Drop Duplicate Rows

Drop Duplicate Rows

What is this problem about?

The Drop Duplicate Rows coding problem is a data manipulation task typically performed using libraries like Pandas in Python. You are given a DataFrame (a tabular dataset) and you need to remove rows that are duplicates based on specific columns or all columns. This ensures that the data is unique and consistent for further analysis.

Why is this asked in interviews?

Companies like Google ask the Drop Duplicate Rows interview question to verify a candidate's proficiency with data cleaning and preparation. Data cleaning is a significant part of a software engineer's or data scientist's job. This task tests familiarity with the Pandas library and the ability to handle data efficiently without writing complex manual loops.

Algorithmic pattern used

This problem follows a Library API usage pattern.

1. In Pandas, the drop_duplicates() method is used.
2. It can take parameters such as subset (to define specific columns to check for duplicates) and keep (to decide whether to keep the first occurrence, the last, or none).
3. The method returns a new DataFrame with duplicates removed.

Example explanation

Suppose we have a table of employees:

id	name	department
1	John	Sales
2	Jane	HR
1	John	Marketing

1. To drop duplicates based on the id and name columns:
   • Row 1 and Row 3 have the same id (1) and name (John).
   • By default, the first row is kept.
   • Result: Row 1 and Row 2 remain. Row 3 is dropped.

Common mistakes candidates make

• In-place modification confusion: Not realizing that drop_duplicates() returns a new object unless inplace=True is specified.
• Subset selection: Forgetting to specify the subset argument when duplicates should only be checked for specific columns rather than the entire row.
• Ignoring indices: Not resetting the index of the DataFrame after dropping rows, which can cause issues in subsequent operations.

Interview preparation tip

While the library handles the heavy lifting, be prepared to explain how you would solve this without a library (e.g., using a hash set to track seen rows). This shows you understand the underlying algorithm.