Python Data Structures

Why Performance Matters in Python Data Structures

When solving real-world problems or working with large datasets, choosing the right data structure can significantly improve performance and reduce memory usage.

Big-O Time Complexity of Python Built-in Data Structures

Common Data Structures in Python (With Best Use Cases)

List

• Best for ordered collections where duplicates are allowed
• Easy to add, remove, update elements
• Not suitable for frequent searches in large data

Tuple

• Immutable, lightweight, and hashable
• Ideal for fixed data structures, function return values, and dictionary keys

Set

• Unordered, no duplicates
• Great for fast membership tests, unions, intersections, and difference

Dictionary

• Key-value mapping
• Ideal for fast lookups, JSON-like structures, and indexed data access

Performance Tips for Python Data Structures

1. Use List Comprehensions Over Loops

# Faster
squares = [x*x for x in range(100)]

# Slower
squares = []
for x in range(100):
    squares.append(x*x)

2. Prefer Sets for Membership Tests

nums = set([1, 2, 3, 4])
print(3 in nums)  # O(1) vs O(n) in list

3. Use defaultdict or Counter for Counting Items

from collections import Counter

words = ["apple", "banana", "apple"]
count = Counter(words)
print(count["apple"])  # 2

4. Avoid Repeated Concatenation in Loops

# Slow
s = ""
for word in ["a", "b", "c"]:
    s += word

# Fast
s = "".join(["a", "b", "c"])

5. Use Generators Instead of Lists When Possible

# Saves memory
def gen():
    for i in range(1000000):
        yield i

6. Use enumerate() Instead of Manual Indexing

for i, value in enumerate(["a", "b", "c"]):
    print(i, value)

7. Use zip() for Pairing Data

names = ["Alice", "Bob"]
scores = [90, 80]

for name, score in zip(names, scores):
    print(name, score)

Advanced Structures for High Performance

Summary of Optimization Tips