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Python is a versatile programming language that offers a wide range of functionalities. One common task in programming is finding the sum of numbers in a given list. In this article, we will explore a number sum finder algorithm in Python 3 that efficiently calculates the sum of numbers in a list.The ProblemGiven a list of numbers, we need to find the sum of all the numbers in the list. This task may seem simple at first, but as the size of the list increases, the time complexity of the algorithm becomes crucial. We aim to develop an algorithm that can handle large lists efficiently.The AlgorithmLet’s dive into the number sum finder algorithm in Python 3. We will use a simple approach that iterates through each number in the list and adds it to a running sum. Here’s the implementation:def find_sum(numbers): sum = 0 for num in numbers: sum += num return sumThe algorithm starts by initializing a variable called “sum” to zero. Then, it iterates through each number in the given list using a for loop. In each iteration, the current number is added to the running sum. Finally, the algorithm returns the sum once all numbers have been processed.Time Complexity AnalysisThe time complexity of this algorithm is O(n), where n is the size of the input list. This means that the time taken to find the sum of numbers in the list increases linearly with the size of the list. The algorithm performs a constant amount of work for each number in the list, resulting in a linear time complexity.Example UsageLet’s see the number sum finder algorithm in action with a simple example:numbers = [1, 2, 3, 4, 5]result = find_sum(numbers)print(result) # Output: 15In this example, we have a list of numbers [1, 2, 3, 4, 5]. The algorithm calculates the sum of these numbers, which is 15. The result is then printed to the console.The number sum finder algorithm in Python 3 provides a simple and efficient solution to find the sum of numbers in a list. By iterating through each number and adding it to a running sum, we can calculate the sum in linear time. This algorithm is suitable for handling large lists and can be easily implemented in Python 3.Number Sum Finder Algorithm in Python 3Here are a few examples of the Number Sum Finder Algorithm in Python 3:def find_number_sum(numbers, target): """ Function to

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Find a pair of numbers in a given list that adds up to a target number. Parameters: numbers (list): List of numbers. target (int): Target number. Returns: tuple: A tuple containing the pair of numbers that adds up to the target, or None if no such pair exists. """ complement_map = {} for num in numbers: complement = target - num if complement in complement_map: return (complement, num) complement_map[num] = True return None# Example usagenumbers = [2, 4, 6, 8, 10]target = 14result = find_number_sum(numbers, target)if result: print(f"A pair of numbers that adds up to {target} is: {result}")else: print("No pair of numbers found.")# Output: A pair of numbers that adds up to 14 is: (4, 10)In the above example, we define a function called find_number_sum that takes a list of numbers and a target number as input. The function uses a dictionary called complement_map to store the complements of the numbers encountered so far. For each number in the list, we calculate its complement (target minus the number) and check if it exists in the complement_map. If it does, we have found a pair of numbers that adds up to the target, and we return the pair. If no such pair is found, we return None.We then demonstrate the usage of the find_number_sum function with an example. We create a list of numbers [2, 4, 6, 8, 10] and set the target to 14. We call the find_number_sum function with these inputs and store the result in the result variable. If a pair of numbers is found, we print it; otherwise, we print a message indicating that no pair of numbers was found.The output of the above example is “A pair of numbers that adds up to 14 is: (4, 10)”, indicating that the pair (4, 10) adds up to the target number 14.In conclusion, the Number Sum Finder Algorithm in Python 3 allows us to efficiently find a pair of numbers in a given list that adds up to a target number. By using a dictionary to store the complements of the numbers encountered, we can achieve a time complexity of O(n), where n is the number of elements in the list. This algorithm can be useful in various scenarios, such as finding pairs of numbers with a specific sum in financial data or solving coding interview questions related to number sums.Reference links:Two-pointer algorithmGeeksforGeeks: Given an array A[] and

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