Python的collections模塊通過提供更靈活、功能更強的數(shù)據(jù)類型來補充內(nèi)置類型。 1.deque適用於兩端快速添加和刪除，解決列表在首部操作效率低的問題；2.Counter簡化頻率統(tǒng)計，支持便捷的數(shù)學運算及常用分析方法；3.OrderedDict在早期版本中保留字典鍵順序，並支持顯式排序與順序敏感的操作。這些工具填補了標準類型的功能空缺，使代碼更簡潔高效。

Python's collections module gives you more flexible and powerful versions of built-in data types. These specialized containers solve common problems that lists, dicts, and tuples aren't always great at. Let's break down a few of the most useful ones.

deque for fast appends and pops from both ends

If you're working with queues or need to frequently add/remove items from both ends of a sequence, regular lists can be inefficient — inserting or deleting at the front takes O(n) time.

That's where deque comes in. It's optimized for fast operations on both ends.

 from collections import deque
d = deque([1, 2, 3])
d.appendleft(0)
d.pop()

Use cases include:

• Implementing queues or stacks
• Maintaining a history of recent actions (eg, undo/redo systems)
• Rotating buffers where old values get pushed out

One thing to note: while indexing is possible, it's slower than with regular lists. So if you're doing a lot of random access, stick with list unless you really need those fast ends.

Counter for easy frequency counting

Need to count how many times something appears in a dataset? You could use a dictionary and manage it manually, but Counter does this cleanly and efficiently.

 from collections import Counter
words = ['apple', 'banana', 'apple', 'orange']
word_count = Counter(words)

It also has handy methods like .most_common() which shows top N results. This is super useful for tasks like:

• Word frequency analysis in NLP
• Tracking item popularity in logs or surveys
• Finding duplicates in a list

A nice trick: you can combine Counters using , - , & , and | operators for quick set-like math.

OrderedDict for insertion-ordered dictionaries (before Python 3.7)

Before Python 3.7, regular dictionaries didn't remember insertion order. If you needed that behavior, OrderedDict was your go-to choice.

Even though newer Python versions preserve order by default, OrderedDict still matters because:

• It makes ordering expectations explicit in your code
• It has .move_to_end() for reordering keys
• Equality checks consider order ( dict ignores it)

For example:

 from collections import OrderedDict
d = OrderedDict()
d['a'] = 1
d['b'] = 2
print(d) # prints keys in insertion order

This was especially helpful when building things like caches or config readers where order mattered.

There are a few other tools in collections like defaultdict , namedtuple , and ChainMap , all with their own roles. But the three above show how the module helps fill gaps in the standard types without reinventing the wheel.

At the end of the day, these alternatives make your code cleaner and faster — especially when dealing with real-world data structures and workflows.

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