Rename Columns

Rename Columns - Problem

Database 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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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

✓ Create New DataFrame

⏱️ Time: O(n×m) Space: O(n×m)

Create a completely new DataFrame by extracting each column's data and assigning it to new column names. This involves accessing each column individually and building a new structure.

Dictionary Rename Method

⏱️ Time: O(m) Space: O(1)

Use the built-in pandas rename() method with a dictionary that maps old column names to new ones. This is the standard and most efficient way to rename columns in pandas.

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>

typedef struct {
    int id;
    char first[100];
    char last[100];
    int age;
} Student;

void parseStudents(char* json, Student* students, int* count) {
    *count = 0;
    if (strlen(json) <= 2) return; // Empty array "[]"
    
    char* ptr = json + 1; // Skip opening '['
    
    while (*ptr && *ptr != ']') {
        // Skip whitespace and commas
        while (*ptr == ' ' || *ptr == ',' || *ptr == '\n' || *ptr == '\t') ptr++;
        if (*ptr == ']') break;
        
        if (*ptr == '{') {
            ptr++; // Skip '{'
            
            // Parse each field
            while (*ptr && *ptr != '}') {
                // Skip whitespace and commas
                while (*ptr == ' ' || *ptr == ',' || *ptr == '\n' || *ptr == '\t') ptr++;
                if (*ptr == '}') break;
                
                if (*ptr == '"') {
                    ptr++; // Skip opening quote
                    
                    if (strncmp(ptr, "id", 2) == 0) {
                        // Find the value after "id":
                        while (*ptr != ':') ptr++;
                        ptr++; // Skip ':'
                        while (*ptr == ' ') ptr++;
                        students[*count].id = strtol(ptr, &ptr, 10);
                    }
                    else if (strncmp(ptr, "first", 5) == 0) {
                        // Find the value after "first":
                        while (*ptr != ':') ptr++;
                        ptr++; // Skip ':'
                        while (*ptr == ' ') ptr++;
                        if (*ptr == '"') {
                            ptr++; // Skip opening quote
                            int i = 0;
                            while (*ptr != '"' && i < 99) {
                                students[*count].first[i++] = *ptr++;
                            }
                            students[*count].first[i] = '\0';
                            if (*ptr == '"') ptr++; // Skip closing quote
                        }
                    }
                    else if (strncmp(ptr, "last", 4) == 0) {
                        // Find the value after "last":
                        while (*ptr != ':') ptr++;
                        ptr++; // Skip ':'
                        while (*ptr == ' ') ptr++;
                        if (*ptr == '"') {
                            ptr++; // Skip opening quote
                            int i = 0;
                            while (*ptr != '"' && i < 99) {
                                students[*count].last[i++] = *ptr++;
                            }
                            students[*count].last[i] = '\0';
                            if (*ptr == '"') ptr++; // Skip closing quote
                        }
                    }
                    else if (strncmp(ptr, "age", 3) == 0) {
                        // Find the value after "age":
                        while (*ptr != ':') ptr++;
                        ptr++; // Skip ':'
                        while (*ptr == ' ') ptr++;
                        students[*count].age = strtol(ptr, &ptr, 10);
                    }
                    else {
                        // Skip unknown field
                        while (*ptr && *ptr != ',' && *ptr != '}') ptr++;
                    }
                }
                else {
                    ptr++;
                }
            }
            
            if (*ptr == '}') {
                ptr++; // Skip '}'
                (*count)++;
            }
        }
        else {
            ptr++;
        }
    }
}

void printResult(Student* students, int count) {
    printf("[");
    for (int i = 0; i < count; i++) {
        if (i > 0) printf(", ");
        printf("{\"student_id\": %d, \"first_name\": \"%s\", \"last_name\": \"%s\", \"age_in_years\": %d}", 
               students[i].id, students[i].first, students[i].last, students[i].age);
    }
    printf("]\n");
}

int main() {
    static char buffer[10000];
    static Student students[1000];
    int count;
    
    fgets(buffer, sizeof(buffer), stdin);
    buffer[strcspn(buffer, "\n")] = 0;
    
    parseStudents(buffer, students, &count);
    printResult(students, count);
    
    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

✓ Linear Space

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Input & Output

Constraints

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Related Problems

Common Approaches

Create New DataFrame — Algorithm Steps

Visualization

Code -

Time & Space Complexity

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