Algorithms in Python

Build your algorithm skills in Python with hands-on tutorials that cover sorting, searching, graphs, greedy techniques, and dynamic programming. You will learn to think in Big O, pick the right data structures, and turn pseudocode into clean, Pythonic solutions you can ship and discuss in interviews.

Work with battle-tested tools from the standard library to write fast, readable code. Use heapq for priority queues, bisect for binary search, collections.deque for BFS, itertools for combinatorics, and functools.lru_cache for memoization. Measure real performance with timeit and cProfile, and reach for NumPy or NetworkX when vectorization or graph tooling helps.

Browse all resources below, or commit to a guided Learning Path with progress tracking:

Learning Path

Classic Data Structures and Algorithms

20 Resources ⋅ Skills: Python, Data Structures, Stacks, Queues, Linked Lists, Hash Tables, Enums, Search Algorithms, Sorting Algorithms, Collections

How do I analyze time complexity in Python?Show/Hide

Use Big O notation to reason about growth: count loops, focus on dominant terms, and watch nested loops and recursion. Validate runtime with timeit, but remember Big O is about scaling behavior, not specific timings.

How do I implement binary search in Python?Show/Hide

Prefer the built-in bisect module for sorted lists. It gives insertion points and supports O(log n) searches. If you write binary search yourself, then maintain two pointers and loop while lo <= hi.

Which data structure should I use for BFS and DFS?Show/Hide

Use collections.deque as the BFS queue and a list or recursion for DFS. Represent graphs as adjacency lists, for example dict[str, set[str]], and track a visited set to avoid repeats.

How can I improve a Python algorithm’s performance?Show/Hide

Pick better data structures, avoid quadratic patterns, and move hot paths to built-ins like sorted, sum, and min. Profile with cProfile, cache results using functools.lru_cache, and consider NumPy for numeric loops.

When should I use a heap in Python?Show/Hide

Use a heap when you need the smallest (or largest with inverted keys) item quickly, or to keep a rolling top-k. heapq.heappush() and heapq.heappop() run in O(log n) and power tasks like Dijkstra and scheduling.