Group Anagrams
Given an array of strings, group the anagrams together.
Output groups one per line, words space-separated and sorted, groups sorted by first word.
Example:
eat,tea,tan,ate,nat,bat
ate eat tea bat nat tan
- First, we identify the anagrams in the input array: "eat", "tea", and "ate" are anagrams, "tan" and "nat" are anagrams, and "bat" has no anagrams.
- Then, we sort the words within each anagram group and sort the groups themselves based on the first word in each group.
- The sorted anagram groups are: ["ate", "eat", "tea"], ["bat"], and ["nat", "tan"].
- Finally, we output each group on a new line, with the words in each group separated by spaces, resulting in:
ate eat tea,bat, andnat tanbecomesbatandnat tanis sorted tonat tan.
Constraints:
- 1 <= len(strs) <= 10^4
- 0 <= len(strs[i]) <= 100
- strs[i] is lowercase English letters
Background Knowledge
The problem of grouping anagrams together involves understanding the concept of anagrams, which are words or phrases formed by rearranging the letters of another word or phrase, typically using all the original letters exactly once. To solve this problem, we need to understand how to identify anagrams and group them together. This requires knowledge of hash maps (also known as dictionaries or associative arrays), which are data structures that store key-value pairs and allow for efficient lookup, insertion, and deletion of elements.
In the context of this problem, we can use hash maps to store the anagrams together. One way to do this is by using a sorted string as the key in the hash map. For example, the words "listen" and "silent" can be sorted to form the key "eilnst", which can be used to group these anagrams together. This approach requires understanding of string sorting and how to use it to create a unique key for each group of anagrams.
Another important concept in this problem is time and space complexity. We need to consider how our solution will scale with the size of the input array and how much memory it will use. This requires understanding of big O notation, which is used to describe the complexity of algorithms. We should aim to find a solution that has a reasonable time and space complexity, such as O(nmlogm), where n is the number of strings and m is the maximum length of a string.
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