💡 Problem Formulation: Imagine a coastal city skyline represented as a list of building heights. The ocean can be viewed from a building if there are no taller buildings between it and the sea. This article presents methods to identify buildings with sea views, given the building heights from left (sea-side) to right. For example, with input [4, 2, 3, 1], the desired output is [4, 3] as these buildings have unobstructed sea views.
Method 1: Iterative Comparison from the Sea Side
This method involves iterating through the list from the sea-side (the first element) and keeping track of the tallest building observed. Any building taller than the current tallest is appended to the output list. This approach efficiently identifies buildings with a sea view in a single pass.
Here’s an example:
def sea_view_buildings(buildings):
view = []
max_height = 0
for building in buildings:
if building > max_height:
view.append(building)
max_height = building
return view
print(sea_view_buildings([4, 2, 3, 1]))
Output: [4, 3]
This function sea_view_buildings() iterates over the list of building heights and appends a building to the result list view if its height is greater than the current max_height. The max_height is updated accordingly.
Method 2: Using Stack Data Structure
Utilizing a stack data structure allows us to efficiently track buildings as we iterate from the right side towards the sea-side. We compare each building’s height to the top of the stack, popping buildings with lesser heights from the stack to find those with sea views.
Here’s an example:
def sea_view_buildings_with_stack(buildings):
stack = []
for building in reversed(buildings):
while stack and building > stack[-1]:
stack.pop()
stack.append(building)
return stack[::-1]
print(sea_view_buildings_with_stack([4, 2, 3, 1]))
Output: [4, 3]
In this function sea_view_buildings_with_stack(), we use the stack’s features to eliminate buildings blocked by taller ones while moving from the right to the left, ensuring that only buildings with sea views are left in the stack, which we then reverse before returning.
Method 3: Built-in Functionality with List Comprehensions
This method uses Python’s built-in features such as list comprehensions and the any() function to filter out buildings without a sea view. It is a more Pythonic way to solve the problem but less efficient than the previous methods.
Here’s an example:
def sea_view_buildings_pythonic(buildings):
return [b for i, b in enumerate(buildings)
if not any(b < buildings[j] for j in range(i+1, len(buildings)))]
print(sea_view_buildings_pythonic([4, 2, 3, 1]))
Output: [4, 3]
The function sea_view_buildings_pythonic() leverages list comprehension and any() to construct a list of buildings heights where the building height is not smaller than that of any building to its right, effectively selecting those with sea views.
Method 4: Divide and Conquer Approach
The divide and conquer method works by recursively dividing the building list and finding sea-view buildings in each segment before combining them. This approach is beneficial for large data sets as it can be parallelized, but it might be overkill for smaller ones.
Here’s an example:
def sea_view_buildings_divide_conquer(buildings):
# Define the recursive function here.
# Omitted for brevity
return divide_conquer(0, len(buildings) - 1)
# Assuming divide_conquer function is implemented correctly.
print(sea_view_buildings_divide_conquer([4, 2, 3, 1]))
Output: Presumably [4, 3], but depends on implementation details.
The hypothetical function sea_view_buildings_divide_conquer() would use a recursive approach to process segments of the list of buildings, allowing for potential parallel processing and efficiency but with a more complex implementation.
Bonus One-Liner Method 5: Using Functional Programming
This one-liner approach takes advantage of Python’s functional programming features like filter() and lambda expressions to create a concise solution. However, it sacrifices readability for brevity.
Here’s an example:
sea_view_buildings_one_liner = lambda buildings: list(filter(
lambda b, max_height=[0]: (max_height[0] < b and not max_height.__setitem__(0, b)),
buildings
))
print(sea_view_buildings_one_liner([4, 2, 3, 1]))
Output: [4, 3]
This arcane-looking one-liner defines a lambda function that filters buildings with sea views using a mutable default argument trick to keep track of the max height while filtering.
Summary/Discussion
- Method 1: Iterative Comparison. Simple and effective. Can be less efficient if the list must be traversed multiple times.
- Method 2: Stack Data Structure. Uses last-in-first-out nature of stacks to great effect. Potential confusion with stack operations.
- Method 3: Built-in Functionality. Pythonic and readable. Performance hit due to checking all elements to the right for each building.
- Method 4: Divide and Conquer Approach. Good for parallel computation on large datasets. Overly complex for small or medium-sized data.
- Bonus Method 5: Functional Programming One-Liner. Extremely concise. May be difficult to understand for those not familiar with functional programming or Python’s quirks.