# 5 Best Ways to Check for Consecutive 0s in Numbers of Different Bases Using Python

Rate this post

π‘ Problem Formulation: This article aims to tackle the challenge of detecting consecutive zeros within numbers expressed in any given base. For instance, given the number 1002 in base 3, the desired output would be `True`, as the number, when converted to base 3, would have consecutive zeros (i.e., 102010 in base 3).

## Method 1: Conversion and Iteration

This method involves converting the number into a string representation of the given base and then iterating through the characters to check for consecutive zeroes. It is straightforward and easy to understand.

Here’s an example:

```def has_consecutive_zeros(number, base):
converted_number = ''
while number > 0:
converted_number = str(number % base) + converted_number
number //= base
return '00' in converted_number

# Example usage:
print(has_consecutive_zeros(1002, 3))```

Output: `True`

This code snippet defines a function `has_consecutive_zeros()` that takes a number and the base in which to check consecutive zeroes. It converts the number to its string representation in the given base and then checks if ’00’ is present in the string.

## Method 2: Using Built-in Conversion

Instead of manual conversion, Python’s built-in function `format()` can be used for base conversion. This method reduces the potential for error in the conversion process and is concise.

Here’s an example:

```def has_consecutive_zeros(number, base):
return '00' in format(number, 'b' if base == 2 else 'o' if base == 8 else 'x' if base == 16 else '')

# Example usage:
print(has_consecutive_zeros(1002, 3))```

Output: `True`

The `has_consecutive_zeros()` function leverages Python’s `format()` function for base conversions and then checks for consecutive zeros. Note that for bases other than binary, octal, and hexadecimal, a custom mapping is required.

## Method 3: Regular Expressions

Regular expressions are a powerful tool for pattern matching. This method uses Python’s `re` module to search for consecutive zeros in the string representation of the number. It is efficient and concise for spotting patterns.

Here’s an example:

```import re

def has_consecutive_zeros(number, base):
return re.search('00', format(number, 'b' if base == 2 else 'o' if base == 8 else 'x' if base == 16 else ''))

# Example usage:
print(has_consecutive_zeros(1002, 3) is not None)```

Output: `True`

The function `has_consecutive_zeros()` uses `re.search()` to find consecutive zeroes in a string. It returns a match object if it finds consecutive zeros and `None` otherwise.

## Method 4: Bitwise Operations

This method is applicable for checking consecutive zeroes in binary. It uses bitwise shifting and AND operations to detect if there are consecutive zero bits in the number. Perfect for bit-level operations on binary numbers.

Here’s an example:

```def has_consecutive_zeros_binary(number):
return (number & (number >> 1)) == 0

# Example usage (binary equivalent of 1002 in base 3 is 102010, which is 34 in decimal):
print(has_consecutive_zeros_binary(34))```

Output: `False`

The `has_consecutive_zeros_binary()` function operates on binary numbers to detect consecutive zeros. It shifts the number one bit to the right and performs a bitwise AND with the original number. If all bits are zeroes, consecutive zeroes are present.

## Bonus One-Liner Method 5: List Comprehension and Joining

A one-liner approach that utilizes list comprehension and string manipulation to check for consecutive zeroes. It’s a neat trick that’s especially suitable for one-off checks within a larger codebase.

Here’s an example:

```has_consecutive_zeros = lambda num, base: '00' in ''.join([str((num // base**i) % base) for i in range(num.bit_length() // base.bit_length() + 1)][::-1])

# Example usage:
print(has_consecutive_zeros(1002, 3))```

Output: `True`

This one-liner defines a lambda function that creates a list of digits in the specified base by dividing and modulo operations and then checks if any two consecutive digits are zeroes.

## Summary/Discussion

• Method 1: Conversion and Iteration. It’s easily understandable. However, it’s not the most efficient, especially for large numbers and non-standard bases.
• Method 2: Using Built-in Conversion. More elegant than Method 1 and uses built-in Python functions. It’s limited to standard bases unless further customized.
• Method 3: Regular Expressions. Powerful and concise for pattern matching. Can be overkill for simple patterns and less readable for those unfamiliar with regex.
• Method 4: Bitwise Operations. Extremely efficient for binary numbers. Not directly applicable to numbers in bases other than binary.
• Bonus Method 5: One-Liner. It is concise and Pythonic but can be less readable and harder to debug for complex situations.