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Grokking the Coding Interview Patterns in Python

Solution: Similar String Groups

Explore how to identify groups of similar strings where similarity is defined by at most one swap difference. Understand the Union-Find data structure to efficiently merge string groups and compute connectivity. This lesson helps you implement and analyze an optimized solution for graph connectivity problems using strings, preparing you for coding interviews involving graph and union-find patterns.

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Statement

Two strings x and y are considered similar if they are either exactly the same or can be made identical by swapping at most two different characters in string x.

We define a similarity group as a set of strings where each string is similar to at least one other string in the group. A string doesn't need to be directly similar to every other string in the group — it just needs to be connected to them through a chain of similarities.

Given a list of strings strs, where each string is an anagram of the others, your task is to determine how many such similarity groups exist in the list.

Constraints:

  • 1≤1 \leq strs.length ≤300\leq 300

  • 1≤1 \leq strs[i].length ≤300\leq 300

  • strs[i] consists of lowercase letters only.

  • All strings in strs have the same length and are anagrams of each other.

Solution

This problem can be seen as a graph connectivity challenge. Each string is a node, and an edge exists between two nodes if their corresponding strings are similar. Our goal is to count how many connected groups (components) exist in this graph.

We solve this problem using the Union-Find (Disjoint Set Union) data structure to efficiently group similar strings. Initially, each string is placed in its own group. We then iterate over all ...