How do you solve Reshape Data: Melt on LeetCode?

Reshape Data: Melt (LeetCode #2890) can be solved using Brute Force or Optimal Solution. The optimal approach is Optimal Solution with O(n) time complexity and O(n) space complexity. Key insight: Using built-in functions can significantly reduce complexity.

What is the brute force solution for Reshape Data: Melt?

The brute force approach for Reshape Data: Melt is Brute Force, with O(n²) time complexity and O(n) space complexity. The brute force approach involves manually iterating through each product and each quarter to create a new list of rows. This method is straightforward but inefficient for larger datasets. The optimal Optimal Solution approach improves this to O(n).

What are the interview tips for Reshape Data: Melt?

Familiarize yourself with DataFrame operations in pandas. Practice explaining your thought process clearly. Be prepared to discuss the time and space complexity of your solutions.

What are common mistakes when solving Reshape Data: Melt?

Not using built-in functions when available. Overcomplicating the reshaping process with manual loops.

#2890

Reshape Data: Melt

Easy

Hash MapArray

LeetCode ↗

Approaches

Brute ForceOptimal

Complexity Comparison

	Brute Force	Optimal Solution★
Time	O(n²)	O(n)
Space	O(n)	O(n)

💡

Intuition

Time O(n)Space O(n)

Using built-in functions in pandas allows us to reshape the DataFrame efficiently without manually iterating through rows and columns. This method leverages optimized internal operations.

⚙️

Algorithm

3 steps

1Step 1: Use the pandas melt function to reshape the DataFrame.
2Step 2: Specify the id_vars as 'product' and value_vars as the quarters.
3Step 3: The melt function will automatically create the desired long format.

solution.py5 lines

1# Full working Python code
2import pandas as pd
3
4def reshape_data_optimal(df):
5    return df.melt(id_vars=['product'], value_vars=['quarter_1', 'quarter_2', 'quarter_3', 'quarter_4'], var_name='quarter', value_name='sales')

ℹ

Complexity note: The time complexity is O(n) because the melt function processes each row once. The space complexity is O(n) as it creates a new DataFrame to store the reshaped data.

1Using built-in functions can significantly reduce complexity.
2Understanding DataFrame operations is crucial for efficient data manipulation.

Solutions and explanations are original Tejav content. Problem titles © LeetCode — use the LeetCode button above for the full problem statement.