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.
Move from brute-force thinking to an efficient approach using core interview patterns strategy.
Write a bash script to calculate the frequency of each word in a text file words.txt.
For simplicity sake, you may assume:
words.txt contains only lowercase characters and space ' ' characters.Example:
Assume that words.txt has the following content:
the day is sunny the the the sunny is is
Your script should output the following, sorted by descending frequency:
the 4 is 3 sunny 2 day 1
Note:
Problem summary: Write a bash script to calculate the frequency of each word in a text file words.txt. For simplicity sake, you may assume: words.txt contains only lowercase characters and space ' ' characters. Each word must consist of lowercase characters only. Words are separated by one or more whitespace characters.
Start with the most direct exhaustive search. That gives a correctness anchor before optimizing.
Pattern signal: General problem-solving
a
top-k-frequent-elements)Source-backed implementations are provided below for direct study and interview prep.
// Accepted solution for LeetCode #192: Word Frequency
// Auto-generated Java example from rust.
class Solution {
public void exampleSolution() {
}
}
// Reference (rust):
// // Accepted solution for LeetCode #192: Word Frequency
// pub fn shell_example() -> &'static str {
// r#"
// # Accepted solution for LeetCode #192: Word Frequency
// # Read from the file words.txt and output the word frequency list to stdout.
// cat words.txt | tr -s ' ' '\n' | sort | uniq -c | sort -nr | awk '{print $2, $1}'
// "#
// }
// Accepted solution for LeetCode #192: Word Frequency
// Auto-generated Go example from rust.
func exampleSolution() {
}
// Reference (rust):
// // Accepted solution for LeetCode #192: Word Frequency
// pub fn shell_example() -> &'static str {
// r#"
// # Accepted solution for LeetCode #192: Word Frequency
// # Read from the file words.txt and output the word frequency list to stdout.
// cat words.txt | tr -s ' ' '\n' | sort | uniq -c | sort -nr | awk '{print $2, $1}'
// "#
// }
# Accepted solution for LeetCode #192: Word Frequency
# Auto-generated Python example from rust.
def example_solution() -> None:
return
# Reference (rust):
# // Accepted solution for LeetCode #192: Word Frequency
# pub fn shell_example() -> &'static str {
# r#"
# # Accepted solution for LeetCode #192: Word Frequency
# # Read from the file words.txt and output the word frequency list to stdout.
# cat words.txt | tr -s ' ' '\n' | sort | uniq -c | sort -nr | awk '{print $2, $1}'
# "#
# }
// Accepted solution for LeetCode #192: Word Frequency
pub fn shell_example() -> &'static str {
r#"
# Accepted solution for LeetCode #192: Word Frequency
# Read from the file words.txt and output the word frequency list to stdout.
cat words.txt | tr -s ' ' '\n' | sort | uniq -c | sort -nr | awk '{print $2, $1}'
"#
}
// Accepted solution for LeetCode #192: Word Frequency
// Auto-generated TypeScript example from rust.
function exampleSolution(): void {
}
// Reference (rust):
// // Accepted solution for LeetCode #192: Word Frequency
// pub fn shell_example() -> &'static str {
// r#"
// # Accepted solution for LeetCode #192: Word Frequency
// # Read from the file words.txt and output the word frequency list to stdout.
// cat words.txt | tr -s ' ' '\n' | sort | uniq -c | sort -nr | awk '{print $2, $1}'
// "#
// }
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.