5 Best Ways to Check if an Array Element Equals the Sum of Remaining Elements in Python

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πŸ’‘ Problem Formulation: In Python, we often face tasks that require data analysis within arrays or lists. A common challenge is determining if an array contains an element that is the sum of all other elements. Consider an input array [1, 2, 3, 6], the desired output would be True because the last element (6) equals the sum of the other elements (1+2+3).

Method 1: Iterative Approach

This method involves iterating through each element in the array and calculating the sum of the remaining elements to check for equality. It is straightforward and easy to understand.

Here’s an example:

def check_sum(arr):
    for i in range(len(arr)):
        if arr[i] == sum(arr[:i] + arr[i+1:]):
            return True
    return False

# Test the function
print(check_sum([1, 2, 3, 6]))

Output: True

This snippet defines a function check_sum that uses a for loop to consider each element. If an element equals the sum of all other elements, it returns True. Otherwise, it returns False after completing the iteration.

Method 2: Using list comprehension

List comprehension offers a more Pythonic and concise way to solve the problem. This approach is typically faster than an iterative approach.

Here’s an example:

def check_sum(arr):
    return any(arr[i] == sum(arr[:i] + arr[i+1:]) for i in range(len(arr)))

# Test the function
print(check_sum([1, 2, 3, 6]))

Output: True

The code uses list comprehension combined with the any function to achieve the same result as the iterative approach in a more compact form, which can be more efficient and easier to read.

Method 3: Optimized Sum Calculation

This method optimizes the sum calculation by first computing the total sum of the array once and then checking each element by subtracting it from the total to avoid summing on each iteration.

Here’s an example:

def check_sum(arr):
    total_sum = sum(arr)
    return any(item == total_sum - item for item in arr)

# Test the function
print(check_sum([1, 2, 3, 6]))

Output: True

By calculating the total sum of the array upfront, this code snippet only has to iterate through the array once. For each element, it subtracts the element from the total to check if it matches the sum of the remaining elements.

Method 4: Using a Set for Lookup

This method improves performance for large datasets by using a set for lookup, which has O(1) time complexity for checking if an element exists.

Here’s an example:

def check_sum(arr):
    total_sum = sum(arr)
    seen = set(arr)
    return any((total_sum - item) in seen and (total_sum - item) != item for item in arr)

# Test the function
print(check_sum([1, 2, 3, 6]))

Output: True

This function calculates the total sum and populates a set with the array’s elements. When iterating through the array, it uses the set to quickly check if the complement of the current element (total minus current element) is present in the set, ensuring not to count the element itself.

Bonus One-Liner Method 5: Using a Functional Approach

A functional approach can be used to solve this problem in a compact and expressive one-liner code using the filter and next functions.

Here’s an example:

arr = [1, 2, 3, 6]
check_sum = lambda arr: bool(next(filter(lambda x: x == sum(arr) - x, arr), False))

# Test the function
print(check_sum(arr))

Output: True

The one-liner uses a lambda function to filter out elements that match the condition and then uses next to check if there’s such an element, defaulting to False if not. This method is compact but might be less readable for those unfamiliar with functional programming.

Summary/Discussion

  • Method 1: Iterative Approach. Simple and readable. Not efficient for large arrays due to repeated sum calculations.
  • Method 2: List Comprehension. More Pythonic and potentially faster for small to medium-sized arrays. Still not the most efficient for very large arrays.
  • Method 3: Optimized Sum Calculation. Efficient and readable. Greatly reduces the number of operations required. Best for larger arrays.
  • Method 4: Using a Set for Lookup. Efficient with large datasets due to O(1) lookup times in a set. Slightly more complex.
  • Method 5: Functional Approach. Compact and elegant. Best for one-off uses or in a functional programming context, but may be less intuitive for some developers.