difflib Namespace Reference

Data Structures
class	SequenceMatcher
class	Differ

Functions
def	get_close_matches
def	IS_LINE_JUNK
def	IS_CHARACTER_JUNK
def	ndiff
def	restore

Variables
list	__all__

Function Documentation

def difflib.get_close_matches	(		word,
			possibilities,
			n = 3,
			cutoff = 0.6
	)

Use SequenceMatcher to return list of the best "good enough" matches.

word is a sequence for which close matches are desired (typically a
string).

possibilities is a list of sequences against which to match word
(typically a list of strings).

Optional arg n (default 3) is the maximum number of close matches to
return.  n must be > 0.

Optional arg cutoff (default 0.6) is a float in [0, 1].  Possibilities
that don't score at least that similar to word are ignored.

The best (no more than n) matches among the possibilities are returned
in a list, sorted by similarity score, most similar first.

>>> get_close_matches("appel", ["ape", "apple", "peach", "puppy"])
['apple', 'ape']
>>> import keyword as _keyword
>>> get_close_matches("wheel", _keyword.kwlist)
['while']
>>> get_close_matches("apple", _keyword.kwlist)
[]
>>> get_close_matches("accept", _keyword.kwlist)
['except']

Definition at line 571 of file difflib.py.

def get_close_matches(word, possibilities, n=3, cutoff=0.6):
    """Use SequenceMatcher to return list of the best "good enough" matches.

    word is a sequence for which close matches are desired (typically a
    string).

    possibilities is a list of sequences against which to match word
    (typically a list of strings).

    Optional arg n (default 3) is the maximum number of close matches to
    return.  n must be > 0.

    Optional arg cutoff (default 0.6) is a float in [0, 1].  Possibilities
    that don't score at least that similar to word are ignored.

    The best (no more than n) matches among the possibilities are returned
    in a list, sorted by similarity score, most similar first.

    >>> get_close_matches("appel", ["ape", "apple", "peach", "puppy"])
    ['apple', 'ape']
    >>> import keyword as _keyword
    >>> get_close_matches("wheel", _keyword.kwlist)
    ['while']
    >>> get_close_matches("apple", _keyword.kwlist)
    []
    >>> get_close_matches("accept", _keyword.kwlist)
    ['except']
    """

    if not n >  0:
        raise ValueError("n must be > 0: " + `n`)
    if not 0.0 <= cutoff <= 1.0:
        raise ValueError("cutoff must be in [0.0, 1.0]: " + `cutoff`)
    result = []
    s = SequenceMatcher()
    s.set_seq2(word)
    for x in possibilities:
        s.set_seq1(x)
        if s.real_quick_ratio() >= cutoff and \
           s.quick_ratio() >= cutoff and \
           s.ratio() >= cutoff:
            result.append((s.ratio(), x))
    # Sort by score.
    result.sort()
    # Retain only the best n.
    result = result[-n:]
    # Move best-scorer to head of list.
    result.reverse()
    # Strip scores.
    return [x for score, x in result]