Rename Columns - Practice Coding Problems

Rename Columns - Problem

Databse Easy

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

Column Name	Type
id	int
first	object
last	object
age	int

Your task is to rename the columns according to the following mapping:

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

Return the DataFrame with the renamed columns.

Input & Output

Example 1 — Basic DataFrame

$ Input: students = [{"id": 1, "first": "John", "last": "Doe", "age": 20}]

› Output: [{"student_id": 1, "first_name": "John", "last_name": "Doe", "age_in_years": 20}]

💡 Note: All columns are renamed: id→student_id, first→first_name, last→last_name, age→age_in_years

Example 2 — Multiple Students

$ Input: students = [{"id": 1, "first": "Alice", "last": "Smith", "age": 19}, {"id": 2, "first": "Bob", "last": "Jones", "age": 21}]

› Output: [{"student_id": 1, "first_name": "Alice", "last_name": "Smith", "age_in_years": 19}, {"student_id": 2, "first_name": "Bob", "last_name": "Jones", "age_in_years": 21}]

💡 Note: Same column renaming applied to all rows in the DataFrame

Example 3 — Empty DataFrame Structure

$ Input: students = []

› Output: []

💡 Note: Empty DataFrame remains empty after column renaming operation

Constraints

DataFrame contains columns: id, first, last, age
Column renaming must follow the exact mapping specified
All data values remain unchanged, only column names are modified

Visualization

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Understanding the Visualization

Input DataFrame

Original columns: id, first, last, age

Apply Renaming

Map each column to more descriptive name

Output DataFrame

New columns: student_id, first_name, last_name, age_in_years

Key Takeaway

🎯 Key Insight: Column renaming is a metadata operation that makes DataFrames more readable without changing the underlying data

Asked in

M Meta 35 N Netflix 28 G Google 25

The key insight is that column renaming is a metadata operation that doesn't require copying data. The best approach uses pandas rename() method with a mapping dictionary for efficient, in-place column renaming. Time: O(m), Space: O(1).

Common Approaches

Approach	Time	Space	Notes
✓ Create New DataFrame	O(n×m)	O(n×m)	Create a new DataFrame with renamed columns by copying data
Dictionary Rename Method	O(m)	O(1)	Use pandas rename() method with a mapping dictionary

Create New DataFrame — Algorithm Steps

Extract data from each original column
Create new DataFrame with desired column names
Assign the extracted data to new columns

Visualization

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Step-by-Step Walkthrough

Original DataFrame

Extract data from each column

Copy Process

Create new structure with renamed columns

New DataFrame

All data copied with new column names

Code -

solution.c — C

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

cJSON* solution(cJSON* students) {
    // Create new JSON array with renamed columns by copying data
    cJSON* newData = cJSON_CreateArray();
    cJSON* row = NULL;
    cJSON_ArrayForEach(row, students) {
        cJSON* newRow = cJSON_CreateObject();
        cJSON_AddItemToObject(newRow, "student_id", cJSON_Duplicate(cJSON_GetObjectItem(row, "id"), 1));
        cJSON_AddItemToObject(newRow, "first_name", cJSON_Duplicate(cJSON_GetObjectItem(row, "first"), 1));
        cJSON_AddItemToObject(newRow, "last_name", cJSON_Duplicate(cJSON_GetObjectItem(row, "last"), 1));
        cJSON_AddItemToObject(newRow, "age_in_years", cJSON_Duplicate(cJSON_GetObjectItem(row, "age"), 1));
        cJSON_AddItemToArray(newData, newRow);
    }
    return newData;
}

int main() {
    char buffer[10000];
    fgets(buffer, sizeof(buffer), stdin);
    buffer[strcspn(buffer, "\n")] = 0;
    
    cJSON* students = cJSON_Parse(buffer);
    cJSON* result = solution(students);
    char* output = cJSON_Print(result);
    printf("%s\n", output);
    
    free(output);
    cJSON_Delete(students);
    cJSON_Delete(result);
    return 0;
}

Time & Space Complexity

Time Complexity

⏱️

O(n×m)

Must copy all n rows × m columns of data

✓ Linear Growth

Space Complexity

O(n×m)

Creates a complete copy of the DataFrame

⚡ Linearithmic Space

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Input & Output

Constraints

Visualization

Related Problems

Common Approaches

Create New DataFrame — Algorithm Steps

Visualization

Code -

Time & Space Complexity

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