# 5 Best Ways to Write a Program to Form a Cumulative Sum List in Python

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π‘ Problem Formulation: In Python, forming a cumulative sum list is a common operation that involves creating a new list where each element is the sum of the elements in the original list up to that index. For instance, given an input list `[1, 2, 3, 4]`, the desired cumulative sum output would be `[1, 3, 6, 10]`.

## Method 1: Using a For Loop

This method involves iterating over the original list and adding each element to a running total, which is then appended to a new list. This approach is clear and illustrative, giving beginners a good comprehension of how cumulative sums are calculated.

Here’s an example:

```from functools import reduce

nums = [1, 2, 3, 4]
cumulative_sum = reduce(lambda c, x: c + [c[-1] + x] if c else [x], nums, [])

print(cumulative_sum)
```

Output: `[1, 3, 6, 10]`

This code uses `reduce()` to build the cumulative sum list. The lambda takes a cumulative list `c` and an element `x`, appends the sum of `x` and the last element in `c` to `c`, and continues this cumulatively through the list.

## Summary/Discussion

• Method 1: For Loop. Easy to understand and implement. Not the most efficient for large datasets due to O(n) space and time complexity.
• Method 2: List Comprehension and Sum. Pythonic and compact. It can be less efficient than other methods, as sum is called repeatedly for each element.
• Method 3: accumulate Function. Pythonic and efficient, especially for iterator-based processing. It is a built-in function, but requires importing `itertools`.
• Method 4: NumPy’s cumsum. Highly efficient for numerical computations on large arrays. However, it requires NumPy, which is not part of Python’s standard library.
• Method 5: reduce with Lambda. Compact and functional programming style. May be less readable for those not familiar with `reduce()` or lambda functions.
```import numpy as np

nums = np.array([1, 2, 3, 4])
cumulative_sum = np.cumsum(nums)

print(cumulative_sum)
```

Output: `[1 3 6 10]`

This snippet uses NumPy’s `cumsum()` function to efficiently compute the cumulative sum of an array, returning a NumPy array with the cumulative totals.

## Bonus One-Liner Method 5: Using Reduce with Lambda

This elegance of functional programming is showcased using the `reduce()` function in combination with a lambda expression to achieve cumulative sum in a one-liner command.

Here’s an example:

```from functools import reduce

nums = [1, 2, 3, 4]
cumulative_sum = reduce(lambda c, x: c + [c[-1] + x] if c else [x], nums, [])

print(cumulative_sum)
```

Output: `[1, 3, 6, 10]`

This code uses `reduce()` to build the cumulative sum list. The lambda takes a cumulative list `c` and an element `x`, appends the sum of `x` and the last element in `c` to `c`, and continues this cumulatively through the list.

## Summary/Discussion

• Method 1: For Loop. Easy to understand and implement. Not the most efficient for large datasets due to O(n) space and time complexity.
• Method 2: List Comprehension and Sum. Pythonic and compact. It can be less efficient than other methods, as sum is called repeatedly for each element.
• Method 3: accumulate Function. Pythonic and efficient, especially for iterator-based processing. It is a built-in function, but requires importing `itertools`.
• Method 4: NumPy’s cumsum. Highly efficient for numerical computations on large arrays. However, it requires NumPy, which is not part of Python’s standard library.
• Method 5: reduce with Lambda. Compact and functional programming style. May be less readable for those not familiar with `reduce()` or lambda functions.
```from itertools import accumulate

nums = [1, 2, 3, 4]
cumulative_sum = list(accumulate(nums))

print(cumulative_sum)
```

Output: `[1, 3, 6, 10]`

The code utilizes the `accumulate()` function, which takes an iterable as input and returns an iterator over the intermediate cumulative sum results, which is then converted into a list.

## Method 4: Using NumPy’s cumsum Function

For those working within the scientific computing realm, NumPy’s `cumsum()` function provides a high-performance method to compute cumulative sums on numerical data.

Here’s an example:

```import numpy as np

nums = np.array([1, 2, 3, 4])
cumulative_sum = np.cumsum(nums)

print(cumulative_sum)
```

Output: `[1 3 6 10]`

This snippet uses NumPy’s `cumsum()` function to efficiently compute the cumulative sum of an array, returning a NumPy array with the cumulative totals.

## Bonus One-Liner Method 5: Using Reduce with Lambda

This elegance of functional programming is showcased using the `reduce()` function in combination with a lambda expression to achieve cumulative sum in a one-liner command.

Here’s an example:

```from functools import reduce

nums = [1, 2, 3, 4]
cumulative_sum = reduce(lambda c, x: c + [c[-1] + x] if c else [x], nums, [])

print(cumulative_sum)
```

Output: `[1, 3, 6, 10]`

This code uses `reduce()` to build the cumulative sum list. The lambda takes a cumulative list `c` and an element `x`, appends the sum of `x` and the last element in `c` to `c`, and continues this cumulatively through the list.

## Summary/Discussion

• Method 1: For Loop. Easy to understand and implement. Not the most efficient for large datasets due to O(n) space and time complexity.
• Method 2: List Comprehension and Sum. Pythonic and compact. It can be less efficient than other methods, as sum is called repeatedly for each element.
• Method 3: accumulate Function. Pythonic and efficient, especially for iterator-based processing. It is a built-in function, but requires importing `itertools`.
• Method 4: NumPy’s cumsum. Highly efficient for numerical computations on large arrays. However, it requires NumPy, which is not part of Python’s standard library.
• Method 5: reduce with Lambda. Compact and functional programming style. May be less readable for those not familiar with `reduce()` or lambda functions.
```nums = [1, 2, 3, 4]
cumulative_sum = [sum(nums[:i+1]) for i in range(len(nums))]

print(cumulative_sum)
```

Output: `[1, 3, 6, 10]`

The list comprehension iterates through the indices of the original list, slicing it up to the current index and using `sum()` to calculate the cumulative total.

## Method 3: Using the accumulate Function from itertools

The `accumulate()` function from the `itertools` module provides a direct way to compute cumulative sums in Python. This function is a fast and memory-efficient tool for such tasks.

Here’s an example:

```from itertools import accumulate

nums = [1, 2, 3, 4]
cumulative_sum = list(accumulate(nums))

print(cumulative_sum)
```

Output: `[1, 3, 6, 10]`

The code utilizes the `accumulate()` function, which takes an iterable as input and returns an iterator over the intermediate cumulative sum results, which is then converted into a list.

## Method 4: Using NumPy’s cumsum Function

For those working within the scientific computing realm, NumPy’s `cumsum()` function provides a high-performance method to compute cumulative sums on numerical data.

Here’s an example:

```import numpy as np

nums = np.array([1, 2, 3, 4])
cumulative_sum = np.cumsum(nums)

print(cumulative_sum)
```

Output: `[1 3 6 10]`

This snippet uses NumPy’s `cumsum()` function to efficiently compute the cumulative sum of an array, returning a NumPy array with the cumulative totals.

## Bonus One-Liner Method 5: Using Reduce with Lambda

This elegance of functional programming is showcased using the `reduce()` function in combination with a lambda expression to achieve cumulative sum in a one-liner command.

Here’s an example:

```from functools import reduce

nums = [1, 2, 3, 4]
cumulative_sum = reduce(lambda c, x: c + [c[-1] + x] if c else [x], nums, [])

print(cumulative_sum)
```

Output: `[1, 3, 6, 10]`

This code uses `reduce()` to build the cumulative sum list. The lambda takes a cumulative list `c` and an element `x`, appends the sum of `x` and the last element in `c` to `c`, and continues this cumulatively through the list.

## Summary/Discussion

• Method 1: For Loop. Easy to understand and implement. Not the most efficient for large datasets due to O(n) space and time complexity.
• Method 2: List Comprehension and Sum. Pythonic and compact. It can be less efficient than other methods, as sum is called repeatedly for each element.
• Method 3: accumulate Function. Pythonic and efficient, especially for iterator-based processing. It is a built-in function, but requires importing `itertools`.
• Method 4: NumPy’s cumsum. Highly efficient for numerical computations on large arrays. However, it requires NumPy, which is not part of Python’s standard library.
• Method 5: reduce with Lambda. Compact and functional programming style. May be less readable for those not familiar with `reduce()` or lambda functions.
```nums = [1, 2, 3, 4]
cumulative_sum = []
total = 0
for num in nums:
total += num
cumulative_sum.append(total)

print(cumulative_sum)
```

Output: `[1, 3, 6, 10]`

The code snippet above demonstrates creating a cumulative sum using a `for` loop. On each iteration, the current number is added to a running total and then appended to the `cumulative_sum` list.

## Method 2: Using List Comprehension and Sum

This method leverages Python’s list comprehension feature combined with the `sum()` function. It’s an elegant and Pythonic way to calculate cumulative sums, utilizing the power of list comprehensions for conciseness.

Here’s an example:

```nums = [1, 2, 3, 4]
cumulative_sum = [sum(nums[:i+1]) for i in range(len(nums))]

print(cumulative_sum)
```

Output: `[1, 3, 6, 10]`

The list comprehension iterates through the indices of the original list, slicing it up to the current index and using `sum()` to calculate the cumulative total.

## Method 3: Using the accumulate Function from itertools

The `accumulate()` function from the `itertools` module provides a direct way to compute cumulative sums in Python. This function is a fast and memory-efficient tool for such tasks.

Here’s an example:

```from itertools import accumulate

nums = [1, 2, 3, 4]
cumulative_sum = list(accumulate(nums))

print(cumulative_sum)
```

Output: `[1, 3, 6, 10]`

The code utilizes the `accumulate()` function, which takes an iterable as input and returns an iterator over the intermediate cumulative sum results, which is then converted into a list.

## Method 4: Using NumPy’s cumsum Function

For those working within the scientific computing realm, NumPy’s `cumsum()` function provides a high-performance method to compute cumulative sums on numerical data.

Here’s an example:

```import numpy as np

nums = np.array([1, 2, 3, 4])
cumulative_sum = np.cumsum(nums)

print(cumulative_sum)
```

Output: `[1 3 6 10]`

This snippet uses NumPy’s `cumsum()` function to efficiently compute the cumulative sum of an array, returning a NumPy array with the cumulative totals.

## Bonus One-Liner Method 5: Using Reduce with Lambda

This elegance of functional programming is showcased using the `reduce()` function in combination with a lambda expression to achieve cumulative sum in a one-liner command.

Here’s an example:

```from functools import reduce

nums = [1, 2, 3, 4]
cumulative_sum = reduce(lambda c, x: c + [c[-1] + x] if c else [x], nums, [])

print(cumulative_sum)
```

Output: `[1, 3, 6, 10]`

This code uses `reduce()` to build the cumulative sum list. The lambda takes a cumulative list `c` and an element `x`, appends the sum of `x` and the last element in `c` to `c`, and continues this cumulatively through the list.

## Summary/Discussion

• Method 1: For Loop. Easy to understand and implement. Not the most efficient for large datasets due to O(n) space and time complexity.
• Method 2: List Comprehension and Sum. Pythonic and compact. It can be less efficient than other methods, as sum is called repeatedly for each element.
• Method 3: accumulate Function. Pythonic and efficient, especially for iterator-based processing. It is a built-in function, but requires importing `itertools`.
• Method 4: NumPy’s cumsum. Highly efficient for numerical computations on large arrays. However, it requires NumPy, which is not part of Python’s standard library.
• Method 5: reduce with Lambda. Compact and functional programming style. May be less readable for those not familiar with `reduce()` or lambda functions.
```import numpy as np

nums = np.array([1, 2, 3, 4])
cumulative_sum = np.cumsum(nums)

print(cumulative_sum)
```

Output: `[1 3 6 10]`

This snippet uses NumPy’s `cumsum()` function to efficiently compute the cumulative sum of an array, returning a NumPy array with the cumulative totals.

## Bonus One-Liner Method 5: Using Reduce with Lambda

This elegance of functional programming is showcased using the `reduce()` function in combination with a lambda expression to achieve cumulative sum in a one-liner command.

Here’s an example:

```from functools import reduce

nums = [1, 2, 3, 4]
cumulative_sum = reduce(lambda c, x: c + [c[-1] + x] if c else [x], nums, [])

print(cumulative_sum)
```

Output: `[1, 3, 6, 10]`

This code uses `reduce()` to build the cumulative sum list. The lambda takes a cumulative list `c` and an element `x`, appends the sum of `x` and the last element in `c` to `c`, and continues this cumulatively through the list.

## Summary/Discussion

• Method 1: For Loop. Easy to understand and implement. Not the most efficient for large datasets due to O(n) space and time complexity.
• Method 2: List Comprehension and Sum. Pythonic and compact. It can be less efficient than other methods, as sum is called repeatedly for each element.
• Method 3: accumulate Function. Pythonic and efficient, especially for iterator-based processing. It is a built-in function, but requires importing `itertools`.
• Method 4: NumPy’s cumsum. Highly efficient for numerical computations on large arrays. However, it requires NumPy, which is not part of Python’s standard library.
• Method 5: reduce with Lambda. Compact and functional programming style. May be less readable for those not familiar with `reduce()` or lambda functions.
```nums = [1, 2, 3, 4]
cumulative_sum = []
total = 0
for num in nums:
total += num
cumulative_sum.append(total)

print(cumulative_sum)
```

Output: `[1, 3, 6, 10]`

The code snippet above demonstrates creating a cumulative sum using a `for` loop. On each iteration, the current number is added to a running total and then appended to the `cumulative_sum` list.

## Method 2: Using List Comprehension and Sum

This method leverages Python’s list comprehension feature combined with the `sum()` function. It’s an elegant and Pythonic way to calculate cumulative sums, utilizing the power of list comprehensions for conciseness.

Here’s an example:

```nums = [1, 2, 3, 4]
cumulative_sum = [sum(nums[:i+1]) for i in range(len(nums))]

print(cumulative_sum)
```

Output: `[1, 3, 6, 10]`

The list comprehension iterates through the indices of the original list, slicing it up to the current index and using `sum()` to calculate the cumulative total.

## Method 3: Using the accumulate Function from itertools

The `accumulate()` function from the `itertools` module provides a direct way to compute cumulative sums in Python. This function is a fast and memory-efficient tool for such tasks.

Here’s an example:

```from itertools import accumulate

nums = [1, 2, 3, 4]
cumulative_sum = list(accumulate(nums))

print(cumulative_sum)
```

Output: `[1, 3, 6, 10]`

The code utilizes the `accumulate()` function, which takes an iterable as input and returns an iterator over the intermediate cumulative sum results, which is then converted into a list.

## Method 4: Using NumPy’s cumsum Function

For those working within the scientific computing realm, NumPy’s `cumsum()` function provides a high-performance method to compute cumulative sums on numerical data.

Here’s an example:

```import numpy as np

nums = np.array([1, 2, 3, 4])
cumulative_sum = np.cumsum(nums)

print(cumulative_sum)
```

Output: `[1 3 6 10]`

This snippet uses NumPy’s `cumsum()` function to efficiently compute the cumulative sum of an array, returning a NumPy array with the cumulative totals.

## Bonus One-Liner Method 5: Using Reduce with Lambda

This elegance of functional programming is showcased using the `reduce()` function in combination with a lambda expression to achieve cumulative sum in a one-liner command.

Here’s an example:

```from functools import reduce

nums = [1, 2, 3, 4]
cumulative_sum = reduce(lambda c, x: c + [c[-1] + x] if c else [x], nums, [])

print(cumulative_sum)
```

Output: `[1, 3, 6, 10]`

This code uses `reduce()` to build the cumulative sum list. The lambda takes a cumulative list `c` and an element `x`, appends the sum of `x` and the last element in `c` to `c`, and continues this cumulatively through the list.

## Summary/Discussion

• Method 1: For Loop. Easy to understand and implement. Not the most efficient for large datasets due to O(n) space and time complexity.
• Method 2: List Comprehension and Sum. Pythonic and compact. It can be less efficient than other methods, as sum is called repeatedly for each element.
• Method 3: accumulate Function. Pythonic and efficient, especially for iterator-based processing. It is a built-in function, but requires importing `itertools`.
• Method 4: NumPy’s cumsum. Highly efficient for numerical computations on large arrays. However, it requires NumPy, which is not part of Python’s standard library.
• Method 5: reduce with Lambda. Compact and functional programming style. May be less readable for those not familiar with `reduce()` or lambda functions.
```from itertools import accumulate

nums = [1, 2, 3, 4]
cumulative_sum = list(accumulate(nums))

print(cumulative_sum)
```

Output: `[1, 3, 6, 10]`

The code utilizes the `accumulate()` function, which takes an iterable as input and returns an iterator over the intermediate cumulative sum results, which is then converted into a list.

## Method 4: Using NumPy’s cumsum Function

For those working within the scientific computing realm, NumPy’s `cumsum()` function provides a high-performance method to compute cumulative sums on numerical data.

Here’s an example:

```import numpy as np

nums = np.array([1, 2, 3, 4])
cumulative_sum = np.cumsum(nums)

print(cumulative_sum)
```

Output: `[1 3 6 10]`

This snippet uses NumPy’s `cumsum()` function to efficiently compute the cumulative sum of an array, returning a NumPy array with the cumulative totals.

## Bonus One-Liner Method 5: Using Reduce with Lambda

This elegance of functional programming is showcased using the `reduce()` function in combination with a lambda expression to achieve cumulative sum in a one-liner command.

Here’s an example:

```from functools import reduce

nums = [1, 2, 3, 4]
cumulative_sum = reduce(lambda c, x: c + [c[-1] + x] if c else [x], nums, [])

print(cumulative_sum)
```

Output: `[1, 3, 6, 10]`

This code uses `reduce()` to build the cumulative sum list. The lambda takes a cumulative list `c` and an element `x`, appends the sum of `x` and the last element in `c` to `c`, and continues this cumulatively through the list.

## Summary/Discussion

• Method 1: For Loop. Easy to understand and implement. Not the most efficient for large datasets due to O(n) space and time complexity.
• Method 2: List Comprehension and Sum. Pythonic and compact. It can be less efficient than other methods, as sum is called repeatedly for each element.
• Method 3: accumulate Function. Pythonic and efficient, especially for iterator-based processing. It is a built-in function, but requires importing `itertools`.
• Method 4: NumPy’s cumsum. Highly efficient for numerical computations on large arrays. However, it requires NumPy, which is not part of Python’s standard library.
• Method 5: reduce with Lambda. Compact and functional programming style. May be less readable for those not familiar with `reduce()` or lambda functions.
```nums = [1, 2, 3, 4]
cumulative_sum = []
total = 0
for num in nums:
total += num
cumulative_sum.append(total)

print(cumulative_sum)
```

Output: `[1, 3, 6, 10]`

The code snippet above demonstrates creating a cumulative sum using a `for` loop. On each iteration, the current number is added to a running total and then appended to the `cumulative_sum` list.

## Method 2: Using List Comprehension and Sum

This method leverages Python’s list comprehension feature combined with the `sum()` function. It’s an elegant and Pythonic way to calculate cumulative sums, utilizing the power of list comprehensions for conciseness.

Here’s an example:

```nums = [1, 2, 3, 4]
cumulative_sum = [sum(nums[:i+1]) for i in range(len(nums))]

print(cumulative_sum)
```

Output: `[1, 3, 6, 10]`

The list comprehension iterates through the indices of the original list, slicing it up to the current index and using `sum()` to calculate the cumulative total.

## Method 3: Using the accumulate Function from itertools

The `accumulate()` function from the `itertools` module provides a direct way to compute cumulative sums in Python. This function is a fast and memory-efficient tool for such tasks.

Here’s an example:

```from itertools import accumulate

nums = [1, 2, 3, 4]
cumulative_sum = list(accumulate(nums))

print(cumulative_sum)
```

Output: `[1, 3, 6, 10]`

The code utilizes the `accumulate()` function, which takes an iterable as input and returns an iterator over the intermediate cumulative sum results, which is then converted into a list.

## Method 4: Using NumPy’s cumsum Function

For those working within the scientific computing realm, NumPy’s `cumsum()` function provides a high-performance method to compute cumulative sums on numerical data.

Here’s an example:

```import numpy as np

nums = np.array([1, 2, 3, 4])
cumulative_sum = np.cumsum(nums)

print(cumulative_sum)
```

Output: `[1 3 6 10]`

This snippet uses NumPy’s `cumsum()` function to efficiently compute the cumulative sum of an array, returning a NumPy array with the cumulative totals.

## Bonus One-Liner Method 5: Using Reduce with Lambda

This elegance of functional programming is showcased using the `reduce()` function in combination with a lambda expression to achieve cumulative sum in a one-liner command.

Here’s an example:

```from functools import reduce

nums = [1, 2, 3, 4]
cumulative_sum = reduce(lambda c, x: c + [c[-1] + x] if c else [x], nums, [])

print(cumulative_sum)
```

Output: `[1, 3, 6, 10]`

This code uses `reduce()` to build the cumulative sum list. The lambda takes a cumulative list `c` and an element `x`, appends the sum of `x` and the last element in `c` to `c`, and continues this cumulatively through the list.

## Summary/Discussion

• Method 1: For Loop. Easy to understand and implement. Not the most efficient for large datasets due to O(n) space and time complexity.
• Method 2: List Comprehension and Sum. Pythonic and compact. It can be less efficient than other methods, as sum is called repeatedly for each element.
• Method 3: accumulate Function. Pythonic and efficient, especially for iterator-based processing. It is a built-in function, but requires importing `itertools`.
• Method 4: NumPy’s cumsum. Highly efficient for numerical computations on large arrays. However, it requires NumPy, which is not part of Python’s standard library.
• Method 5: reduce with Lambda. Compact and functional programming style. May be less readable for those not familiar with `reduce()` or lambda functions.
```nums = [1, 2, 3, 4]
cumulative_sum = [sum(nums[:i+1]) for i in range(len(nums))]

print(cumulative_sum)
```

Output: `[1, 3, 6, 10]`

The list comprehension iterates through the indices of the original list, slicing it up to the current index and using `sum()` to calculate the cumulative total.

## Method 3: Using the accumulate Function from itertools

The `accumulate()` function from the `itertools` module provides a direct way to compute cumulative sums in Python. This function is a fast and memory-efficient tool for such tasks.

Here’s an example:

```from itertools import accumulate

nums = [1, 2, 3, 4]
cumulative_sum = list(accumulate(nums))

print(cumulative_sum)
```

Output: `[1, 3, 6, 10]`

The code utilizes the `accumulate()` function, which takes an iterable as input and returns an iterator over the intermediate cumulative sum results, which is then converted into a list.

## Method 4: Using NumPy’s cumsum Function

For those working within the scientific computing realm, NumPy’s `cumsum()` function provides a high-performance method to compute cumulative sums on numerical data.

Here’s an example:

```import numpy as np

nums = np.array([1, 2, 3, 4])
cumulative_sum = np.cumsum(nums)

print(cumulative_sum)
```

Output: `[1 3 6 10]`

This snippet uses NumPy’s `cumsum()` function to efficiently compute the cumulative sum of an array, returning a NumPy array with the cumulative totals.

## Bonus One-Liner Method 5: Using Reduce with Lambda

This elegance of functional programming is showcased using the `reduce()` function in combination with a lambda expression to achieve cumulative sum in a one-liner command.

Here’s an example:

```from functools import reduce

nums = [1, 2, 3, 4]
cumulative_sum = reduce(lambda c, x: c + [c[-1] + x] if c else [x], nums, [])

print(cumulative_sum)
```

Output: `[1, 3, 6, 10]`

This code uses `reduce()` to build the cumulative sum list. The lambda takes a cumulative list `c` and an element `x`, appends the sum of `x` and the last element in `c` to `c`, and continues this cumulatively through the list.

## Summary/Discussion

• Method 1: For Loop. Easy to understand and implement. Not the most efficient for large datasets due to O(n) space and time complexity.
• Method 2: List Comprehension and Sum. Pythonic and compact. It can be less efficient than other methods, as sum is called repeatedly for each element.
• Method 3: accumulate Function. Pythonic and efficient, especially for iterator-based processing. It is a built-in function, but requires importing `itertools`.
• Method 4: NumPy’s cumsum. Highly efficient for numerical computations on large arrays. However, it requires NumPy, which is not part of Python’s standard library.
• Method 5: reduce with Lambda. Compact and functional programming style. May be less readable for those not familiar with `reduce()` or lambda functions.
```nums = [1, 2, 3, 4]
cumulative_sum = []
total = 0
for num in nums:
total += num
cumulative_sum.append(total)

print(cumulative_sum)
```

Output: `[1, 3, 6, 10]`

The code snippet above demonstrates creating a cumulative sum using a `for` loop. On each iteration, the current number is added to a running total and then appended to the `cumulative_sum` list.

## Method 2: Using List Comprehension and Sum

This method leverages Python’s list comprehension feature combined with the `sum()` function. It’s an elegant and Pythonic way to calculate cumulative sums, utilizing the power of list comprehensions for conciseness.

Here’s an example:

```nums = [1, 2, 3, 4]
cumulative_sum = [sum(nums[:i+1]) for i in range(len(nums))]

print(cumulative_sum)
```

Output: `[1, 3, 6, 10]`

The list comprehension iterates through the indices of the original list, slicing it up to the current index and using `sum()` to calculate the cumulative total.

## Method 3: Using the accumulate Function from itertools

The `accumulate()` function from the `itertools` module provides a direct way to compute cumulative sums in Python. This function is a fast and memory-efficient tool for such tasks.

Here’s an example:

```from itertools import accumulate

nums = [1, 2, 3, 4]
cumulative_sum = list(accumulate(nums))

print(cumulative_sum)
```

Output: `[1, 3, 6, 10]`

The code utilizes the `accumulate()` function, which takes an iterable as input and returns an iterator over the intermediate cumulative sum results, which is then converted into a list.

## Method 4: Using NumPy’s cumsum Function

For those working within the scientific computing realm, NumPy’s `cumsum()` function provides a high-performance method to compute cumulative sums on numerical data.

Here’s an example:

```import numpy as np

nums = np.array([1, 2, 3, 4])
cumulative_sum = np.cumsum(nums)

print(cumulative_sum)
```

Output: `[1 3 6 10]`

This snippet uses NumPy’s `cumsum()` function to efficiently compute the cumulative sum of an array, returning a NumPy array with the cumulative totals.

## Bonus One-Liner Method 5: Using Reduce with Lambda

This elegance of functional programming is showcased using the `reduce()` function in combination with a lambda expression to achieve cumulative sum in a one-liner command.

Here’s an example:

```from functools import reduce

nums = [1, 2, 3, 4]
cumulative_sum = reduce(lambda c, x: c + [c[-1] + x] if c else [x], nums, [])

print(cumulative_sum)
```

Output: `[1, 3, 6, 10]`

This code uses `reduce()` to build the cumulative sum list. The lambda takes a cumulative list `c` and an element `x`, appends the sum of `x` and the last element in `c` to `c`, and continues this cumulatively through the list.

## Summary/Discussion

• Method 1: For Loop. Easy to understand and implement. Not the most efficient for large datasets due to O(n) space and time complexity.
• Method 2: List Comprehension and Sum. Pythonic and compact. It can be less efficient than other methods, as sum is called repeatedly for each element.
• Method 3: accumulate Function. Pythonic and efficient, especially for iterator-based processing. It is a built-in function, but requires importing `itertools`.
• Method 4: NumPy’s cumsum. Highly efficient for numerical computations on large arrays. However, it requires NumPy, which is not part of Python’s standard library.
• Method 5: reduce with Lambda. Compact and functional programming style. May be less readable for those not familiar with `reduce()` or lambda functions.
```import numpy as np

nums = np.array([1, 2, 3, 4])
cumulative_sum = np.cumsum(nums)

print(cumulative_sum)
```

Output: `[1 3 6 10]`

This snippet uses NumPy’s `cumsum()` function to efficiently compute the cumulative sum of an array, returning a NumPy array with the cumulative totals.

## Bonus One-Liner Method 5: Using Reduce with Lambda

This elegance of functional programming is showcased using the `reduce()` function in combination with a lambda expression to achieve cumulative sum in a one-liner command.

Here’s an example:

```from functools import reduce

nums = [1, 2, 3, 4]
cumulative_sum = reduce(lambda c, x: c + [c[-1] + x] if c else [x], nums, [])

print(cumulative_sum)
```

Output: `[1, 3, 6, 10]`

This code uses `reduce()` to build the cumulative sum list. The lambda takes a cumulative list `c` and an element `x`, appends the sum of `x` and the last element in `c` to `c`, and continues this cumulatively through the list.

## Summary/Discussion

• Method 1: For Loop. Easy to understand and implement. Not the most efficient for large datasets due to O(n) space and time complexity.
• Method 2: List Comprehension and Sum. Pythonic and compact. It can be less efficient than other methods, as sum is called repeatedly for each element.
• Method 3: accumulate Function. Pythonic and efficient, especially for iterator-based processing. It is a built-in function, but requires importing `itertools`.
• Method 4: NumPy’s cumsum. Highly efficient for numerical computations on large arrays. However, it requires NumPy, which is not part of Python’s standard library.
• Method 5: reduce with Lambda. Compact and functional programming style. May be less readable for those not familiar with `reduce()` or lambda functions.
```nums = [1, 2, 3, 4]
cumulative_sum = [sum(nums[:i+1]) for i in range(len(nums))]

print(cumulative_sum)
```

Output: `[1, 3, 6, 10]`

The list comprehension iterates through the indices of the original list, slicing it up to the current index and using `sum()` to calculate the cumulative total.

## Method 3: Using the accumulate Function from itertools

The `accumulate()` function from the `itertools` module provides a direct way to compute cumulative sums in Python. This function is a fast and memory-efficient tool for such tasks.

Here’s an example:

```from itertools import accumulate

nums = [1, 2, 3, 4]
cumulative_sum = list(accumulate(nums))

print(cumulative_sum)
```

Output: `[1, 3, 6, 10]`

The code utilizes the `accumulate()` function, which takes an iterable as input and returns an iterator over the intermediate cumulative sum results, which is then converted into a list.

## Method 4: Using NumPy’s cumsum Function

For those working within the scientific computing realm, NumPy’s `cumsum()` function provides a high-performance method to compute cumulative sums on numerical data.

Here’s an example:

```import numpy as np

nums = np.array([1, 2, 3, 4])
cumulative_sum = np.cumsum(nums)

print(cumulative_sum)
```

Output: `[1 3 6 10]`

This snippet uses NumPy’s `cumsum()` function to efficiently compute the cumulative sum of an array, returning a NumPy array with the cumulative totals.

## Bonus One-Liner Method 5: Using Reduce with Lambda

This elegance of functional programming is showcased using the `reduce()` function in combination with a lambda expression to achieve cumulative sum in a one-liner command.

Here’s an example:

```from functools import reduce

nums = [1, 2, 3, 4]
cumulative_sum = reduce(lambda c, x: c + [c[-1] + x] if c else [x], nums, [])

print(cumulative_sum)
```

Output: `[1, 3, 6, 10]`

This code uses `reduce()` to build the cumulative sum list. The lambda takes a cumulative list `c` and an element `x`, appends the sum of `x` and the last element in `c` to `c`, and continues this cumulatively through the list.

## Summary/Discussion

• Method 1: For Loop. Easy to understand and implement. Not the most efficient for large datasets due to O(n) space and time complexity.
• Method 2: List Comprehension and Sum. Pythonic and compact. It can be less efficient than other methods, as sum is called repeatedly for each element.
• Method 3: accumulate Function. Pythonic and efficient, especially for iterator-based processing. It is a built-in function, but requires importing `itertools`.
• Method 4: NumPy’s cumsum. Highly efficient for numerical computations on large arrays. However, it requires NumPy, which is not part of Python’s standard library.
• Method 5: reduce with Lambda. Compact and functional programming style. May be less readable for those not familiar with `reduce()` or lambda functions.
```nums = [1, 2, 3, 4]
cumulative_sum = []
total = 0
for num in nums:
total += num
cumulative_sum.append(total)

print(cumulative_sum)
```

Output: `[1, 3, 6, 10]`

The code snippet above demonstrates creating a cumulative sum using a `for` loop. On each iteration, the current number is added to a running total and then appended to the `cumulative_sum` list.

## Method 2: Using List Comprehension and Sum

This method leverages Python’s list comprehension feature combined with the `sum()` function. It’s an elegant and Pythonic way to calculate cumulative sums, utilizing the power of list comprehensions for conciseness.

Here’s an example:

```nums = [1, 2, 3, 4]
cumulative_sum = [sum(nums[:i+1]) for i in range(len(nums))]

print(cumulative_sum)
```

Output: `[1, 3, 6, 10]`

The list comprehension iterates through the indices of the original list, slicing it up to the current index and using `sum()` to calculate the cumulative total.

## Method 3: Using the accumulate Function from itertools

The `accumulate()` function from the `itertools` module provides a direct way to compute cumulative sums in Python. This function is a fast and memory-efficient tool for such tasks.

Here’s an example:

```from itertools import accumulate

nums = [1, 2, 3, 4]
cumulative_sum = list(accumulate(nums))

print(cumulative_sum)
```

Output: `[1, 3, 6, 10]`

The code utilizes the `accumulate()` function, which takes an iterable as input and returns an iterator over the intermediate cumulative sum results, which is then converted into a list.

## Method 4: Using NumPy’s cumsum Function

For those working within the scientific computing realm, NumPy’s `cumsum()` function provides a high-performance method to compute cumulative sums on numerical data.

Here’s an example:

```import numpy as np

nums = np.array([1, 2, 3, 4])
cumulative_sum = np.cumsum(nums)

print(cumulative_sum)
```

Output: `[1 3 6 10]`

This snippet uses NumPy’s `cumsum()` function to efficiently compute the cumulative sum of an array, returning a NumPy array with the cumulative totals.

## Bonus One-Liner Method 5: Using Reduce with Lambda

This elegance of functional programming is showcased using the `reduce()` function in combination with a lambda expression to achieve cumulative sum in a one-liner command.

Here’s an example:

```from functools import reduce

nums = [1, 2, 3, 4]
cumulative_sum = reduce(lambda c, x: c + [c[-1] + x] if c else [x], nums, [])

print(cumulative_sum)
```

Output: `[1, 3, 6, 10]`

This code uses `reduce()` to build the cumulative sum list. The lambda takes a cumulative list `c` and an element `x`, appends the sum of `x` and the last element in `c` to `c`, and continues this cumulatively through the list.

## Summary/Discussion

• Method 1: For Loop. Easy to understand and implement. Not the most efficient for large datasets due to O(n) space and time complexity.
• Method 2: List Comprehension and Sum. Pythonic and compact. It can be less efficient than other methods, as sum is called repeatedly for each element.
• Method 3: accumulate Function. Pythonic and efficient, especially for iterator-based processing. It is a built-in function, but requires importing `itertools`.
• Method 4: NumPy’s cumsum. Highly efficient for numerical computations on large arrays. However, it requires NumPy, which is not part of Python’s standard library.
• Method 5: reduce with Lambda. Compact and functional programming style. May be less readable for those not familiar with `reduce()` or lambda functions.
```from itertools import accumulate

nums = [1, 2, 3, 4]
cumulative_sum = list(accumulate(nums))

print(cumulative_sum)
```

Output: `[1, 3, 6, 10]`

The code utilizes the `accumulate()` function, which takes an iterable as input and returns an iterator over the intermediate cumulative sum results, which is then converted into a list.

## Method 4: Using NumPy’s cumsum Function

For those working within the scientific computing realm, NumPy’s `cumsum()` function provides a high-performance method to compute cumulative sums on numerical data.

Here’s an example:

```import numpy as np

nums = np.array([1, 2, 3, 4])
cumulative_sum = np.cumsum(nums)

print(cumulative_sum)
```

Output: `[1 3 6 10]`

This snippet uses NumPy’s `cumsum()` function to efficiently compute the cumulative sum of an array, returning a NumPy array with the cumulative totals.

## Bonus One-Liner Method 5: Using Reduce with Lambda

This elegance of functional programming is showcased using the `reduce()` function in combination with a lambda expression to achieve cumulative sum in a one-liner command.

Here’s an example:

```from functools import reduce

nums = [1, 2, 3, 4]
cumulative_sum = reduce(lambda c, x: c + [c[-1] + x] if c else [x], nums, [])

print(cumulative_sum)
```

Output: `[1, 3, 6, 10]`

This code uses `reduce()` to build the cumulative sum list. The lambda takes a cumulative list `c` and an element `x`, appends the sum of `x` and the last element in `c` to `c`, and continues this cumulatively through the list.

## Summary/Discussion

• Method 1: For Loop. Easy to understand and implement. Not the most efficient for large datasets due to O(n) space and time complexity.
• Method 2: List Comprehension and Sum. Pythonic and compact. It can be less efficient than other methods, as sum is called repeatedly for each element.
• Method 3: accumulate Function. Pythonic and efficient, especially for iterator-based processing. It is a built-in function, but requires importing `itertools`.
• Method 4: NumPy’s cumsum. Highly efficient for numerical computations on large arrays. However, it requires NumPy, which is not part of Python’s standard library.
• Method 5: reduce with Lambda. Compact and functional programming style. May be less readable for those not familiar with `reduce()` or lambda functions.
```nums = [1, 2, 3, 4]
cumulative_sum = [sum(nums[:i+1]) for i in range(len(nums))]

print(cumulative_sum)
```

Output: `[1, 3, 6, 10]`

The list comprehension iterates through the indices of the original list, slicing it up to the current index and using `sum()` to calculate the cumulative total.

## Method 3: Using the accumulate Function from itertools

The `accumulate()` function from the `itertools` module provides a direct way to compute cumulative sums in Python. This function is a fast and memory-efficient tool for such tasks.

Here’s an example:

```from itertools import accumulate

nums = [1, 2, 3, 4]
cumulative_sum = list(accumulate(nums))

print(cumulative_sum)
```

Output: `[1, 3, 6, 10]`

The code utilizes the `accumulate()` function, which takes an iterable as input and returns an iterator over the intermediate cumulative sum results, which is then converted into a list.

## Method 4: Using NumPy’s cumsum Function

For those working within the scientific computing realm, NumPy’s `cumsum()` function provides a high-performance method to compute cumulative sums on numerical data.

Here’s an example:

```import numpy as np

nums = np.array([1, 2, 3, 4])
cumulative_sum = np.cumsum(nums)

print(cumulative_sum)
```

Output: `[1 3 6 10]`

This snippet uses NumPy’s `cumsum()` function to efficiently compute the cumulative sum of an array, returning a NumPy array with the cumulative totals.

## Bonus One-Liner Method 5: Using Reduce with Lambda

This elegance of functional programming is showcased using the `reduce()` function in combination with a lambda expression to achieve cumulative sum in a one-liner command.

Here’s an example:

```from functools import reduce

nums = [1, 2, 3, 4]
cumulative_sum = reduce(lambda c, x: c + [c[-1] + x] if c else [x], nums, [])

print(cumulative_sum)
```

Output: `[1, 3, 6, 10]`

This code uses `reduce()` to build the cumulative sum list. The lambda takes a cumulative list `c` and an element `x`, appends the sum of `x` and the last element in `c` to `c`, and continues this cumulatively through the list.

## Summary/Discussion

• Method 1: For Loop. Easy to understand and implement. Not the most efficient for large datasets due to O(n) space and time complexity.
• Method 2: List Comprehension and Sum. Pythonic and compact. It can be less efficient than other methods, as sum is called repeatedly for each element.
• Method 3: accumulate Function. Pythonic and efficient, especially for iterator-based processing. It is a built-in function, but requires importing `itertools`.
• Method 4: NumPy’s cumsum. Highly efficient for numerical computations on large arrays. However, it requires NumPy, which is not part of Python’s standard library.
• Method 5: reduce with Lambda. Compact and functional programming style. May be less readable for those not familiar with `reduce()` or lambda functions.
```nums = [1, 2, 3, 4]
cumulative_sum = []
total = 0
for num in nums:
total += num
cumulative_sum.append(total)

print(cumulative_sum)
```

Output: `[1, 3, 6, 10]`

The code snippet above demonstrates creating a cumulative sum using a `for` loop. On each iteration, the current number is added to a running total and then appended to the `cumulative_sum` list.

## Method 2: Using List Comprehension and Sum

This method leverages Python’s list comprehension feature combined with the `sum()` function. It’s an elegant and Pythonic way to calculate cumulative sums, utilizing the power of list comprehensions for conciseness.

Here’s an example:

```nums = [1, 2, 3, 4]
cumulative_sum = [sum(nums[:i+1]) for i in range(len(nums))]

print(cumulative_sum)
```

Output: `[1, 3, 6, 10]`

The list comprehension iterates through the indices of the original list, slicing it up to the current index and using `sum()` to calculate the cumulative total.

## Method 3: Using the accumulate Function from itertools

The `accumulate()` function from the `itertools` module provides a direct way to compute cumulative sums in Python. This function is a fast and memory-efficient tool for such tasks.

Here’s an example:

```from itertools import accumulate

nums = [1, 2, 3, 4]
cumulative_sum = list(accumulate(nums))

print(cumulative_sum)
```

Output: `[1, 3, 6, 10]`

The code utilizes the `accumulate()` function, which takes an iterable as input and returns an iterator over the intermediate cumulative sum results, which is then converted into a list.

## Method 4: Using NumPy’s cumsum Function

For those working within the scientific computing realm, NumPy’s `cumsum()` function provides a high-performance method to compute cumulative sums on numerical data.

Here’s an example:

```import numpy as np

nums = np.array([1, 2, 3, 4])
cumulative_sum = np.cumsum(nums)

print(cumulative_sum)
```

Output: `[1 3 6 10]`

This snippet uses NumPy’s `cumsum()` function to efficiently compute the cumulative sum of an array, returning a NumPy array with the cumulative totals.

## Bonus One-Liner Method 5: Using Reduce with Lambda

This elegance of functional programming is showcased using the `reduce()` function in combination with a lambda expression to achieve cumulative sum in a one-liner command.

Here’s an example:

```from functools import reduce

nums = [1, 2, 3, 4]
cumulative_sum = reduce(lambda c, x: c + [c[-1] + x] if c else [x], nums, [])

print(cumulative_sum)
```

Output: `[1, 3, 6, 10]`

This code uses `reduce()` to build the cumulative sum list. The lambda takes a cumulative list `c` and an element `x`, appends the sum of `x` and the last element in `c` to `c`, and continues this cumulatively through the list.

## Summary/Discussion

• Method 1: For Loop. Easy to understand and implement. Not the most efficient for large datasets due to O(n) space and time complexity.
• Method 2: List Comprehension and Sum. Pythonic and compact. It can be less efficient than other methods, as sum is called repeatedly for each element.
• Method 3: accumulate Function. Pythonic and efficient, especially for iterator-based processing. It is a built-in function, but requires importing `itertools`.
• Method 4: NumPy’s cumsum. Highly efficient for numerical computations on large arrays. However, it requires NumPy, which is not part of Python’s standard library.
• Method 5: reduce with Lambda. Compact and functional programming style. May be less readable for those not familiar with `reduce()` or lambda functions.
```import numpy as np

nums = np.array([1, 2, 3, 4])
cumulative_sum = np.cumsum(nums)

print(cumulative_sum)
```

Output: `[1 3 6 10]`

This snippet uses NumPy’s `cumsum()` function to efficiently compute the cumulative sum of an array, returning a NumPy array with the cumulative totals.

## Bonus One-Liner Method 5: Using Reduce with Lambda

This elegance of functional programming is showcased using the `reduce()` function in combination with a lambda expression to achieve cumulative sum in a one-liner command.

Here’s an example:

```from functools import reduce

nums = [1, 2, 3, 4]
cumulative_sum = reduce(lambda c, x: c + [c[-1] + x] if c else [x], nums, [])

print(cumulative_sum)
```

Output: `[1, 3, 6, 10]`

This code uses `reduce()` to build the cumulative sum list. The lambda takes a cumulative list `c` and an element `x`, appends the sum of `x` and the last element in `c` to `c`, and continues this cumulatively through the list.

## Summary/Discussion

• Method 1: For Loop. Easy to understand and implement. Not the most efficient for large datasets due to O(n) space and time complexity.
• Method 2: List Comprehension and Sum. Pythonic and compact. It can be less efficient than other methods, as sum is called repeatedly for each element.
• Method 3: accumulate Function. Pythonic and efficient, especially for iterator-based processing. It is a built-in function, but requires importing `itertools`.
• Method 4: NumPy’s cumsum. Highly efficient for numerical computations on large arrays. However, it requires NumPy, which is not part of Python’s standard library.
• Method 5: reduce with Lambda. Compact and functional programming style. May be less readable for those not familiar with `reduce()` or lambda functions.
```from itertools import accumulate

nums = [1, 2, 3, 4]
cumulative_sum = list(accumulate(nums))

print(cumulative_sum)
```

Output: `[1, 3, 6, 10]`

The code utilizes the `accumulate()` function, which takes an iterable as input and returns an iterator over the intermediate cumulative sum results, which is then converted into a list.

## Method 4: Using NumPy’s cumsum Function

For those working within the scientific computing realm, NumPy’s `cumsum()` function provides a high-performance method to compute cumulative sums on numerical data.

Here’s an example:

```import numpy as np

nums = np.array([1, 2, 3, 4])
cumulative_sum = np.cumsum(nums)

print(cumulative_sum)
```

Output: `[1 3 6 10]`

This snippet uses NumPy’s `cumsum()` function to efficiently compute the cumulative sum of an array, returning a NumPy array with the cumulative totals.

## Bonus One-Liner Method 5: Using Reduce with Lambda

This elegance of functional programming is showcased using the `reduce()` function in combination with a lambda expression to achieve cumulative sum in a one-liner command.

Here’s an example:

```from functools import reduce

nums = [1, 2, 3, 4]
cumulative_sum = reduce(lambda c, x: c + [c[-1] + x] if c else [x], nums, [])

print(cumulative_sum)
```

Output: `[1, 3, 6, 10]`

This code uses `reduce()` to build the cumulative sum list. The lambda takes a cumulative list `c` and an element `x`, appends the sum of `x` and the last element in `c` to `c`, and continues this cumulatively through the list.

## Summary/Discussion

• Method 1: For Loop. Easy to understand and implement. Not the most efficient for large datasets due to O(n) space and time complexity.
• Method 2: List Comprehension and Sum. Pythonic and compact. It can be less efficient than other methods, as sum is called repeatedly for each element.
• Method 3: accumulate Function. Pythonic and efficient, especially for iterator-based processing. It is a built-in function, but requires importing `itertools`.
• Method 4: NumPy’s cumsum. Highly efficient for numerical computations on large arrays. However, it requires NumPy, which is not part of Python’s standard library.
• Method 5: reduce with Lambda. Compact and functional programming style. May be less readable for those not familiar with `reduce()` or lambda functions.
```nums = [1, 2, 3, 4]
cumulative_sum = [sum(nums[:i+1]) for i in range(len(nums))]

print(cumulative_sum)
```

Output: `[1, 3, 6, 10]`

The list comprehension iterates through the indices of the original list, slicing it up to the current index and using `sum()` to calculate the cumulative total.

## Method 3: Using the accumulate Function from itertools

The `accumulate()` function from the `itertools` module provides a direct way to compute cumulative sums in Python. This function is a fast and memory-efficient tool for such tasks.

Here’s an example:

```from itertools import accumulate

nums = [1, 2, 3, 4]
cumulative_sum = list(accumulate(nums))

print(cumulative_sum)
```

Output: `[1, 3, 6, 10]`

The code utilizes the `accumulate()` function, which takes an iterable as input and returns an iterator over the intermediate cumulative sum results, which is then converted into a list.

## Method 4: Using NumPy’s cumsum Function

For those working within the scientific computing realm, NumPy’s `cumsum()` function provides a high-performance method to compute cumulative sums on numerical data.

Here’s an example:

```import numpy as np

nums = np.array([1, 2, 3, 4])
cumulative_sum = np.cumsum(nums)

print(cumulative_sum)
```

Output: `[1 3 6 10]`

This snippet uses NumPy’s `cumsum()` function to efficiently compute the cumulative sum of an array, returning a NumPy array with the cumulative totals.

## Bonus One-Liner Method 5: Using Reduce with Lambda

This elegance of functional programming is showcased using the `reduce()` function in combination with a lambda expression to achieve cumulative sum in a one-liner command.

Here’s an example:

```from functools import reduce

nums = [1, 2, 3, 4]
cumulative_sum = reduce(lambda c, x: c + [c[-1] + x] if c else [x], nums, [])

print(cumulative_sum)
```

Output: `[1, 3, 6, 10]`

This code uses `reduce()` to build the cumulative sum list. The lambda takes a cumulative list `c` and an element `x`, appends the sum of `x` and the last element in `c` to `c`, and continues this cumulatively through the list.

## Summary/Discussion

• Method 1: For Loop. Easy to understand and implement. Not the most efficient for large datasets due to O(n) space and time complexity.
• Method 2: List Comprehension and Sum. Pythonic and compact. It can be less efficient than other methods, as sum is called repeatedly for each element.
• Method 3: accumulate Function. Pythonic and efficient, especially for iterator-based processing. It is a built-in function, but requires importing `itertools`.
• Method 4: NumPy’s cumsum. Highly efficient for numerical computations on large arrays. However, it requires NumPy, which is not part of Python’s standard library.
• Method 5: reduce with Lambda. Compact and functional programming style. May be less readable for those not familiar with `reduce()` or lambda functions.
```nums = [1, 2, 3, 4]
cumulative_sum = []
total = 0
for num in nums:
total += num
cumulative_sum.append(total)

print(cumulative_sum)
```

Output: `[1, 3, 6, 10]`

The code snippet above demonstrates creating a cumulative sum using a `for` loop. On each iteration, the current number is added to a running total and then appended to the `cumulative_sum` list.

## Method 2: Using List Comprehension and Sum

This method leverages Python’s list comprehension feature combined with the `sum()` function. It’s an elegant and Pythonic way to calculate cumulative sums, utilizing the power of list comprehensions for conciseness.

Here’s an example:

```nums = [1, 2, 3, 4]
cumulative_sum = [sum(nums[:i+1]) for i in range(len(nums))]

print(cumulative_sum)
```

Output: `[1, 3, 6, 10]`

The list comprehension iterates through the indices of the original list, slicing it up to the current index and using `sum()` to calculate the cumulative total.

## Method 3: Using the accumulate Function from itertools

The `accumulate()` function from the `itertools` module provides a direct way to compute cumulative sums in Python. This function is a fast and memory-efficient tool for such tasks.

Here’s an example:

```from itertools import accumulate

nums = [1, 2, 3, 4]
cumulative_sum = list(accumulate(nums))

print(cumulative_sum)
```

Output: `[1, 3, 6, 10]`

The code utilizes the `accumulate()` function, which takes an iterable as input and returns an iterator over the intermediate cumulative sum results, which is then converted into a list.

## Method 4: Using NumPy’s cumsum Function

For those working within the scientific computing realm, NumPy’s `cumsum()` function provides a high-performance method to compute cumulative sums on numerical data.

Here’s an example:

```import numpy as np

nums = np.array([1, 2, 3, 4])
cumulative_sum = np.cumsum(nums)

print(cumulative_sum)
```

Output: `[1 3 6 10]`

This snippet uses NumPy’s `cumsum()` function to efficiently compute the cumulative sum of an array, returning a NumPy array with the cumulative totals.

## Bonus One-Liner Method 5: Using Reduce with Lambda

This elegance of functional programming is showcased using the `reduce()` function in combination with a lambda expression to achieve cumulative sum in a one-liner command.

Here’s an example:

```from functools import reduce

nums = [1, 2, 3, 4]
cumulative_sum = reduce(lambda c, x: c + [c[-1] + x] if c else [x], nums, [])

print(cumulative_sum)
```

Output: `[1, 3, 6, 10]`

This code uses `reduce()` to build the cumulative sum list. The lambda takes a cumulative list `c` and an element `x`, appends the sum of `x` and the last element in `c` to `c`, and continues this cumulatively through the list.

## Summary/Discussion

• Method 1: For Loop. Easy to understand and implement. Not the most efficient for large datasets due to O(n) space and time complexity.
• Method 2: List Comprehension and Sum. Pythonic and compact. It can be less efficient than other methods, as sum is called repeatedly for each element.
• Method 3: accumulate Function. Pythonic and efficient, especially for iterator-based processing. It is a built-in function, but requires importing `itertools`.
• Method 4: NumPy’s cumsum. Highly efficient for numerical computations on large arrays. However, it requires NumPy, which is not part of Python’s standard library.
• Method 5: reduce with Lambda. Compact and functional programming style. May be less readable for those not familiar with `reduce()` or lambda functions.