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Algorithmic iteration techniques
Algorithmic iteration techniques
Topics covered: Multiple Pointers, Sliding Window Pattern, Reverse Iteration, Modifying While Looping, Using any() and all()
Pointers from Both Ends The most common pattern uses one pointer at the start and one at the end, moving them toward each other. This efficiently solves problems like finding pairs, checking palindromes, and partitioning arrays: Because the list is sorted, we know that if the sum is too small, we need a larger left value. If too large, we need a smaller right value. This lets us eliminate many possibilities with each comparison. Palindrome Check Checking if a string is a palindrome is a classic
The sliding window pattern maintains a "window" over a contiguous portion of a sequence. The window slides through the data, adding elements at one end and removing them from the other. This efficiently solves problems involving contiguous subarrays or substrings. Fixed-Size Window The simplest form uses a fixed window size. Rather than recalculating from scratch for each position, we update the window incrementally: The fixed-size sliding window follows a simple four-step recipe on every iterat
Iterating backwards through sequences is often necessary for algorithms that build results from end to beginning, or when modifications affect indices of later elements. Python provides several ways to iterate in reverse. The reversed() Function range() with Negative Step Why Reverse Iteration Reverse iteration is essential when modifying a list based on indices. Deleting from the beginning shifts all later indices, but deleting from the end keeps earlier indices valid: This code tries to remove
Modifying a collection while iterating over it is dangerous and often causes bugs. Elements get skipped or the iterator becomes invalid. However, with the right techniques, you can safely modify collections during iteration. Before looking at specific techniques, here are the three safe strategies you can rely on whenever you need to change a collection mid-loop. The Problem Removing elements while iterating forward causes items to be skipped because indices shift: When you remove index 0, eleme
These two functions cover the vast majority of sequence-wide condition checks you will ever need: The any() Function The all() Function Replacing Loop Patterns Try switching between any() and all() to see how each evaluates the same list of values differently: Validation Examples These functions shine in data validation scenarios: Combining any() and all() Complex conditions can combine both functions: Here is a side-by-side comparison of how any() and all() evaluate elements. Advanced iteration