How do you solve Reshape Data: Pivot on LeetCode?

Reshape Data: Pivot (LeetCode #2889) 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 and improve readability.

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

The brute force approach for Reshape Data: Pivot is Brute Force, with O(n²) time complexity and O(1) space complexity. In the brute-force approach, we manually create a new DataFrame by iterating through each city and month combination, filling in the temperatures. This is straightforward but inefficient. The optimal Optimal Solution approach improves this to O(n).

What are the interview tips for Reshape Data: Pivot?

Always clarify the requirements and edge cases before jumping into coding. Discuss your thought process and approach with the interviewer. Practice using built-in functions in libraries like pandas to become familiar with their capabilities.

What are common mistakes when solving Reshape Data: Pivot?

Failing to handle missing data when pivoting. Not resetting the index after pivoting, leading to unexpected DataFrame structures.

#2889

Reshape Data: Pivot

Easy

Hash MapDataFrame Manipulation

LeetCode ↗

Approaches

Brute ForceOptimal

Complexity Comparison

	Brute Force	Optimal Solution★
Time	O(n²)	O(n)
Space	O(1)	O(n)

💡

Intuition

Time O(n)Space O(n)

Using the built-in pivot functionality in pandas allows us to efficiently transform the DataFrame without manual iteration. This is faster and cleaner.

⚙️

Algorithm

4 steps

1Step 1: Use the pivot_table function from pandas.
2Step 2: Set 'month' as the index, 'city' as the columns, and 'temperature' as the values.
3Step 3: Fill any missing values with NaN or a specified value.
4Step 4: Reset the index to get a clean DataFrame.

solution.py4 lines

1import pandas as pd
2
3def pivot_weather(df):
4    return df.pivot_table(index='month', columns='city', values='temperature', aggfunc='first').reset_index()

ℹ

Complexity note: The time complexity is O(n) because we are processing each entry in the DataFrame only once, making it efficient. The space complexity is O(n) due to the storage of the pivoted DataFrame.

1Using built-in functions can significantly reduce complexity and improve readability.
2Understanding how to manipulate DataFrames is crucial for data analysis tasks.

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