LeetCode Problem Workspace
Select Data
Learn to efficiently select specific rows and columns in a dataset using the Select Data core interview pattern for accurate results.
0
Topics
1
Code langs
0
Related
Practice Focus
Easy · Select Data core interview pattern
Answer-first summary
Learn to efficiently select specific rows and columns in a dataset using the Select Data core interview pattern for accurate results.
Ace coding interviews with Interview AiBoxInterview AiBox guidance for Select Data core interview pattern
The Select Data problem focuses on extracting targeted information from a table by filtering rows and selecting specific columns. Applying both row and column filtering ensures you retrieve the exact data required without extra computation. This problem tests understanding of simple yet precise data selection techniques that are fundamental in database and DataFrame operations.
Problem Statement
Given a table of student information with columns student_id, name, and age, write a query or operation to select the name and age of the student whose student_id is 101. Ensure that only the relevant row and the specific columns are returned in the result.
Your solution should return the data in a structured format, preserving the column names. For example, selecting name and age for student_id 101 should output a single row with those values. Focus on applying both row and column selection efficiently according to the Select Data core interview pattern.
Examples
Example 1
Input: See original problem statement.
Output: See original problem statement.
DataFrame students +-------------+--------+ | Column Name | Type | +-------------+--------+ | student_id | int | | name | object | | age | int | +-------------+--------+
Example 2
Input: +------------+---------+-----+ | student_id | name | age | +------------+---------+-----+ | 101 | Ulysses | 13 | | 53 | William | 10 | | 128 | Henry | 6 | | 3 | Henry | 11 | +------------+---------+-----+
Output: +---------+-----+ | name | age | +---------+-----+ | Ulysses | 13 | +---------+-----+
Student Ulysses has student_id = 101, we select the name and age.
Constraints
Solution Approach
Filter Rows by Condition
Apply a condition to select only the row where student_id equals 101. This ensures you are working with the exact row needed before selecting columns.
Select Specific Columns
After filtering the row, select only the name and age columns. Avoid selecting all columns to follow the core pattern and reduce unnecessary data processing.
Combine Row and Column Selection
Chain row filtering and column selection to produce the final result. This pattern prevents common mistakes like returning extra rows or columns and guarantees correct output format.
Complexity Analysis
| Metric | Value |
|---|---|
| Time | Depends on the final approach |
| Space | Depends on the final approach |
Time complexity depends on the method used; filtering a single row is generally O(n) in table size. Space complexity is minimal since only one row and two columns are selected.
What Interviewers Usually Probe
- Expect precise row filtering using the student_id column.
- Check for column-specific selection to ensure only name and age are returned.
- Notice if the candidate avoids unnecessary full-table selection or extra columns.
Common Pitfalls or Variants
Common pitfalls
- Selecting all columns instead of only name and age, returning more data than needed.
- Using incorrect conditions, e.g., filtering with the wrong student_id.
- Returning multiple rows when the query should only produce one row for student_id 101.
Follow-up variants
- Select multiple students by providing a list of student_ids instead of a single ID.
- Retrieve additional columns such as grade while still filtering by student_id.
- Filter by conditions other than student_id, such as age greater than a certain value.
FAQ
What is the easiest way to select specific columns for student_id 101?
First filter the row where student_id equals 101, then select only the name and age columns to match the expected output.
Can I use a single SQL query or DataFrame operation to solve this?
Yes, combining row filtering and column selection in one query or operation efficiently solves the problem.
What if multiple students have the same student_id?
The problem assumes student_id is unique; otherwise, all matching rows will be returned unless additional constraints are applied.
Is this problem testing general selection or a specific pattern?
This problem specifically tests the Select Data core interview pattern, emphasizing precise row and column selection.
How can I verify my solution is correct?
Compare the output with the expected format, ensuring only the name and age of student_id 101 are returned in a single row.
Solution
Solution 1
#### Python3
import pandas as pd
def selectData(students: pd.DataFrame) -> pd.DataFrame:
return students[students['student_id'] == 101][['name', 'age']]