Change Data Type - Practice Coding Problems

Change Data Type - Problem

Databse Easy

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

Column Name	Type
student_id	int
name	object
age	int
grade	float

The grade column is currently stored as floats, but it should be stored as integers. Write a solution to convert the grade column from float to int data type.

Return the modified DataFrame with the grade column converted to integers.

Input & Output

Example 1 — Basic Grade Conversion

$ Input: students = [{"student_id": 1, "name": "Alice", "age": 20, "grade": 85.0}, {"student_id": 2, "name": "Bob", "age": 21, "grade": 92.0}]

› Output: [{"student_id": 1, "name": "Alice", "age": 20, "grade": 85}, {"student_id": 2, "name": "Bob", "age": 21, "grade": 92}]

💡 Note: The grade column values 85.0 and 92.0 are converted to integers 85 and 92 respectively

Example 2 — Single Student

$ Input: students = [{"student_id": 1, "name": "Carol", "age": 19, "grade": 78.0}]

› Output: [{"student_id": 1, "name": "Carol", "age": 19, "grade": 78}]

💡 Note: Single row conversion: grade 78.0 becomes integer 78

Example 3 — Multiple Students

$ Input: students = [{"student_id": 1, "name": "Dave", "age": 22, "grade": 95.0}, {"student_id": 2, "name": "Eve", "age": 20, "grade": 88.0}, {"student_id": 3, "name": "Frank", "age": 21, "grade": 91.0}]

› Output: [{"student_id": 1, "name": "Dave", "age": 22, "grade": 95}, {"student_id": 2, "name": "Eve", "age": 20, "grade": 88}, {"student_id": 3, "name": "Frank", "age": 21, "grade": 91}]

💡 Note: All grades (95.0, 88.0, 91.0) are converted to their integer equivalents (95, 88, 91)

Constraints

1 ≤ students.length ≤ 10⁴
All grade values are valid floats
Grade values are in range [0.0, 100.0]

Visualization

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Understanding the Visualization

Input DataFrame

Students data with float grades

Type Conversion

Convert grade column from float to int

Output DataFrame

Same data with integer grades

Key Takeaway

🎯 Key Insight: Use pandas astype() method for efficient column data type conversion

Asked in

N Netflix 25 M Microsoft 20

The key insight is to use pandas' built-in astype() method for efficient data type conversion. The optimal approach uses students['grade'].astype(int) to convert the entire column in one vectorized operation. Time: O(n), Space: O(1)

Common Approaches

Approach	Time	Space	Notes
✓ Pandas astype() Method	O(n)	O(1)	Use pandas built-in astype() method for efficient column conversion
Manual Row-by-Row Conversion	O(n)	O(1)	Iterate through each row and manually convert grade values

Pandas astype() Method — Algorithm Steps

Step 1: Access the grade column
Step 2: Apply astype(int) conversion
Step 3: Return modified DataFrame

Visualization

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Step-by-Step Walkthrough

DataFrame Input

Original DataFrame with float grades

astype() Operation

Apply vectorized conversion to entire column

Updated DataFrame

All grades converted to integers efficiently

Code -

solution.c — C

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

typedef struct {
    int student_id;
    char name[100];
    int age;
    int grade;
} Student;

Student* solution(Student* students, int size) {
    // In C, grades are already integers
    // No conversion needed
    return students;
}

int main() {
    Student students[1000];
    int size = 0;
    
    Student* result = solution(students, size);
    // Output result
    return 0;
}

Time & Space Complexity

Time Complexity

⏱️

O(n)

Pandas internally processes n rows but with optimized vectorized operations

✓ Linear Growth

Space Complexity

O(1)

Modifies DataFrame in-place, no additional memory needed

✓ Linear Space

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Input & Output

Constraints

Visualization

Related Problems

Common Approaches

Pandas astype() Method — Algorithm Steps

Visualization

Code -

Time & Space Complexity

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