Find Common Characters

Find Common Characters - Problem

Given a string array words, return an array of all characters that show up in all strings within the words (including duplicates). You may return the answer in any order.

For example, if a character occurs 3 times in each string and 2 times in another string, you should include that character 2 times in the final answer (the minimum occurrence across all strings).

Input & Output

Example 1 — Basic Case

$ Input: words = ["bella","label","roller"]

› Output: ["e","l","l"]

💡 Note: Character 'e' appears once in each word, 'l' appears twice in each word, so result is ["e", "l", "l"]

Example 2 — Different Frequencies

$ Input: words = ["cool","lock","cook"]

› Output: ["c","o"]

💡 Note: Character 'c' appears once in each word, 'o' appears twice in 'cool' and 'cook' but once in 'lock', so we take minimum count of 1

Example 3 — No Common Characters

$ Input: words = ["abc","def","ghi"]

› Output: []

💡 Note: No characters appear in all three words, so result is empty array

Constraints

1 ≤ words.length ≤ 100
1 ≤ words[i].length ≤ 100
words[i] consists of lowercase English letters.

Visualization

Tap to expand

Asked in

G Google 15 a Amazon 12 f Facebook 8

The key insight is that we need to find the minimum frequency of each character across all strings. The best approach is frequency counting - build frequency maps for each string, then for each character that appears in all strings, take the minimum count. Time: O(n × m), Space: O(n × k).

Common Approaches

✓ Dp 2d

⏱️ Time: N/A Space: N/A

Greedy

⏱️ Time: N/A Space: N/A

Brute Force Character Check

⏱️ Time: O(n × m²) Space: O(n × m)

For each character in the first string, iterate through all other strings to see if that character exists. Remove used characters to handle duplicates correctly.

Frequency Counting

⏱️ Time: O(n × m) Space: O(n × k)

Create frequency maps for each string, then for each character that appears in all strings, take the minimum count across all frequency maps.

Algorithm Steps — Algorithm Steps

Code -

solution.c — C

Time & Space Complexity

Time Complexity

⏱️

✓ Linear Growth

Space Complexity

✓ Linear Space

28.5K Views

Medium Frequency

~15 min Avg. Time

892 Likes

Ln 1, Col 1

Smart Actions

💡 Explanation

AI Ready

💡 Suggestion Tab to accept Esc to dismiss

// Output will appear here after running code

Code Editor Closed

Click the red button to reopen