Python One-Liner: Find Duplicate Rows in a Dataset Using Pandas 🔎🐼 #PythonTips

Duplicate records are a common issue in datasets and can affect data quality, analysis results, and machine learning models. Detecting duplicates is an important step in data cleaning and preprocessing. In this example, we use Pandas, one of the most widely used libraries in data science, to identify duplicate rows in a dataset. 🧠 STEP-BY-STEP EXPLANATION 1️⃣ Install Pandas pip install pandas 2️⃣ Create Dataset pd.DataFrame(...) We create a simple dataset with Name and Age columns. Example dataset: Name Age Alice 25 Bob 30 Alice 25 David 40 Bob 30 3️⃣ Detect Duplicate Rows df.duplicated() This function returns True for rows that are duplicates. Example output: 0 False 1 False 2 True 3 False 4 True Rows 2 and 4 are duplicates. 4️⃣ Filter Duplicate Rows df[df.duplicated()] This returns only duplicate records: Name Age Alice 25 Bob 30 ⚡ WHY THE ONE-LINER WORKS df[df.duplicated()] It: Detects duplicate rows Filters them directly Returns only duplicated records All in a single expression. 🧪 PRACTICAL USE CASES This technique is used in: Data cleaning pipelines Data engineering workflows ETL processes Machine learning preprocessing Business analytics reports 🎯 INTERVIEW QUESTIONS & ANSWERS 1️⃣ What does duplicated() do in Pandas? It identifies duplicate rows in a DataFrame. 2️⃣ How to remove duplicates instead of detecting them? df.drop_duplicates() 3️⃣ How to check duplicates based on specific columns? df.duplicated(subset=["Name"]) 4️⃣ How to keep the last duplicate instead of first? df.duplicated(keep="last") 5️⃣ Why is duplicate detection important in data science? Duplicate data can bias statistics and machine learning models. Codes: # Long Way: Find duplicate rows in a dataset import pandas as pd # Sample dataset data = { "Name": ["Alice", "Bob", "Alice", "David", "Bob"], "Age": [25, 30, 25, 40, 30] } df = pd.DataFrame(data) # Detect duplicate rows duplicates = df[df.duplicated()] print(duplicates) # TRUE One-L