Display the First Three Rows

Display the First Three Rows - Problem

Pandas Easy

You are given a DataFrame named employees with the following structure:

Column Name	Type
employee_id	int
name	object
department	object
salary	int

Write a solution to display the first 3 rows of this DataFrame.

Note: This is a Pandas DataFrame operation problem.

Input & Output

Example 1 — Basic Employee Data

$ Input: employees = [{"employee_id": 1, "name": "John", "department": "Engineering", "salary": 75000}, {"employee_id": 2, "name": "Sarah", "department": "Marketing", "salary": 65000}, {"employee_id": 3, "name": "Mike", "department": "Sales", "salary": 55000}, {"employee_id": 4, "name": "Lisa", "department": "HR", "salary": 60000}]

› Output: [{"employee_id": 1, "name": "John", "department": "Engineering", "salary": 75000}, {"employee_id": 2, "name": "Sarah", "department": "Marketing", "salary": 65000}, {"employee_id": 3, "name": "Mike", "department": "Sales", "salary": 55000}]

💡 Note: The first 3 rows are returned: John (Engineering), Sarah (Marketing), and Mike (Sales)

Example 2 — Exactly 3 Rows

$ Input: employees = [{"employee_id": 10, "name": "Alice", "department": "IT", "salary": 80000}, {"employee_id": 20, "name": "Bob", "department": "Finance", "salary": 70000}, {"employee_id": 30, "name": "Carol", "department": "Operations", "salary": 62000}]

› Output: [{"employee_id": 10, "name": "Alice", "department": "IT", "salary": 80000}, {"employee_id": 20, "name": "Bob", "department": "Finance", "salary": 70000}, {"employee_id": 30, "name": "Carol", "department": "Operations", "salary": 62000}]

💡 Note: When DataFrame has exactly 3 rows, all rows are returned

Example 3 — Less than 3 Rows

$ Input: employees = [{"employee_id": 100, "name": "David", "department": "Research", "salary": 85000}, {"employee_id": 101, "name": "Eve", "department": "Design", "salary": 72000}]

› Output: [{"employee_id": 100, "name": "David", "department": "Research", "salary": 85000}, {"employee_id": 101, "name": "Eve", "department": "Design", "salary": 72000}]

💡 Note: When DataFrame has less than 3 rows, all available rows are returned

Constraints

DataFrame contains at least 0 rows
Each row has employee_id, name, department, and salary columns
employee_id is a positive integer
salary is a non-negative integer

Visualization

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Asked in

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

The key insight is to use Pandas' built-in head() method, which is specifically designed for this common operation. The optimal approach is employees.head(3) which returns the first 3 rows efficiently. Time: O(1), Space: O(1)

Common Approaches

✓ Manual Row Selection

⏱️ Time: O(1) Space: O(1)

Use iloc to manually slice the first 3 rows by specifying the exact row indices [0, 1, 2]. This approach works but is more verbose than necessary.

Pandas head() Method

⏱️ Time: O(1) Space: O(1)

Use the Pandas head() method which is specifically designed to return the first n rows of a DataFrame. This is the most readable and conventional approach.

Manual Row Selection — Algorithm Steps

Use iloc[0:3] to select rows 0, 1, and 2
Return the sliced DataFrame

Visualization

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Step-by-Step Walkthrough

DataFrame Input

Original DataFrame with all rows

Apply iloc[0:3]

Select rows at indices 0, 1, 2

Result

First 3 rows returned

Code -

solution.c — C

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

char* solution(const char* employees_str) {
    // Parse JSON and manually select first 3 rows
    cJSON *json = cJSON_Parse(employees_str);
    cJSON *result = cJSON_CreateArray();
    
    int count = cJSON_GetArraySize(json);
    for (int i = 0; i < 3 && i < count; i++) {
        cJSON *item = cJSON_GetArrayItem(json, i);
        cJSON_AddItemToArray(result, cJSON_Duplicate(item, 1));
    }
    
    char *result_str = cJSON_Print(result);
    cJSON_Delete(json);
    cJSON_Delete(result);
    return result_str;
}

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(1)

Slicing operation is constant time for small fixed number of rows

✓ Linear Growth

Space Complexity

O(1)

Returns a view of existing data, no additional storage needed

✓ Linear Space

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Input & Output

Constraints

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Related Problems

Common Approaches

Manual Row Selection — Algorithm Steps

Visualization

Code -

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