Reshape Data: Concatenate - Practice Coding Problems

Reshape Data: Concatenate - Problem

Databse Easy

You are given two DataFrames with identical structure:

DataFrame df1

Column Name	Type
student_id	int
name	object
age	int

DataFrame df2

Column Name	Type
student_id	int
name	object
age	int

Write a function to concatenate these two DataFrames vertically into one DataFrame. The rows from df2 should be appended below the rows from df1.

Return the concatenated DataFrame.

Input & Output

Example 1 — Basic Concatenation

$ Input: df1 = [{"student_id": 1, "name": "Alice", "age": 20}, {"student_id": 2, "name": "Bob", "age": 22}], df2 = [{"student_id": 3, "name": "Charlie", "age": 19}, {"student_id": 4, "name": "Diana", "age": 21}]

› Output: [{"student_id": 1, "name": "Alice", "age": 20}, {"student_id": 2, "name": "Bob", "age": 22}, {"student_id": 3, "name": "Charlie", "age": 19}, {"student_id": 4, "name": "Diana", "age": 21}]

💡 Note: All rows from df1 come first, followed by all rows from df2, maintaining original order within each DataFrame

Example 2 — Single Row DataFrames

$ Input: df1 = [{"student_id": 1, "name": "Eve", "age": 23}], df2 = [{"student_id": 2, "name": "Frank", "age": 24}]

› Output: [{"student_id": 1, "name": "Eve", "age": 23}, {"student_id": 2, "name": "Frank", "age": 24}]

💡 Note: Each DataFrame has only one row, result combines both into a two-row DataFrame

Example 3 — Empty DataFrame

$ Input: df1 = [{"student_id": 5, "name": "Grace", "age": 20}], df2 = []

› Output: [{"student_id": 5, "name": "Grace", "age": 20}]

💡 Note: When one DataFrame is empty, result contains only rows from the non-empty DataFrame

Constraints

Both DataFrames have identical column structure
0 ≤ number of rows in each DataFrame ≤ 1000
Column names are consistent across both DataFrames

Visualization

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Understanding the Visualization

Input

Two DataFrames with identical column structure

Process

Vertical concatenation preserving row order

Output

Single DataFrame with all rows combined

Key Takeaway

🎯 Key Insight: Vertical concatenation efficiently combines DataFrames while preserving row order from each input

Asked in

M Meta 15 G Google 12

The key insight is to use built-in concatenation functions like pd.concat() for efficient vertical DataFrame combination. Best approach is Built-in Concatenation with Time: O(n+m), Space: O(n+m).

Common Approaches

Approach	Time	Space	Notes
✓ Row-by-Row Manual Copy	O(n + m)	O(n + m)	Manually iterate through each row and copy to new DataFrame
Built-in Concatenation	O(n + m)	O(n + m)	Use pandas concat() function for efficient DataFrame concatenation

Row-by-Row Manual Copy — Algorithm Steps

Create empty result DataFrame with same columns
Iterate through df1 rows and append each
Iterate through df2 rows and append each
Return combined DataFrame

Visualization

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Step-by-Step Walkthrough

Initialize

Create empty result DataFrame

Copy DF1

Iterate and copy each row from first DataFrame

Copy DF2

Iterate and copy each row from second DataFrame

Code -

solution.c — C

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

// Simplified C implementation for demonstration
// In practice, C would need a JSON parsing library

int main() {
    printf("C implementation would require JSON parsing library\n");
    return 0;
}

Time & Space Complexity

Time Complexity

⏱️

O(n + m)

Must iterate through all n rows of df1 and all m rows of df2

✓ Linear Growth

Space Complexity

O(n + m)

Creates new DataFrame to store all rows from both input DataFrames

⚡ Linearithmic Space

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Input & Output

Constraints

Visualization

Related Problems

Common Approaches

Row-by-Row Manual Copy — Algorithm Steps

Visualization

Code -

Time & Space Complexity

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