Levenshtein Distance
Implement the classic Levenshtein distance algorithm to measure string similarity.
Longest Common Subsequence
Given two strings, compute the length of the longest subsequence common to both.
Longest Palindromic Subsequence
Compute the length of the longest palindromic subsequence in a given string.
Edit Distance (Levenshtein Distance)
Implement the classic edit distance algorithm to find the minimum number of single-character edits required to transform one string into another.
Word Break DP
Implement a function to check if a string can be segmented into space-separated dictionary words.
Palindrome Partitioning Minimum Cuts
Given a string, return the minimum number of cuts needed such that every substring in the partition is a palindrome.
Shortest Common Supersequence
Given two strings, return any shortest supersequence that contains both as subsequences.
Interleaving string
Given three strings s1, s2, and s3, check if s3 is formed by interleaving s1 and s2 while preserving the order of each input string.
Showing 1–8 of 8 challenges · Dynamic Programming
Dynamic Programming — Python coding challenges
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This page lists dynamic programming challenges — real Python problems you solve in the browser IDE with instant test feedback. Each challenge includes a clear brief, starter code, and automated checks.
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Challenges test what you can build under constraints. For guided teaching, use our Python tutorials. For quick checks, try quizzes or copy snippets from code samples.