How do you solve Rename Columns on LeetCode?

Rename Columns (LeetCode #2885) 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 greatly simplify code and improve performance.

What is the brute force solution for Rename Columns?

The brute force approach for Rename Columns is Brute Force, with O(n²) time complexity and O(n) space complexity. In the brute force approach, we manually create a new DataFrame with the desired column names and copy the data from the original DataFrame. This method is straightforward but inefficient. The optimal Optimal Solution approach improves this to O(n).

What are the interview tips for Rename Columns?

Always consider built-in functions for common tasks to save time. Explain your thought process clearly while coding, as it shows your understanding. Practice renaming and manipulating DataFrames to become comfortable with the syntax.

What are common mistakes when solving Rename Columns?

Not using built-in functions when available, leading to unnecessary complexity. Confusing the original column names with the new ones during renaming.

#2885

Rename Columns

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)

The optimal solution leverages built-in functions in pandas to rename columns directly, which is efficient and concise. This approach minimizes the need for manual iteration and copying.

⚙️

Algorithm

2 steps

1Step 1: Create a mapping dictionary for the old column names to the new column names.
2Step 2: Use the pandas rename function with the mapping dictionary to rename the columns in the DataFrame.

solution.py4 lines

1import pandas as pd
2
3def rename_columns_optimal(df):
4    return df.rename(columns={'id': 'student_id', 'first': 'first_name', 'last': 'last_name', 'age': 'age_in_years'})

ℹ

Complexity note: The time complexity is O(n) because we are iterating through the DataFrame once. The space complexity is O(n) for the new DataFrame.

1Using built-in functions can greatly simplify code and improve performance.
2Understanding the structure of data (like DataFrames) is crucial for efficient manipulation.

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