Select Data - Practice Coding Problems

Select Data - Problem

Pandas/Dataframe Easy

You are given a DataFrame called students with the following structure:

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 should be a DataFrame containing only the name and age columns for the matching student.

Input & Output

Example 1 — Basic Selection

$ Input: students = [{"student_id": 100, "name": "Alice", "age": 20}, {"student_id": 101, "name": "Bob", "age": 22}, {"student_id": 102, "name": "Charlie", "age": 19}]

› Output: [{"name": "Bob", "age": 22}]

💡 Note: Filter for student_id = 101 (Bob) and select only name and age columns

Example 2 — First Student Match

$ Input: students = [{"student_id": 101, "name": "Diana", "age": 21}, {"student_id": 103, "name": "Eve", "age": 23}]

› Output: [{"name": "Diana", "age": 21}]

💡 Note: Student 101 is Diana, so return her name and age

Example 3 — No Match Found

$ Input: students = [{"student_id": 100, "name": "Alice", "age": 20}, {"student_id": 102, "name": "Charlie", "age": 19}]

› Output: []

💡 Note: No student with ID 101 exists, so return empty result

Constraints

1 ≤ students.length ≤ 1000
student_id is unique for each student
1 ≤ age ≤ 100
name is a non-empty string

Visualization

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Understanding the Visualization

Input DataFrame

Students table with student_id, name, age columns

Filter by ID

Find rows where student_id equals 101

Select Columns

Extract only name and age from matching rows

Key Takeaway

🎯 Key Insight: Use pandas boolean indexing to combine filtering and column selection in one elegant operation

Asked in

G Google 25 A Amazon 20 M Microsoft 15 N Netflix 10

The key insight is to use pandas boolean indexing: students[students['student_id'] == 101][['name', 'age']]. This creates a boolean mask to filter matching rows, then selects only the required columns. Best approach is boolean indexing. Time: O(n), Space: O(1)

Common Approaches

Approach	Time	Space	Notes
✓ Iterative Row-by-Row Search	O(n)	O(1)	Loop through each row manually to find the matching student_id
Boolean Indexing	O(n)	O(1)	Use pandas boolean indexing to filter and select in one operation

Iterative Row-by-Row Search — Algorithm Steps

Step 1: Initialize empty result DataFrame
Step 2: Loop through each row in the DataFrame
Step 3: Check if current row's student_id equals 101
Step 4: If match found, extract name and age columns
Step 5: Return the result DataFrame

Visualization

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Step-by-Step Walkthrough

Start Search

Begin iterating from first row

Check Each Row

Compare student_id with 101

Found Match

Extract name and age columns

Code -

solution.c — C

#include <stdio.h>
#include <stdlib.h>
#include <string.h>
#include <cjson/cJSON.h>

char* solution(const char* studentsJson) {
    cJSON *json = cJSON_Parse(studentsJson);
    cJSON *result = cJSON_CreateArray();
    
    // Iterate through each student
    cJSON *student = NULL;
    cJSON_ArrayForEach(student, json) {
        cJSON *studentId = cJSON_GetObjectItem(student, "student_id");
        if (cJSON_IsNumber(studentId) && studentId->valueint == 101) {
            // Found the student, create result with name and age
            cJSON *selectedStudent = cJSON_CreateObject();
            cJSON *name = cJSON_GetObjectItem(student, "name");
            cJSON *age = cJSON_GetObjectItem(student, "age");
            
            cJSON_AddStringToObject(selectedStudent, "name", name->valuestring);
            cJSON_AddNumberToObject(selectedStudent, "age", age->valueint);
            
            cJSON_AddItemToArray(result, selectedStudent);
            break;
        }
    }
    
    char *resultString = cJSON_Print(result);
    cJSON_Delete(json);
    cJSON_Delete(result);
    
    return resultString;
}

int main() {
    char input[10000];
    fgets(input, sizeof(input), stdin);
    input[strcspn(input, "\n")] = 0;
    
    char* result = solution(input);
    printf("%s\n", result);
    
    free(result);
    return 0;
}

Time & Space Complexity

Time Complexity

⏱️

O(n)

Must potentially check every row in the DataFrame

✓ Linear Growth

Space Complexity

O(1)

Only stores result DataFrame with one row

✓ Linear Space

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Input & Output

Constraints

Visualization

Related Problems

Common Approaches

Iterative Row-by-Row Search — Algorithm Steps

Visualization

Code -

Time & Space Complexity

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