重構複雜的嵌套迴路，以獲得可讀性和可維護性

提取嵌套邏輯到獨立函數以降低複雜度並提升可讀性；2. 在適用時使用列表推導式或生成器表達式使代碼更簡潔；3. 通過迭代工具或數據預處理展平數據結構以減少嵌套；4. 利用itertools等內置庫函數優化循環結構；5. 考慮面向對像或函數式編程模式封裝重複邏輯；最終目標是通過清晰的抽象和命名使代碼意圖明確，避免因深層嵌套導致的理解困難，從而提升可維護性和可讀性。

Nested loops are often necessary when working with multidimensional data or complex iteration logic, but deeply nested structures can quickly become hard to read, debug, and maintain. Refactoring such code improves clarity, reduces cognitive load, and makes future changes safer. Here's how to approach refactoring complex nested loops effectively.

Extract Nested Logic into Separate Functions

One of the most effective ways to simplify nested loops is to extract the inner logic into well-named, focused functions. This reduces the main loop's complexity and gives meaning to what the inner operations do.

For example, consider this nested loop:

 for user in users:
    for order in user.orders:
        if order.status == 'active':
            for item in order.items:
                if item.price > 100:
                    apply_discount(item)

This triple-nested structure is hard to scan. We can extract the inner logic:

 def process_high_value_items(order):
    for item in order.items:
        if item.price > 100:
            apply_discount(item)

def process_active_orders(user):
    for order in user.orders:
        if order.status == 'active':
            process_high_value_items(order)

for user in users:
    process_active_orders(user)

Now each function has a single responsibility, and the main flow is easier to follow.

Use List Comprehensions or Generator Expressions (When Appropriate)

For simple filtering or transformations, list comprehensions or generator expressions can replace inner loops and make the intent clearer.

Instead of:

 results = []
for user in users:
    for order in user.orders:
        if order.is_overdue():
            for item in order.items:
                results.append(item)

You could write:

 results = [
    item
    for user in users
    for order in user.orders
    if order.is_overdue()
    for item in order.items
]

While still nested, this is more compact and declarative. However, use this only when readability isn't compromised — avoid over-nesting in comprehensions.

Flatten with Iteration Helpers or Data Preprocessing

Sometimes, the need for nesting comes from data structure rather than logic. In such cases, flattening the data upfront can eliminate layers.

 all_items = (
    item
    for user in users
    for order in user.orders
    for item in order.items
)

for item in all_items:
    if hasattr(item.order, 'is_overdue') and item.order.is_overdue():
        # process item

This approach separates data traversal from business logic, making each part simpler.

Leverage Built-in Functions and Libraries

Functions like itertools , filter() , map() , or chain() can reduce explicit nesting.

Example using itertools.chain :

 from itertools import chain

all_orders = chain.from_iterable(user.orders for user in users)
active_orders = (order for order in all_orders if order.status == 'active')
high_value_items = (item for order in active_orders for item in order.items if item.price > 100)

for item in high_value_items:
    apply_discount(item)

This is not only more readable but also memory-efficient with generators.

Consider Object-Oriented or Functional Patterns

If nested loops appear frequently around certain data types, encapsulate the behavior:

 class User:
    def get_high_value_active_items(self):
        for order in self.orders:
            if order.status == 'active':
                for item in order.items:
                    if item.price > 100:
                        yield item

Then the usage becomes:

 for user in users:
    for item in user.get_high_value_active_items():
        apply_discount(item)

Now the complexity is hidden behind a clear interface.

The key is to prioritize clarity over cleverness. Refactoring nested loops isn't just about reducing indentation — it's about making the code's intent obvious. Extract functions, use descriptive names, and choose the right abstraction level for your context.

Basically, if you have to pause and count indentation levels to understand what's happening, it's time to refactor.

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