5 Best Ways to Validate Credit Card Numbers Using Python

Rate this post

πŸ’‘ Problem Formulation: Validating a credit card number is essential to ensuring it follows the industry-standard format before processing transactions. This article provides Python programmers with methods to verify if a credit card number is valid or not. We will explore various algorithms and techniques, aiming to receive a credit card number as input and return a boolean indicating its validity as output.

Method 1: Using the Luhn Algorithm

The Luhn algorithm, also known as the “modulus 10” or “mod 10” algorithm, is a simple checksum formula used to validate a variety of identification numbers, especially credit card numbers. This method checks the validity of a credit card number by summing certain digits and performing a modulo operation.

Here’s an example:

def validate_credit_card(number):
    def digits_of(n):
        return [int(d) for d in str(n)]
    digits = digits_of(number)
    odd_digits = digits[-1::-2]
    even_digits = digits[-2::-2]
    checksum = sum(odd_digits)
    for d in even_digits:
        checksum += sum(digits_of(d*2))
    return checksum % 10 == 0

# Example usage:
card_number = 1234567890123456


This code snippet defines a function validate_credit_card that implements the Luhn algorithm to verify the validity of a given credit card number. It separates the digits of the provided number into odd and even positions, doubles the even digits, sums up all the individual digits, and then checks if the total modulo 10 is equal to zero, implying the number is valid.

Method 2: Using Regular Expressions for Pattern Matching

Regular expressions can be used to check if a credit card number matches the expected pattern for various card types, like Visa, MasterCard, etc. This method doesn’t validate the card’s actual legitimacy but checks if it’s plausibly valid.

Here’s an example:

import re

def validate_credit_card_pattern(number):
    pattern = r'^4[0-9]{12}(?:[0-9]{3})?$'  # Visa card pattern example
    match = re.match(pattern, str(number))
    return bool(match)

# Example usage:
card_number = '4123456789012'


The code snippet uses regular expressions to detect whether a credit card number corresponds to the pattern of a Visa card. It defines a function validate_credit_card_pattern that creates a regex pattern string and uses it to match the provided number, returning True for a match or False otherwise.

Method 3: Using the Python creditcard Module

The creditcard module in Python can be leveraged to validate credit card numbers using predefined functions. It provides a straightforward way to not only check for validity but also determine the card type.

Here’s an example:

from creditcard import validator

# Example usage:
card_number = '5399838383838381'


By importing the validator function from the creditcard module, the code snippet allows for an immediate check on the credit card number’s validity. This external library simplifies the process, as you don’t have to write the validation logic yourself.

Method 4: Using Python Classes to Encapsulate Validation Logic

Using a Python class to encapsulate the validation logic can aid in creating a more structured codebase. This method allows the programmer to extend the functionality and maintain the code more effectively.

Here’s an example:

class CreditCardValidator:
    def luhn_checksum(card_number):
        """Implement Luhn Algorithm."""
        # Luhn logic...

    def is_valid(self, card_number):
        # Validation logic using self.luhn_checksum...

validator = CreditCardValidator()


This snippet demonstrates how to define a class CreditCardValidator to handle credit card validation. Using methods within the class, such as the static method for the Luhn checksum, makes the validation logic more modular and reusable within different parts of a codebase.

Bonus One-Liner Method 5: Using a Lambda Function

Lambda functions in Python are small anonymous functions that can be used to encapsulate simple expressions into a compact form. This one-liner method offers a brief way to define the validation logic.

Here’s an example:

luhn_validator = lambda num: sum(map(lambda x: x if x < 10 else x - 9, [int(x) if idx % 2 == 0 else int(x) * 2 for idx, x in enumerate(str(num)[::-1])])) % 10 == 0


The one-liner defines a lambda function luhn_validator that compresses the Luhn algorithm into a single line of code. It uses list comprehension combined with conditional logic to duplicate and transform the necessary digits, summing them and checking the modulo for validation.


  • Method 1: Luhn Algorithm. Reliable and widely used for checksum validation. May not identify card type or specificity beyond basic validity.
  • Method 2: Regex Pattern Matching. Quick for identifying card types based on patterns. Limited as it doesn’t perform a full validity check on the card number.
  • Method 3: Python creditcard Module. Handy and easy to use for checking validity and identifying card type. Reliant on third-party modules, which may not be ideal in all environments.
  • Method 4: Python Classes. Provides a structured approach to maintain and extend validation logic. Requires more code and understanding of object-oriented programming concepts.
  • Method 5: Lambda Function. Concise and functional. However, it can be less readable, especially for those unfamiliar with lambda functions or complex list comprehensions.