🧠 Smart Text Matching: RapidFuzz vs Difflib

Have you ever typed a restaurant name into a food app — only to realize you misspelled it, used a local nickname, or added an extra space? Yet somehow, the app still finds what you meant.
That’s fuzzy matching in action — a smart way of connecting almost-right text to the right result.

In the era of big data and AI, matching text strings intelligently is a superpower. Whether deduplicating records, powering search engines, or normalizing messy user input, smart text matching goes beyond exact equality. It understands similarities despite typos, reorderings, or variations—turning chaos into clarity.

In this article, we’ll explore how to build a simple but powerful fuzzy matching system for restaurant names, using two different Python approaches:

• Part 1: Using RapidFuzz for lightning-fast, token-based similarity
• Part 2: Using Difflib, Python’s built-in (but slower) alternative

We’ll walk through real examples and see how both methods handle variations, typos, and multilingual data.

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🍽️ The Scenario: Matching Restaurant Names

Let’s say you have a list of restaurants from multiple regions:

restaurants = [
    {"name": "The Shawarma Professor"},
    {"name": "Dr. Falafel"},
    {"name": "Taco Republic (Mexican Grill)"},
    {"name": "مطعم الدكتور فلافل"},
    {"name": "Shawarma Prof."},
]

Now a hungry user types:
query = "shawarma prof"

We want to find the closest matching restaurants, even if:

• They typed “prof” instead of “professor”
• The name has parentheses, prefixes, or translation
• It’s written in another language (like Arabic)

Part 1: ⚡ Using RapidFuzz for Fast and Accurate Matching

Why RapidFuzz?
RapidFuzz is a high-performance library built in C++.
It's perfect for large-scale search tasks, offering token-based comparison, speed, and Unicode support - all essential when dealing with real-world restaurant names.
A Quick Demo

from rapidfuzz import fuzz

score = fuzz.token_sort_ratio("The Shawarma Professor", "Shawarma Prof.")
print(score)

# Output
72.22

RapidFuzz recognizes that even with abbreviations and reordered words, the two names are nearly identical.

Building the Matcher

Let’s build a lightweight matcher using RapidFuzz:

import re
from rapidfuzz import fuzz

class RestaurantMatcher:

    def _normalize(self, text):
        text = ' '.join(text.strip().split())
        text = re.sub(r'[\u064B-\u065F\u0670]', '', text)  # remove Arabic diacritics
        return text

    def _calculate_similarity(self, a, b):
        return fuzz.token_sort_ratio(a, b)

    def match(self, query, restaurants):
        query_norm = self._normalize(query)
        results = []
        for r in restaurants:
            name = self._normalize(r["name"])
            score = self._calculate_similarity(query_norm, name)
            results.append((r["name"], score))
        return results

Let's use it

if __name__ == "__main__":
    restaurants = [
        {"name": "The Shawarma Professor"},
        {"name": "Dr. Falafel"},
        {"name": "Taco Republic (Mexican Grill)"},
        {"name": "مطعم الدكتور فلافل"},
        {"name": "Shawarma Prof."},
    ]
    matcher = RestaurantMatcher()
    matches = matcher.match("shawarma prof", restaurants)
    if not matches:
        print("No matches found")
    else:
        for name, score in matches:
            print(f"{name}: {score:.1f}")

Output:

The Shawarma Professor: 62.9
Dr. Falafel: 33.3
Taco Republic (Mexican Grill): 14.3
مطعم الدكتور فلافل: 6.5
Shawarma Prof.: 81.5

✅ RapidFuzz successfully detects that "Shawarma Prof." and "The Shawarma Professor" are basically the same name - even with abbreviation and missing words.
Note: There are also multiple ways to enhance the score
You can use the following match function to filterout the results:

# do not forget to set you value here: self.HIGH_CONFIDENCE
def match(self, query, restaurants):
        query_norm = self._normalize(self._remove_titles(query))
        results = []
        for r in restaurants:
            name = self._normalize(self._remove_titles(r["name"]))
            score = self._calculate_similarity(query_norm, name)
            if score >= self.HIGH_CONFIDENCE:
                results.append((r["name"], score))
        return sorted(results, key=lambda x: x[1], reverse=True

RapidFuzz Advantages

⚡ Super fast (C++ backend)
🔤 Handles multilingual data like Arabic and English
🔁 Understands reordered words
🎯 Ideal for search engines, autocompletes, or recommendation systems

Part 2: 🐢 Difflib - The Simpler, Built-In Alternative

Now let's assume we can't install external libraries - maybe our system runs on a restricted environment.
 Enter Difflib, a standard Python module for comparing sequences.
Let's swap the matching engine:

from difflib import SequenceMatcher

class RestaurantMatcher:

    def _normalize(self, text):
        text = ' '.join(text.strip().split())
        text = re.sub(r'[\u064B-\u065F\u0670]', '', text)  # remove Arabic diacritics
        return text

    def _calculate_similarity(self, a, b):
        return SequenceMatcher(None, a.lower(), b.lower()).ratio() * 100

    def match(self, query, restaurants):
        query_norm = self._normalize(query)
        results = []
        for r in restaurants:
            name = self._normalize(r["name"])
            score = self._calculate_similarity(query_norm, name)
            results.append((r["name"], score))
        return results

Same code, different engine.
Let's use it

if __name__ == "__main__":
    restaurants = [
        {"name": "The Shawarma Professor"},
        {"name": "Dr. Falafel"},
        {"name": "Taco Republic (Mexican Grill)"},
        {"name": "مطعم الدكتور فلافل"},
        {"name": "Shawarma Prof."},
    ]
    matcher = RestaurantMatcher2()
    matches = matcher.match("shawarma prof", restaurants)
    for name, score in matches:
        print(f"{name}: {score:.1f}")

Output:

The Shawarma Professor: 74.3
Dr. Falafel: 25.0
Taco Republic (Mexican Grill): 14.3
مطعم الدكتور فلافل: 6.5
Shawarma Prof.: 96.3

When Difflib Is Enough

✅ Built into Python - no installation needed
✅ Perfect for small datasets
✅ Easier to debug and explain to beginners

If your use case is simple - like comparing a few names or verifying duplicates - Difflib might be all you need.

In short:

Use RapidFuzz if you need speed and accuracy
Use Difflib if you need simplicity and zero dependencies

🎯 Conclusion: Smarter Search Starts with Fuzzy Matching

Smart text matching levels up your data game, and RapidFuzz vs Difflib is the ultimate duel. RapidFuzz dominates with speed, token intelligence, and accuracy - ideal for demanding apps. Difflib holds the fort with zero-setup reliability for everyday tasks.

i.e Fuzzy matching makes search feel effortless.

• RapidFuzz gives you the power to scale - fast, accurate, and language-aware.
• Difflib gives you a lightweight fallback for simpler projects.

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Which side are you on - RapidFuzz or Difflib? Drop your benchmarks in the comments.