Why duplicate detection matters in Pandas - See It in Action

Why duplicate detection matters

📖 Scenario: Imagine you work in a store that keeps track of customer purchases. Sometimes, the same purchase gets recorded twice by mistake. This can cause problems when you try to understand how many unique purchases were made.

🎯 Goal: You will learn how to find duplicate entries in a list of purchases using pandas. This helps keep data clean and accurate.

📋 What You'll Learn

Create a pandas DataFrame with purchase data

Set a variable to count duplicates

Use pandas to find duplicate rows

Print the number of duplicate purchases

💡 Why This Matters

🌍 Real World

Duplicate detection helps businesses keep their data clean and accurate, avoiding mistakes in reports and decisions.

💼 Career

Data analysts and scientists often clean data by finding and removing duplicates to ensure trustworthy analysis.

1

Create purchase data

Create a pandas DataFrame called purchases with these exact rows: {'Customer': ['Alice', 'Bob', 'Alice', 'David', 'Bob'], 'Item': ['Apple', 'Banana', 'Apple', 'Carrot', 'Banana']}

Pandas

import pandas as pd
# Create the purchases DataFrame with the given data
# Your code here

Need a hint?

Use pd.DataFrame with a dictionary where keys are column names and values are lists of entries.

2

Set duplicate counter

Create a variable called duplicate_count and set it to 0 to prepare for counting duplicates.

Pandas

import pandas as pd
purchases = pd.DataFrame({'Customer': ['Alice', 'Bob', 'Alice', 'David', 'Bob'], 'Item': ['Apple', 'Banana', 'Apple', 'Carrot', 'Banana']})
# Create duplicate_count variable and set to 0
# Your code here

Need a hint?

Just write duplicate_count = 0 to start counting duplicates.

3

Find duplicates in purchases

Use purchases.duplicated() to find duplicate rows and assign the sum of duplicates to duplicate_count.

Pandas

import pandas as pd
purchases = pd.DataFrame({'Customer': ['Alice', 'Bob', 'Alice', 'David', 'Bob'], 'Item': ['Apple', 'Banana', 'Apple', 'Carrot', 'Banana']})
duplicate_count = 0
# Find duplicates in purchases and update duplicate_count
# Your code here

Need a hint?

Use purchases.duplicated() to get a boolean Series of duplicates, then sum it to count.

4

Print number of duplicates

Print the text "Number of duplicate purchases:" followed by the value of duplicate_count.

Pandas

import pandas as pd
purchases = pd.DataFrame({'Customer': ['Alice', 'Bob', 'Alice', 'David', 'Bob'], 'Item': ['Apple', 'Banana', 'Apple', 'Carrot', 'Banana']})
duplicate_count = 0
duplicate_count = purchases.duplicated().sum()
# Print the number of duplicate purchases
# Your code here

Need a hint?

Use print("Number of duplicate purchases:", duplicate_count) to show the result.