Algorithms

The standard library covers most everyday algorithmic needs. sorted, bisect, heapq, itertools, functools, hashlib, and graphlib together handle sorting, searching, ordered sequences, priority queues, lazy combinators, memoization, graph traversal, and hashing. Reach for them before rolling your own.

This section breaks the catalogue into one page per algorithm family. For the containers the algorithms run over, see Data Structures.

Family

Sorting

Timsort, sort / sorted, custom keys, itemgetter / attrgetter, stable multi-key sorting.

Sorting

Searching

Linear, in, bisect_left / bisect_right, insort, set / dict for repeated membership.

Searching

Recursion

setrecursionlimit, memoization with @cache and @lru_cache.

Recursion

Dynamic programming

Top-down memoization vs bottom-up tabulation. LCS, edit distance, knapsack, coin change.

Dynamic programming

Greedy

Interval scheduling, token bucket, when greedy is correct.

Greedy

Graphs

Adjacency representation, BFS, DFS, Dijkstra, graphlib.TopologicalSorter.

Graphs

Strings

Built-in methods, regex, substring search, distance metrics.

Strings

Hashing

hash vs hashlib, content addressing, password stretching, HMAC.

Hashing

Rate limiting

Token bucket, leaky bucket, sliding window.

Rate limiting

Scheduling

Bounded concurrency with asyncio.Semaphore and ThreadPoolExecutor.

Scheduling

Parsing

Built-ins, regex, and real parsers. When each tier earns its place.

Parsing

Big-O

Common-operation complexities for built-in containers in CPython.

Big-O

References

• Data Structures for the containers these algorithms run over.

• Libraries for functools, itertools, collections, heapq, bisect, graphlib, hashlib, hmac, difflib.

• Python Standard Library, Functional Programming — the reference for itertools, functools, operator.

• Time complexity wiki — CPython container performance.

• Introduction to Algorithms, Cormen, Leiserson, Rivest, Stein (CLRS) — standard reference text.