Is Select Data easy or hard?

Select Data is generally classified as an easy problem. The core idea is straightforward filtering using iteration. The HashMap variation introduces a common optimization pattern but remains beginner-friendly and focuses on fundamental data structure usage.

Select Data Python/Java solution

In Python, you typically iterate through the list and append matching elements to a result list or use dictionary lookups for faster retrieval. In Java, a loop over the collection with conditional checks works for filtering, while a HashMap can store key-to-record mappings for constant-time access.

How to solve Select Data in O(n)?

Traverse the dataset once and apply a conditional check to each record. If the record satisfies the selection rule, add it to the result. Since each element is processed exactly once, the algorithm runs in O(n) time with constant auxiliary space.

What is the best approach for Select Data?

The most practical solution is a single pass iteration that filters records matching the required condition. This runs in O(n) time and requires minimal extra memory. If the problem involves repeated lookups by a key or identifier, building a HashMap first allows O(1) average-time access for each query.

Is Select Data asked at Google/Amazon/Meta?

Problems involving filtering datasets and using hash-based lookups appear frequently in interviews at large tech companies. While the exact problem title may vary, the underlying pattern of linear scanning or HashMap-based retrieval is commonly tested in coding interviews.

What data structure is used in Select Data?

The simplest implementation uses arrays or lists for sequential traversal. For optimized retrieval, a HashMap (dictionary) is used to map keys to records, enabling constant-time lookups and avoiding repeated scans of the dataset.

What is the time complexity of Select Data?

The standard filtering solution runs in O(n) time because every record must be checked once. Space complexity is O(1) if only scanning the dataset, or O(k) if storing the filtered results. A HashMap-based approach still requires O(n) preprocessing but enables O(1) average lookup afterward.

Description Hints 1 Solution Video 1:42

2880. Select Data

Easy79.2% acceptance3 companies

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.

Asked at 3 companies

Microsoft

Google Bloomberg

Hints open one at a time

Hint 1

Consider applying both row and column filtering to select the desired data.

1:42

Still stuck?Read 2 approaches, or watch a walkthrough.

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1class Solution:
  def solve(self):
      pass


Starter codePython 3Spaces: 4

Examples

Case 1

Input

{"headers":{"students":["student_id","name","age"]},"rows":{"students":[[101,"Ulysses",13],[53,"William",10],[128,"Henry",6],[3,"Henry",11]]}}

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