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 core interview patterns fundamentals.
DataFrame employees
+-------------+--------+
| Column Name | Type. |
+-------------+--------+
| name | object |
| salary | int. |
+-------------+--------+
A company plans to provide its employees with a bonus.
Write a solution to create a new column name bonus that contains the doubled values of the salary column.
The result format is in the following example.
Example 1:
Input: DataFrame employees +---------+--------+ | name | salary | +---------+--------+ | Piper | 4548 | | Grace | 28150 | | Georgia | 1103 | | Willow | 6593 | | Finn | 74576 | | Thomas | 24433 | +---------+--------+ Output: +---------+--------+--------+ | name | salary | bonus | +---------+--------+--------+ | Piper | 4548 | 9096 | | Grace | 28150 | 56300 | | Georgia | 1103 | 2206 | | Willow | 6593 | 13186 | | Finn | 74576 | 149152 | | Thomas | 24433 | 48866 | +---------+--------+--------+ Explanation: A new column bonus is created by doubling the value in the column salary.
Problem summary: DataFrame employees +-------------+--------+ | Column Name | Type. | +-------------+--------+ | name | object | | salary | int. | +-------------+--------+ A company plans to provide its employees with a bonus. Write a solution to create a new column name bonus that contains the doubled values of the salary column. The result format is in the following example.
Start with the most direct exhaustive search. That gives a correctness anchor before optimizing.
Pattern signal: General problem-solving
{"headers":{"employees":["name","salary"]},"rows":{"employees":[["Piper",4548],["Grace",28150],["Georgia",1103],["Willow",6593],["Finn",74576],["Thomas",24433]]}}Source-backed implementations are provided below for direct study and interview prep.
// Accepted solution for LeetCode #2881: Create a New Column
// Auto-generated Java example from py.
class Solution {
public void exampleSolution() {
}
}
// Reference (py):
// # Accepted solution for LeetCode #2881: Create a New Column
// import pandas as pd
//
//
// def createBonusColumn(employees: pd.DataFrame) -> pd.DataFrame:
// employees['bonus'] = employees['salary'] * 2
// return employees
// Accepted solution for LeetCode #2881: Create a New Column
// Auto-generated Go example from py.
func exampleSolution() {
}
// Reference (py):
// # Accepted solution for LeetCode #2881: Create a New Column
// import pandas as pd
//
//
// def createBonusColumn(employees: pd.DataFrame) -> pd.DataFrame:
// employees['bonus'] = employees['salary'] * 2
// return employees
# Accepted solution for LeetCode #2881: Create a New Column
import pandas as pd
def createBonusColumn(employees: pd.DataFrame) -> pd.DataFrame:
employees['bonus'] = employees['salary'] * 2
return employees
// Accepted solution for LeetCode #2881: Create a New Column
// Rust example auto-generated from py reference.
// Replace the signature and local types with the exact LeetCode harness for this problem.
impl Solution {
pub fn rust_example() {
// Port the logic from the reference block below.
}
}
// Reference (py):
// # Accepted solution for LeetCode #2881: Create a New Column
// import pandas as pd
//
//
// def createBonusColumn(employees: pd.DataFrame) -> pd.DataFrame:
// employees['bonus'] = employees['salary'] * 2
// return employees
// Accepted solution for LeetCode #2881: Create a New Column
// Auto-generated TypeScript example from py.
function exampleSolution(): void {
}
// Reference (py):
// # Accepted solution for LeetCode #2881: Create a New Column
// import pandas as pd
//
//
// def createBonusColumn(employees: pd.DataFrame) -> pd.DataFrame:
// employees['bonus'] = employees['salary'] * 2
// return employees
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.