The Algorithms - Python Tours

Walk the README disclaimer, the CONTRIBUTING.md algorithm requirements, the canonical insertion_sort file shape, the auto-generated DIRECTORY.md, the pytest --doctest-modules gate, and the path to deeper algorithm tours.

Sample one canonical algorithm from each major category (a sort, a search, a graph traversal, a DP class, a cipher) and end on the autoapi_dirs list that names every category.

Compare adjacent elements, swap if out of order, repeat. The algorithm that appears in every first-year course because the swapping motion is easy to picture.

Pick the next element, shift everything larger one position right, drop the element into the gap. The algorithm that outperforms quicksort on small runs.

Find the minimum of the unsorted region, swap it to the front, repeat. O(n squared) always, but only O(n) swaps total.

Split the list in half, sort each half recursively, merge the two sorted halves. The first algorithm written for a stored-program computer.

A random pivot, a partition into lesser and greater, two recursive calls. The 1959 algorithm that still drives most stdlib sorts.

Build a max-heap over the array, then repeatedly swap the root to the back and restore the heap. O(n log n) worst case with no extra memory.

Sort by least significant digit, then next digit, repeat. Non-comparison sort that runs in O(d times n) rather than O(n log n).

No comparisons, no pivots. Count key frequencies, prefix-sum them, then place each element at its exact output index in one backward pass.

Scan every element in order, return the index on match, return -1 on miss. The lower bound for searching unsorted data.

Halve the search space on every step. The 1946 algorithm that still trips up professionals on overflow and off-by-one errors.

Jump through sorted data in square-root-sized blocks, then linear-search the candidate block. O(sqrt(n)) with no recursion, no pivots.

Visit every neighbor before going deeper. The FIFO queue invariant that guarantees shortest-path distances on unweighted graphs.

Follow a path as deep as it goes before backtracking. The iterative stack-based implementation that mirrors the recursive structure without blowing the call stack.

A min-heap of (cost, node) tuples, a visited set, and a single relaxation loop. The 1956 algorithm at the core of OSPF and IS-IS routing.

Combine actual path cost with a heuristic estimate of remaining distance. The 1968 algorithm built for a robot, now in every map and game engine.

Relax every edge V-1 times, then run one more pass to detect negative cycles. The algorithm Dijkstra cannot replace.

Sort edges by weight, then add each one unless it creates a cycle. Union-find with path compression in 46 lines.

Cache the sequence on a class instance, extend it on demand, return a slice. The canonical example of memoization replacing exponential recursion.

Fill a capacity-by-item table bottom-up, then trace back through it to recover which items go in the bag.

A 2-D DP table, one recurrence, and a traceback walk. The algorithm at the core of diff, git-merge, and DNA sequence alignment.

Count the minimum insertions, deletions, and substitutions to transform one string into another. The 1965 algorithm behind spell-check, fuzzy search, and DNA alignment.

Mark every multiple of each found prime starting at its square. What remains is a list of primes, produced in O(n log log n).

A modular shift over an alphabet, mirrored to decrypt, exhausted to brute-force. The oldest attested cipher in the Western record.

Split the message into fixed-size integer blocks, raise each block to a modular power, and undo it with the private exponent.

Pad the message, split into 512-bit blocks, expand each block to 80 words, run 80 rounds of bit operations, accumulate five 32-bit words into a 160-bit digest.

Four 32-bit state words, 64 rounds, a sin-derived constant table. The 1991 algorithm that still shows up in checksums everywhere.

Preprocess the pattern into a failure array, then scan the text without backtracking. The 1977 algorithm that makes grep and DNA search O(n+m).

Hash the pattern, slide a rolling hash across the text, confirm on match. The 1987 algorithm that makes multi-pattern search practical.

A bitarray, two hash functions, and a membership check that never gives false-negatives. Burton Bloom's 1970 data structure still in production databases.

A doubly-linked list plus a hashmap gives O(1) get and put. The eviction policy at the core of CPU caches, browser caches, and functools.lru_cache.

Seven sorting strategies side by side: from the classroom bubble loop to radix buckets.

Seven search strategies from a simple scan to partition-based selection, each with a different trade-off.

Seven ciphers from ancient Rome to Diffie-Hellman: each a different answer to the question of what makes a message unreadable.

Six hash algorithms from a 13-byte checksum to 256-bit cryptographic digests: each trading reliability for speed differently.

Seven fundamental data structures side by side: the pointer chain, the key-ordered tree, the self-balancing tree, the heap, the hash table, the union-find, and the prefix tree.

Seven base, numeral, and unit conversions showing how repeated division, recursion, lookup tables, and pivot factors each solve the same family of problems.

Seven techniques that use bitwise operations to count, test, flip, reverse, and encode integers without arithmetic.

Seven string search and comparison algorithms from KMP failure arrays to Aho-Corasick automata.

Seven mathematical algorithms from prime sieves to the Chinese Remainder Theorem.

Four stops from primitive shapes to convex hull algorithms, with cross-product orientation as the shared computational primitive.

Six matrix operations from elementwise addition to Pascal's triangle generation.

Seven graph traversal and shortest-path strategies from BFS queues to Prim's greedy expansion.

Seven constraint-satisfaction problems solved by the same pattern: try a placement, recurse, and undo it when the branch fails.

Five problems where the locally optimal choice at each step produces the globally optimal result.

Six algorithms that split a problem in half, solve each half independently, and combine the results: from sorting to matrix multiplication to geometry.

Seven DP patterns from memoized sequences to all-pairs shortest paths.

Seven direct solvers and iterative methods, from Gaussian elimination to conjugate gradient, each exposing a different trade-off in numerical linear algebra.

Seven classical ML algorithms from linear models to kernel machines, each implemented from scratch in plain NumPy.

Five files tracing how a network learns: from a single-neuron forward pass to backpropagation, multi-layer weight matrices, and the activation functions that make it possible.

The first seven Project Euler problems, each solved in a single Python function with doctests, showing how mathematical insight reduces brute force to a handful of lines.