Python的collections模塊提供 specialized 數(shù)據(jù)結(jié)構(gòu)提升效率與可讀性。1.namedtuple賦予元組字段名增強(qiáng)清晰度；2.deque優(yōu)化雙端增刪適合隊(duì)列滑窗；3.Counter統(tǒng)計(jì)元素頻次；4.defaultdict自動(dòng)初始化缺失鍵；5.OrderedDict保留插入順序；6.ChainMap合并多字典視圖。適用于數(shù)據(jù)分析、高效隊(duì)列、分組項(xiàng)、自定義結(jié)構(gòu)等場(chǎng)景，當(dāng)需避免手動(dòng)初始化或提升性能時(shí)應(yīng)使用該模塊。

Python's collections module is a built-in library that provides specialized data structures beyond the standard ones like lists, tuples, and dictionaries. These structures are designed to make certain tasks easier and more efficient by offering additional functionality tailored for specific use cases.

Commonly Used Structures in collections

1. namedtuple()
   This function creates tuple-like objects that have named fields, making code more readable. For example:

   from collections import namedtuple
   Point = namedtuple('Point', ['x', 'y'])
   p = Point(1, 2)

   Here, p.x gives you access to the x-coordinate directly, which makes it clearer than using p[0].

   

2. deque
   A double-ended queue, optimized for fast appends and pops from both ends. It’s especially useful for implementing queues or sliding window algorithms.

   from collections import deque
   dq = deque([1, 2, 3])
   dq.appendleft(0)  # Adds to the front
   dq.pop()          # Removes from the end

3. Counter
   This class is used for counting hashable objects. It returns a dictionary-like object where keys are elements and values are their counts.

   

   from collections import Counter
   c = Counter(['a', 'b', 'a', 'c'])
   # Output: Counter({'a': 2, 'b': 1, 'c': 1})

4. defaultdict
   A subclass of dict, this structure automatically initializes missing keys with a default value based on the type you specify.

   from collections import defaultdict
   dd = defaultdict(int)
   dd['key']  = 1  # No KeyError even if 'key' wasn't there before

5. OrderedDict
   This dictionary remembers insertion order. While regular dictionaries in Python 3.7 also preserve order, OrderedDict offers extra methods for reordering items.

6. ChainMap
   Combines multiple dictionaries into a single view. When looking up keys, it searches through all the maps in order.

   from collections import ChainMap
   d1 = {'a': 1}
   d2 = {'b': 2}
   cm = ChainMap(d1, d2)

Practical Uses of collections

• Data analysis: Using Counter is great when you need to tally occurrences, such as counting word frequencies in text processing.
• Efficient queue operations: If you're building a task queue or doing breadth-first search (BFS), deque is much faster than using a list for popping from the front.
• Grouping items: defaultdict comes in handy when grouping items by some key, like categorizing log entries by severity level.
• Custom structured data: namedtuple helps avoid confusion in large datasets by giving meaningful names to positions in a tuple.

Each of these tools solves a common problem in a clean and efficient way, so they’re worth getting familiar with.

When to Use collections

If your code involves repetitive patterns like manually initializing dictionary keys, keeping track of insertion order, or needing a lightweight object with named properties, then collections likely has a tool that can simplify things.

You don’t always need them—sometimes a basic dict or list will do—but when performance or clarity becomes an issue, the collections module can offer just what you need without writing custom logic.

基本上就這些。

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