Rename Columns - Practice Coding Problems

Rename Columns - Problem

Database Easy

You are given a pandas DataFrame containing student information with the following columns: id, first, last, and age.

Your task is to rename these columns to make them more descriptive and follow a consistent naming convention:

id → student_id
first → first_name
last → last_name
age → age_in_years

This is a common data preprocessing task when working with datasets that have abbreviated or unclear column names. Good column naming is crucial for data clarity and maintainability!

Input & Output

example_1.py — Basic Student Data

$ Input: students = pd.DataFrame({ 'id': [1, 2, 3], 'first': ['Alice', 'Bob', 'Charlie'], 'last': ['Johnson', 'Smith', 'Davis'], 'age': [20, 21, 19] })

› Output: student_id first_name last_name age_in_years 0 1 Alice Johnson 20 1 2 Bob Smith 21 2 3 Charlie Davis 19

💡 Note: All four columns are successfully renamed according to the mapping: id→student_id, first→first_name, last→last_name, age→age_in_years

example_2.py — Single Student Record

$ Input: students = pd.DataFrame({ 'id': [1001], 'first': ['Emma'], 'last': ['Watson'], 'age': [22] })

› Output: student_id first_name last_name age_in_years 0 1001 Emma Watson 22

💡 Note: Even with a single row, the column renaming works exactly the same way - only the headers change, data remains intact

example_3.py — Empty DataFrame Edge Case

$ Input: students = pd.DataFrame({ 'id': [], 'first': [], 'last': [], 'age': [] })

› Output: Empty DataFrame Columns: [student_id, first_name, last_name, age_in_years] Index: []

💡 Note: Column renaming works even on empty DataFrames - the structure is preserved with new column names, just no data rows

Visualization

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Understanding the Visualization

Original DataFrame Structure

DataFrame with abbreviated column names that need clarification

Create Column Mapping

Define a dictionary mapping old names to more descriptive new names

Apply rename() Method

Use pandas built-in rename() function with the mapping dictionary

Headers Updated Efficiently

Only column headers change - data stays in place with no copying overhead

Key Takeaway

🎯 Key Insight: Column renaming is a metadata operation that only changes headers, making it extremely efficient for any DataFrame size.

Time & Space Complexity

Time Complexity

⏱️

O(1)

Only column headers are modified, data is not copied

✓ Linear Growth

Space Complexity

O(1)

No additional space needed, columns renamed in place

✓ Linear Space

Constraints

DataFrame will always have exactly 4 columns: id, first, last, age
Number of rows can be 0 ≤ n ≤ 10³
Column names must be renamed exactly as specified
Data types and values should remain unchanged

Asked in

f Meta 25 G Google 20 N Netflix 15 a Amazon 12

The optimal solution uses pandas' rename() method with a dictionary mapping to efficiently rename columns. This approach has O(1) time and space complexity since it only modifies column headers without copying data. The dictionary maps old column names to new ones: {'id': 'student_id', 'first': 'first_name', 'last': 'last_name', 'age': 'age_in_years'}.

Common Approaches

Approach	Time	Space	Notes
✓ Manual Column Recreation	O(n*m)	O(n*m)	Create new DataFrame with renamed columns by manually copying each column
Dictionary Mapping (Optimal)	O(1)	O(1)	Use pandas rename() method with column mapping dictionary in one operation

Manual Column Recreation — Algorithm Steps

Create a new empty DataFrame
Manually copy each column with the new name
Assign the new DataFrame back to the original variable

Visualization

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Step-by-Step Walkthrough

Original DataFrame

Start with DataFrame containing id, first, last, age columns

Create New DataFrame

Initialize empty DataFrame with new column names

Copy Column Data

Manually copy each column's data to corresponding new column

Result

New DataFrame with renamed columns but same data

Code -

solution.c — C

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

// C approach using struct renaming
typedef struct {
    int student_id;
    char first_name[50];
    char last_name[50];
    int age_in_years;
} RenamedStudent;

typedef struct {
    int id;
    char first[50];
    char last[50];
    int age;
} OriginalStudent;

RenamedStudent* renameColumns(OriginalStudent* students, int size) {
    RenamedStudent* renamed = malloc(sizeof(RenamedStudent) * size);
    for (int i = 0; i < size; i++) {
        renamed[i].student_id = students[i].id;
        strcpy(renamed[i].first_name, students[i].first);
        strcpy(renamed[i].last_name, students[i].last);
        renamed[i].age_in_years = students[i].age;
    }
    return renamed;
}

Time & Space Complexity

Time Complexity

⏱️

O(n*m)

Where n is number of rows and m is number of columns - we copy all data

✓ Linear Growth

Space Complexity

O(n*m)

Creates a complete copy of the DataFrame in memory

⚡ Linearithmic Space

Constraints

DataFrame will always have exactly 4 columns: id, first, last, age
Number of rows can be 0 ≤ n ≤ 10³
Column names must be renamed exactly as specified
Data types and values should remain unchanged

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Input & Output

Visualization

Time & Space Complexity

Related Problems

Constraints

Common Approaches

Manual Column Recreation — Algorithm Steps

Visualization

Code -

Time & Space Complexity

Constraints

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