Select Data - Practice Coding Problems

Select Data - Problem

Pandas Easy

You are working as a data analyst for a university, and you need to extract specific student information from a large DataFrame containing student records.

Given a DataFrame students with columns student_id, name, and age, your task is to select and return only the name and age of the student with student_id = 101.

DataFrame Schema:

Column Name	Type
student_id	int
name	object
age	int

The result should be a DataFrame containing only the name and age columns for the specified student.

Input & Output

example_1.py — Basic Selection

$ Input: students = pd.DataFrame({ 'student_id': [101, 102, 103], 'name': ['Bob', 'Carol', 'David'], 'age': [21, 22, 20] })

› Output: name age 0 Bob 21

💡 Note: Student with ID 101 is found in the first row, so we return their name 'Bob' and age 21.

example_2.py — Student Not First

$ Input: students = pd.DataFrame({ 'student_id': [100, 101, 102], 'name': ['Alice', 'Bob', 'Carol'], 'age': [20, 21, 22] })

› Output: name age 1 Bob 21

💡 Note: Student with ID 101 is in the second row (index 1), so we return their name 'Bob' and age 21.

example_3.py — Large Dataset

$ Input: students = pd.DataFrame({ 'student_id': [100, 99, 101, 103, 104], 'name': ['Alice', 'Zoe', 'Bob', 'Carol', 'Eve'], 'age': [20, 19, 21, 22, 18] })

› Output: name age 2 Bob 21

💡 Note: Even with more students, we efficiently find student 101 (Bob, age 21) using pandas indexing.

Visualization

Tap to expand

students['student_id'] == 101 creates boolean maskStep 2: students[mask] filters rows where mask is TrueStep 3: [['name', 'age']] selects only specified columnsResult: New DataFrame with filtered rows and selected columns

Understanding the Visualization

Boolean Indexing

Create a True/False mask for each row based on student_id == 101

Row Filtering

Apply the boolean mask to select only matching rows

Column Selection

From the filtered rows, select only 'name' and 'age' columns

Result DataFrame

Return the filtered and projected DataFrame

Key Takeaway

🎯 Key Insight: Pandas boolean indexing allows us to filter and select data in a single, highly optimized operation that's both readable and performant.

Time & Space Complexity

Time Complexity

⏱️

O(n)

Pandas uses optimized C code for filtering, much faster than Python loops

✓ Linear Growth

Space Complexity

O(1)

Only stores the result row(s), pandas handles memory efficiently

✓ Linear Space

Constraints

1 ≤ students.shape[0] ≤ 10⁵
student_id values are integers
Student ID 101 may or may not exist in the DataFrame
Name values are non-empty strings
Age values are positive integers

Asked in

G Google 25 f Meta 20 a Amazon 18 ⊞ Microsoft 15

The optimal solution uses pandas boolean indexing to filter rows where student_id == 101 and then selects only the name and age columns. This approach: students[students['student_id'] == 101][['name', 'age']] leverages pandas' optimized C implementations for maximum performance.

Common Approaches

Approach	Time	Space	Notes
✓ Pandas Boolean Indexing (Optimal)	O(n)	O(1)	Use pandas' optimized filtering and column selection in one operation
Iterative Row-by-Row Search	O(n)	O(1)	Loop through each row manually to find the target student

Pandas Boolean Indexing (Optimal) — Algorithm Steps

Create boolean mask where student_id equals 101
Apply the mask to filter the DataFrame
Select only 'name' and 'age' columns
Return the filtered and projected result

Visualization

Tap to expand

Step-by-Step Walkthrough

Create Boolean Mask

Generate True/False for each row based on condition

Apply Filter

Pandas uses the mask to select matching rows

Column Selection

Extract only the specified columns from filtered rows

Code -

solution.c — C

#include <stdio.h>
#include <stdlib.h>
#include <string.h>

typedef struct {
    int student_id;
    char name[50];
    int age;
} Student;

typedef struct {
    char name[50];
    int age;
} Result;

Result* selectData(Student* students, int size, int* resultSize) {
    Result* results = malloc(sizeof(Result) * size);
    *resultSize = 0;
    
    // Filter and project efficiently
    for (int i = 0; i < size; i++) {
        if (students[i].student_id == 101) {
            strcpy(results[*resultSize].name, students[i].name);
            results[*resultSize].age = students[i].age;
            (*resultSize)++;
        }
    }
    
    return results;
}

Time & Space Complexity

Time Complexity

⏱️

O(n)

Pandas uses optimized C code for filtering, much faster than Python loops

✓ Linear Growth

Space Complexity

O(1)

Only stores the result row(s), pandas handles memory efficiently

✓ Linear Space

Constraints

1 ≤ students.shape[0] ≤ 10⁵
student_id values are integers
Student ID 101 may or may not exist in the DataFrame
Name values are non-empty strings
Age values are positive integers

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Input & Output

Visualization

Time & Space Complexity

Related Problems

Constraints

Common Approaches

Pandas Boolean Indexing (Optimal) — Algorithm Steps

Visualization

Code -

Time & Space Complexity

Constraints

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