4.4 difflib -- Helpers for computing deltas
Description: difflib -- Helpers for computing deltas
Keywords: lib

# 4.4 `difflib` -- Helpers for computing deltas

New in version 2.1.

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.

class `Differ`
  This is a class for comparing sequences of lines of text, and producing human-readable differences or deltas. Differ uses `SequenceMatcher` both to compare sequences of lines, and to compare sequences of characters within similar (near-matching) lines.

  Each line of a `Differ` delta begins with a two-letter code:

  Code | Meaning
  --- | ---
  `'- '` | line unique to sequence 1
  `'+ '` | line unique to sequence 2
  `' '` | line common to both sequences
  `'? '` | line not present in either input sequence

  Lines beginning with ``?` ' attempt to guide the eye to intraline differences, and were not present in either input sequence. These lines can be confusing if the sequences contain tab characters.

`context_diff`( | a, b[, fromfile[, tofile [, fromfiledate[, tofiledate[, n [, lineterm]]]]]])

  Compare a and b (lists of strings); return a delta (a generator generating the delta lines) in context diff format.

  Context diffs are a compact way of showing just the lines that have changed plus a few lines of context. The changes are shown in a before/after style. The number of context lines is set by n which defaults to three.

  By default, the diff control lines (those with `***` or `---`) are created with a trailing newline. This is helpful so that inputs created from `file.readlines()` result in diffs that are suitable for use with `file.writelines()` since both the inputs and outputs have trailing newlines.

  For inputs that do not have trailing newlines, set the lineterm argument to `""` so that the output will be uniformly newline free.

  The context diff format normally has a header for filenames and modification times. Any or all of these may be specified using strings for fromfile, tofile, fromfiledate, and tofiledate. The modification times are normally expressed in the format returned by `time.ctime()`. If not specified, the strings default to blanks.

  Tools/scripts/diff.py is a command-line front-end for this function.

  New in version 2.3.

`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']

`ndiff`( | a, b[, linejunk[, charjunk]])

  Compare a and b (lists of strings); return a `Differ`-style delta (a generator generating the delta lines).

  Optional keyword parameters linejunk and charjunk are for filter functions (or `None`):

  linejunk: A function that accepts a single string argument, and returns true if the string is junk, or false if not. The default is (`None`), starting with Python 2.3. Before then, the default was the module-level function `IS_LINE_JUNK()`, which filters out lines without visible characters, except for at most one pound character ("`#`"). As of Python 2.3, the underlying `SequenceMatcher` class does a dynamic analysis of which lines are so frequent as to constitute noise, and this usually works better than the pre-2.3 default.

  charjunk: A function that accepts a character (a string of length 1), and returns if the character is junk, or false if not. The default is module-level function `IS_CHARACTER_JUNK()`, which filters out whitespace characters (a blank or tab; note: bad idea to include newline in this!).

  Tools/scripts/ndiff.py is a command-line front-end to this function.

      >>> diff = ndiff('one\ntwo\nthree\n'.splitlines(1),
      ...              'ore\ntree\nemu\n'.splitlines(1))
      >>> print ''.join(diff),
      - one
      ?  ^
      + ore
      ?  ^
      - two
      - three
      ?  -
      + tree
      + emu

`restore`( | sequence, which)

  Return one of the two sequences that generated a delta.

  Given a sequence produced by `Differ.compare()` or `ndiff()`, extract lines originating from file 1 or 2 (parameter which), stripping off line prefixes.

  Example:

      >>> diff = ndiff('one\ntwo\nthree\n'.splitlines(1),
      ...              'ore\ntree\nemu\n'.splitlines(1))
      >>> diff = list(diff) # materialize the generated delta into a list
      >>> print ''.join(restore(diff, 1)),
      one
      two
      three
      >>> print ''.join(restore(diff, 2)),
      ore
      tree
      emu

`unified_diff`( | a, b[, fromfile[, tofile [, fromfiledate[, tofiledate[, n [, lineterm]]]]]])

  Compare a and b (lists of strings); return a delta (a generator generating the delta lines) in unified diff format.

  Unified diffs are a compact way of showing just the lines that have changed plus a few lines of context. The changes are shown in a inline style (instead of separate before/after blocks). The number of context lines is set by n which defaults to three.

  By default, the diff control lines (those with `---`, `+++`, or `@@`) are created with a trailing newline. This is helpful so that inputs created from `file.readlines()` result in diffs that are suitable for use with `file.writelines()` since both the inputs and outputs have trailing newlines.

  For inputs that do not have trailing newlines, set the lineterm argument to `""` so that the output will be uniformly newline free.

  The context diff format normally has a header for filenames and modification times. Any or all of these may be specified using strings for fromfile, tofile, fromfiledate, and tofiledate. The modification times are normally expressed in the format returned by `time.ctime()`. If not specified, the strings default to blanks.

  Tools/scripts/diff.py is a command-line front-end for this function.

  New in version 2.3.

`IS_LINE_JUNK`( | line)

  Return true for ignorable lines. The line line is ignorable if line is blank or contains a single "`#`", otherwise it is not ignorable. Used as a default for parameter linejunk in `ndiff()` before Python 2.3.

`IS_CHARACTER_JUNK`( | ch)

  Return true for ignorable characters. The character ch is ignorable if ch is a space or tab, otherwise it is not ignorable. Used as a default for parameter charjunk in `ndiff()`.

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.