# 5 Best Ways to Find the Sum of Unique Elements in Python

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π‘ Problem Formulation: Given a list of integers, the goal is to calculate the sum of only the unique elements, where “unique” refers to those elements that appear exactly once within the list. For example, if the input is `[7, 3, 5, 3, 4, 5]`, the unique elements are `[7, 4]`, and their sum is `11`.

## Method 1: Using a Dictionary

In this method, a dictionary is used to count the occurrences of each element. Then, the sum is taken over elements which have a count of 1. This approach is intuitive and enables us to easily identify unique elements based on their counts.

Here’s an example:

```def sum_of_unique(nums):
element_count = {}
for num in nums:
element_count[num] = element_count.get(num, 0) + 1
return sum(key for key, value in element_count.items() if value == 1)

print(sum_of_unique([7, 3, 5, 3, 4, 5]))```

Output:

`11`

This function iterates through the input list and populates a dictionary with element counts. Then, a dictionary comprehension is used to iterate over key-value pairs and sum the keys where the value is 1, indicating a unique element.

## Method 2: Using set and list operations

Another approach utilizes set and list operations to find unique elements. It involves creating a set from the list to identify unique elements and then filtering the original list to include only those occurrences. Finally, sum the filtered list.

Here’s an example:

```def sum_of_unique(nums):
unique_elements = set(nums)
return sum(e for e in unique_elements if nums.count(e) == 1)

print(sum_of_unique([7, 3, 5, 3, 4, 5]))```

Output:

`11`

This code defines a function that first determines the unique elements by casting the list to a set. Then it sums those elements that appear exactly once in the original list by using a generator expression.

## Method 3: Using Collections Counter

The `Collections` module provides a Counter class which is perfect for this task since it counts occurrences of elements in an iterable. We can then sum the keys for which the count is exactly one to find the answer.

Here’s an example:

```from collections import Counter

def sum_of_unique(nums):
counts = Counter(nums)
return sum(key for key, value in counts.items() if value == 1)

print(sum_of_unique([7, 3, 5, 3, 4, 5]))```

Output:

`11`

This snippet imports the Counter class from the `Collections` module, and uses it to create a dictionary-like object that stores the counts of each element in the list. Then, it iterates through this object to calculate the sum of keys with a count of 1.

## Method 4: Using numpy library

For lists with numerical values, the numpy library provides a fast and efficient way to handle data. It can be used to perform unique element extraction and summation effectively. This method works well with large datasets.

Here’s an example:

```import numpy as np

def sum_of_unique(nums):
vals, counts = np.unique(nums, return_counts=True)
return np.sum(vals[counts == 1])

print(sum_of_unique([7, 3, 5, 3, 4, 5]))```

Output:

`11`

The function uses numpy’s `unique` method with the `return_counts` parameter set to True to both identify unique values and count their occurrences. It then sums the values which have an occurrence count of one.

## Bonus One-Liner Method 5: Using generator expressions and set operations

This one-liner method combines set operations with a generator expression to identify and sum the unique elements in a concise manner. This is great for writing quick and readable code.

Here’s an example:

```nums = [7, 3, 5, 3, 4, 5]
print(sum(e for e in set(nums) if nums.count(e) == 1))```

Output:

`11`

This one-liner uses a generator expression within the `sum` function to iterate over a set created from the list, ensuring each element is evaluated only once, and sums those that appear exactly once in the original list.

## Summary/Discussion

• Method 1: Using a Dictionary. Straightforward and easy to understand. Performance might decline with a large number of elements because of dictionary overhead.
• Method 2: Using set and list operations. It’s more intuitive but less efficient with large lists due to repeated use of the `count()` method.
• Method 3: Using Collections Counter. Quick and concise, intended for counting items. It is the most pythonic way among those listed but adds a dependency on the Collections module.
• Method 4: Using numpy library. Offers excellent performance especially for large datasets but requires numpy, an external library that is not typically included with Python’s standard library.
• Bonus Method 5: One-Liner using generator expressions and set operations. Extremely concise code, perfect for short scripts. However, like Method 2, it might suffer in terms of performance due to the use of the `count()` operation.