Off-by-one on range boundaries
Wrong move: Loop endpoints miss first/last candidate.
Usually fails on: Fails on minimal arrays and exact-boundary answers.
Fix: Re-derive loops from inclusive/exclusive ranges before coding.
Build confidence with an intuition-first walkthrough focused on array fundamentals.
Given a string paragraph and a string array of the banned words banned, return the most frequent word that is not banned. It is guaranteed there is at least one word that is not banned, and that the answer is unique.
The words in paragraph are case-insensitive and the answer should be returned in lowercase.
Note that words can not contain punctuation symbols.
Example 1:
Input: paragraph = "Bob hit a ball, the hit BALL flew far after it was hit.", banned = ["hit"] Output: "ball" Explanation: "hit" occurs 3 times, but it is a banned word. "ball" occurs twice (and no other word does), so it is the most frequent non-banned word in the paragraph. Note that words in the paragraph are not case sensitive, that punctuation is ignored (even if adjacent to words, such as "ball,"), and that "hit" isn't the answer even though it occurs more because it is banned.
Example 2:
Input: paragraph = "a.", banned = [] Output: "a"
Constraints:
1 <= paragraph.length <= 1000' ', or one of the symbols: "!?',;.".0 <= banned.length <= 1001 <= banned[i].length <= 10banned[i] consists of only lowercase English letters.Problem summary: Given a string paragraph and a string array of the banned words banned, return the most frequent word that is not banned. It is guaranteed there is at least one word that is not banned, and that the answer is unique. The words in paragraph are case-insensitive and the answer should be returned in lowercase. Note that words can not contain punctuation symbols.
Start with the most direct exhaustive search. That gives a correctness anchor before optimizing.
Pattern signal: Array · Hash Map
"Bob hit a ball, the hit BALL flew far after it was hit." ["hit"]
"a." []
Source-backed implementations are provided below for direct study and interview prep.
// Accepted solution for LeetCode #819: Most Common Word
import java.util.regex.Matcher;
import java.util.regex.Pattern;
class Solution {
private static Pattern pattern = Pattern.compile("[a-z]+");
public String mostCommonWord(String paragraph, String[] banned) {
Set<String> bannedWords = new HashSet<>();
for (String word : banned) {
bannedWords.add(word);
}
Map<String, Integer> counter = new HashMap<>();
Matcher matcher = pattern.matcher(paragraph.toLowerCase());
while (matcher.find()) {
String word = matcher.group();
if (bannedWords.contains(word)) {
continue;
}
counter.put(word, counter.getOrDefault(word, 0) + 1);
}
int max = Integer.MIN_VALUE;
String ans = null;
for (Map.Entry<String, Integer> entry : counter.entrySet()) {
if (entry.getValue() > max) {
max = entry.getValue();
ans = entry.getKey();
}
}
return ans;
}
}
// Accepted solution for LeetCode #819: Most Common Word
func mostCommonWord(paragraph string, banned []string) string {
s := make(map[string]bool)
for _, w := range banned {
s[w] = true
}
counter := make(map[string]int)
var ans string
for i, mx, n := 0, 0, len(paragraph); i < n; {
if !unicode.IsLetter(rune(paragraph[i])) {
i++
continue
}
j := i
var word []byte
for j < n && unicode.IsLetter(rune(paragraph[j])) {
word = append(word, byte(unicode.ToLower(rune(paragraph[j]))))
j++
}
i = j + 1
t := string(word)
if s[t] {
continue
}
counter[t]++
if counter[t] > mx {
ans = t
mx = counter[t]
}
}
return ans
}
# Accepted solution for LeetCode #819: Most Common Word
class Solution:
def mostCommonWord(self, paragraph: str, banned: List[str]) -> str:
s = set(banned)
p = Counter(re.findall('[a-z]+', paragraph.lower()))
return next(word for word, _ in p.most_common() if word not in s)
// Accepted solution for LeetCode #819: Most Common Word
use std::collections::{HashMap, HashSet};
impl Solution {
pub fn most_common_word(mut paragraph: String, banned: Vec<String>) -> String {
paragraph.make_ascii_lowercase();
let banned: HashSet<&str> = banned.iter().map(String::as_str).collect();
let mut map = HashMap::new();
for word in paragraph.split(|c| !matches!(c, 'a'..='z')) {
if word.is_empty() || banned.contains(word) {
continue;
}
let val = map.get(&word).unwrap_or(&0) + 1;
map.insert(word, val);
}
map.into_iter()
.max_by_key(|&(_, v)| v)
.unwrap()
.0
.to_string()
}
}
// Accepted solution for LeetCode #819: Most Common Word
function mostCommonWord(paragraph: string, banned: string[]): string {
const s = paragraph.toLocaleLowerCase();
const map = new Map<string, number>();
const set = new Set<string>(banned);
for (const word of s.split(/[^A-z]/)) {
if (word === '' || set.has(word)) {
continue;
}
map.set(word, (map.get(word) ?? 0) + 1);
}
return [...map.entries()].reduce((r, v) => (v[1] > r[1] ? v : r), ['', 0])[0];
}
Use this to step through a reusable interview workflow for this problem.
Two nested loops check every pair or subarray. The outer loop fixes a starting point, the inner loop extends or searches. For n elements this gives up to n²/2 operations. No extra space, but the quadratic time is prohibitive for large inputs.
Most array problems have an O(n²) brute force (nested loops) and an O(n) optimal (single pass with clever state tracking). The key is identifying what information to maintain as you scan: a running max, a prefix sum, a hash map of seen values, or two pointers.
Review these before coding to avoid predictable interview regressions.
Wrong move: Loop endpoints miss first/last candidate.
Usually fails on: Fails on minimal arrays and exact-boundary answers.
Fix: Re-derive loops from inclusive/exclusive ranges before coding.
Wrong move: Zero-count keys stay in map and break distinct/count constraints.
Usually fails on: Window/map size checks are consistently off by one.
Fix: Delete keys when count reaches zero.