Given an array of strings strs, group the anagrams together. You can return the answer in any order.
Example 1:
Input: strs = ["eat","tea","tan","ate","nat","bat"]
Output: [["bat"],["nat","tan"],["ate","eat","tea"]]
Explanation:
"bat"."nat" and "tan" are anagrams as they can be rearranged to form each other."ate", "eat", and "tea" are anagrams as they can be rearranged to form each other.Example 2:
Input: strs = [""]
Output: [[""]]
Example 3:
Input: strs = ["a"]
Output: [["a"]]
Constraints:
1 <= strs.length <= 1040 <= strs[i].length <= 100strs[i] consists of lowercase English letters.The key idea behind Group Anagrams is that words which are anagrams share the same character composition. If two strings contain the same letters with the same frequencies, they belong in the same group. The most common approach is to use a hash table where each group is identified by a canonical representation of the word.
One practical strategy is to sort the characters of each string and use the sorted string as a key in a hash map. All words producing the same sorted key are placed in the same list. Another efficient technique is to build a character frequency signature (for example, a 26-length count for lowercase letters) and use that signature as the key.
Both approaches rely on constant-time hash lookups for grouping. Sorting-based grouping is simple to implement, while the frequency-count method can reduce repeated sorting overhead. The final groups are collected from the hash map values.
| Approach | Time Complexity | Space Complexity |
|---|---|---|
| Sorting each string + Hash Map | O(n * k log k) | O(n * k) |
| Character Frequency Count + Hash Map | O(n * k) | O(n * k) |
NeetCode
This approach involves using dynamic programming to store solutions to subproblems in a table and build up to the solution of the original problem. By doing so, we can avoid redundant calculations and achieve a more efficient solution.
Time Complexity: O(n)
Space Complexity: O(n)
1/* C code example */The dynamic programming approach in C involves initializing a dp array to store intermediate results and filling it up based on the recurrence relation derived from the problem's requirements.
A greedy algorithm is an approach that constructs a solution by choosing the best option at each step. This approach may not always yield the optimal global solution, but for certain problems, especially those with optima formed by greedy choices, it can be very efficient.
Time Complexity: O(n log n) /* or other depending on the specific problem */
Space Complexity: O(1) /* if in-place, depending on conditions */
1/* C code example */Watch expert explanations and walkthroughs
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Sorting a string arranges its characters in a consistent order. Since anagrams contain the same characters, their sorted versions will be identical. This makes the sorted string an effective key for grouping related words together.
Yes, Group Anagrams is a common interview problem at companies like Amazon, Google, and Meta. It tests understanding of hash maps, string manipulation, and efficient grouping techniques.
The optimal approach uses a hash map to group words by a canonical key. This key can be a sorted version of the string or a character frequency signature. Words that produce the same key are placed in the same list, forming an anagram group.
A hash table (or hash map) is the most suitable data structure because it allows constant-time insertion and lookup. Each key represents a normalized representation of a word, and the value stores all strings that match that pattern.
This C solution applies a greedy technique where at each step, the locally optimal choice is made with hopes of finding the global optimum.