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difflib

Helpers for computing deltas between sequences. Provides unified diff generation, similarity ratios, and close-match finding: matching Python 3’s difflib module behavior.

Available Functions

Function Description
unified_diff(a, b, fromfile="", tofile="", n=3) Return a unified format diff string.
ratio(a, b) Return a similarity ratio between 0.0 and 1.0.
opcodes(a, b) Return a list of edit operations turning a into b.
get_close_matches(word, possibilities, n=3, cutoff=0.6) Return the best matches for word from a list of possibilities.

Functions

unified_diff(a, b, fromfile="", tofile="", n=3)

Returns a unified diff string comparing two multi-line strings, line by line. The output format matches diff -u and is suitable for display or passing to LLMs. Returns an empty string if the inputs are identical.

Parameters:

  • a (str): The original text.
  • b (str): The modified text.
  • fromfile (str, keyword-only, optional): Label used for the --- header line. Default: "".
  • tofile (str, keyword-only, optional): Label used for the +++ header line. Default: "".
  • n (int, keyword-only, optional): Number of lines of context shown around each change. Default: 3.

Returns: str: a unified diff, or an empty string if a and b are identical.

import difflib

a = "line1\nline2\nline3\n"
b = "line1\nLINE2\nline3\n"

diff = difflib.unified_diff(a, b, fromfile="before.txt", tofile="after.txt")
print(diff)
# --- before.txt
# +++ after.txt
# @@ -1,3 +1,3 @@
#  line1
# -line2
# +LINE2
#  line3

ratio(a, b)

Returns a float between 0.0 (completely different) and 1.0 (identical) indicating how similar two strings are. Operates character-by-character, matching Python’s SequenceMatcher behavior. The result is rounded to two decimal places.

Parameters:

  • a (str): The first string.
  • b (str): The second string.

Returns: float: similarity ratio between 0.0 and 1.0.

import difflib

print(difflib.ratio("hello", "hello"))   # 1.0
print(difflib.ratio("hello", "world"))   # 0.4
print(difflib.ratio("", ""))             # 1.0

opcodes(a, b)

Returns a list of (tag, i1, i2, j1, j2) tuples describing the edit operations needed to turn a into b, operating on lines. Tags are "equal", "insert", "delete", or "replace".

Parameters:

  • a (str): The original text.
  • b (str): The modified text.

Returns: list: a list of (tag, i1, i2, j1, j2) tuples.

import difflib

ops = difflib.opcodes("line1\nline2\nline3\n", "line1\nLINE2\nline3\n")
for tag, i1, i2, j1, j2 in ops:
    print(tag, i1, i2, j1, j2)
# equal 0 1 0 1
# replace 1 2 1 2
# equal 2 3 2 3

get_close_matches(word, possibilities, n=3, cutoff=0.6)

Returns a list of the best matches for word from possibilities, sorted by similarity (best match first). Returns at most n matches, each with a similarity ratio of at least cutoff.

Parameters:

  • word (str): The string to match against.
  • possibilities (list): List of candidate strings.
  • n (int, optional): Maximum number of matches to return. Default: 3.
  • cutoff (float, optional): Minimum similarity ratio (0.0 to 1.0) for a candidate to be included. Default: 0.6.

Returns: list: matching strings from possibilities, ordered by similarity.

import difflib

matches = difflib.get_close_matches("appel", ["ape", "apple", "peach", "puppy"])
print(matches)  # ["apple", "ape"]

# Stricter cutoff
matches = difflib.get_close_matches("appel", ["ape", "apple", "peach"], cutoff=0.8)
print(matches)  # ["apple"]

# Limit results
matches = difflib.get_close_matches("appel", ["ape", "apple", "peach"], n=1)
print(matches)  # ["apple"]

Examples

Comparing API responses

import difflib
import requests

before = requests.get("https://api.example.com/config/v1").text
after  = requests.get("https://api.example.com/config/v2").text

diff = difflib.unified_diff(before, after, fromfile="v1", tofile="v2")
if diff:
    print("Changes detected:")
    print(diff)
else:
    print("No changes")

Fuzzy command matching

import difflib

commands = ["start", "stop", "restart", "status", "reload"]
user_input = "statsu"

suggestions = difflib.get_close_matches(user_input, commands)
if suggestions:
    print(f"Did you mean: {suggestions[0]}?")

Similarity check before update

import difflib

def has_significant_change(old, new, threshold=0.9):
    return difflib.ratio(old, new) < threshold

if has_significant_change(old_content, new_content):
    print("Warning: large change detected")

See Also

  • string - String constants for character classification
  • textwrap - Text wrapping and filling utilities
  • regex - Regular expressions for pattern matching