Reshape Data: Concatenate - Practice Coding Problems

Reshape Data: Concatenate - Problem

Database Easy

DataFrame Concatenation Challenge

You're working as a data analyst and need to combine student records from two separate DataFrames into one comprehensive dataset. Both DataFrames contain the same structure with student information including student_id, name, and age.

Goal: Concatenate two DataFrames vertically (stack them on top of each other) to create a single unified DataFrame containing all student records.

Input: Two DataFrames with identical column structures
Output: One DataFrame containing all rows from both input DataFrames

This is a fundamental data manipulation operation used frequently in data preprocessing and ETL (Extract, Transform, Load) processes.

Input & Output

basic_concatenation.py — Python

$ Input: df1 = pd.DataFrame({'student_id': [1, 2], 'name': ['Alice', 'Bob'], 'age': [20, 21]}) df2 = pd.DataFrame({'student_id': [3, 4], 'name': ['Charlie', 'Diana'], 'age': [22, 19]})

› Output: student_id name age 0 1 Alice 20 1 2 Bob 21 2 3 Charlie 22 3 4 Diana 19

💡 Note: The two DataFrames are stacked vertically, with the result having a new sequential index from 0 to 3

single_row_dataframes.py — Python

$ Input: df1 = pd.DataFrame({'student_id': [100], 'name': ['Eve'], 'age': [25]}) df2 = pd.DataFrame({'student_id': [200], 'name': ['Frank'], 'age': [23]})

› Output: student_id name age 0 100 Eve 25 1 200 Frank 23

💡 Note: Even with single-row DataFrames, concatenation works the same way, creating a two-row result

empty_dataframe_edge_case.py — Python

$ Input: df1 = pd.DataFrame({'student_id': [1, 2], 'name': ['Alice', 'Bob'], 'age': [20, 21]}) df2 = pd.DataFrame(columns=['student_id', 'name', 'age'])

› Output: student_id name age 0 1 Alice 20.0 1 2 Bob 21.0

💡 Note: When one DataFrame is empty, the result is essentially a copy of the non-empty DataFrame with potential data type changes

Constraints

Both DataFrames have identical column structure
DataFrames can have 0 to 10⁶ rows each
Column names must match exactly
Data types should be compatible for proper concatenation

Visualization

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Understanding the Visualization

Identify Source DataFrames

Two separate DataFrames with identical column structure

Apply concat() Function

Pandas efficiently combines the data with optimal memory management

Index Management

New sequential index is created (0, 1, 2, 3, ...) when ignore_index=True

Return Unified DataFrame

Single DataFrame containing all rows from both inputs

Key Takeaway

🎯 Key Insight: Pandas concat() function provides optimal O(n) performance by efficiently managing memory allocation and data copying, making it the preferred approach for combining DataFrames in production environments.

Asked in

G Google 45 f Meta 38 a Amazon 35 N Netflix 28

The optimal solution uses pandas' built-in pd.concat() function with O(n) time complexity. This approach efficiently handles memory allocation, index management, and data alignment automatically, making it the preferred method for DataFrame concatenation in production environments.

Common Approaches

Approach	Time	Space	Notes
✓ Manual Row-by-Row Concatenation	O(n²)	O(n)	Manually iterate through each DataFrame and append rows one by one
Pandas concat() - Optimal Solution	O(n)	O(n)	Use pandas built-in concat() function for efficient DataFrame concatenation

Manual Row-by-Row Concatenation — Algorithm Steps

Create an empty result DataFrame with the same columns
Iterate through each row in the first DataFrame and append to result
Iterate through each row in the second DataFrame and append to result
Return the concatenated result

Visualization

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Step-by-Step Walkthrough

Initialize Empty DataFrame

Start with empty result DataFrame

Append First DataFrame Rows

Copy each row from df1 individually

Append Second DataFrame Rows

Copy each row from df2 individually

Return Combined Result

Final DataFrame with all rows

Code -

solution.c — C

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

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

Student* concatenateDataFrames(Student* df1, int size1, Student* df2, int size2, int* resultSize) {
    *resultSize = size1 + size2;
    Student* result = (Student*)malloc(*resultSize * sizeof(Student));
    
    // Copy all students from first DataFrame
    for (int i = 0; i < size1; i++) {
        result[i] = df1[i];
    }
    
    // Copy all students from second DataFrame
    for (int i = 0; i < size2; i++) {
        result[size1 + i] = df2[i];
    }
    
    return result;
}

int main() {
    Student df1[] = {
        {1, "Alice", 20},
        {2, "Bob", 21}
    };
    
    Student df2[] = {
        {3, "Charlie", 22},
        {4, "Diana", 19}
    };
    
    int resultSize;
    Student* result = concatenateDataFrames(df1, 2, df2, 2, &resultSize);
    
    for (int i = 0; i < resultSize; i++) {
        printf("ID: %d, Name: %s, Age: %d\n", result[i].student_id, result[i].name, result[i].age);
    }
    
    free(result);
    return 0;
}

Time & Space Complexity

Time Complexity

⏱️

O(n²)

Each append operation creates a new DataFrame copy, leading to quadratic time complexity

⚠ Quadratic Growth

Space Complexity

O(n)

Space needed for the result DataFrame plus temporary copies during appends

⚡ Linearithmic Space

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Input & Output

Constraints

Visualization

Related Problems

Common Approaches

Manual Row-by-Row Concatenation — Algorithm Steps

Visualization

Code -

Time & Space Complexity

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