5 Best Ways to Program to Find Minimum Jumps to Reach a Value with Different Parity in Python

Rate this post

πŸ’‘ Problem Formulation: This article delves into the intriguing problem of calculating the minimum number of jumps needed to reach a number with different parity from a given starting number in Python. Parity refers to whether a number is odd or even. The challenge is to find the least amount of increments or decrements (single jumps) to reach any number from the starting number that has different parity. For instance, starting at 5 (odd), the goal might be to reach an even number with the smallest number of jumps.

Method 1: Iterative Approach

The iterative approach towards finding the minimum jumps required to reach a value with different parity involves looping from the starting number and incrementing or decrementing the value until a number with the opposite parity is reached. This method is straightforward and easy to understand.

Here’s an example:

def min_jumps_to_diff_parity(start):
    jumps = 0
    while True:
        start += 1
        jumps += 1
        if start % 2 == 0:
    return jumps


Output: 1

This snippet defines a function min_jumps_to_diff_parity that takes a starting integer, and sequentially increases it until it reaches an even number. It tracks the number of increments in the ‘jumps’ variable, which it returns as the result.

Method 2: Mathematical Deduction

The mathematical deduction method leverages arithmetic properties to determine the minimum jumps to a differently paritied number without iteration. Odd numbers become even after one jump, and for even numbers, two jumps are always sufficient (one to become odd, another to become even).

Here’s an example:

def min_jumps_to_diff_parity(start):
    # Odd numbers need only one jump to become even
    if start % 2 != 0:
        return 1
        # Even numbers need two jumps to become odd, then even again
        return 2


Output: 2

This code defines the same function min_jumps_to_diff_parity, but this time it uses conditional logic to determine the result based on whether the starting number is odd or even, avoiding the need for a loop entirely.

Method 3: Bit Manipulation

Bit manipulation is a clever and efficient method to determine minimum jumps. Since the least significant bit (LSB) determines parity, we can simply check the LSB of the starting number to decide how many jumps are needed.

Here’s an example:

def min_jumps_to_diff_parity(start):
    return 1 if start & 1 else 2


Output: 1

This elegant snippet uses the bitwise AND operator to check if the starting number is odd (LSB is 1) or even (LSB is 0) and returns the corresponding number of jumps.

Method 4: Direct Evaluation

Direct evaluation involves using a ternary operator to output the minimum jumps in a single line of code, which is essentially a more compact version of the mathematical deduction method.

Here’s an example:

min_jumps_to_diff_parity = lambda start: 1 if start % 2 != 0 else 2


Output: 1

This time, a lambda function is defined inline, assigning to min_jumps_to_diff_parity. It uses the modulo operator to determine parity and ternary conditional to return the minimum jumps.

Bonus One-Liner Method 5: Functional Approach

Python’s functional programming features, like the next() function, can be used to create a one-liner that finds the next number with different parity, subsequently calculating the minimum jumps.

Here’s an example:

min_jumps_to_diff_parity = lambda start: next(i for i in range(1, 3) if (start + i) % 2 == 0)


Output: 1

Here we have another lambda function using next() with a generator expression. It generates the numbers 1 or 2, selects the first one that when added to the starting number results in a number with even parity (different from the starting number’s parity).


In summary, we discussed several methods to solve the problem of finding the minimum jumps to a value with different parity:

  • Method 1: Iterative Approach. Simple to understand. However, it’s less efficient than other methods since it requires looping.
  • Method 2: Mathematical Deduction. Efficient and doesn’t require looping. However, less direct than using bit manipulation.
  • Method 3: Bit Manipulation. Highly efficient and succinct. Might be less clear for those unfamiliar with bitwise operations.
  • Method 4: Direct Evaluation. Compact and efficient. Utilizes similar logic to Method 2 but in a more condensed form.
  • Method 5: Functional Approach. One-liner that is clever and efficient. However, it can be less intuitive for readers not familiar with Python’s functional capabilities.