How do you solve Drop Duplicate Rows on LeetCode?

Drop Duplicate Rows (LeetCode #2882) 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 Drop Duplicate Rows?

The brute force approach for Drop Duplicate Rows is Brute Force, with O(n²) time complexity and O(1) space complexity. The brute-force approach involves checking each row against all previous rows to see if the email has already been encountered. This is straightforward but inefficient as it requires multiple comparisons. The optimal Optimal Solution approach improves this to O(n).

What are the interview tips for Drop Duplicate Rows?

Always clarify whether to keep the first or last occurrence of duplicates. Discuss your thought process and why you choose a specific approach. Practice using built-in functions in your preferred programming language.

What are common mistakes when solving Drop Duplicate Rows?

Not considering the order of rows when dropping duplicates. Assuming that all duplicates are adjacent.

#2882

Drop Duplicate Rows

Easy

Hash MapArray

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)

The optimal approach leverages built-in functions in pandas to efficiently drop duplicates based on the email column. This is much faster as it uses optimized algorithms under the hood.

⚙️

Algorithm

4 steps

1Step 1: Use the pandas 'drop_duplicates' method on the DataFrame.
2Step 2: Specify the 'email' column to check for duplicates.
3Step 3: Set 'keep' parameter to 'first' to retain the first occurrence.
4Step 4: Return the modified DataFrame.

solution.py5 lines

1# Full working Python code
2import pandas as pd
3
4def drop_duplicates_optimal(df):
5    return df.drop_duplicates(subset='email', keep='first')

ℹ

Complexity note: The time complexity is O(n) because we are processing each row only once. The space complexity is O(n) due to storing unique emails in a set or map.

1Using built-in functions can significantly reduce complexity.
2Understanding the data structure helps in choosing the right approach.

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