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 students +-------------+--------+ | Column Name | Type | +-------------+--------+ | student_id | int | | name | object | | age | int | +-------------+--------+
Write a solution to select the name and age of the student with student_id = 101.
The result format is in the following example.
Example 1: Input: +------------+---------+-----+ | student_id | name | age | +------------+---------+-----+ | 101 | Ulysses | 13 | | 53 | William | 10 | | 128 | Henry | 6 | | 3 | Henry | 11 | +------------+---------+-----+ Output: +---------+-----+ | name | age | +---------+-----+ | Ulysses | 13 | +---------+-----+ Explanation: Student Ulysses has student_id = 101, we select the name and age.
Problem summary: DataFrame students +-------------+--------+ | Column Name | Type | +-------------+--------+ | student_id | int | | name | object | | age | int | +-------------+--------+ Write a solution to select the name and age of the student with student_id = 101. 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":{"students":["student_id","name","age"]},"rows":{"students":[[101,"Ulysses",13],[53,"William",10],[128,"Henry",6],[3,"Henry",11]]}}Source-backed implementations are provided below for direct study and interview prep.
// Accepted solution for LeetCode #2880: Select Data
// Auto-generated Java example from py.
class Solution {
public void exampleSolution() {
}
}
// Reference (py):
// # Accepted solution for LeetCode #2880: Select Data
// import pandas as pd
//
//
// def selectData(students: pd.DataFrame) -> pd.DataFrame:
// return students[students['student_id'] == 101][['name', 'age']]
// Accepted solution for LeetCode #2880: Select Data
// Auto-generated Go example from py.
func exampleSolution() {
}
// Reference (py):
// # Accepted solution for LeetCode #2880: Select Data
// import pandas as pd
//
//
// def selectData(students: pd.DataFrame) -> pd.DataFrame:
// return students[students['student_id'] == 101][['name', 'age']]
# Accepted solution for LeetCode #2880: Select Data
import pandas as pd
def selectData(students: pd.DataFrame) -> pd.DataFrame:
return students[students['student_id'] == 101][['name', 'age']]
// Accepted solution for LeetCode #2880: Select Data
// 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 #2880: Select Data
// import pandas as pd
//
//
// def selectData(students: pd.DataFrame) -> pd.DataFrame:
// return students[students['student_id'] == 101][['name', 'age']]
// Accepted solution for LeetCode #2880: Select Data
// Auto-generated TypeScript example from py.
function exampleSolution(): void {
}
// Reference (py):
// # Accepted solution for LeetCode #2880: Select Data
// import pandas as pd
//
//
// def selectData(students: pd.DataFrame) -> pd.DataFrame:
// return students[students['student_id'] == 101][['name', 'age']]
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.