Source code: Lib/difflib.py
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re — Regular expression operations
textwrap — Text wrapping and filling
Source code: Lib/difflib.py
This module provides classes and functions for comparing sequences. It
can be used for example, for comparing files, and can produce information
about file differences in various formats, including HTML and context and unified
diffs. For comparing directories and files, see also, the filecmp module.
difflib.SequenceMatcherThis 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; these “junk” elements are ones that are uninteresting in some sense, such as blank lines or whitespace. (Handling junk is an extension to the Ratcliff and Obershelp algorithm.) 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.
Automatic junk heuristic: SequenceMatcher supports a heuristic that
automatically treats certain sequence items as junk. The heuristic counts how many
times each individual item appears in the sequence. If an item’s duplicates (after
the first one) account for more than 1% of the sequence and the sequence is at least
200 items long, this item is marked as “popular” and is treated as junk for
the purpose of sequence matching. This heuristic can be turned off by setting
the autojunk argument to False when creating the SequenceMatcher.
New in version 3.2: The autojunk parameter.
difflib.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.
difflib.HtmlDiff¶This class can be used to create an HTML table (or a complete HTML file containing the table) showing a side by side, line by line comparison of text with inter-line and intra-line change highlights. The table can be generated in either full or contextual difference mode.
The constructor for this class is:
__init__(tabsize=8, wrapcolumn=None, linejunk=None, charjunk=IS_CHARACTER_JUNK)¶
Initializes instance of HtmlDiff.
tabsize is an optional keyword argument to specify tab stop spacing and
defaults to 8.
wrapcolumn is an optional keyword to specify column number where lines are
broken and wrapped, defaults to None where lines are not wrapped.
linejunk and charjunk are optional keyword arguments passed into ndiff()
(used by HtmlDiff to generate the side by side HTML differences). See
ndiff() documentation for argument default values and descriptions.
The following methods are public:
make_file(fromlines, tolines, fromdesc='', todesc='', context=False, numlines=5, *, charset='utf-8')¶Compares fromlines and tolines (lists of strings) and returns a string which is a complete HTML file containing a table showing line by line differences with inter-line and intra-line changes highlighted.
fromdesc and todesc are optional keyword arguments to specify from/to file column header strings (both default to an empty string).
context and numlines are both optional keyword arguments. Set contexttoTrue when contextual differences are to be shown, else the default is
False to show the full files. numlines defaults to 5. When contextisTrue numlines controls the number of context lines which surround the
difference highlights. When contextisFalse numlines controls the
number of lines which are shown before a difference highlight when using the
“next” hyperlinks (setting to zero would cause the “next” hyperlinks to place
the next difference highlight at the top of the browser without any leading
context).
Note
fromdesc and todesc are interpreted as unescaped HTML and should be properly escaped while receiving input from untrusted sources.
Changed in version 3.5: charset keyword-only argument was added. The default charset of
HTML document changed from 'ISO-8859-1'to'utf-8'.
make_table(fromlines, tolines, fromdesc='', todesc='', context=False, numlines=5)¶Compares fromlines and tolines (lists of strings) and returns a string which is a complete HTML table showing line by line differences with inter-line and intra-line changes highlighted.
The arguments for this method are the same as those for the make_file()
method.
Tools/scripts/diff.py is a command-line front-end to this class and
contains a good example of its use.
difflib.context_diff(a, b, fromfile='', tofile='', fromfiledate='', tofiledate='', n=3, lineterm='\n')¶Compare a and b (lists of strings); return a delta (agenerator 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
io.IOBase.readlines() result in diffs that are suitable for use with
io.IOBase.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 ISO 8601 format. If not specified, the strings default to blanks.
>>> 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
See A command-line interface to difflib for a more detailed example.
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 (agenerator generating the delta lines).
Optional keyword parameters linejunk and charjunk are filtering functions
(orNone):
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()orndiff(), extract
lines originating from file 1 or 2 (parameter which), stripping off line
prefixes.
Example:
>>> 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 (agenerator 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.
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 ISO 8601 format. If not specified, the strings default to blanks.
>>> 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
See A command-line interface to difflib for a more detailed example.
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()orcontext_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.
New in 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 linejunkinndiff() 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 charjunkinndiff().
See also
Discussion of a similar algorithm by John W. Ratcliff and D. E. Metzener. This was published in Dr. Dobb’s Journal in July, 1988.
The SequenceMatcher class has this constructor:
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.
New in version 3.2: The autojunk parameter.
SequenceMatcher objects get three data attributes: bjunk is the
set of elements of b for which isjunkisTrue; 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()orset_seq2().
New in 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=0, ahi=None, blo=0, bhi=None)¶
Find longest matching block in a[alo:ahi] and b[blo:bhi].
Ifisjunk 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)
Ifisjunk 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).
Changed in version 3.9: Added default arguments.
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'orj+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:
Value |
Meaning |
|---|---|
|
|
|
|
|
|
|
|
For example:
>>> 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()orget_opcodes() hasn’t already been called, in which case you may want
to try quick_ratio()orreal_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
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]
See also
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.
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:
difflib.Differ(linejunk=None, charjunk=None)
Optional keyword parameters linejunk and charjunk are for filter functions
(orNone):
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.
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.
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()
re — Regular expression operations
textwrap — Text wrapping and filling