# 4.4 `difflib` -- Helpers for computing deltas

New in version 2.1.

`get_close_matches`(word, possibilities[, n[, cutoff]])
  Return a list of the best ``good enough'' matches. word is a sequence for which close matches are desired (typically a string), and possibilities is a list of sequences against which to match word (typically a list of strings).

  Optional argument n (default `3`) is the maximum number of close matches to return; n must be greater than `0`.

  Optional argument cutoff (default `0.6`) is a float in the range [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
        >>> get_close_matches('wheel', keyword.kwlist)
        ['while']
        >>> get_close_matches('apple', keyword.kwlist)
        []
        >>> get_close_matches('accept', keyword.kwlist)
        ['except']

class `SequenceMatcher`(...)
  This is a flexible class for comparing pairs of sequences of any type, so long as the sequence elements are hashable. The basic algorithm predates, and is a little fancier than, an algorithm published in the late 1980's by Ratcliff and Obershelp under the hyperbolic name ``gestalt pattern matching.'' The idea is to find the longest contiguous matching subsequence that contains no ``junk'' elements (the Ratcliff and Obershelp algorithm doesn't address junk). The same idea is then applied recursively to the pieces of the sequences to the left and to the right of the matching subsequence. This does not yield minimal edit sequences, but does tend to yield matches that ``look right'' to people.

  Timing: The basic Ratcliff-Obershelp algorithm is cubic time in the worst case and quadratic time in the expected case. `SequenceMatcher` is quadratic time for the worst case and has expected-case behavior dependent in a complicated way on how many elements the sequences have in common; best case time is linear.

See Also:

Pattern Matching: The Gestalt Approach
  Discussion of a similar algorithm by John W. Ratcliff and D. E. Metzener. This was published in Dr. Dobb's Journal in July, 1988.

Subsections

- 4.4.1 SequenceMatcher Objects
- 4.4.2 Examples