5 Best Ways to Check if a Binary Representation Has Equal Number of 0s and 1s in Blocks in Python

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π‘ Problem Formulation: When working with binary numbers, an interesting problem is to determine if the binary representation of a number includes blocks with equal quantities of 0s and 1s. For example, given the binary number `110011`, you should be able to ascertain whether it contains blocks of equal count of 0s and 1s, such as two ’11’ and two ’00’.

Method 1: Iterative Comparison

This method involves iterating over the binary representation of the number and comparing subsequent blocks of digits. It requires a function that converts the number to binary, counts consecutive zeroes and ones, and then compares these counts to check if they are equal.

Here’s an example:

```def has_equal_zero_one_blocks(n):
binary = bin(n)[2:]
count = 1
for i in range(1, len(binary)):
if binary[i] == binary[i-1]:
count += 1
else:
if count % 2 != 0:
return False
count = 1
return count % 2 == 0

# Testing the function
print(has_equal_zero_one_blocks(6)) # Binary: 110
```

The output of this code snippet:

`False`

This code snippet defines a function `has_equal_zero_one_blocks` that first converts a given number to its binary string representation. Then, it iterates through the binary digits, counting consecutive similar bits and ensuring that each block of zeroes and ones has the same length.

Method 2: Regular Expressions

The regular expression method leverages the Python `re` module to match patterns in the binary string and verify if there are equal blocks of 0s and 1s. It’s a high-level approach that abstracts much of the manual checking involved in other methods.

Here’s an example:

```import re

def equal_blocks_regex(n):
binary = bin(n)[2:]
return bool(re.fullmatch('(10)*', binary))

# Testing the function
print(equal_blocks_regex(10)) # Binary: 1010
```

The output of this code snippet:

`True`

This snippet uses the `re.fullmatch` function to check if the binary string representation of the number matches a pattern where ‘1’ and ‘0’ appear in consecutive pairs from the beginning to the end of the string.

Method 3: Using Groupby from itertools

The Python itertools’s `groupby` function can group elements of a sequence. Here, it is used to group consecutive bits and then compare the lengths of alternate groups to ensure they are of equal size in the binary representation.

Here’s an example:

```from itertools import groupby

def equal_blocks_groupby(n):
binary = bin(n)[2:]
groups = [list(g) for _, g in groupby(binary)]
return all(len(groups[i]) == len(groups[i+1]) for i in range(0, len(groups), 2))

# Testing the function
print(equal_blocks_groupby(27)) # Binary: 11011
```

The output of this code snippet:

`False`

In this code, the `groupby` function from the itertools module is used to group consecutive duplicate bits in the binary string, and then the sizes of these grouped blocks are compared to each other.

Method 4: Counting and Pairing

This method entails counting the number of 1’s and 0’s in pairs as they appear in the binary representation. It’s a straightforward check that fails early if any unmatched block is found during counting. The strength lies in its simplicity and efficiency.

Here’s an example:

```def has_matching_pairs(n):
binary = bin(n)[2:]
ones = zeros = 0
for bit in binary:
if bit == '0':
if ones and (ones != zeros):
return False
zeros += 1
else:
if zeros and (zeros != ones):
return False
ones += 1
return ones == zeros

# Testing the function
print(has_matching_pairs(3)) # Binary: 11
```

The output of this code snippet:

`False`

The code defines a function `has_matching_pairs` which iterates over the binary string and counts the number of 0s and 1s. If at any point the counts do not match, the function returns `False`. If the loop completes, it checks the final counts for equality.

Bonus One-Liner Method 5: Slice Checking

A concise one-liner approach, this method relies on Python’s ability to take slices of strings and compare them directly. This is less versatile and may only apply to simple scenarios, but is included here for the sake of completeness.

Here’s an example:

```is_balanced = lambda x: bin(x)[2:][:len(bin(x)[2:])//2] == bin(x)[2:][len(bin(x)[2:])//2:]

# Testing the lambda
print(is_balanced(9)) # Binary: 1001
```

The output of this code snippet:

`True`

This one-liner lambda function `is_balanced` compares two halves of the binary string to check for balance. It’s a straightforward comparison that works well when the binary representation has an even number of digits and is balanced in the middle.

Summary/Discussion

• Method 1: Iterative Comparison. Strong in terms of simplicity and directness. Weak in case of sequential inefficient looping in large binary strings.
• Method 2: Regular Expressions. Powerful for pattern-based checks. Limited by the necessity of knowing regex patterns and less intuitive than simple iterations.
• Method 3: Using Groupby from itertools. Effectively abstracts away some of the manual looping, but creates additional lists in memory which might be unnecessary overhead for large strings.
• Method 4: Counting and Pairing. Highly efficient and straightforward; however, it may not be flexible for more complex conditions for balance checks outside of simple pairs.
• Method 5: Slice Checking. The simplest method but also the least flexible. It won’t work well for strings with an odd number of characters or complex block patterns.