- name
- fuzzy-match
- description
- A toolkit for fuzzy string matching and data reconciliation. Useful for matching entity names (companies, people) across different datasets where spelling variations, typos, or formatting differences exist.
- license
- MIT
Fuzzy Matching Guide
Overview
This skill provides methods to compare strings and find the best matches using Levenshtein distance and other similarity metrics. It is essential when joining datasets on string keys that are not identical.
Quick Start
from difflib import SequenceMatcher
def similarity(a, b):
return SequenceMatcher(None, a, b).ratio()
print(similarity("Apple Inc.", "Apple Incorporated"))
# Output: 0.7...Python Libraries
difflib (Standard Library)
The difflib module provides classes and functions for comparing sequences.
Basic Similarity
from difflib import SequenceMatcher
def get_similarity(str1, str2):
"""Returns a ratio between 0 and 1."""
return SequenceMatcher(None, str1, str2).ratio()
# Example
s1 = "Acme Corp"
s2 = "Acme Corporation"
print(f"Similarity: {get_similarity(s1, s2)}")Finding Best Match in a List
from difflib import get_close_matches
word = "appel"
possibilities = ["ape", "apple", "peach", "puppy"]
matches = get_close_matches(word, possibilities, n=1, cutoff=0.6)
print(matches)
# Output: ['apple']rapidfuzz (Recommended for Performance)
If rapidfuzz is available (pip install rapidfuzz), it is much faster and offers more metrics.
from rapidfuzz import fuzz, process
# Simple Ratio
score = fuzz.ratio("this is a test", "this is a test!")
print(score)
# Partial Ratio (good for substrings)
score = fuzz.partial_ratio("this is a test", "this is a test!")
print(score)
# Extraction
choices = ["Atlanta Falcons", "New York Jets", "New York Giants", "Dallas Cowboys"]
best_match = process.extractOne("new york jets", choices)
print(best_match)
# Output: ('New York Jets', 100.0, 1)Common Patterns
Normalization before Matching
Always normalize strings before comparing to improve accuracy.
import re
def normalize(text):
# Convert to lowercase
text = text.lower()
# Remove special characters
text = re.sub(r'[^\w\s]', '', text)
# Normalize whitespace
text = " ".join(text.split())
# Common abbreviations
text = text.replace("limited", "ltd").replace("corporation", "corp")
return text
s1 = "Acme Corporation, Inc."
s2 = "acme corp inc"
print(normalize(s1) == normalize(s2))Entity Resolution
When matching a list of dirty names to a clean database:
clean_names = ["Google LLC", "Microsoft Corp", "Apple Inc"]
dirty_names = ["google", "Microsft", "Apple"]
results = {}
for dirty in dirty_names:
# simple containment check first
match = None
for clean in clean_names:
if dirty.lower() in clean.lower():
match = clean
break
# fallback to fuzzy
if not match:
matches = get_close_matches(dirty, clean_names, n=1, cutoff=0.6)
if matches:
match = matches[0]
results[dirty] = match