• Computer Science
• Python
• TypeScript

LeetCode Meditations: Group Anagrams

Let's start with the description for Group Anagrams:

Given an array of strings strs, group the anagrams together. You can return the answer in any order.

An Anagram is a word or phrase formed by rearranging the letters of a different word or phrase, typically using all the original letters exactly once.

For example:

groupAnagrams(['eat', 'tea', 'tan', 'ate', 'nat', 'bat']);
// -> [ ['bat'], ['nat', 'tan'], ['ate', 'eat', 'tea'] ]

groupAnagrams(['']);
// -> [ [''] ]

groupAnagrams(['a']);
// -> [ ['a'] ]

groupAnagrams(['eat', 'tea', 'tan', 'ate', 'nat', 'bat']);
// -> [ ['bat'], ['nat', 'tan'], ['ate', 'eat', 'tea'] ]

groupAnagrams(['']);
// -> [ [''] ]

groupAnagrams(['a']);
// -> [ ['a'] ]

And, as one of the constraints says, each of the strings will consist of lowercase English letters.

One thing to remember from the previous Valid Anagram problem is that we can easily check if two strings are anagrams of each other by comparing their sorted versions.

So, we can use a hash table to store the sorted words. In that case, all words that are anagrams of each other will be grouped together in an array, and share the same key:

function groupAnagrams(strs: string[]): string[][] {
  let words: { [word: string]: string[] } = {};

  for (let s of strs) {
    let sortedWord = [...s].sort().join('');
    (sortedWord in words) ? words[sortedWord].push(s) : words[sortedWord] = [s];
  }

  return Object.values(words);
};

function groupAnagrams(strs: string[]): string[][] {
  let words: { [word: string]: string[] } = {};

  for (let s of strs) {
    let sortedWord = [...s].sort().join('');
    (sortedWord in words) ? words[sortedWord].push(s) : words[sortedWord] = [s];
  }

  return Object.values(words);
};

Time and space complexity

Since we're using the sorting operation, time complexity will be $O(n \ log \ n)$ as it is the best we can do with sorting. But we're doing the sorting operation for each element in strs, so the loop itself has an $O(n)$ time complexity. To not confuse ourselves, we'll use another variable, $m$, to denote the length of strs, that is, the number of times we'll iterate for each element. Overall, the time complexity will be $O(m \cdot n \ log \ n)$.

We can say that space complexity is $O(m \cdot n)$ where $m$ is the length of strs and $n$ is the length of the longest string, because in the worst case where all strings are anagrams of each other, the value array can contain $m$ strings, and the key's length will be $n$, so, words will grow proportionally.

Using Python

It might look like this in Python:

class Solution:
    def groupAnagrams(self, strs: List[str]) -> List[List[str]]:
        words = {}

        for s in strs:
            sorted_word = ''.join(sorted(s))
            words.setdefault(sorted_word, []).append(s)

        return words.values()

class Solution:
    def groupAnagrams(self, strs: List[str]) -> List[List[str]]:
        words = {}

        for s in strs:
            sorted_word = ''.join(sorted(s))
            words.setdefault(sorted_word, []).append(s)

        return words.values()

Now, after taking a deep breath, we can look at NeetCode's solution.

And, voilà, a more efficient solution exists:

from collections import defaultdict

class Solution:
    def groupAnagrams(self, strs: List[str]) -> List[List[str]]:
        res = defaultdict(list)

        for s in strs:
            count = [0] * 26 # a ... z

            for c in s:
                count[ord(c) - ord('a')] += 1

            res[tuple(count)].append(s)

        return res.values()

from collections import defaultdict

class Solution:
    def groupAnagrams(self, strs: List[str]) -> List[List[str]]:
        res = defaultdict(list)

        for s in strs:
            count = [0] * 26 # a ... z

            for c in s:
                count[ord(c) - ord('a')] += 1

            res[tuple(count)].append(s)

        return res.values()

Here, the constraint we mentioned in the beginning gives some perspective to this solution: For each string, we can count the number of characters from 'a' to 'z', because the strings will be just lowercase English letters.

We can still use a hash table, and map each of the 26 letters to an index, and increase the value at that index every time we see that letter. The keys will be these arrays of length 26, and the values will be the arrays of strings themselves.

For example, if we have these strings:

['eat', 'tea', 'tan']

['eat', 'tea', 'tan']

Then, res will look like this:

{
    (1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0): ['tan'],
    (1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0): ['eat', 'tea']
}

{
    (1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0): ['tan'],
    (1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0): ['eat', 'tea']
}

Also note that in the code, we use the ASCII numbers of the characters to get their index, for example, the count of 'a' will be at the 0th index, so it's basic offset arithmetic.

For instance, the ASCII number of 'z' is 122, and 'a' is 97, when you get the difference, it will be 25, meaning that the 'z' will be at the end of the array, that is, the 25th index.

After taking another deep breath, let's try converting it into TypeScript:

function groupAnagrams(strs: string[]): string[][] {
  let result: { [count: string]: string[] } = {};

  for (let s of strs) {
    let count = new Array(26).fill(0);

    for (let c of s) {
      count[c.charCodeAt(0) - 'a'.charCodeAt(0)]++;
    }

    const key = count.toString();

    !(key in result) ? result[key] = [s] : result[key].push(s);
  }

  return Object.values(result);
};

function groupAnagrams(strs: string[]): string[][] {
  let result: { [count: string]: string[] } = {};

  for (let s of strs) {
    let count = new Array(26).fill(0);

    for (let c of s) {
      count[c.charCodeAt(0) - 'a'.charCodeAt(0)]++;
    }

    const key = count.toString();

    !(key in result) ? result[key] = [s] : result[key].push(s);
  }

  return Object.values(result);
};

Time and space complexity

The time complexity will be $O(m \cdot n)$ where $m$ is the total number of strings and the $n$ is the length of a string.

For the space complexity, the dominant item will be the res variable (the count array won't matter much because it won't grow with the input size, it is constant, or $O(1)$). In the case where each key is unique, the space complexity will be $O(m \cdot n)$ where $m$ is the total number of strings, and $n$ is the length of the longest string.

And, that's the end of Group Arrays. The next one will be Top K Frequent Elements, until then, happy coding.