Data Structures in Python
Data structures organize and manipulate information every time you write Python code. Master built-in types like lists, tuples, dictionaries, and sets to handle collections efficiently. Understand when to use each structure based on performance characteristics and your program’s needs.
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Custom data structures like stacks, queues, linked lists, and hash tables fill the gaps when built-ins don’t fit your requirements. Learn about the collections module for specialized containers, explore time complexity for common operations, and choose the right structure to optimize your algorithms. Apply these concepts to solve real-world problems efficiently.
Python provides lists for ordered mutable sequences, tuples for immutable sequences, dictionaries for key-value mappings, and sets for unique unordered elements. Each has different performance characteristics. Lists and dictionaries are the most commonly used.
Use lists when you need to modify elements, add items, or remove items after creation. Use tuples for fixed collections that won’t change, as dictionary keys, or to return multiple values from functions. Tuples are slightly faster and use less memory.
Use a list with append() to push and pop() to remove from the end. For thread-safe operations, use queue.LifoQueue. The list approach is simplest for single-threaded code. Both provide O(1) push and pop operations.
A deque from the collections module supports fast O(1) appends and pops from both ends, while lists are optimized for operations at the end only. Use deques for queues or when you need efficient operations on both sides of your collection.
Dictionaries use hash tables to store key-value pairs, providing O(1) average time for lookups, insertions, and deletions. Keys must be hashable immutable types. Python 3.7+ preserves insertion order. Dictionaries resize automatically when they grow.
