What is the time complexity of using melt?

O(n), where n is the total number of cells in the DataFrame.

What is the space complexity of the melt approach?

O(n), as it creates a new DataFrame containing all the original data.

Is melt more efficient than manual union?

Yes, melt is generally more efficient and concise than manually selecting and unioning columns.

Reshape Data: Melt

Difficulty: Easy | Acceptance: 85.90% | Paid: No Topics: N/A

Write a solution to reshape the data as shown in the output format.

An output should have the student id, the subject name, and the score for each subject.

Examples
Constraints
Approach 1: Using melt
Approach 2: Manual Unpivoting with Union

Examples

Example 1

Input:

+-------------+-----------+-----------+-----------+-----------+
| product     | quarter_1 | quarter_2 | quarter_3 | quarter_4 |
+-------------+-----------+-----------+-----------+-----------+
| Umbrella    | 417       | 224       | 379       | 611       |
| SleepingBag | 800       | 936       | 93        | 875       |
+-------------+-----------+-----------+-----------+-----------+

Output:

+-------------+-----------+-------+
| product     | quarter   | sales |
+-------------+-----------+-------+
| Umbrella    | quarter_1 | 417   |
| SleepingBag | quarter_1 | 800   |
| Umbrella    | quarter_2 | 224   |
| SleepingBag | quarter_2 | 936   |
| Umbrella    | quarter_3 | 379   |
| SleepingBag | quarter_3 | 93    |
| Umbrella    | quarter_4 | 611   |
| SleepingBag | quarter_4 | 875   |
+-------------+-----------+-------+

Explanation: The DataFrame is reshaped from wide to long format. Each row represents the sales of a product in a quarter.

Constraints

2 <= number of rows <= 100
2 <= number of columns <= 100

Approach 1: Using melt

Intuition The melt function is specifically designed to unpivot a DataFrame from wide format to long format, converting column headers into data values.

Steps

Identify the identifier column (student) to keep fixed.
Identify the value columns (math, physics, chemistry) to unpivot.
Specify the new column names for the unpivoted headers (subject) and values (score).

python

import pandas as pd

def reshapeData(df: pd.DataFrame) -&gt; pd.DataFrame:
    return pd.melt(df, id_vars=['student'], var_name='subject', value_name='score')

Complexity

Time: O(n) where n is the number of cells in the DataFrame.
Space: O(n) to store the reshaped DataFrame.
Notes: This is the most idiomatic and efficient approach for this problem.

Approach 2: Manual Unpivoting with Union

Intuition Simulate the melting process by manually selecting each subject column, renaming it appropriately, and then combining (unioning) all the resulting DataFrames.

Steps

Select the student column and the first subject column (e.g., math).
Rename the subject column to subject (with literal value ‘math’) and the value column to score.
Repeat this for all other subject columns.
Union all the intermediate DataFrames together.

python

import pandas as pd

def reshapeData(df: pd.DataFrame) -&gt; pd.DataFrame:
    subjects = ['math', 'physics', 'chemistry']
    dfs = []
    for sub in subjects:
        temp = df[['student']].copy()
        temp['subject'] = sub
        temp['score'] = df[sub]
        dfs.append(temp)
    return pd.concat(dfs)

Complexity

Time: O(n * m) where n is rows and m is columns (subjects), as we scan the table multiple times.
Space: O(n * m) to store the intermediate and final results.
Notes: More verbose and less efficient than melt, but demonstrates the underlying mechanics.