Ce document est relatif à une vieille version de Python qui n'est plus soutenue. Mettez à jour votre version et lisez Bienvenue sur la documentation de la version stable actuelle de Python.

# `difflib` — Utilitaires pour le calcul des deltas

Code source: Lib/difflib.py

This module provides classes and functions for comparing sequences. It can be used for example, for comparing files, and can produce difference information in various formats, including HTML and context and unified diffs. For comparing directories and files, see also, the `filecmp` module.

class `difflib.``SequenceMatcher`

  C'est une classe flexible permettant de comparer des séquences deux à deux de n'importe quel type, tant que les éléments des séquences sont hachables. L'algorithme de base est antérieur, et un peu plus sophistiqué, à un algorithme publié à la fin des années 1980 par Ratcliff et Obershelp sous le nom hyperbolique de gestalt pattern matching. L'idée est de trouver la plus longue sous-séquence d'appariement contiguë qui ne contient pas d'éléments « indésirables » ; ces éléments « indésirables » sont ceux qui sont inintéressants dans un certain sens, comme les lignes blanches ou les espaces. (Le traitement des éléments indésirables est une extension de l'algorithme de Ratcliff et Obershelp). La même idée est ensuite appliquée récursivement aux morceaux des séquences à gauche et à droite de la sous-séquence correspondante. Cela ne donne pas des séquences de montage minimales, mais tend à donner des correspondances qui « semblent correctes » pour les gens.

  Compléxité temporelle : l'algorithme de base de Ratcliff-Obershelp est de complexité cubique dans le pire cas et de complexité quadratique dans le cas attendu. `SequenceMatcher` est de complexité quadratique pour le pire cas et son comportement dans le cas attendu dépend de façon complexe du nombre d'éléments que les séquences ont en commun ; le temps dans le meilleur cas est linéaire.

  Heuristique automatique des indésirables: `SequenceMatcher` utilise une heuristique qui traite automatiquement certains éléments de la séquence comme indésirables. L'heuristique compte combien de fois chaque élément individuel apparaît dans la séquence. Si les doublons d'un élément (après le premier) représentent plus de 1 % de la séquence et que la séquence compte au moins 200 éléments, cet élément est marqué comme « populaire » et est traité comme indésirable aux fins de la comparaison des séquences. Cette heuristique peut être désactivée en réglant l'argument `autojunk` sur `False` lors de la création de la classe `SequenceMatcher`.

  Nouveau dans la version 3.2: Le paramètre autojunk.

class `difflib.``Differ`

  Il s'agit d'une classe permettant de comparer des séquences de lignes de texte et de produire des différences ou deltas humainement lisibles. Differ utilise `SequenceMatcher` à la fois pour comparer des séquences de lignes, et pour comparer des séquences de caractères dans des lignes similaires (quasi-correspondantes).

  Chaque ligne d'un delta `Differ` commence par un code de deux lettres :

  Code | Signification
  --- | ---
  `'- '` | ligne n'appartenant qu'à la séquence 1
  `'+ '` | ligne n'appartenant qu'à la séquence 2
  `' '` | ligne commune aux deux séquences
  `'? '` | ligne non présente dans l'une ou l'autre des séquences d'entrée

  Les lignes commençant par `'?'` tentent de guider l'œil vers les différences intralignes, et n'étaient présentes dans aucune des séquences d'entrée. Ces lignes peuvent être déroutantes si les séquences contiennent des caractères de tabulation.

class `difflib.``HtmlDiff`

  Cette classe peut être utilisée pour créer un tableau HTML (ou un fichier HTML complet contenant le tableau) montrant une comparaison côte à côte, ligne par ligne, du texte avec les changements inter-lignes et intralignes. Le tableau peut être généré en mode de différence complet ou contextuel.

  Le constructeur pour cette classe est :

  `__init__`(tabsize=8, wrapcolumn=None, linejunk=None, charjunk=IS_CHARACTER_JUNK)

    Initialise l'instance de `HtmlDiff`.

    tabsize est un mot-clé optionnel pour spécifier l'espacement des tabulations et sa valeur par défaut est `8`.

    wrapcolumn est un mot-clé optionnel pour spécifier le numéro de la colonne où les lignes sont coupées pour être ré-agencées, la valeur par défaut est `None` lorsque les lignes ne sont pas ré-agencées.

    linejunk et charjunk sont des arguments de mots-clés optionnels passés dans `ndiff()` (utilisés par `HtmlDiff` pour générer les différences HTML côte à côte). Voir la documentation de `ndiff()` pour les valeurs par défaut des arguments et les descriptions.

  Les méthodes suivantes sont publiques :

  `make_file`(fromlines, tolines, fromdesc='', todesc='', context=False, numlines=5, *, charset='utf-8')

    Compare fromlines et tolines (listes de chaînes de caractères) et renvoie une chaîne de caractères qui est un fichier HTML complet contenant un tableau montrant les différences ligne par ligne avec les changements inter-lignes et intralignes mis en évidence.

    fromdesc et todesc sont des arguments mot-clé optionnels pour spécifier les chaînes d'en-tête des colonnes from/to du fichier (les deux sont des chaînes vides par défaut).

    context et numlines sont tous deux des arguments mots-clés facultatifs. Mettre context à `True` lorsque les différences contextuelles doivent être affichées, sinon la valeur par défaut est `False` pour afficher les fichiers complets. Les numlines ont pour valeur par défaut `5`. Lorsque context est True`, numlines contrôle le nombre de lignes de contexte qui entourent les différences mise en évidence. Lorsque context est `False`, numlines contrôle le nombre de lignes qui sont affichées avant un surlignage de différence lors de l'utilisation des hyperliens « suivants » (un réglage à zéro ferait en sorte que les hyperliens « suivants » placeraient le surlignage de différence suivant en haut du navigateur sans aucun contexte introductif).

    Modifié dans la version 3.5: l'argument mot-clé charset a été ajouté. Le jeu de caractères par défaut du document HTML est passé de `'ISO-8859-1'` à `'utf-8'`.

  `make_table`(fromlines, tolines, fromdesc='', todesc='', context=False, numlines=5)

    Compare fromlines et tolines (listes de chaînes) et renvoie une chaîne qui est un tableau HTML complet montrant les différences ligne par ligne avec les changements inter-lignes et intralignes mis en évidence.

    Les arguments pour cette méthode sont les mêmes que ceux de la méthode `make_file()`.

  `Tools/scripts/diff.py` est un frontal en ligne de commande de cette classe et contient un bon exemple de son utilisation.

`difflib.``context_diff`(a, b, fromfile='', tofile='', fromfiledate='', tofiledate='', n=3, lineterm='\n')

  Compare a et b (listes de chaînes de caractères) ; renvoie un delta (un generateur générant les lignes delta) dans un format de différence de contexte.

  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.

  Par défaut, les lignes de contrôle de la différence (celles avec `***` ou `---`) sont créées avec un saut de ligne à la fin. Ceci est utile pour que les entrées créées à partir de `io.IOBase.readlines()` résultent en des différences qui peuvent être utilisées avec `io.IOBase.writelines()` puisque les entrées et les sorties ont des nouvelles lignes de fin.

  Pour les entrées qui n'ont pas de retour à la ligne, mettre l'argument lineterm à `""` afin que la sortie soit uniformément sans retour à la ligne.

  Le format de contexte de différence comporte normalement un en-tête pour les noms de fichiers et les heures de modification. Tout ou partie de ces éléments peuvent être spécifiés en utilisant les chaînes de caractères fromfile, tofile, fromfiledate et tofiledate. Les heures de modification sont normalement exprimées dans le format ISO 8601. Si elles ne sont pas spécifiées, les chaînes de caractères sont par défaut vierges.

      >>> s1 = ['bacon\n', 'eggs\n', 'ham\n', 'guido\n']
      >>> s2 = ['python\n', 'eggy\n', 'hamster\n', 'guido\n']
      >>> sys.stdout.writelines(context_diff(s1, s2, fromfile='before.py', tofile='after.py'))
      *** before.py
      --- after.py
      ***************
      *** 1,4 ****
      ! bacon
      ! eggs
      ! ham
        guido
      --- 1,4 ----
      ! python
      ! eggy
      ! hamster
        guido

  Voir A command-line interface to difflib pour un exemple plus détaillé.

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

  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('pineapple', keyword.kwlist)
      []
      >>> get_close_matches('accept', keyword.kwlist)
      ['except']

`difflib.``ndiff`(a, b, linejunk=None, charjunk=IS_CHARACTER_JUNK)

  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 filtering 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`. There is also a module-level function `IS_LINE_JUNK()`, which filters out lines without visible characters, except for at most one pound character (`'#'`) -- however the underlying `SequenceMatcher` class does a dynamic analysis of which lines are so frequent as to constitute noise, and this usually works better than using this function.

  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; it's a 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(keepends=True),
      ...              'ore\ntree\nemu\n'.splitlines(keepends=True))
      >>> print(''.join(diff), end="")
      - one
      ?  ^
      + ore
      ?  ^
      - two
      - three
      ?  -
      + tree
      + emu

`difflib.``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.

  Exemple :

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

`difflib.``unified_diff`(a, b, fromfile='', tofile='', fromfiledate='', tofiledate='', n=3, lineterm='\n')

  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 an 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 `io.IOBase.readlines()` result in diffs that are suitable for use with `io.IOBase.writelines()` since both the inputs and outputs have trailing newlines.

  Pour les entrées qui n'ont pas de retour à la ligne, mettre l'argument lineterm à `""` afin que la sortie soit uniformément sans retour à la ligne.

  Le format de contexte de différence comporte normalement un en-tête pour les noms de fichiers et les heures de modification. Tout ou partie de ces éléments peuvent être spécifiés en utilisant les chaînes de caractères fromfile, tofile, fromfiledate et tofiledate. Les heures de modification sont normalement exprimées dans le format ISO 8601. Si elles ne sont pas spécifiées, les chaînes de caractères sont par défaut vierges.

      >>> s1 = ['bacon\n', 'eggs\n', 'ham\n', 'guido\n']
      >>> s2 = ['python\n', 'eggy\n', 'hamster\n', 'guido\n']
      >>> sys.stdout.writelines(unified_diff(s1, s2, fromfile='before.py', tofile='after.py'))
      --- before.py
      +++ after.py
      @@ -1,4 +1,4 @@
      -bacon
      -eggs
      -ham
      +python
      +eggy
      +hamster
       guido

  Voir A command-line interface to difflib pour un exemple plus détaillé.

`difflib.``diff_bytes`(dfunc, a, b, fromfile=b'', tofile=b'', fromfiledate=b'', tofiledate=b'', n=3, lineterm=b'\n')

  Compare a and b (lists of bytes objects) using dfunc; yield a sequence of delta lines (also bytes) in the format returned by dfunc. dfunc must be a callable, typically either `unified_diff()` or `context_diff()`.

  Allows you to compare data with unknown or inconsistent encoding. All inputs except n must be bytes objects, not str. Works by losslessly converting all inputs (except n) to str, and calling `dfunc(a, b, fromfile, tofile, fromfiledate, tofiledate, n, lineterm)`. The output of dfunc is then converted back to bytes, so the delta lines that you receive have the same unknown/inconsistent encodings as a and b.

  Nouveau dans la version 3.5.

`difflib.``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()` in older versions.

`difflib.``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()`.

Voir aussi

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.

## SequenceMatcher Objects

The `SequenceMatcher` class has this constructor:

class `difflib.``SequenceMatcher`(isjunk=None, a='', b='', autojunk=True)

  Optional argument isjunk must be `None` (the default) or a one-argument function that takes a sequence element and returns true if and only if the element is "junk" and should be ignored. Passing `None` for isjunk is equivalent to passing `lambda x: False`; in other words, no elements are ignored. For example, pass:

      lambda x: x in " \t"

  if you're comparing lines as sequences of characters, and don't want to synch up on blanks or hard tabs.

  The optional arguments a and b are sequences to be compared; both default to empty strings. The elements of both sequences must be hashable.

  The optional argument autojunk can be used to disable the automatic junk heuristic.

  Nouveau dans la version 3.2: Le paramètre autojunk.

  SequenceMatcher objects get three data attributes: bjunk is the set of elements of b for which isjunk is `True`; bpopular is the set of non-junk elements considered popular by the heuristic (if it is not disabled); b2j is a dict mapping the remaining elements of b to a list of positions where they occur. All three are reset whenever b is reset with `set_seqs()` or `set_seq2()`.

  Nouveau dans la version 3.2: The bjunk and bpopular attributes.

  `SequenceMatcher` objects have the following methods:

  `set_seqs`(a, b)

    Set the two sequences to be compared.

  `SequenceMatcher` computes and caches detailed information about the second sequence, so if you want to compare one sequence against many sequences, use `set_seq2()` to set the commonly used sequence once and call `set_seq1()` repeatedly, once for each of the other sequences.

  `set_seq1`(a)

    Set the first sequence to be compared. The second sequence to be compared is not changed.

  `set_seq2`(b)

    Set the second sequence to be compared. The first sequence to be compared is not changed.

  `find_longest_match`(alo, ahi, blo, bhi)

    Find longest matching block in `a[alo:ahi]` and `b[blo:bhi]`.

    If isjunk was omitted or `None`, `find_longest_match()` returns `(i, j, k)` such that `a[i:i+k]` is equal to `b[j:j+k]`, where `alo <= i <= i+k <= ahi` and `blo <= j <= j+k <= bhi`. For all `(i', j', k')` meeting those conditions, the additional conditions `k >= k'`, `i <= i'`, and if `i == i'`, `j <= j'` are also met. In other words, of all maximal matching blocks, return one that starts earliest in a, and of all those maximal matching blocks that start earliest in a, return the one that starts earliest in b.

        >>> s = SequenceMatcher(None, " abcd", "abcd abcd")
        >>> s.find_longest_match(0, 5, 0, 9)
        Match(a=0, b=4, size=5)

    If isjunk was provided, first the longest matching block is determined as above, but with the additional restriction that no junk element appears in the block. Then that block is extended as far as possible by matching (only) junk elements on both sides. So the resulting block never matches on junk except as identical junk happens to be adjacent to an interesting match.

    Here's the same example as before, but considering blanks to be junk. That prevents `' abcd'` from matching the `' abcd'` at the tail end of the second sequence directly. Instead only the `'abcd'` can match, and matches the leftmost `'abcd'` in the second sequence:

        >>> s = SequenceMatcher(lambda x: x==" ", " abcd", "abcd abcd")
        >>> s.find_longest_match(0, 5, 0, 9)
        Match(a=1, b=0, size=4)

    If no blocks match, this returns `(alo, blo, 0)`.

    This method returns a named tuple `Match(a, b, size)`.

  `get_matching_blocks`()

    Return list of triples describing non-overlapping matching subsequences. Each triple is of the form `(i, j, n)`, and means that `a[i:i+n] == b[j:j+n]`. The triples are monotonically increasing in i and j.

    The last triple is a dummy, and has the value `(len(a), len(b), 0)`. It is the only triple with `n == 0`. If `(i, j, n)` and `(i', j', n')` are adjacent triples in the list, and the second is not the last triple in the list, then `i+n < i'` or `j+n < j'`; in other words, adjacent triples always describe non-adjacent equal blocks.

        >>> s = SequenceMatcher(None, "abxcd", "abcd")
        >>> s.get_matching_blocks()
        [Match(a=0, b=0, size=2), Match(a=3, b=2, size=2), Match(a=5, b=4, size=0)]

  `get_opcodes`()

    Return list of 5-tuples describing how to turn a into b. Each tuple is of the form `(tag, i1, i2, j1, j2)`. The first tuple has `i1 == j1 == 0`, and remaining tuples have i1 equal to the i2 from the preceding tuple, and, likewise, j1 equal to the previous j2.

    The tag values are strings, with these meanings:

    Valeur | Signification
    --- | ---
    `'replace'` | `a[i1:i2]` should be replaced by `b[j1:j2]`.
    `'delete'` | `a[i1:i2]` should be deleted. Note that `j1 == j2` in this case.
    `'insert'` | `b[j1:j2]` should be inserted at `a[i1:i1]`. Note that `i1 == i2` in this case.
    `'equal'` | `a[i1:i2] == b[j1:j2]` (the sub-sequences are equal).

    Par exemple :

        >>> a = "qabxcd"
        >>> b = "abycdf"
        >>> s = SequenceMatcher(None, a, b)
        >>> for tag, i1, i2, j1, j2 in s.get_opcodes():
        ...     print('{:7}   a[{}:{}] --> b[{}:{}] {!r:>8} --> {!r}'.format(
        ...         tag, i1, i2, j1, j2, a[i1:i2], b[j1:j2]))
        delete    a[0:1] --> b[0:0]      'q' --> ''
        equal     a[1:3] --> b[0:2]     'ab' --> 'ab'
        replace   a[3:4] --> b[2:3]      'x' --> 'y'
        equal     a[4:6] --> b[3:5]     'cd' --> 'cd'
        insert    a[6:6] --> b[5:6]       '' --> 'f'

  `get_grouped_opcodes`(n=3)

    Return a generator of groups with up to n lines of context.

    Starting with the groups returned by `get_opcodes()`, this method splits out smaller change clusters and eliminates intervening ranges which have no changes.

    The groups are returned in the same format as `get_opcodes()`.

  `ratio`()

    Return a measure of the sequences' similarity as a float in the range [0, 1].

    Where T is the total number of elements in both sequences, and M is the number of matches, this is 2.0*M / T. Note that this is `1.0` if the sequences are identical, and `0.0` if they have nothing in common.

    This is expensive to compute if `get_matching_blocks()` or `get_opcodes()` hasn't already been called, in which case you may want to try `quick_ratio()` or `real_quick_ratio()` first to get an upper bound.

    Note

    Caution: The result of a `ratio()` call may depend on the order of the arguments. For instance:

        >>> SequenceMatcher(None, 'tide', 'diet').ratio()
        0.25
        >>> SequenceMatcher(None, 'diet', 'tide').ratio()
        0.5

  `quick_ratio`()

    Return an upper bound on `ratio()` relatively quickly.

  `real_quick_ratio`()

    Return an upper bound on `ratio()` very quickly.

The three methods that return the ratio of matching to total characters can give different results due to differing levels of approximation, although `quick_ratio()` and `real_quick_ratio()` are always at least as large as `ratio()`:

    >>> s = SequenceMatcher(None, "abcd", "bcde")
    >>> s.ratio()
    0.75
    >>> s.quick_ratio()
    0.75
    >>> s.real_quick_ratio()
    1.0

## SequenceMatcher Examples

This example compares two strings, considering blanks to be "junk":

    >>> s = SequenceMatcher(lambda x: x == " ",
    ...                     "private Thread currentThread;",
    ...                     "private volatile Thread currentThread;")

`ratio()` returns a float in [0, 1], measuring the similarity of the sequences. As a rule of thumb, a `ratio()` value over 0.6 means the sequences are close matches:

    >>> print(round(s.ratio(), 3))
    0.866

If you're only interested in where the sequences match, `get_matching_blocks()` is handy:

    >>> for block in s.get_matching_blocks():
    ...     print("a[%d] and b[%d] match for %d elements" % block)
    a[0] and b[0] match for 8 elements
    a[8] and b[17] match for 21 elements
    a[29] and b[38] match for 0 elements

Note that the last tuple returned by `get_matching_blocks()` is always a dummy, `(len(a), len(b), 0)`, and this is the only case in which the last tuple element (number of elements matched) is `0`.

If you want to know how to change the first sequence into the second, use `get_opcodes()`:

    >>> for opcode in s.get_opcodes():
    ...     print("%6s a[%d:%d] b[%d:%d]" % opcode)
     equal a[0:8] b[0:8]
    insert a[8:8] b[8:17]
     equal a[8:29] b[17:38]

Voir aussi

- The `get_close_matches()` function in this module which shows how simple code building on `SequenceMatcher` can be used to do useful work.

- Simple version control recipe for a small application built with `SequenceMatcher`.

## Differ Objects

Note that `Differ`-generated deltas make no claim to be minimal diffs. To the contrary, minimal diffs are often counter-intuitive, because they synch up anywhere possible, sometimes accidental matches 100 pages apart. Restricting synch points to contiguous matches preserves some notion of locality, at the occasional cost of producing a longer diff.

The `Differ` class has this constructor:

class `difflib.``Differ`(linejunk=None, charjunk=None)

  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. The default is `None`, meaning that no line is considered junk.

  charjunk: A function that accepts a single character argument (a string of length 1), and returns true if the character is junk. The default is `None`, meaning that no character is considered junk.

  These junk-filtering functions speed up matching to find differences and do not cause any differing lines or characters to be ignored. Read the description of the `find_longest_match()` method's isjunk parameter for an explanation.

  `Differ` objects are used (deltas generated) via a single method:

  `compare`(a, b)

    Compare two sequences of lines, and generate the delta (a sequence of lines).

    Each sequence must contain individual single-line strings ending with newlines. Such sequences can be obtained from the `readlines()` method of file-like objects. The delta generated also consists of newline-terminated strings, ready to be printed as-is via the `writelines()` method of a file-like object.

## Differ Example

This example compares two texts. First we set up the texts, sequences of individual single-line strings ending with newlines (such sequences can also be obtained from the `readlines()` method of file-like objects):

    >>> text1 = '''  1. Beautiful is better than ugly.
    ...   2. Explicit is better than implicit.
    ...   3. Simple is better than complex.
    ...   4. Complex is better than complicated.
    ... '''.splitlines(keepends=True)
    >>> len(text1)
    4
    >>> text1[0][-1]
    '\n'
    >>> text2 = '''  1. Beautiful is better than ugly.
    ...   3.   Simple is better than complex.
    ...   4. Complicated is better than complex.
    ...   5. Flat is better than nested.
    ... '''.splitlines(keepends=True)

Next we instantiate a Differ object:

    >>> d = Differ()

Note that when instantiating a `Differ` object we may pass functions to filter out line and character "junk." See the `Differ()` constructor for details.

Finally, we compare the two:

    >>> result = list(d.compare(text1, text2))

`result` is a list of strings, so let's pretty-print it:

    >>> from pprint import pprint
    >>> pprint(result)
    ['    1. Beautiful is better than ugly.\n',
     '-   2. Explicit is better than implicit.\n',
     '-   3. Simple is better than complex.\n',
     '+   3.   Simple is better than complex.\n',
     '?     ++\n',
     '-   4. Complex is better than complicated.\n',
     '?            ^                     ---- ^\n',
     '+   4. Complicated is better than complex.\n',
     '?           ++++ ^                      ^\n',
     '+   5. Flat is better than nested.\n']

As a single multi-line string it looks like this:

    >>> import sys
    >>> sys.stdout.writelines(result)
        1. Beautiful is better than ugly.
    -   2. Explicit is better than implicit.
    -   3. Simple is better than complex.
    +   3.   Simple is better than complex.
    ?     ++
    -   4. Complex is better than complicated.
    ?            ^                     ---- ^
    +   4. Complicated is better than complex.
    ?           ++++ ^                      ^
    +   5. Flat is better than nested.

## A command-line interface to difflib

This example shows how to use difflib to create a `diff`-like utility. It is also contained in the Python source distribution, as `Tools/scripts/diff.py`.

    #!/usr/bin/env python3
    """ Command line interface to difflib.py providing diffs in four formats:

    * ndiff:    lists every line and highlights interline changes.
    * context:  highlights clusters of changes in a before/after format.
    * unified:  highlights clusters of changes in an inline format.
    * html:     generates side by side comparison with change highlights.

    """

    import sys, os, difflib, argparse
    from datetime import datetime, timezone

    def file_mtime(path):
        t = datetime.fromtimestamp(os.stat(path).st_mtime,
                                   timezone.utc)
        return t.astimezone().isoformat()

    def main():

        parser = argparse.ArgumentParser()
        parser.add_argument('-c', action='store_true', default=False,
                            help='Produce a context format diff (default)')
        parser.add_argument('-u', action='store_true', default=False,
                            help='Produce a unified format diff')
        parser.add_argument('-m', action='store_true', default=False,
                            help='Produce HTML side by side diff '
                                 '(can use -c and -l in conjunction)')
        parser.add_argument('-n', action='store_true', default=False,
                            help='Produce a ndiff format diff')
        parser.add_argument('-l', '--lines', type=int, default=3,
                            help='Set number of context lines (default 3)')
        parser.add_argument('fromfile')
        parser.add_argument('tofile')
        options = parser.parse_args()

        n = options.lines
        fromfile = options.fromfile
        tofile = options.tofile

        fromdate = file_mtime(fromfile)
        todate = file_mtime(tofile)
        with open(fromfile) as ff:
            fromlines = ff.readlines()
        with open(tofile) as tf:
            tolines = tf.readlines()

        if options.u:
            diff = difflib.unified_diff(fromlines, tolines, fromfile, tofile, fromdate, todate, n=n)
        elif options.n:
            diff = difflib.ndiff(fromlines, tolines)
        elif options.m:
            diff = difflib.HtmlDiff().make_file(fromlines,tolines,fromfile,tofile,context=options.c,numlines=n)
        else:
            diff = difflib.context_diff(fromlines, tolines, fromfile, tofile, fromdate, todate, n=n)

        sys.stdout.writelines(diff)

    if __name__ == '__main__':
        main()