rapidfuzz.process#

cdist#

rapidfuzz.process.cdist(queries, choices, *, scorer=<cyfunction ratio>, processor=None, score_cutoff=None, dtype=None, workers=1, **kwargs)#

Compute distance/similarity between each pair of the two collections of inputs.

Parameters:
  • queries (Collection[Sequence[Hashable]]) – list of all strings the queries

  • choices (Collection[Sequence[Hashable]]) – list of all strings the query should be compared

  • scorer (Callable, optional) –

    Optional callable that is used to calculate the matching score between the query and each choice. This can be:

    • a scorer using the RapidFuzz C-API like the builtin scorers in RapidFuzz, which can return a distance or similarity between two strings. Further details can be found here.

    • a Python function which returns a similarity between two strings in the range 0-100. This is not recommended, since it is far slower than a scorer using the RapidFuzz C-API.

    fuzz.ratio is used by default.

  • processor (Callable, optional) – Optional callable that is used to preprocess the strings before comparing them. Default is None, which deactivates this behaviour.

  • score_cutoff (Any, optional) – Optional argument for a score threshold. When an edit distance is used this represents the maximum edit distance and matches with a distance <= score_cutoff are inserted as -1. When a normalized edit distance is used this represents the minimal similarity and matches with a similarity >= score_cutoff are inserted as 0. Default is None, which deactivates this behaviour.

  • dtype (data-type, optional) –

    The desired data-type for the result array.Depending on the scorer type the following dtypes are supported:

    • similarity: - np.float32, np.float64 - np.uint8 -> stores fixed point representation of the result scaled to a range 0-100

    • distance: - np.int8, np.int16, np.int32, np.int64

    If not given, then the type will be np.float32 for similarities and np.int32 for distances.

  • workers (int, optional) – The calculation is subdivided into workers sections and evaluated in parallel. Supply -1 to use all available CPU cores. This argument is only available for scorers using the RapidFuzz C-API so far, since it releases the Python GIL.

  • **kwargs (Any, optional) – any other named parameters are passed to the scorer. This can be used to pass e.g. weights to string_metric.levenshtein

Returns:

Returns a matrix of dtype with the distance/similarity between each pair of the two collections of inputs.

Return type:

ndarray

extract#

rapidfuzz.process.extract(query, choices, *, scorer=<cyfunction WRatio>, processor=<cyfunction default_process>, limit=5, score_cutoff=None, **kwargs)#

Find the best matches in a list of choices. The list is sorted by the similarity. When multiple choices have the same similarity, they are sorted by their index

Parameters:
  • query (Sequence[Hashable]) – string we want to find

  • choices (Collection[Sequence[Hashable]] | Mapping[Sequence[Hashable]]) – list of all strings the query should be compared with or dict with a mapping {<result>: <string to compare>}

  • scorer (Callable, optional) – Optional callable that is used to calculate the matching score between the query and each choice. This can be any of the scorers included in RapidFuzz (both scorers that calculate the edit distance or the normalized edit distance), or a custom function, which returns a normalized edit distance. fuzz.WRatio is used by default.

  • processor (Callable, optional) – Optional callable that reformats the strings. utils.default_process is used by default, which lowercases the strings and trims whitespace

  • limit (int) – maximum amount of results to return

  • score_cutoff (Any, optional) – Optional argument for a score threshold. When an edit distance is used this represents the maximum edit distance and matches with a distance <= score_cutoff are ignored. When a normalized edit distance is used this represents the minimal similarity and matches with a similarity >= score_cutoff are ignored. For edit distances this defaults to -1, while for normalized edit distances this defaults to 0.0, which deactivates this behaviour.

  • **kwargs (Any, optional) – any other named parameters are passed to the scorer. This can be used to pass e.g. weights to string_metric.levenshtein

Returns:

The return type is always a List of Tuples with 3 elements. However the values stored in the tuple depend on the types of the input arguments.

  • The first element is always the choice, which is the value thats compared to the query.

  • The second value represents the similarity calculated by the scorer. This can be:

    • An edit distance (distance is 0 for a perfect match and > 0 for non perfect matches). In this case only choices which have a distance <= max are returned. An example of a scorer with this behavior is string_metric.levenshtein.

    • A normalized edit distance (similarity is a score between 0 and 100, with 100 being a perfect match). In this case only choices which have a similarity >= score_cutoff are returned. An example of a scorer with this behavior is string_metric.normalized_levenshtein.

    Note, that for all scorers, which are not provided by RapidFuzz, only normalized edit distances are supported.

  • The third parameter depends on the type of the choices argument it is:

    • The index of choice when choices is a simple iterable like a list

    • The key of choice when choices is a mapping like a dict, or a pandas Series

The list is sorted by score_cutoff or max depending on the scorer used. The first element in the list has the highest similarity/smallest distance.

Return type:

List[Tuple[Sequence[Hashable], Any, Any]]

extract_iter#

rapidfuzz.process.extract_iter(query, choices, *, scorer=<cyfunction WRatio>, processor=<cyfunction default_process>, score_cutoff=None, **kwargs)#

Find the best match in a list of choices

Parameters:
  • query (Sequence[Hashable]) – string we want to find

  • choices (Iterable[Sequence[Hashable]] | Mapping[Sequence[Hashable]]) – list of all strings the query should be compared with or dict with a mapping {<result>: <string to compare>}

  • scorer (Callable, optional) – Optional callable that is used to calculate the matching score between the query and each choice. This can be any of the scorers included in RapidFuzz (both scorers that calculate the edit distance or the normalized edit distance), or a custom function, which returns a normalized edit distance. fuzz.WRatio is used by default.

  • processor (Callable, optional) – Optional callable that reformats the strings. utils.default_process is used by default, which lowercases the strings and trims whitespace

  • score_cutoff (Any, optional) – Optional argument for a score threshold. When an edit distance is used this represents the maximum edit distance and matches with a distance <= score_cutoff are ignored. When a normalized edit distance is used this represents the minimal similarity and matches with a similarity >= score_cutoff are ignored. For edit distances this defaults to -1, while for normalized edit distances this defaults to 0.0, which deactivates this behaviour.

  • **kwargs (Any, optional) – any other named parameters are passed to the scorer. This can be used to pass e.g. weights to string_metric.levenshtein

Yields:

Tuple[Sequence[Hashable], Any, Any] – Yields similarity between the query and each choice in form of a Tuple with 3 elements. The values stored in the tuple depend on the types of the input arguments.

  • The first element is always the current choice, which is the value thats compared to the query.

  • The second value represents the similarity calculated by the scorer. This can be:

    • An edit distance (distance is 0 for a perfect match and > 0 for non perfect matches). In this case only choices which have a distance <= max are yielded. An example of a scorer with this behavior is string_metric.levenshtein.

    • A normalized edit distance (similarity is a score between 0 and 100, with 100 being a perfect match). In this case only choices which have a similarity >= score_cutoff are yielded. An example of a scorer with this behavior is string_metric.normalized_levenshtein.

    Note, that for all scorers, which are not provided by RapidFuzz, only normalized edit distances are supported.

  • The third parameter depends on the type of the choices argument it is:

    • The index of choice when choices is a simple iterable like a list

    • The key of choice when choices is a mapping like a dict, or a pandas Series

extractOne#

rapidfuzz.process.extractOne(query, choices, *, scorer=<cyfunction WRatio>, processor=<cyfunction default_process>, score_cutoff=None, **kwargs)#

Find the best match in a list of choices. When multiple elements have the same similarity, the first element is returned.

Parameters:
  • query (Sequence[Hashable]) – string we want to find

  • choices (Iterable[Sequence[Hashable]] | Mapping[Sequence[Hashable]]) – list of all strings the query should be compared with or dict with a mapping {<result>: <string to compare>}

  • scorer (Callable, optional) – Optional callable that is used to calculate the matching score between the query and each choice. This can be any of the scorers included in RapidFuzz (both scorers that calculate the edit distance or the normalized edit distance), or a custom function, which returns a normalized edit distance. fuzz.WRatio is used by default.

  • processor (Callable, optional) – Optional callable that reformats the strings. utils.default_process is used by default, which lowercases the strings and trims whitespace

  • score_cutoff (Any, optional) – Optional argument for a score threshold. When an edit distance is used this represents the maximum edit distance and matches with a distance <= score_cutoff are ignored. When a normalized edit distance is used this represents the minimal similarity and matches with a similarity >= score_cutoff are ignored. For edit distances this defaults to -1, while for normalized edit distances this defaults to 0.0, which deactivates this behaviour.

  • **kwargs (Any, optional) – any other named parameters are passed to the scorer. This can be used to pass e.g. weights to string_metric.levenshtein

Returns:

  • Tuple[Sequence[Hashable], Any, Any] – Returns the best match in form of a Tuple with 3 elements. The values stored in the tuple depend on the types of the input arguments.

    • The first element is always the choice, which is the value thats compared to the query.

    • The second value represents the similarity calculated by the scorer. This can be:

      • An edit distance (distance is 0 for a perfect match and > 0 for non perfect matches). In this case only choices which have a distance <= score_cutoff are returned. An example of a scorer with this behavior is string_metric.levenshtein.

      • A normalized edit distance (similarity is a score between 0 and 100, with 100 being a perfect match). In this case only choices which have a similarity >= score_cutoff are returned. An example of a scorer with this behavior is string_metric.normalized_levenshtein.

      Note, that for all scorers, which are not provided by RapidFuzz, only normalized edit distances are supported.

    • The third parameter depends on the type of the choices argument it is:

      • The index of choice when choices is a simple iterable like a list

      • The key of choice when choices is a mapping like a dict, or a pandas Series

  • None – When no choice has a similarity >= score_cutoff/distance <= score_cutoff None is returned

Examples

>>> from rapidfuzz.process import extractOne
>>> from rapidfuzz.string_metric import levenshtein, normalized_levenshtein
>>> from rapidfuzz.fuzz import ratio

extractOne can be used with normalized edit distances.

>>> extractOne("abcd", ["abce"], scorer=ratio)
("abcd", 75.0, 1)
>>> extractOne("abcd", ["abce"], scorer=normalized_levenshtein)
("abcd", 75.0, 1)

extractOne can be used with edit distances as well.

>>> extractOne("abcd", ["abce"], scorer=levenshtein)
("abce", 1, 0)

additional settings of the scorer can be passed as keyword arguments to extractOne

>>> extractOne("abcd", ["abce"], scorer=levenshtein, weights=(1,1,2))
("abcde", 2, 1)

when a mapping is used for the choices the key of the choice is returned instead of the List index

>>> extractOne("abcd", {"key": "abce"}, scorer=ratio)
("abcd", 75.0, "key")

By default each string is preprocessed using utils.default_process, which lowercases the strings, replaces non alphanumeric characters with whitespaces and trims whitespaces from start and end of them. This behavior can be changed by passing a custom function, or None to disable the behavior. Preprocessing can take a significant part of the runtime, so it makes sense to disable it, when it is not required.

>>> extractOne("abcd", ["abdD"], scorer=ratio)
("abcD", 100.0, 0)
>>> extractOne("abcd", ["abdD"], scorer=ratio, processor=None)
("abcD", 75.0, 0)
>>> extractOne("abcd", ["abdD"], scorer=ratio, processor=lambda s: s.upper())
("abcD", 100.0, 0)

When only results with a similarity above a certain threshold are relevant, the parameter score_cutoff can be used to filter out results with a lower similarity. This threshold is used by some of the scorers to exit early, when they are sure, that the similarity is below the threshold. For normalized edit distances all results with a similarity below score_cutoff are filtered out

>>> extractOne("abcd", ["abce"], scorer=ratio)
("abce", 75.0, 0)
>>> extractOne("abcd", ["abce"], scorer=ratio, score_cutoff=80)
None

For edit distances all results with an edit distance above the score_cutoff are filtered out

>>> extractOne("abcd", ["abce"], scorer=levenshtein, weights=(1,1,2))
("abce", 2, 0)
>>> extractOne("abcd", ["abce"], scorer=levenshtein, weights=(1,1,2), score_cutoff=1)
None