=regex_extract,
        set_to_lowercase=set_to_lowercase,
        higher_is_more_similar=True,
        include_colname_in_charts_label=include_colname_in_charts_label,
        m_probability=m_probability,
    )

Bases: DistanceFunctionLevelBase

Source code in splink/comparison_level_library.py 
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
750
751
752
753
754
755
756
757
758
759
760
761
762
763
764
765
766
767
768
769
770
771
772
773
774
775
776
777
778
779
780
781
782
783
784
class JaroWinklerLevelBase(DistanceFunctionLevelBase):
    def __init__(
        self,
        col_name: str,
        distance_threshold: float,
        regex_extract: str = None,
        set_to_lowercase=False,
        include_colname_in_charts_label=False,
        manual_col_name_for_charts_label=None,
        m_probability=None,
    ) -> ComparisonLevel:
        """Represents a comparison level using the jaro winkler distance function

        Args:
            col_name (str): Input column name
            distance_threshold (Union[int, float]): The threshold to use to assess
                similarity
            regex_extract (str): Regular expression pattern to evaluate a match on.
            set_to_lowercase (bool): If True, sets all entries to lowercase.
            include_colname_in_charts_label (bool, optional): If True, includes
                col_name in charts label
            manual_col_name_for_charts_label (str, optional): string to include as
                 column name in chart label. Acts as a manual overwrite of the
                 colname when include_colname_in_charts_label is True.
            m_probability (float, optional): Starting value for m probability.
                Defaults to None.

        Examples:
            === ":simple-duckdb: DuckDB"
                Comparison level with jaro-winkler score greater than 0.9
                ``` python
                import splink.duckdb.comparison_level_library as cll
                cll.jaro_winkler_level("name", 0.9)
                ```
                Comparison level with jaro-winkler score greater than 0.9 on a
                substring of name column as determined by a regular expression.
                ``` python
                import splink.duckdb.comparison_level_library as cll
                cll.jaro_winkler_level("name", 0.9, regex_extract="^[A-Z]{1,4}")
                ```
            === ":simple-apachespark: Spark"
                Comparison level with jaro-winkler score greater than 0.9
                ``` python
                import splink.spark.comparison_level_library as cll
                cll.jaro_winkler_level("name", 0.9)
                ```
                Comparison level with jaro-winkler score greater than 0.9 on a
                substring of name column as determined by a regular expression.
                ``` python
                import splink.spark.comparison_level_library as cll
                cll.jaro_winkler_level("name", 0.9, regex_extract="^[A-Z]{1,4}")
                ```
            === ":simple-sqlite: SQLite"
                Comparison level with jaro-winkler score greater than 0.9
                ``` python
                import splink.sqlite.comparison_level_library as cll
                cll.jaro_winkler_level("name", 0.9)
                ```

        Returns:
            ComparisonLevel: A comparison level that evaluates the
                jaro winkler similarity
        """

        super().__init__(
            col_name,
            self._jaro_winkler_name,
            distance_threshold=distance_threshold,
            regex_extract=regex_extract,
            set_to_lowercase=set_to_lowercase,
            higher_is_more_similar=True,
            include_colname_in_charts_label=include_colname_in_charts_label,
            m_probability=m_probability,
        )

    @property
    def _jaro_winkler_name(self):
        raise NotImplementedError(
            "Jaro-winkler function name not defined on base class"
        )

__init__(col_name, distance_threshold, regex_extract=None, set_to_lowercase=False, include_colname_in_charts_label=False, manual_col_name_for_charts_label=None, m_probability=None)

Represents a comparison level using the jaro winkler distance function

Parameters:

Name Type Description Default
col_name str

Input column name

 required
distance_threshold Union[int, float]

The threshold to use to assess similarity

 required
regex_extract str

Regular expression pattern to evaluate a match on.

 None
set_to_lowercase bool

If True, sets all entries to lowercase.

 False
include_colname_in_charts_label bool

If True, includes col_name in charts label

 False
manual_col_name_for_charts_label str

string to include as column name in chart label. Acts as a manual overwrite of the colname when include_colname_in_charts_label is True.

 None
m_probability float

Starting value for m probability. Defaults to None.

 None

Examples:

Comparison level with jaro-winkler score greater than 0.9 

import splink.duckdb.comparison_level_library as cll
cll.jaro_winkler_level("name", 0.9)
Comparison level with jaro-winkler score greater than 0.9 on a substring of name column as determined by a regular expression. 
import splink.duckdb.comparison_level_library as cll
cll.jaro_winkler_level("name", 0.9, regex_extract="^[A-Z]{1,4}")

Comparison level with jaro-winkler score greater than 0.9 

import splink.spark.comparison_level_library as cll
cll.jaro_winkler_level("name", 0.9)
Comparison level with jaro-winkler score greater than 0.9 on a substring of name column as determined by a regular expression. 
import splink.spark.comparison_level_library as cll
cll.jaro_winkler_level("name", 0.9, regex_extract="^[A-Z]{1,4}")

Comparison level with jaro-winkler score greater than 0.9 

import splink.sqlite.comparison_level_library as cll
cll.jaro_winkler_level("name", 0.9)

Returns:

Name Type Description
ComparisonLevel ComparisonLevel

A comparison level that evaluates the jaro winkler similarity
Source code in splink/comparison_level_library.py 
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
750
751
752
753
754
755
756
757
758
759
760
761
762
763
764
765
766
767
768
769
770
771
772
773
774
775
776
777
778
def __init__(
    self,
    col_name: str,
    distance_threshold: float,
    regex_extract: str = None,
    set_to_lowercase=False,
    include_colname_in_charts_label=False,
    manual_col_name_for_charts_label=None,
    m_probability=None,
) -> ComparisonLevel:
    """Represents a comparison level using the jaro winkler distance function

    Args:
        col_name (str): Input column name
        distance_threshold (Union[int, float]): The threshold to use to assess
            similarity
        regex_extract (str): Regular expression pattern to evaluate a match on.
        set_to_lowercase (bool): If True, sets all entries to lowercase.
        include_colname_in_charts_label (bool, optional): If True, includes
            col_name in charts label
        manual_col_name_for_charts_label (str, optional): string to include as
             column name in chart label. Acts as a manual overwrite of the
             colname when include_colname_in_charts_label is True.
        m_probability (float, optional): Starting value for m probability.
            Defaults to None.

    Examples:
        === ":simple-duckdb: DuckDB"
            Comparison level with jaro-winkler score greater than 0.9
            ``` python
            import splink.duckdb.comparison_level_library as cll
            cll.jaro_winkler_level("name", 0.9)
            ```
            Comparison level with jaro-winkler score greater than 0.9 on a
            substring of name column as determined by a regular expression.
            ``` python
            import splink.duckdb.comparison_level_library as cll
            cll.jaro_winkler_level("name", 0.9, regex_extract="^[A-Z]{1,4}")
            ```
        === ":simple-apachespark: Spark"
            Comparison level with jaro-winkler score greater than 0.9
            ``` python
            import splink.spark.comparison_level_library as cll
            cll.jaro_winkler_level("name", 0.9)
            ```
            Comparison level with jaro-winkler score greater than 0.9 on a
            substring of name column as determined by a regular expression.
            ``` python
            import splink.spark.comparison_level_library as cll
            cll.jaro_winkler_level("name", 0.9, regex_extract="^[A-Z]{1,4}")
            ```
        === ":simple-sqlite: SQLite"
            Comparison level with jaro-winkler score greater than 0.9
            ``` python
            import splink.sqlite.comparison_level_library as cll
            cll.jaro_winkler_level("name", 0.9)
            ```

    Returns:
        ComparisonLevel: A comparison level that evaluates the
            jaro winkler similarity
    """

    super().__init__(
        col_name,
        self._jaro_winkler_name,
        distance_threshold=distance_threshold,
        regex_extract=regex_extract,
        set_to_lowercase=set_to_lowercase,
        higher_is_more_similar=True,
        include_colname_in_charts_label=include_colname_in_charts_label,
        m_probability=m_probability,
    )

Bases: DistanceFunctionLevelBase

Source code in splink/comparison_level_library.py 
787
788
789
790
791
792
793
794
795
796
797
798
799
800
801
802
803
804
805
806
807
808
809
810
811
812
813
814
815
816
817
818
819
820
821
822
823
824
825
826
827
828
829
830
831
832
833
834
835
836
837
838
839
840
841
842
843
844
845
846
847
848
849
850
851
class JaccardLevelBase(DistanceFunctionLevelBase):
    def __init__(
        self,
        col_name: str,
        distance_threshold: int | float,
        regex_extract: str = None,
        set_to_lowercase=False,
        include_colname_in_charts_label=False,
        manual_col_name_for_charts_label=None,
        m_probability=None,
    ) -> ComparisonLevel:
        """Represents a comparison level using a jaccard distance function

        Args:
            col_name (str): Input column name
            distance_threshold (Union[int, float]): The threshold to use to assess
                similarity
            regex_extract (str): Regular expression pattern to evaluate a match on.
            set_to_lowercase (bool): If True, sets all entries to lowercase.
            include_colname_in_charts_label (bool, optional): If True, includes
                col_name in charts label
            manual_col_name_for_charts_label (str, optional): string to include as
                 column name in chart label. Acts as a manual overwrite of the
                 colname when include_colname_in_charts_label is True.
            m_probability (float, optional): Starting value for m probability.
                Defaults to None.
        Examples:
            === ":simple-duckdb: DuckDB"
                Comparison level with jaccard score greater than 0.9
                ``` python
                import splink.duckdb.comparison_level_library as cll
                cll.jaccard_level("name", 0.9)
                ```
                Comparison level with jaccard score greater than 0.9 on a
                substring of name column as determined by a regular expression.
                ``` python
                import splink.duckdb.comparison_level_library as cll
                cll.jaccard_level("name", 0.9, regex_extract="^[A-Z]{1,4}")
                ```
            === ":simple-apachespark: Spark"
                Comparison level with jaccard score greater than 0.9
                ``` python
                import splink.spark.comparison_level_library as cll
                cll.jaccard_level("name", 0.9)
                ```
                Comparison level with jaccard score greater than 0.9 on a
                substring of name column as determined by a regular expression.
                ``` python
                import splink.spark.comparison_level_library as cll
                cll.jaccard_level("name", 0.9, regex_extract="^[A-Z]{1,4}")
                ```

        Returns:
            ComparisonLevel: A comparison level that evaluates the jaccard similarity
        """
        super().__init__(
            col_name,
            self._jaccard_name,
            distance_threshold=distance_threshold,
            regex_extract=regex_extract,
            set_to_lowercase=set_to_lowercase,
            higher_is_more_similar=True,
            include_colname_in_charts_label=include_colname_in_charts_label,
            m_probability=m_probability,
        )

__init__(col_name, distance_threshold, regex_extract=None, set_to_lowercase=False, include_colname_in_charts_label=False, manual_col_name_for_charts_label=None, m_probability=None)

Represents a comparison level using a jaccard distance function

Parameters:

Name Type Description Default
col_name str

Input column name

 required
distance_threshold Union[int, float]

The threshold to use to assess similarity

 required
regex_extract str

Regular expression pattern to evaluate a match on.

 None
set_to_lowercase bool

If True, sets all entries to lowercase.

 False
include_colname_in_charts_label bool

If True, includes col_name in charts label

 False
manual_col_name_for_charts_label str

string to include as column name in chart label. Acts as a manual overwrite of the colname when include_colname_in_charts_label is True.

 None
m_probability float

Starting value for m probability. Defaults to None.

 None

Returns:

Name Type Description
ComparisonLevel ComparisonLevel

A comparison level that evaluates the jaccard similarity
Source code in splink/comparison_level_library.py 
788
789
790
791
792
793
794
795
796
797
798
799
800
801
802
803
804
805
806
807
808
809
810
811
812
813
814
815
816
817
818
819
820
821
822
823
824
825
826
827
828
829
830
831
832
833
834
835
836
837
838
839
840
841
842
843
844
845
846
847
848
849
850
851
def __init__(
    self,
    col_name: str,
    distance_threshold: int | float,
    regex_extract: str = None,
    set_to_lowercase=False,
    include_colname_in_charts_label=False,
    manual_col_name_for_charts_label=None,
    m_probability=None,
) -> ComparisonLevel:
    """Represents a comparison level using a jaccard distance function

    Args:
        col_name (str): Input column name
        distance_threshold (Union[int, float]): The threshold to use to assess
            similarity
        regex_extract (str): Regular expression pattern to evaluate a match on.
        set_to_lowercase (bool): If True, sets all entries to lowercase.
        include_colname_in_charts_label (bool, optional): If True, includes
            col_name in charts label
        manual_col_name_for_charts_label (str, optional): string to include as
             column name in chart label. Acts as a manual overwrite of the
             colname when include_colname_in_charts_label is True.
        m_probability (float, optional): Starting value for m probability.
            Defaults to None.
    Examples:
        === ":simple-duckdb: DuckDB"
            Comparison level with jaccard score greater than 0.9
            ``` python
            import splink.duckdb.comparison_level_library as cll
            cll.jaccard_level("name", 0.9)
            ```
            Comparison level with jaccard score greater than 0.9 on a
            substring of name column as determined by a regular expression.
            ``` python
            import splink.duckdb.comparison_level_library as cll
            cll.jaccard_level("name", 0.9, regex_extract="^[A-Z]{1,4}")
            ```
        === ":simple-apachespark: Spark"
            Comparison level with jaccard score greater than 0.9
            ``` python
            import splink.spark.comparison_level_library as cll
            cll.jaccard_level("name", 0.9)
            ```
            Comparison level with jaccard score greater than 0.9 on a
            substring of name column as determined by a regular expression.
            ``` python
            import splink.spark.comparison_level_library as cll
            cll.jaccard_level("name", 0.9, regex_extract="^[A-Z]{1,4}")
            ```

    Returns:
        ComparisonLevel: A comparison level that evaluates the jaccard similarity
    """
    super().__init__(
        col_name,
        self._jaccard_name,
        distance_threshold=distance_threshold,
        regex_extract=regex_extract,
        set_to_lowercase=set_to_lowercase,
        higher_is_more_similar=True,
        include_colname_in_charts_label=include_colname_in_charts_label,
        m_probability=m_probability,
    )

Bases: ComparisonLevel

Source code in splink/comparison_level_library.py 
854
855
856
857
858
859
860
861
862
863
864
865
866
867
868
869
870
871
872
873
874
875
876
877
878
879
880
881
882
883
884
885
886
887
888
889
890
891
892
893
894
895
896
897
898
899
900
901
902
903
904
905
906
907
908
909
910
911
912
913
914
915
916
917
918
919
920
921
922
923
924
925
926
927
928
929
930
931
932
933
934
935
936
937
938
939
940
941
942
943
944
945
946
947
948
949
950
951
952
953
954
955
956
957
958
959
960
961
962
963
964
965
966
967
class ColumnsReversedLevelBase(ComparisonLevel):
    def __init__(
        self,
        col_name_1: str,
        col_name_2: str,
        regex_extract: str = None,
        set_to_lowercase=False,
        m_probability=None,
        tf_adjustment_column=None,
    ) -> ComparisonLevel:
        """Represents a comparison level where the columns are reversed.  For example,
        if surname is in the forename field and vice versa

        Args:
            col_name_1 (str): First column, e.g. forename
            col_name_2 (str): Second column, e.g. surname
            regex_extract (str): Regular expression pattern to evaluate a match on.
            set_to_lowercase (bool): If True, sets all entries to lowercase.
            m_probability (float, optional): Starting value for m probability.
                Defaults to None.
            tf_adjustment_column (str, optional): Column to use for term frequency
                adjustments if an exact match is observed. Defaults to None.

        Examples:
            === ":simple-duckdb: DuckDB"
                Comparison level on first_name and surname columns reversed
                ``` python
                import splink.duckdb.comparison_level_library as cll
                cll.columns_reversed_level("first_name", "surname")
                ```
                Comparison level on first_name and surname column reversed
                on a substring of each column as determined by a regular expression.
                ``` python
                import splink.duckdb.comparison_level_library as cll
                cll.columns_reversed_level("first_name",
                                           "surname",
                                           regex_extract="^[A-Z]{1,4}")
                ```
            === ":simple-apachespark: Spark"
                Comparison level on first_name and surname columns reversed
                ``` python
                import splink.spark.comparison_level_library as cll
                cll.columns_reversed_level("first_name", "surname")
                ```
                Comparison level on first_name and surname column reversed
                on a substring of each column as determined by a regular expression.
                ``` python
                import splink.spark.comparison_level_library as cll
                cll.columns_reversed_level("first_name",
                                           "surname",
                                           regex_extract="^[A-Z]{1,4}")
                ```
            === ":simple-amazonaws: Athena"
                Comparison level on first_name and surname columns reversed
                ``` python
                import splink.athena.comparison_level_library as cll
                cll.columns_reversed_level("first_name", "surname")
                ```
                Comparison level on first_name and surname column reversed
                on a substring of each column as determined by a regular expression.
                ``` python
                import splink.athena.comparison_level_library as cll
                cll.columns_reversed_level("first_name",
                                           "surname",
                                           regex_extract="^[A-Z]{1,4}")
                ```
            === ":simple-sqlite: SQLite"
                Comparison level on first_name and surname columns reversed
                ``` python
                import splink.sqlite.comparison_level_library as cll
                cll.columns_reversed_level("first_name", "surname")
                ```
            === ":simple-postgresql: PostgreSql"
                ``` python
                import splink.postgres.comparison_level_library as cll
                cll.columns_reversed_level("first_name", "surname")
                ```


        Returns:
            ComparisonLevel: A comparison level that evaluates the exact match of two
                columns.
        """

        col_1 = InputColumn(col_name_1, sql_dialect=self._sql_dialect)
        col_2 = InputColumn(col_name_2, sql_dialect=self._sql_dialect)

        col_1_l, col_1_r = col_1.name_l, col_1.name_r
        col_2_l, col_2_r = col_2.name_l, col_2.name_r

        if set_to_lowercase:
            col_1_l = f"lower({col_1_l})"
            col_1_r = f"lower({col_1_r})"
            col_2_l = f"lower({col_2_l})"
            col_2_r = f"lower({col_2_r})"

        if regex_extract:
            col_1_l = self._regex_extract_function(col_1_l, regex_extract)
            col_1_r = self._regex_extract_function(col_1_r, regex_extract)
            col_2_l = self._regex_extract_function(col_2_l, regex_extract)
            col_2_r = self._regex_extract_function(col_2_r, regex_extract)

        s = f"{col_1_l} = {col_2_r} and " f"{col_1_r} = {col_2_l}"
        level_dict = {
            "sql_condition": s,
            "label_for_charts": "Exact match on reversed cols",
        }
        if m_probability:
            level_dict["m_probability"] = m_probability

        if tf_adjustment_column:
            level_dict["tf_adjustment_column"] = tf_adjustment_column

        super().__init__(level_dict, sql_dialect=self._sql_dialect)

__init__(col_name_1, col_name_2, regex_extract=None, set_to_lowercase=False, m_probability=None, tf_adjustment_column=None)

Represents a comparison level where the columns are reversed. For example, if surname is in the forename field and vice versa

Parameters:

Name Type Description Default
col_name_1 str

First column, e.g. forename

 required
col_name_2 str

Second column, e.g. surname

 required
regex_extract str

Regular expression pattern to evaluate a match on.

 None
set_to_lowercase bool

If True, sets all entries to lowercase.

 False
m_probability float

Starting value for m probability. Defaults to None.

 None
tf_adjustment_column str

Column to use for term frequency adjustments if an exact match is observed. Defaults to None.

 None

Examples:

Comparison level on first_name and surname columns reversed 

import splink.duckdb.comparison_level_library as cll
cll.columns_reversed_level("first_name", "surname")
Comparison level on first_name and surname column reversed on a substring of each column as determined by a regular expression. 
import splink.duckdb.comparison_level_library as cll
cll.columns_reversed_level("first_name",
                           "surname",
                           regex_extract="^[A-Z]{1,4}")

Comparison level on first_name and surname columns reversed 

import splink.spark.comparison_level_library as cll
cll.columns_reversed_level("first_name", "surname")
Comparison level on first_name and surname column reversed on a substring of each column as determined by a regular expression. 
import splink.spark.comparison_level_library as cll
cll.columns_reversed_level("first_name",
                           "surname",
                           regex_extract="^[A-Z]{1,4}")

Comparison level on first_name and surname columns reversed 

import splink.athena.comparison_level_library as cll
cll.columns_reversed_level("first_name", "surname")
Comparison level on first_name and surname column reversed on a substring of each column as determined by a regular expression. 
import splink.athena.comparison_level_library as cll
cll.columns_reversed_level("first_name",
                           "surname",
                           regex_extract="^[A-Z]{1,4}")

Comparison level on first_name and surname columns reversed 

import splink.sqlite.comparison_level_library as cll
cll.columns_reversed_level("first_name", "surname")

import splink.postgres.comparison_level_library as cll
cll.columns_reversed_level("first_name", "surname")

Returns:

Name Type Description
ComparisonLevel ComparisonLevel

A comparison level that evaluates the exact match of two columns.
Source code in splink/comparison_level_library.py 
855
856
857
858
859
860
861
862
863
864
865
866
867
868
869
870
871
872
873
874
875
876
877
878
879
880
881
882
883
884
885
886
887
888
889
890
891
892
893
894
895
896
897
898
899
900
901
902
903
904
905
906
907
908
909
910
911
912
913
914
915
916
917
918
919
920
921
922
923
924
925
926
927
928
929
930
931
932
933
934
935
936
937
938
939
940
941
942
943
944
945
946
947
948
949
950
951
952
953
954
955
956
957
958
959
960
961
962
963
964
965
966
967
def __init__(
    self,
    col_name_1: str,
    col_name_2: str,
    regex_extract: str = None,
    set_to_lowercase=False,
    m_probability=None,
    tf_adjustment_column=None,
) -> ComparisonLevel:
    """Represents a comparison level where the columns are reversed.  For example,
    if surname is in the forename field and vice versa

    Args:
        col_name_1 (str): First column, e.g. forename
        col_name_2 (str): Second column, e.g. surname
        regex_extract (str): Regular expression pattern to evaluate a match on.
        set_to_lowercase (bool): If True, sets all entries to lowercase.
        m_probability (float, optional): Starting value for m probability.
            Defaults to None.
        tf_adjustment_column (str, optional): Column to use for term frequency
            adjustments if an exact match is observed. Defaults to None.

    Examples:
        === ":simple-duckdb: DuckDB"
            Comparison level on first_name and surname columns reversed
            ``` python
            import splink.duckdb.comparison_level_library as cll
            cll.columns_reversed_level("first_name", "surname")
            ```
            Comparison level on first_name and surname column reversed
            on a substring of each column as determined by a regular expression.
            ``` python
            import splink.duckdb.comparison_level_library as cll
            cll.columns_reversed_level("first_name",
                                       "surname",
                                       regex_extract="^[A-Z]{1,4}")
            ```
        === ":simple-apachespark: Spark"
            Comparison level on first_name and surname columns reversed
            ``` python
            import splink.spark.comparison_level_library as cll
            cll.columns_reversed_level("first_name", "surname")
            ```
            Comparison level on first_name and surname column reversed
            on a substring of each column as determined by a regular expression.
            ``` python
            import splink.spark.comparison_level_library as cll
            cll.columns_reversed_level("first_name",
                                       "surname",
                                       regex_extract="^[A-Z]{1,4}")
            ```
        === ":simple-amazonaws: Athena"
            Comparison level on first_name and surname columns reversed
            ``` python
            import splink.athena.comparison_level_library as cll
            cll.columns_reversed_level("first_name", "surname")
            ```
            Comparison level on first_name and surname column reversed
            on a substring of each column as determined by a regular expression.
            ``` python
            import splink.athena.comparison_level_library as cll
            cll.columns_reversed_level("first_name",
                                       "surname",
                                       regex_extract="^[A-Z]{1,4}")
            ```
        === ":simple-sqlite: SQLite"
            Comparison level on first_name and surname columns reversed
            ``` python
            import splink.sqlite.comparison_level_library as cll
            cll.columns_reversed_level("first_name", "surname")
            ```
        === ":simple-postgresql: PostgreSql"
            ``` python
            import splink.postgres.comparison_level_library as cll
            cll.columns_reversed_level("first_name", "surname")
            ```


    Returns:
        ComparisonLevel: A comparison level that evaluates the exact match of two
            columns.
    """

    col_1 = InputColumn(col_name_1, sql_dialect=self._sql_dialect)
    col_2 = InputColumn(col_name_2, sql_dialect=self._sql_dialect)

    col_1_l, col_1_r = col_1.name_l, col_1.name_r
    col_2_l, col_2_r = col_2.name_l, col_2.name_r

    if set_to_lowercase:
        col_1_l = f"lower({col_1_l})"
        col_1_r = f"lower({col_1_r})"
        col_2_l = f"lower({col_2_l})"
        col_2_r = f"lower({col_2_r})"

    if regex_extract:
        col_1_l = self._regex_extract_function(col_1_l, regex_extract)
        col_1_r = self._regex_extract_function(col_1_r, regex_extract)
        col_2_l = self._regex_extract_function(col_2_l, regex_extract)
        col_2_r = self._regex_extract_function(col_2_r, regex_extract)

    s = f"{col_1_l} = {col_2_r} and " f"{col_1_r} = {col_2_l}"
    level_dict = {
        "sql_condition": s,
        "label_for_charts": "Exact match on reversed cols",
    }
    if m_probability:
        level_dict["m_probability"] = m_probability

    if tf_adjustment_column:
        level_dict["tf_adjustment_column"] = tf_adjustment_column

    super().__init__(level_dict, sql_dialect=self._sql_dialect)

Bases: ComparisonLevel

Source code in splink/comparison_level_library.py 
 970
 971
 972
 973
 974
 975
 976
 977
 978
 979
 980
 981
 982
 983
 984
 985
 986
 987
 988
 989
 990
 991
 992
 993
 994
 995
 996
 997
 998
 999
1000
1001
1002
1003
1004
1005
1006
1007
1008
1009
1010
1011
1012
1013
1014
1015
1016
1017
1018
1019
1020
1021
1022
1023
1024
1025
1026
1027
1028
1029
1030
1031
1032
1033
1034
1035
1036
1037
1038
1039
1040
1041
1042
1043
1044
1045
1046
1047
1048
1049
1050
1051
1052
1053
1054
class DistanceInKMLevelBase(ComparisonLevel):
    def __init__(
        self,
        lat_col: str,
        long_col: str,
        km_threshold: int | float,
        not_null: bool = False,
        m_probability=None,
    ) -> ComparisonLevel:
        """Use the haversine formula to transform comparisons of lat,lngs
        into distances measured in kilometers

        Arguments:
            lat_col (str): The name of a latitude column or the respective array
                or struct column column containing the information
                For example: long_lat['lat'] or long_lat[0]
            long_col (str): The name of a longitudinal column or the respective array
                or struct column column containing the information, plus an index.
                For example: long_lat['long'] or long_lat[1]
            km_threshold (int): The total distance in kilometers to evaluate your
                comparisons against
            not_null (bool): If true, remove any . This is only necessary if you are not
                capturing nulls elsewhere in your comparison level.
            m_probability (float, optional): Starting value for m probability.
                Defaults to None.

        Examples:
            === ":simple-duckdb: DuckDB"
                ``` python
                import splink.duckdb.comparison_level_library as cll
                cll.distance_in_km_level("lat_col",
                                        "long_col",
                                        km_threshold=5)
                ```
            === ":simple-apachespark: Spark"
                ``` python
                import splink.spark.comparison_level_library as cll
                cll.distance_in_km_level("lat_col",
                                        "long_col",
                                        km_threshold=5)
                ```
            === ":simple-amazonaws: Athena"
                ``` python
                import splink.athena.comparison_level_library as cll
                cll.distance_in_km_level("lat_col",
                                        "long_col",
                                        km_threshold=5)
                ```
            === ":simple-postgresql: PostgreSql"
                ``` python
                import splink.postgres.comparison_level_library as cll
                cll.distance_in_km_level("lat_col",
                                        "long_col",
                                        km_threshold=5)
                ```

        Returns:
            ComparisonLevel: A comparison level that evaluates the distance between
                two coordinates
        """

        lat = InputColumn(lat_col, sql_dialect=self._sql_dialect)
        long = InputColumn(long_col, sql_dialect=self._sql_dialect)
        lat_l, lat_r = lat.names_l_r
        long_l, long_r = long.names_l_r

        distance_km_sql = f"""
        {great_circle_distance_km_sql(lat_l, lat_r, long_l, long_r)} <= {km_threshold}
        """

        if not_null:
            null_sql = " AND ".join(
                [f"{c} is not null" for c in [lat_r, lat_l, long_l, long_r]]
            )
            distance_km_sql = f"({null_sql}) AND {distance_km_sql}"

        level_dict = {
            "sql_condition": distance_km_sql,
            "label_for_charts": f"Distance less than {km_threshold}km",
        }

        if m_probability:
            level_dict["m_probability"] = m_probability

        super().__init__(level_dict, sql_dialect=self._sql_dialect)

__init__(lat_col, long_col, km_threshold, not_null=False, m_probability=None)

Use the haversine formula to transform comparisons of lat,lngs into distances measured in kilometers

Parameters:

Name Type Description Default
lat_col str

The name of a latitude column or the respective array or struct column column containing the information For example: long_lat['lat'] or long_lat[0]

 required
long_col str

The name of a longitudinal column or the respective array or struct column column containing the information, plus an index. For example: long_lat['long'] or long_lat[1]

 required
km_threshold int

The total distance in kilometers to evaluate your comparisons against

 required
not_null bool

If true, remove any . This is only necessary if you are not capturing nulls elsewhere in your comparison level.

 False
m_probability float

Starting value for m probability. Defaults to None.

 None

Examples:

import splink.duckdb.comparison_level_library as cll
cll.distance_in_km_level("lat_col",
                        "long_col",
                        km_threshold=5)

import splink.spark.comparison_level_library as cll
cll.distance_in_km_level("lat_col",
                        "long_col",
                        km_threshold=5)

import splink.athena.comparison_level_library as cll
cll.distance_in_km_level("lat_col",
                        "long_col",
                        km_threshold=5)

import splink.postgres.comparison_level_library as cll
cll.distance_in_km_level("lat_col",
                        "long_col",
                        km_threshold=5)

Returns:

Name Type Description
ComparisonLevel ComparisonLevel

A comparison level that evaluates the distance between two coordinates
Source code in splink/comparison_level_library.py 
 971
 972
 973
 974
 975
 976
 977
 978
 979
 980
 981
 982
 983
 984
 985
 986
 987
 988
 989
 990
 991
 992
 993
 994
 995
 996
 997
 998
 999
1000
1001
1002
1003
1004
1005
1006
1007
1008
1009
1010
1011
1012
1013
1014
1015
1016
1017
1018
1019
1020
1021
1022
1023
1024
1025
1026
1027
1028
1029
1030
1031
1032
1033
1034
1035
1036
1037
1038
1039
1040
1041
1042
1043
1044
1045
1046
1047
1048
1049
1050
1051
1052
1053
1054
def __init__(
    self,
    lat_col: str,
    long_col: str,
    km_threshold: int | float,
    not_null: bool = False,
    m_probability=None,
) -> ComparisonLevel:
    """Use the haversine formula to transform comparisons of lat,lngs
    into distances measured in kilometers

    Arguments:
        lat_col (str): The name of a latitude column or the respective array
            or struct column column containing the information
            For example: long_lat['lat'] or long_lat[0]
        long_col (str): The name of a longitudinal column or the respective array
            or struct column column containing the information, plus an index.
            For example: long_lat['long'] or long_lat[1]
        km_threshold (int): The total distance in kilometers to evaluate your
            comparisons against
        not_null (bool): If true, remove any . This is only necessary if you are not
            capturing nulls elsewhere in your comparison level.
        m_probability (float, optional): Starting value for m probability.
            Defaults to None.

    Examples:
        === ":simple-duckdb: DuckDB"
            ``` python
            import splink.duckdb.comparison_level_library as cll
            cll.distance_in_km_level("lat_col",
                                    "long_col",
                                    km_threshold=5)
            ```
        === ":simple-apachespark: Spark"
            ``` python
            import splink.spark.comparison_level_library as cll
            cll.distance_in_km_level("lat_col",
                                    "long_col",
                                    km_threshold=5)
            ```
        === ":simple-amazonaws: Athena"
            ``` python
            import splink.athena.comparison_level_library as cll
            cll.distance_in_km_level("lat_col",
                                    "long_col",
                                    km_threshold=5)
            ```
        === ":simple-postgresql: PostgreSql"
            ``` python
            import splink.postgres.comparison_level_library as cll
            cll.distance_in_km_level("lat_col",
                                    "long_col",
                                    km_threshold=5)
            ```

    Returns:
        ComparisonLevel: A comparison level that evaluates the distance between
            two coordinates
    """

    lat = InputColumn(lat_col, sql_dialect=self._sql_dialect)
    long = InputColumn(long_col, sql_dialect=self._sql_dialect)
    lat_l, lat_r = lat.names_l_r
    long_l, long_r = long.names_l_r

    distance_km_sql = f"""
    {great_circle_distance_km_sql(lat_l, lat_r, long_l, long_r)} <= {km_threshold}
    """

    if not_null:
        null_sql = " AND ".join(
            [f"{c} is not null" for c in [lat_r, lat_l, long_l, long_r]]
        )
        distance_km_sql = f"({null_sql}) AND {distance_km_sql}"

    level_dict = {
        "sql_condition": distance_km_sql,
        "label_for_charts": f"Distance less than {km_threshold}km",
    }

    if m_probability:
        level_dict["m_probability"] = m_probability

    super().__init__(level_dict, sql_dialect=self._sql_dialect)

Bases: ComparisonLevel

Source code in splink/comparison_level_library.py 
1057
1058
1059
1060
1061
1062
1063
1064
1065
1066
1067
1068
1069
1070
1071
1072
1073
1074
1075
1076
1077
1078
1079
1080
1081
1082
1083
1084
1085
1086
1087
1088
1089
1090
1091
1092
1093
1094
1095
1096
1097
1098
1099
1100
1101
1102
1103
1104
1105
1106
1107
1108
1109
1110
1111
1112
1113
1114
1115
1116
1117
1118
1119
1120
1121
1122
1123
1124
1125
1126
class PercentageDifferenceLevelBase(ComparisonLevel):
    def __init__(
        self,
        col_name: str,
        percentage_distance_threshold: float,
        m_probability=None,
    ) -> ComparisonLevel:
        """Represents a comparison level based around the percentage difference between
        two numbers.

        Note: the percentage is calculated by dividing the absolute difference between
        the values by the largest value

        Args:
            col_name (str): Input column name
            percentage_distance_threshold (float): Percentage difference threshold for
                the comparison level
            m_probability (float, optional): Starting value for m probability. Defaults
                to None.

        Examples:
            === ":simple-duckdb: DuckDB"
                ``` python
                import splink.duckdb.comparison_level_library as cll
                cll.percentage_difference_level("value", 0.5)
                ```
            === ":simple-apachespark: Spark"
                ``` python
                import splink.spark.comparison_level_library as cll
                cll.percentage_difference_level("value", 0.5)
                ```
            === ":simple-amazonaws: Athena"
                ``` python
                import splink.athena.comparison_level_library as cll
                cll.percentage_difference_level("value", 0.5)
                ```
            === ":simple-sqlite: SQLite"
                ``` python
                import splink.sqlite.comparison_level_library as cll
                cll.percentage_difference_level("value", 0.5)
                ```
            === ":simple-postgresql: PostgreSql"
                ``` python
                import splink.postgres.comparison_level_library as cll
                cll.percentage_difference_level("value", 0.5)
                ```

        Returns:
            ComparisonLevel: A comparison level that evaluates the percentage difference
                between two values

        """
        col = InputColumn(col_name, sql_dialect=self._sql_dialect)

        s = f"""(abs({col.name_l} - {col.name_r})/
            (case
                when {col.name_r} > {col.name_l}
                then {col.name_r}
                else {col.name_l}
            end))
            < {percentage_distance_threshold}"""

        level_dict = {
            "sql_condition": s,
            "label_for_charts": f"< {percentage_distance_threshold:,.2%} diff",
        }
        if m_probability:
            level_dict["m_probability"] = m_probability

        super().__init__(level_dict, sql_dialect=self._sql_dialect)

__init__(col_name, percentage_distance_threshold, m_probability=None)

Represents a comparison level based around the percentage difference between two numbers.

Note: the percentage is calculated by dividing the absolute difference between the values by the largest value

Parameters:

Name Type Description Default
col_name str

Input column name

 required
percentage_distance_threshold float

Percentage difference threshold for the comparison level

 required
m_probability float

Starting value for m probability. Defaults to None.

 None

Examples:

import splink.duckdb.comparison_level_library as cll
cll.percentage_difference_level("value", 0.5)

import splink.spark.comparison_level_library as cll
cll.percentage_difference_level("value", 0.5)

import splink.athena.comparison_level_library as cll
cll.percentage_difference_level("value", 0.5)

import splink.sqlite.comparison_level_library as cll
cll.percentage_difference_level("value", 0.5)

import splink.postgres.comparison_level_library as cll
cll.percentage_difference_level("value", 0.5)

Returns:

Name Type Description
ComparisonLevel ComparisonLevel

A comparison level that evaluates the percentage difference between two values
Source code in splink/comparison_level_library.py 
1058
1059
1060
1061
1062
1063
1064
1065
1066
1067
1068
1069
1070
1071
1072
1073
1074
1075
1076
1077
1078
1079
1080
1081
1082
1083
1084
1085
1086
1087
1088
1089
1090
1091
1092
1093
1094
1095
1096
1097
1098
1099
1100
1101
1102
1103
1104
1105
1106
1107
1108
1109
1110
1111
1112
1113
1114
1115
1116
1117
1118
1119
1120
1121
1122
1123
1124
1125
1126
def __init__(
    self,
    col_name: str,
    percentage_distance_threshold: float,
    m_probability=None,
) -> ComparisonLevel:
    """Represents a comparison level based around the percentage difference between
    two numbers.

    Note: the percentage is calculated by dividing the absolute difference between
    the values by the largest value

    Args:
        col_name (str): Input column name
        percentage_distance_threshold (float): Percentage difference threshold for
            the comparison level
        m_probability (float, optional): Starting value for m probability. Defaults
            to None.

    Examples:
        === ":simple-duckdb: DuckDB"
            ``` python
            import splink.duckdb.comparison_level_library as cll
            cll.percentage_difference_level("value", 0.5)
            ```
        === ":simple-apachespark: Spark"
            ``` python
            import splink.spark.comparison_level_library as cll
            cll.percentage_difference_level("value", 0.5)
            ```
        === ":simple-amazonaws: Athena"
            ``` python
            import splink.athena.comparison_level_library as cll
            cll.percentage_difference_level("value", 0.5)
            ```
        === ":simple-sqlite: SQLite"
            ``` python
            import splink.sqlite.comparison_level_library as cll
            cll.percentage_difference_level("value", 0.5)
            ```
        === ":simple-postgresql: PostgreSql"
            ``` python
            import splink.postgres.comparison_level_library as cll
            cll.percentage_difference_level("value", 0.5)
            ```

    Returns:
        ComparisonLevel: A comparison level that evaluates the percentage difference
            between two values

    """
    col = InputColumn(col_name, sql_dialect=self._sql_dialect)

    s = f"""(abs({col.name_l} - {col.name_r})/
        (case
            when {col.name_r} > {col.name_l}
            then {col.name_r}
            else {col.name_l}
        end))
        < {percentage_distance_threshold}"""

    level_dict = {
        "sql_condition": s,
        "label_for_charts": f"< {percentage_distance_threshold:,.2%} diff",
    }
    if m_probability:
        level_dict["m_probability"] = m_probability

    super().__init__(level_dict, sql_dialect=self._sql_dialect)

Bases: ComparisonLevel

Source code in splink/comparison_level_library.py 
1129
1130
1131
1132
1133
1134
1135
1136
1137
1138
1139
1140
1141
1142
1143
1144
1145
1146
1147
1148
1149
1150
1151
1152
1153
1154
1155
1156
1157
1158
1159
1160
1161
1162
1163
1164
1165
1166
1167
1168
1169
1170
1171
1172
1173
1174
1175
1176
1177
1178
1179
1180
1181
1182
1183
1184
1185
1186
1187
1188
1189
1190
1191
1192
1193
1194
1195
1196
1197
1198
1199
1200
1201
1202
1203
class ArrayIntersectLevelBase(ComparisonLevel):
    def __init__(
        self,
        col_name,
        m_probability=None,
        term_frequency_adjustments=False,
        min_intersection=1,
        include_colname_in_charts_label=False,
    ) -> ComparisonLevel:
        """Represents a comparison level based around the size of an intersection of
        arrays

        Args:
            col_name (str): Input column name
            m_probability (float, optional): Starting value for m probability. Defaults
                to None.
            term_frequency_adjustments (bool, optional): If True, apply term frequency
                adjustments to the exact match level. Defaults to False.
            min_intersection (int, optional): The minimum cardinality of the
                intersection of arrays for this comparison level. Defaults to 1
            include_colname_in_charts_label (bool, optional): Should the charts label
                contain the column name? Defaults to False

        Examples:
            === ":simple-duckdb: DuckDB"
                ``` python
                import splink.duckdb.comparison_level_library as cll
                cll.array_intersect_level("name")
                ```
            === ":simple-apachespark: Spark"
                ``` python
                import splink.spark.comparison_level_library as cll
                cll.array_intersect_level("name")
                ```
            === ":simple-amazonaws: Athena"
                ``` python
                import splink.athena.comparison_level_library as cll
                cll.array_intersect_level("name")
                ```
            === ":simple-postgresql: PostgreSql"
                ``` python
                import splink.postgres.comparison_level_library as cll
                cll.array_intersect_level("name")
                ```

        Returns:
            ComparisonLevel: A comparison level that evaluates the size of intersection
                of arrays
        """
        col = InputColumn(col_name, sql_dialect=self._sql_dialect)

        size_array_intersection = (
            f"{self._size_array_intersect_function(col.name_l, col.name_r)}"
        )
        sql = f"{size_array_intersection} >= {min_intersection}"

        label_prefix = (
            f"{col_name} arrays" if include_colname_in_charts_label else "Arrays"
        )
        if min_intersection == 1:
            label = f"{label_prefix} intersect"
        else:
            label = f"{label_prefix} intersect size >= {min_intersection}"

        level_dict = {"sql_condition": sql, "label_for_charts": label}
        if m_probability:
            level_dict["m_probability"] = m_probability
        if term_frequency_adjustments:
            level_dict["tf_adjustment_column"] = col_name

        super().__init__(level_dict, sql_dialect=self._sql_dialect)

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

__init__(col_name, m_probability=None, term_frequency_adjustments=False, min_intersection=1, include_colname_in_charts_label=False)

Represents a comparison level based around the size of an intersection of arrays

Parameters:

Name Type Description Default
col_name str

Input column name

 required
m_probability float

Starting value for m probability. Defaults to None.

 None
term_frequency_adjustments bool

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

 False
min_intersection int

The minimum cardinality of the intersection of arrays for this comparison level. Defaults to 1

 1
include_colname_in_charts_label bool

Should the charts label contain the column name? Defaults to False

 False

Examples:

import splink.duckdb.comparison_level_library as cll
cll.array_intersect_level("name")

import splink.spark.comparison_level_library as cll
cll.array_intersect_level("name")

import splink.athena.comparison_level_library as cll
cll.array_intersect_level("name")

import splink.postgres.comparison_level_library as cll
cll.array_intersect_level("name")

Returns:

Name Type Description
ComparisonLevel ComparisonLevel

A comparison level that evaluates the size of intersection of arrays
Source code in splink/comparison_level_library.py 
1130
1131
1132
1133
1134
1135
1136
1137
1138
1139
1140
1141
1142
1143
1144
1145
1146
1147
1148
1149
1150
1151
1152
1153
1154
1155
1156
1157
1158
1159
1160
1161
1162
1163
1164
1165
1166
1167
1168
1169
1170
1171
1172
1173
1174
1175
1176
1177
1178
1179
1180
1181
1182
1183
1184
1185
1186
1187
1188
1189
1190
1191
1192
1193
1194
1195
1196
1197
1198
1199
def __init__(
    self,
    col_name,
    m_probability=None,
    term_frequency_adjustments=False,
    min_intersection=1,
    include_colname_in_charts_label=False,
) -> ComparisonLevel:
    """Represents a comparison level based around the size of an intersection of
    arrays

    Args:
        col_name (str): Input column name
        m_probability (float, optional): Starting value for m probability. Defaults
            to None.
        term_frequency_adjustments (bool, optional): If True, apply term frequency
            adjustments to the exact match level. Defaults to False.
        min_intersection (int, optional): The minimum cardinality of the
            intersection of arrays for this comparison level. Defaults to 1
        include_colname_in_charts_label (bool, optional): Should the charts label
            contain the column name? Defaults to False

    Examples:
        === ":simple-duckdb: DuckDB"
            ``` python
            import splink.duckdb.comparison_level_library as cll
            cll.array_intersect_level("name")
            ```
        === ":simple-apachespark: Spark"
            ``` python
            import splink.spark.comparison_level_library as cll
            cll.array_intersect_level("name")
            ```
        === ":simple-amazonaws: Athena"
            ``` python
            import splink.athena.comparison_level_library as cll
            cll.array_intersect_level("name")
            ```
        === ":simple-postgresql: PostgreSql"
            ``` python
            import splink.postgres.comparison_level_library as cll
            cll.array_intersect_level("name")
            ```

    Returns:
        ComparisonLevel: A comparison level that evaluates the size of intersection
            of arrays
    """
    col = InputColumn(col_name, sql_dialect=self._sql_dialect)

    size_array_intersection = (
        f"{self._size_array_intersect_function(col.name_l, col.name_r)}"
    )
    sql = f"{size_array_intersection} >= {min_intersection}"

    label_prefix = (
        f"{col_name} arrays" if include_colname_in_charts_label else "Arrays"
    )
    if min_intersection == 1:
        label = f"{label_prefix} intersect"
    else:
        label = f"{label_prefix} intersect size >= {min_intersection}"

    level_dict = {"sql_condition": sql, "label_for_charts": label}
    if m_probability:
        level_dict["m_probability"] = m_probability
    if term_frequency_adjustments:
        level_dict["tf_adjustment_column"] = col_name

    super().__init__(level_dict, sql_dialect=self._sql_dialect)

Bases: ComparisonLevel

Source code in splink/comparison_level_library.py 
1206
1207
1208
1209
1210
1211
1212
1213
1214
1215
1216
1217
1218
1219
1220
1221
1222
1223
1224
1225
1226
1227
1228
1229
1230
1231
1232
1233
1234
1235
1236
1237
1238
1239
1240
1241
1242
1243
1244
1245
1246
1247
1248
1249
1250
1251
1252
1253
1254
1255
1256
1257
1258
1259
1260
1261
1262
1263
1264
1265
1266
1267
1268
1269
1270
1271
1272
1273
1274
1275
1276
1277
1278
1279
1280
1281
1282
1283
1284
1285
1286
1287
1288
1289
1290
1291
1292
1293
1294
1295
1296
1297
1298
1299
1300
1301
1302
1303
1304
1305
1306
1307
1308
1309
1310
1311
1312
1313
1314
1315
1316
1317
1318
1319
1320
1321
1322
1323
1324
1325
1326
1327
1328
1329
1330
1331
1332
1333
1334
1335
1336
1337
1338
1339
1340
1341
1342
1343
1344
1345
1346
1347
1348
1349
1350
1351
1352
1353
1354
1355
1356
1357
1358
1359
1360
1361
1362
1363
1364
1365
1366
1367
1368
1369
1370
1371
1372
1373
1374
1375
1376
1377
1378
1379
1380
1381
1382
1383
1384
1385
1386
1387
1388
class DatediffLevelBase(ComparisonLevel):
    def __init__(
        self,
        date_col: str,
        date_threshold: int,
        date_metric: str = "day",
        m_probability=None,
        cast_strings_to_date=False,
        date_format=None,
    ) -> ComparisonLevel:
        """Represents a comparison level based around the difference between dates
        within a column

        Arguments:
            date_col (str): Input column name
            date_threshold (int): The total difference in time between two given
                dates. This is used in tandem with `date_metric` to determine .
                If you are using `year` as your metric, then a value of 1 would
                require that your dates lie within 1 year of one another.
            date_metric (str): The unit of time with which to measure your
                `date_threshold`.
                Your metric should be one of `day`, `month` or `year`.
                Defaults to `day`.
            m_probability (float, optional): Starting value for m probability.
                Defaults to None.
            cast_strings_to_date (bool, optional): Set to true and adjust
                date_format param when input dates are strings to enable
                date-casting. 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).

        Examples:
            === ":simple-duckdb: DuckDB"
                Date Difference comparison level at threshold 1 year
                ``` python
                import splink.duckdb.comparison_level_library as cll
                cll.datediff_level("date",
                                    date_threshold=1,
                                    date_metric="year"
                                    )
                ```
                Date Difference comparison with date-casting and unspecified
                date_format (default = %Y-%m-%d)
                ``` python
                import splink.duckdb.comparison_level_library as cll
                cll.datediff_level("dob",
                                    date_threshold=3,
                                    date_metric='month',
                                    cast_strings_to_date=True
                                    )
                ```
                Date Difference comparison with date-casting and specified date_format
                ``` python
                import splink.duckdb.comparison_level_library as cll
                cll.datediff_level("dob",
                                    date_threshold=3,
                                    date_metric='month',
                                    cast_strings_to_date=True,
                                    date_format='%d/%m/%Y'
                                    )
                ```
            === ":simple-apachespark: Spark"
                Date Difference comparison level at threshold 1 year
                ``` python
                import splink.spark.comparison_level_library as cll
                cll.datediff_level("date",
                                    date_threshold=1,
                                    date_metric="year"
                                    )
                ```
                Date Difference comparison with date-casting and unspecified
                date_format (default = %Y-%m-%d)
                ``` python
                import splink.spark.comparison_level_library as cll
                cll.datediff_level("dob",
                                    date_threshold=3,
                                    date_metric='month',
                                    cast_strings_to_date=True
                                    )
                ```
                Date Difference comparison with date-casting and specified date_format
                ``` python
                import splink.spark.comparison_level_library as cll
                cll.datediff_level("dob",
                                    date_threshold=3,
                                    date_metric='month',
                                    cast_strings_to_date=True,
                                    date_format='%d/%m/%Y'
                                    )
                ```
            === ":simple-amazonaws: Athena"
                Date Difference comparison level at threshold 1 year
                ``` python
                import splink.athena.comparison_level_library as cll
                cll.datediff_level("date",
                                    date_threshold=1,
                                    date_metric="year"
                                    )
                ```
                Date Difference comparison with date-casting and unspecified
                date_format (default = %Y-%m-%d)
                ``` python
                import splink.athena.comparison_level_library as cll
                cll.datediff_level("dob",
                                    date_threshold=3,
                                    date_metric='month',
                                    cast_strings_to_date=True
                                    )
                ```
                Date Difference comparison with date-casting and specified date_format
                ``` python
                import splink.athena.comparison_level_library as cll
                cll.datediff_level("dob",
                                    date_threshold=3,
                                    date_metric='month',
                                    cast_strings_to_date=True,
                                    date_format='%d/%m/%Y'
                                    )
                ```
            === ":simple-postgresql: PostgreSql"
                Date Difference comparison level at threshold 1 year
                ``` python
                import splink.postgres.comparison_level_library as cll
                cll.datediff_level("date",
                                    date_threshold=1,
                                    date_metric="year"
                                    )
                ```
                Date Difference comparison with date-casting and unspecified
                date_format (default = yyyy-MM-dd)
                ``` python
                import splink.postgres.comparison_level_library as cll
                cll.datediff_level("dob",
                                    date_threshold=3,
                                    date_metric='month',
                                    cast_strings_to_date=True
                                    )
                ```
                Date Difference comparison with date-casting and specified date_format
                ``` python
                import splink.postgres.comparison_level_library as cll
                cll.datediff_level("dob",
                                    date_threshold=3,
                                    date_metric='month',
                                    cast_strings_to_date=True,
                                    date_format='dd/MM/yyyy'
                                    )
                ```
        Returns:
            ComparisonLevel: A comparison level that evaluates whether two dates fall
                within a given interval.
        """

        date = InputColumn(date_col, sql_dialect=self._sql_dialect)
        date_l, date_r = date.names_l_r

        datediff_sql = self._datediff_function(
            date_l,
            date_r,
            date_threshold,
            date_metric,
            cast_strings_to_date,
            date_format,
        )
        label = f"Within {date_threshold} {date_metric}"
        if date_threshold > 1:
            label += "s"

        level_dict = {
            "sql_condition": datediff_sql,
            "label_for_charts": label,
        }

        if m_probability:
            level_dict["m_probability"] = m_probability

        super().__init__(level_dict, sql_dialect=self._sql_dialect)

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

__init__(date_col, date_threshold, date_metric='day', m_probability=None, cast_strings_to_date=False, date_format=None)

Represents a comparison level based around the difference between dates within a column

Parameters:

Name Type Description Default
date_col str

Input column name

 required
date_threshold int

The total difference in time between two given dates. This is used in tandem with date_metric to determine . If you are using year as your metric, then a value of 1 would require that your dates lie within 1 year of one another.

 required
date_metric str

The unit of time with which to measure your date_threshold. Your metric should be one of day, month or year. Defaults to day.

 'day'
m_probability float

Starting value for m probability. Defaults to None.

 None
cast_strings_to_date bool

Set to true and adjust date_format param when input dates are strings to enable date-casting. Defaults to False.

 False
date_format str

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).

 None

Examples:

Date Difference comparison level at threshold 1 year 

import splink.duckdb.comparison_level_library as cll
cll.datediff_level("date",
                    date_threshold=1,
                    date_metric="year"
                    )
Date Difference comparison with date-casting and unspecified date_format (default = %Y-%m-%d) 
import splink.duckdb.comparison_level_library as cll
cll.datediff_level("dob",
                    date_threshold=3,
                    date_metric='month',
                    cast_strings_to_date=True
                    )
Date Difference comparison with date-casting and specified date_format 
import splink.duckdb.comparison_level_library as cll
cll.datediff_level("dob",
                    date_threshold=3,
                    date_metric='month',
                    cast_strings_to_date=True,
                    date_format='%d/%m/%Y'
                    )

Date Difference comparison level at threshold 1 year 

import splink.spark.comparison_level_library as cll
cll.datediff_level("date",
                    date_threshold=1,
                    date_metric="year"
                    )
Date Difference comparison with date-casting and unspecified date_format (default = %Y-%m-%d) 
import splink.spark.comparison_level_library as cll
cll.datediff_level("dob",
                    date_threshold=3,
                    date_metric='month',
                    cast_strings_to_date=True
                    )
Date Difference comparison with date-casting and specified date_format 
import splink.spark.comparison_level_library as cll
cll.datediff_level("dob",
                    date_threshold=3,
                    date_metric='month',
                    cast_strings_to_date=True,
                    date_format='%d/%m/%Y'
                    )

Date Difference comparison level at threshold 1 year 

import splink.athena.comparison_level_library as cll
cll.datediff_level("date",
                    date_threshold=1,
                    date_metric="year"
                    )
Date Difference comparison with date-casting and unspecified date_format (default = %Y-%m-%d) 
import splink.athena.comparison_level_library as cll
cll.datediff_level("dob",
                    date_threshold=3,
                    date_metric='month',
                    cast_strings_to_date=True
                    )
Date Difference comparison with date-casting and specified date_format 
import splink.athena.comparison_level_library as cll
cll.datediff_level("dob",
                    date_threshold=3,
                    date_metric='month',
                    cast_strings_to_date=True,
                    date_format='%d/%m/%Y'
                    )

Date Difference comparison level at threshold 1 year 

import splink.postgres.comparison_level_library as cll
cll.datediff_level("date",
                    date_threshold=1,
                    date_metric="year"
                    )
Date Difference comparison with date-casting and unspecified date_format (default = yyyy-MM-dd) 
import splink.postgres.comparison_level_library as cll
cll.datediff_level("dob",
                    date_threshold=3,
                    date_metric='month',
                    cast_strings_to_date=True
                    )
Date Difference comparison with date-casting and specified date_format 
import splink.postgres.comparison_level_library as cll
cll.datediff_level("dob",
                    date_threshold=3,
                    date_metric='month',
                    cast_strings_to_date=True,
                    date_format='dd/MM/yyyy'
                    )
Source code in splink/comparison_level_library.py 
1207
1208
1209
1210
1211
1212
1213
1214
1215
1216
1217
1218
1219
1220
1221
1222
1223
1224
1225
1226
1227
1228
1229
1230
1231
1232
1233
1234
1235
1236
1237
1238
1239
1240
1241
1242
1243
1244
1245
1246
1247
1248
1249
1250
1251
1252
1253
1254
1255
1256
1257
1258
1259
1260
1261
1262
1263
1264
1265
1266
1267
1268
1269
1270
1271
1272
1273
1274
1275
1276
1277
1278
1279
1280
1281
1282
1283
1284
1285
1286
1287
1288
1289
1290
1291
1292
1293
1294
1295
1296
1297
1298
1299
1300
1301
1302
1303
1304
1305
1306
1307
1308
1309
1310
1311
1312
1313
1314
1315
1316
1317
1318
1319
1320
1321
1322
1323
1324
1325
1326
1327
1328
1329
1330
1331
1332
1333
1334
1335
1336
1337
1338
1339
1340
1341
1342
1343
1344
1345
1346
1347
1348
1349
1350
1351
1352
1353
1354
1355
1356
1357
1358
1359
1360
1361
1362
1363
1364
1365
1366
1367
1368
1369
1370
1371
1372
1373
1374
1375
1376
1377
1378
1379
1380
1381
1382
1383
1384
def __init__(
    self,
    date_col: str,
    date_threshold: int,
    date_metric: str = "day",
    m_probability=None,
    cast_strings_to_date=False,
    date_format=None,
) -> ComparisonLevel:
    """Represents a comparison level based around the difference between dates
    within a column

    Arguments:
        date_col (str): Input column name
        date_threshold (int): The total difference in time between two given
            dates. This is used in tandem with `date_metric` to determine .
            If you are using `year` as your metric, then a value of 1 would
            require that your dates lie within 1 year of one another.
        date_metric (str): The unit of time with which to measure your
            `date_threshold`.
            Your metric should be one of `day`, `month` or `year`.
            Defaults to `day`.
        m_probability (float, optional): Starting value for m probability.
            Defaults to None.
        cast_strings_to_date (bool, optional): Set to true and adjust
            date_format param when input dates are strings to enable
            date-casting. 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).

    Examples:
        === ":simple-duckdb: DuckDB"
            Date Difference comparison level at threshold 1 year
            ``` python
            import splink.duckdb.comparison_level_library as cll
            cll.datediff_level("date",
                                date_threshold=1,
                                date_metric="year"
                                )
            ```
            Date Difference comparison with date-casting and unspecified
            date_format (default = %Y-%m-%d)
            ``` python
            import splink.duckdb.comparison_level_library as cll
            cll.datediff_level("dob",
                                date_threshold=3,
                                date_metric='month',
                                cast_strings_to_date=True
                                )
            ```
            Date Difference comparison with date-casting and specified date_format
            ``` python
            import splink.duckdb.comparison_level_library as cll
            cll.datediff_level("dob",
                                date_threshold=3,
                                date_metric='month',
                                cast_strings_to_date=True,
                                date_format='%d/%m/%Y'
                                )
            ```
        === ":simple-apachespark: Spark"
            Date Difference comparison level at threshold 1 year
            ``` python
            import splink.spark.comparison_level_library as cll
            cll.datediff_level("date",
                                date_threshold=1,
                                date_metric="year"
                                )
            ```
            Date Difference comparison with date-casting and unspecified
            date_format (default = %Y-%m-%d)
            ``` python
            import splink.spark.comparison_level_library as cll
            cll.datediff_level("dob",
                                date_threshold=3,
                                date_metric='month',
                                cast_strings_to_date=True
                                )
            ```
            Date Difference comparison with date-casting and specified date_format
            ``` python
            import splink.spark.comparison_level_library as cll
            cll.datediff_level("dob",
                                date_threshold=3,
                                date_metric='month',
                                cast_strings_to_date=True,
                                date_format='%d/%m/%Y'
                                )
            ```
        === ":simple-amazonaws: Athena"
            Date Difference comparison level at threshold 1 year
            ``` python
            import splink.athena.comparison_level_library as cll
            cll.datediff_level("date",
                                date_threshold=1,
                                date_metric="year"
                                )
            ```
            Date Difference comparison with date-casting and unspecified
            date_format (default = %Y-%m-%d)
            ``` python
            import splink.athena.comparison_level_library as cll
            cll.datediff_level("dob",
                                date_threshold=3,
                                date_metric='month',
                                cast_strings_to_date=True
                                )
            ```
            Date Difference comparison with date-casting and specified date_format
            ``` python
            import splink.athena.comparison_level_library as cll
            cll.datediff_level("dob",
                                date_threshold=3,
                                date_metric='month',
                                cast_strings_to_date=True,
                                date_format='%d/%m/%Y'
                                )
            ```
        === ":simple-postgresql: PostgreSql"
            Date Difference comparison level at threshold 1 year
            ``` python
            import splink.postgres.comparison_level_library as cll
            cll.datediff_level("date",
                                date_threshold=1,
                                date_metric="year"
                                )
            ```
            Date Difference comparison with date-casting and unspecified
            date_format (default = yyyy-MM-dd)
            ``` python
            import splink.postgres.comparison_level_library as cll
            cll.datediff_level("dob",
                                date_threshold=3,
                                date_metric='month',
                                cast_strings_to_date=True
                                )
            ```
            Date Difference comparison with date-casting and specified date_format
            ``` python
            import splink.postgres.comparison_level_library as cll
            cll.datediff_level("dob",
                                date_threshold=3,
                                date_metric='month',
                                cast_strings_to_date=True,
                                date_format='dd/MM/yyyy'
                                )
            ```
    Returns:
        ComparisonLevel: A comparison level that evaluates whether two dates fall
            within a given interval.
    """

    date = InputColumn(date_col, sql_dialect=self._sql_dialect)
    date_l, date_r = date.names_l_r

    datediff_sql = self._datediff_function(
        date_l,
        date_r,
        date_threshold,
        date_metric,
        cast_strings_to_date,
        date_format,
    )
    label = f"Within {date_threshold} {date_metric}"
    if date_threshold > 1:
        label += "s"

    level_dict = {
        "sql_condition": datediff_sql,
        "label_for_charts": label,
    }

    if m_probability:
        level_dict["m_probability"] = m_probability

    super().__init__(level_dict, sql_dialect=self._sql_dialect)