📊 Collections Module

Specialized container datatypes for every need

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🎯 Learning Objectives

By the end of this section, you'll understand: - What Counter is and how to count elements efficiently - How defaultdict eliminates KeyError checks - When to use namedtuple for readable, lightweight objects - Why deque is superior for queue operations

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🔢 Counter - Counting Made Easy

Think of Counter as a smart dictionary that automatically counts things for you.

Real-World Analogy

Imagine you're tallying votes. Instead of checking "have I seen this candidate before?" every time, Counter just handles it:

from collections import Counter

# Count letters in a word
letters = Counter("banana")
print(letters)  # Counter({'a': 3, 'n': 2, 'b': 1})

# Most common elements
print(letters.most_common(2))  # [('a', 3), ('n', 2)]

Key Features

Method	Description
Counter(iterable)	Create a counter from any iterable
.most_common(n)	Get the n most common elements
.elements()	Expand back to individual elements
+, -	Combine counters with arithmetic

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🗝️ defaultdict - Never Get KeyError Again

A defaultdict automatically creates a default value for missing keys.

Real-World Analogy

It's like having a vending machine that dispenses an empty box if you press a button for something that doesn't exist — instead of crashing.

from collections import defaultdict

# Group words by length
words = ["cat", "dog", "elephant", "bird", "fish"]
by_length = defaultdict(list)

for word in words:
    by_length[len(word)].append(word)

print(dict(by_length))
# {3: ['cat', 'dog'], 8: ['elephant'], 4: ['bird'], 4: ['fish']}

Common Default Factory Functions

defaultdict(int)      # Starts at 0
defaultdict(list)     # Starts with []
defaultdict(set)      # Starts with set()
defaultdict(dict)     # Starts with {}

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🏷️ namedtuple - Self-Documenting Tuples

namedtuple creates tuple-like objects with named fields.

Real-World Analogy

A regular tuple is like a row in a spreadsheet with no headers. A namedtuple adds the headers so you know what each column means.

from collections import namedtuple

# Define a Point class
Point = namedtuple('Point', ['x', 'y'])

p = Point(3, 4)
print(p.x, p.y)      # 3 4
print(p[0], p[1])    # 3 4 (also works as tuple)

Benefits

• Immutable - Like regular tuples
• Named access - p.x instead of p[0]
• Unpackable - x, y = p
• Memory efficient - Faster than regular classes

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🔄 deque - Double-Ended Queue

deque (pronounced "deck") is a list optimized for operations at both ends.

Real-World Analogy

Imagine a line of people where you can efficiently add or remove people from either the front OR the back. With a regular list, adding/removing from the front is slow.

from collections import deque

# Create a deque
d = deque([1, 2, 3])

# Fast operations at both ends
d.appendleft(0)      # [0, 1, 2, 3]
d.append(4)          # [0, 1, 2, 3, 4]
d.popleft()          # Returns 0

Performance Comparison

Operation	List	Deque
Append right	O(1)	O(1)
Append left	O(n)	O(1)
Pop right	O(1)	O(1)
Pop left	O(n)	O(1)

Use deque for: - Queues (FIFO) - Stacks (LIFO) - Sliding window algorithms - Maintaining recent history

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⚠️ Common Mistakes

1. Modifying Counter directly - Use update() for adding counts
2. Forgetting to import - These are in collections, not built-in
3. Using list as default - Use list function, not []
4. Mutable defaults in namedtuple - Fields should be immutable

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📝 Quick Reference

from collections import Counter, defaultdict, namedtuple, deque

# Counter
c = Counter(['a', 'b', 'a', 'c', 'a'])
c.most_common(1)  # [('a', 3)]

# defaultdict
d = defaultdict(list)
d['missing']  # Returns [], no KeyError

# namedtuple
Person = namedtuple('Person', 'name age')
p = Person('Alice', 30)

# deque
dq = deque(maxlen=3)  # Fixed size, auto-discards old items
dq.extend([1, 2, 3, 4])  # Only keeps [2, 3, 4]

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🎓 Next Steps

Run examples.py to see these in action, then try exercises.py!