difflib — Helpers for computing deltas
New in version 2.1.
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
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 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.
-
class 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][, wrapcolumn][, linejunk][, charjunk])
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][, numlines])
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 context to
True when contextual differences are to be shown, else the default is
False to show the full files. numlines defaults to 5. When context
is True numlines controls the number of context lines which surround the
difference highlights. When context is False 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).
-
make_table(fromlines, tolines[, fromdesc][, todesc][, context][, numlines])
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.
New in version 2.4.
-
difflib.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.
>>> s1 = ['bacon\n', 'eggs\n', 'ham\n', 'guido\n']
>>> s2 = ['python\n', 'eggy\n', 'hamster\n', 'guido\n']
>>> for line in context_diff(s1, s2, fromfile='before.py', tofile='after.py'):
... sys.stdout.write(line) # doctest: +NORMALIZE_WHITESPACE
*** 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.
New in version 2.3.
-
difflib.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']
-
difflib.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
-
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.
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
-
difflib.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.
>>> s1 = ['bacon\n', 'eggs\n', 'ham\n', 'guido\n']
>>> s2 = ['python\n', 'eggy\n', 'hamster\n', 'guido\n']
>>> for line in unified_diff(s1, s2, fromfile='before.py', tofile='after.py'):
... sys.stdout.write(line) # doctest: +NORMALIZE_WHITESPACE
--- 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.
New in version 2.3.
-
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() before Python 2.3.
-
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().
SequenceMatcher Objects
The SequenceMatcher class has this constructor:
-
class difflib.SequenceMatcher([isjunk[, a[, b]]])
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: 0; in other words, no elements are ignored.
For example, pass:
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.
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).
Changed in version 2.6: This method returns a named tuple Match(a, b, size).
-
get_matching_blocks()
Return list of triples describing 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.
Changed in version 2.5: The guarantee that adjacent triples always describe non-adjacent blocks
was implemented.
>>> 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 |
|---|
| '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). |
For example:
>>> a = "qabxcd"
>>> b = "abycdf"
>>> s = SequenceMatcher(None, a, b)
>>> for tag, i1, i2, j1, j2 in s.get_opcodes():
... print ("%7s a[%d:%d] (%s) b[%d:%d] (%s)" %
... (tag, i1, i2, a[i1:i2], j1, j2, b[j1:j2]))
delete a[0:1] (q) b[0:0] ()
equal a[1:3] (ab) b[0:2] (ab)
replace a[3:4] (x) b[2:3] (y)
equal a[4:6] (cd) b[3:5] (cd)
insert a[6:6] () b[5:6] (f)
-
get_grouped_opcodes([n])
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().
New in version 2.3.
-
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.
-
quick_ratio()
Return an upper bound on ratio() relatively quickly.
This isn’t defined beyond that it is an upper bound on ratio(), and
is faster to compute.
-
real_quick_ratio()
Return an upper bound on ratio() very quickly.
This isn’t defined beyond that it is an upper bound on ratio(), and
is faster to compute than either ratio() or quick_ratio().
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]
See also the function get_close_matches() in this module, which shows how
simple code building on SequenceMatcher can be used to do useful work.
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[, charjunk]])
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.
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(1)
>>> 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(1)
Next we instantiate a Differ object:
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.
""" 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, time, difflib, optparse
def main():
# Configure the option parser
usage = "usage: %prog [options] fromfile tofile"
parser = optparse.OptionParser(usage)
parser.add_option("-c", action="store_true", default=False,
help='Produce a context format diff (default)')
parser.add_option("-u", action="store_true", default=False,
help='Produce a unified format diff')
hlp = 'Produce HTML side by side diff (can use -c and -l in conjunction)'
parser.add_option("-m", action="store_true", default=False, help=hlp)
parser.add_option("-n", action="store_true", default=False,
help='Produce a ndiff format diff')
parser.add_option("-l", "--lines", type="int", default=3,
help='Set number of context lines (default 3)')
(options, args) = parser.parse_args()
if len(args) == 0:
parser.print_help()
sys.exit(1)
if len(args) != 2:
parser.error("need to specify both a fromfile and tofile")
n = options.lines
fromfile, tofile = args # as specified in the usage string
# we're passing these as arguments to the diff function
fromdate = time.ctime(os.stat(fromfile).st_mtime)
todate = time.ctime(os.stat(tofile).st_mtime)
fromlines = open(fromfile, 'U').readlines()
tolines = open(tofile, 'U').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)
# we're using writelines because diff is a generator
sys.stdout.writelines(diff)
if __name__ == '__main__':
main()
|
