Change Data Type - Practice Coding Problems

Change Data Type - Problem

Database Easy

You're working as a data analyst and have received a student dataset with a critical data type error! 📊

The DataFrame contains student information where the grade column is incorrectly stored as float values, but grades should be represented as integers for proper analysis and reporting.

Your task: Convert the grade column from float to integer data type while preserving all other columns unchanged.

Column Name	Current Type	Expected Type
student_id	int	int ✅
name	object	object ✅
age	int	int ✅
grade	float ❌	int 🎯

Note: This is a common data preprocessing step in pandas that ensures data consistency and proper analysis.

Input & Output

example_1.py — Basic Grade Conversion

$ Input: DataFrame: student_id | name | age | grade 1 | Alice | 20 | 85.0 2 | Bob | 21 | 92.0 3 | Charlie | 19 | 78.0

› Output: DataFrame: student_id | name | age | grade 1 | Alice | 20 | 85 2 | Bob | 21 | 92 3 | Charlie | 19 | 78 Data types: grade column changed from float64 to int64

💡 Note: All grade values (85.0, 92.0, 78.0) are successfully converted to integers (85, 92, 78) while preserving other columns

example_2.py — Single Student Record

$ Input: DataFrame: student_id | name | age | grade 100 | Emma | 22 | 95.0

💡 Note: Even with a single record, the conversion works correctly, changing 95.0 to 95

example_3.py — Edge Case with Whole Number Floats

$ Input: DataFrame: student_id | name | age | grade 1 | Alex | 19 | 88.0 2 | Sarah | 20 | 100.0 3 | Mike | 21 | 75.0

› Output: DataFrame: student_id | name | age | grade 1 | Alex | 19 | 88 2 | Sarah | 20 | 100 3 | Mike | 21 | 75 Data types: grade column changed from float64 to int64

💡 Note: All grades are whole numbers stored as floats, conversion preserves the numeric values perfectly

Visualization

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Understanding the Visualization

Identify Column

Locate the 'grade' column with float64 data type

Apply Conversion

Use pandas astype(int) for vectorized conversion

Verify Result

Confirm grade column is now int64 type

Key Takeaway

🎯 Key Insight: Use pandas' built-in `astype()` method for efficient, vectorized data type conversions instead of manual row-by-row processing

Time & Space Complexity

Time Complexity

⏱️

O(n)

Must iterate through each of the n rows to convert grades individually

✓ Linear Growth

Space Complexity

O(1)

Only modifying existing DataFrame in-place, no additional data structures needed

✓ Linear Space

Constraints

1 ≤ number of students ≤ 10⁴
All grade values are valid whole numbers stored as floats
Grade values range from 0.0 to 100.0
DataFrame structure must remain unchanged except for grade column data type

Asked in

N Netflix 15 ♪ Spotify 12 U Uber 10 A Airbnb 8

The optimal solution uses pandas' built-in astype() method: students['grade'] = students['grade'].astype(int). This leverages pandas' vectorized operations for maximum efficiency, converting the entire grade column in one operation rather than processing rows individually.

Common Approaches

Approach	Time	Space	Notes
✓ Row-by-Row Manual Conversion	O(n)	O(1)	Iterate through each row and manually convert grade values one by one

Row-by-Row Manual Conversion — Algorithm Steps

Iterate through each row index in the DataFrame
Access the grade value for current row
Convert the float grade to integer using int()
Assign the converted value back to the DataFrame
Repeat for all rows

Visualization

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Step-by-Step Walkthrough

Start with Float Grades

DataFrame has grades as 85.0, 92.0, 78.0

Process Row 1

Convert 85.0 → 85 for Alice

Process Row 2

Convert 92.0 → 92 for Bob

Process Row 3

Convert 78.0 → 78 for Charlie

Code -

solution.c — C

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

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

void changeDatatype(Student* students, int size) {
    // Brute force: manually convert each student's grade
    for (int i = 0; i < size; i++) {
        students[i].grade = (int)students[i].grade;
    }
}

int main() {
    Student students[] = {
        {1, "Alice", 20, 85.0},
        {2, "Bob", 21, 92.0},
        {3, "Charlie", 19, 78.0}
    };
    
    int size = sizeof(students) / sizeof(students[0]);
    changeDatatype(students, size);
    
    for (int i = 0; i < size; i++) {
        printf("Grade: %.0f\n", students[i].grade);
    }
    
    return 0;
}

Time & Space Complexity

Time Complexity

⏱️

O(n)

Must iterate through each of the n rows to convert grades individually

✓ Linear Growth

Space Complexity

O(1)

Only modifying existing DataFrame in-place, no additional data structures needed

✓ Linear Space

Constraints

1 ≤ number of students ≤ 10⁴
All grade values are valid whole numbers stored as floats
Grade values range from 0.0 to 100.0
DataFrame structure must remain unchanged except for grade column data type

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Input & Output

Visualization

Time & Space Complexity

Related Problems

Constraints

Common Approaches

Row-by-Row Manual Conversion — Algorithm Steps

Visualization

Code -

Time & Space Complexity

Constraints

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