Small games and the code behind them. Short coding lessons with real code and real output.
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![Slide reading 'Dynamic programming in Python, solve it once'. The next slides show Python dynamic programming: @lru_cache on a recursive fib function, the knapsack loop best[c] = max(best[c], best[c - w] + v), and the takeaway that top down is memoisation and bottom up is tabulation.](https://pbs.twimg.com/media/HVMyB9vbMAAOEcU.png?name=orig)
ALT Slide reading 'Dynamic programming in Python, solve it once'. The next slides show Python dynamic programming: @lru_cache on a recursive fib function, the knapsack loop best[c] = max(best[c], best[c - w] + v), and the takeaway that top down is memoisation and bottom up is tabulation.
ALT Slide reading 'Python heapq with tuples'. The next slides show heappush and heappop on (distance, node) tuples and end with: (cost, node) equals cheapest first.
ALT Slide reading 'Cheapest route in a weighted graph with heapq'. The next slides show Python Dijkstra's algorithm: the graph as a dictionary of (node, weight) pairs, the relaxation lines that keep the cheaper distance, and the takeaway that Dijkstra needs no negative weights.
ALT Slide reading 'Shortest path with a queue'. The next slides show the BFS code with a deque of paths and end with: deque plus paths equals fewest hops.
ALT Slide reading 'Shortest path in a graph with a queue'. The next slides show the graph as a dictionary, the lines that queue unseen neighbours, and the takeaway 'BFS = shortest path, DFS = go deep'.
ALT Slide reading 'Dependencies live in a comment'. The next slides show the script, its output 200, and the comment block rules, and end with: One file, its own packages.
