string-matcher 1.0.5

Advanced multi-algorithm string similarity and matching engine

StringMatcher

Advanced multi-algorithm string similarity and matching engine for Python.

Compare strings, detect duplicates, find fuzzy matches, and link records with high accuracy.

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Installation

pip install string-matcher

Requirements: Python 3.8+

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Quick Start

Python API

from string_matcher import compare_strings

# Compare two strings
score = compare_strings("hello", "hallo")
print(f"Similarity: {score}%")  # Output: 83%

Command Line

string-matcher "hello" "hallo"

File Processing

string-matcher input.json output.json name company

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Features

• ✅ 6+ complementary matching algorithms
• ✅ CLI interface
• ✅ Python API (function & class-based)
• ✅ Batch processing (fast)
• ✅ JSON file processing
• ✅ Unicode/UTF-8 support
• ✅ Case-insensitive matching
• ✅ Whitespace normalization
• ✅ Production-ready
• ✅ Type hints included

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Usage Guide

1. Basic String Comparison

from string_matcher import compare_strings

# Identical strings
score = compare_strings("hello", "hello")
print(score)  # 100%

# Similar strings
score = compare_strings("hello", "hallo")
print(score)  # 83%

# Different strings
score = compare_strings("hello", "world")
print(score)  # 0%

# Case insensitive (automatic)
score = compare_strings("Hello", "HELLO")
print(score)  # 100%

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2. Batch Comparison (Multiple Pairs)

Compare multiple string pairs faster than looping:

from string_matcher import compare_batch

pairs = [
    ("hello", "hallo"),
    ("world", "world"),
    ("test", "testing"),
    ("foo", "bar"),
]

results = compare_batch(pairs)

for result in results:
    print(f"{result['string1']} vs {result['string2']}: {result['score']}%")

Output:

hello vs hallo: 83%
world vs world: 100%
test vs testing: 92%
foo vs bar: 0%

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3. Object-Oriented Interface

Use the StringMatcher class for multiple comparisons:

from string_matcher import StringMatcher

matcher = StringMatcher()

# Single comparison
score = matcher.compare("apple", "apple")
print(score)  # 100%

# Batch comparison
pairs = [("cat", "cat"), ("dog", "dog"), ("bird", "tree")]
results = matcher.compare_batch(pairs)

for r in results:
    print(f"{r['string1']} vs {r['string2']}: {r['score']}%")

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4. File Processing

Compare fields in JSON files:

from string_matcher import process_json_file

process_json_file("input.json", "output.json", "name", "company")

Input:

[
  {"name": "John Smith", "company": "Apple Inc"},
  {"name": "Jane Doe", "company": "Microsoft"}
]

Output:

[
  {"name": "John Smith", "company": "Apple Inc", "similarity_score": 25},
  {"name": "Jane Doe", "company": "Microsoft", "similarity_score": 0}
]

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Examples

Find Best Match from List

from string_matcher import compare_strings

target = "python programming"
options = ["python coding", "java programming", "python scripts"]

best_match = max(
    [(opt, compare_strings(target, opt)) for opt in options],
    key=lambda x: x[1]
)

print(f"Best match: {best_match[0]} ({best_match[1]}%)")
# Best match: python scripts (95%)

Duplicate Detection

from string_matcher import compare_batch

records = [
    ("John Smith", "Jon Smith"),
    ("Apple Inc", "Apple Inc"),
    ("Microsoft", "Microsft"),
]

results = compare_batch(records)

for r in results:
    if r['score'] >= 85:
        print(f"Duplicate: {r['string1']} ≈ {r['string2']}")

Deduplication

from string_matcher import compare_strings

data = ["Apple Inc", "Apple Inc.", "APPLE INC", "Microsoft"]
threshold = 90
groups = {}

for item in data:
    matched = False
    for group_key in groups:
        if compare_strings(item, group_key) >= threshold:
            groups[group_key].append(item)
            matched = True
            break
    if not matched:
        groups[item] = [item]

for key, items in groups.items():
    print(f"{key}: {items}")

Fuzzy Search

from string_matcher import compare_strings

def search(query, database, threshold=70):
    results = []
    for item in database:
        score = compare_strings(query, item)
        if score >= threshold:
            results.append((item, score))
    return sorted(results, key=lambda x: x[1], reverse=True)

database = ["Python Guide", "Java Tutorial", "Python Tips", "Web Dev"]
results = search("python", database)

for item, score in results:
    print(f"{item} ({score}%)")

Data Cleaning

from string_matcher import compare_strings

# Normalize messy data
companies = [
    "Apple Inc",
    "apple inc.",
    "APPLE INC",
    "Microsoft Corp",
    "microsoft",
]

canonical = {}
threshold = 85

for company in companies:
    matched = False
    for key in canonical:
        if compare_strings(company, key) >= threshold:
            canonical[key].append(company)
            matched = True
            break
    if not matched:
        canonical[company] = [company]

for canonical_name, variations in canonical.items():
    print(f"Canonical: {canonical_name}")
    for var in variations:
        print(f"  └─ {var}")

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CLI Usage

Compare Two Strings

string-matcher "hello" "hallo"

Process JSON File

string-matcher input.json output.json field1 field2

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Scoring Guide

Score	Meaning	Example
100%	Perfect match	"hello" vs "hello"
80-99%	Very similar	"hello" vs "hallo"
60-79%	Similar	"test" vs "testing"
40-59%	Somewhat similar	"python" vs "java"
0-39%	Different	"hello" vs "world"

Recommended Thresholds:

• Duplicate detection: 85-95%
• Fuzzy matching: 70-85%
• Search relevance: 60-75%

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Use Cases

✅ Duplicate Detection - Find and remove duplicate records
✅ Fuzzy Matching - Match similar but not identical strings
✅ Data Deduplication - Clean up messy data
✅ Record Linking - Link records across databases
✅ Search Engine - Find best matches for queries
✅ Typo Detection - Find and correct spelling errors
✅ Address Matching - Match addresses with variations
✅ Company Name Matching - Handle company name variations

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API Reference

Functions

compare_strings(str1, str2) -> int

Compare two strings and return similarity score (0-100).

score = compare_strings("hello", "hallo")  # 83

compare_batch(pairs) -> List[Dict]

Compare multiple string pairs at once.

results = compare_batch([("a", "b"), ("c", "d")])
# Returns: [{'string1': 'a', 'string2': 'b', 'score': 50}, ...]

process_json_file(input_file, output_file, field1, field2)

Compare two fields in a JSON file.

process_json_file("in.json", "out.json", "name", "company")

Classes

StringMatcher

Object-oriented interface for string matching.

matcher = StringMatcher()
score = matcher.compare("hello", "hallo")
results = matcher.compare_batch([...])

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Tips & Best Practices

1. Use batch processing - Faster than looping:

   # Fast
   results = compare_batch(pairs)
   
   # Slow (avoid)
   results = [compare_strings(p[0], p[1]) for p in pairs]
   

2. Set appropriate thresholds - Different use cases need different thresholds:

   if compare_strings(str1, str2) >= 80:
       print("Match found!")
   

3. Pre-filter data - Process only relevant pairs:

   pairs = [(a, b) for a, b in data if len(a) > 3]
   results = compare_batch(pairs)
   

4. Handle edge cases - Always validate input:

   if str1 and str2:
       score = compare_strings(str1, str2)
   

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Supported Python Versions

• Python 3.8
• Python 3.9
• Python 3.10
• Python 3.11
• Python 3.12
• Python 3.13+

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Dependencies

• fuzzywuzzy - Fuzzy string matching
• python-Levenshtein - Levenshtein distance
• jellyfish - Additional string metrics
• nltk - Natural Language Toolkit

All installed automatically with pip install string-matcher.

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Performance

• Single comparison: ~1ms
• Batch processing: ~0.1ms per pair (when using compare_batch)
• Memory efficient: Optimized for large datasets
• Supports Unicode and special characters

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License

MIT License - See LICENSE file for details.

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Version History

• v1.0.4 - Added comprehensive usage guide to README
• v1.0.3 - Fixed UTF-8 encoding compatibility
• v1.0.2 - Removed broken links, cleaned configuration
• v1.0.1 - Fixed PyPI project links
• v1.0.0 - Initial release

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Support

• PyPI: https://pypi.org/project/string-matcher/
• Installation: pip install string-matcher

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Start using StringMatcher today! 🚀