Removing duplicates (drop_duplicates) in Data Analysis Python - Mini Project: Build & Apply

Removing duplicates (drop_duplicates)

📖 Scenario: You work in a small store that keeps track of sales data. Sometimes, the same sale is recorded twice by mistake. You want to clean the data by removing these duplicate sales records.

🎯 Goal: You will create a sales data table, set a rule to identify duplicates, remove the duplicate rows, and then show the cleaned data.

📋 What You'll Learn

Create a pandas DataFrame with sales data including duplicates

Set a variable to specify which columns to check for duplicates

Use drop_duplicates to remove duplicate rows based on the specified columns

Print the cleaned DataFrame

💡 Why This Matters

🌍 Real World

Cleaning duplicate records is a common task in data analysis to ensure accurate reports and decisions.

💼 Career

Data analysts and scientists often need to remove duplicate data entries before analysis to avoid errors.

1

Create the sales data table

Create a pandas DataFrame called sales_data with these exact columns and rows:
SaleID: [101, 102, 103, 102, 104]
Product: ['Apple', 'Banana', 'Apple', 'Banana', 'Orange']
Quantity: [5, 3, 5, 3, 2]

Data Analysis Python

import pandas as pd
# Create the DataFrame sales_data with the exact data
# Your code here

Hint

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

2

Set columns to check for duplicates

Create a variable called check_columns and set it to a list containing the column names 'SaleID' and 'Product'.

Data Analysis Python

import pandas as pd
sales_data = pd.DataFrame({
    'SaleID': [101, 102, 103, 102, 104],
    'Product': ['Apple', 'Banana', 'Apple', 'Banana', 'Orange'],
    'Quantity': [5, 3, 5, 3, 2]
})
# Create check_columns list with 'SaleID' and 'Product'
# Your code here

Hint

Make a list with the two column names exactly as strings.

3

Remove duplicate sales records

Create a new DataFrame called cleaned_sales by removing duplicate rows from sales_data using drop_duplicates with the subset set to check_columns.

Data Analysis Python

import pandas as pd
sales_data = pd.DataFrame({
    'SaleID': [101, 102, 103, 102, 104],
    'Product': ['Apple', 'Banana', 'Apple', 'Banana', 'Orange'],
    'Quantity': [5, 3, 5, 3, 2]
})
check_columns = ['SaleID', 'Product']
# Use drop_duplicates on sales_data with subset=check_columns
# Your code here

Hint

Use drop_duplicates with the subset parameter set to the list check_columns.

4

Show the cleaned sales data

Print the cleaned_sales DataFrame to display the sales data after removing duplicates.

Data Analysis Python

import pandas as pd
sales_data = pd.DataFrame({
    'SaleID': [101, 102, 103, 102, 104],
    'Product': ['Apple', 'Banana', 'Apple', 'Banana', 'Orange'],
    'Quantity': [5, 3, 5, 3, 2]
})
check_columns = ['SaleID', 'Product']
cleaned_sales = sales_data.drop_duplicates(subset=check_columns)
# Print cleaned_sales
# Your code here

Hint

Use print(cleaned_sales) to show the DataFrame.

Practice

1. What does the drop_duplicates() function do in a pandas DataFrame?

easy

A. Fills missing values with zeros

B. Removes repeated rows based on all columns by default

C. Sorts the DataFrame rows in ascending order

D. Changes the data type of columns

Solution

1. Step 1: Understand the function purpose

   drop_duplicates() is used to remove repeated rows in a DataFrame.

2. Step 2: Default behavior

   By default, it checks all columns to find duplicates and keeps the first occurrence.

3. Final Answer:

   Removes repeated rows based on all columns by default -> Option B

4. Quick Check:

   drop_duplicates() removes repeated rows [OK]

Hint: Remember: drop_duplicates removes repeated rows [OK]

Common Mistakes:

• Thinking it sorts data
• Confusing with fillna()
• Assuming it changes data types

2. Which of the following is the correct syntax to remove duplicate rows from a DataFrame df?

easy

A. df.drop_duplicates[]

B. df.remove_duplicates()

C. df.drop_duplicates()

D. df.drop_duplicates{}

Solution

1. Step 1: Identify correct method call

   The method to remove duplicates is drop_duplicates(), called with parentheses.

2. Step 2: Check syntax correctness

   Only df.drop_duplicates() is valid syntax; brackets or braces are incorrect.

3. Final Answer:

   df.drop_duplicates() -> Option C

4. Quick Check:

   Use parentheses to call methods [OK]

Hint: Use parentheses () to call drop_duplicates method [OK]

Common Mistakes:

• Using square brackets instead of parentheses
• Using a non-existent method name
• Using curly braces instead of parentheses

3. Given the DataFrame df below, what will df.drop_duplicates(subset=['Name']) return?

import pandas as pd
data = {'Name': ['Anna', 'Bob', 'Anna', 'Cody'], 'Age': [25, 30, 25, 22]}
df = pd.DataFrame(data)

medium

A. [{'Name': 'Anna', 'Age': 25}, {'Name': 'Bob', 'Age': 30}, {'Name': 'Cody', 'Age': 22}]

B. [{'Name': 'Anna', 'Age': 25}, {'Name': 'Anna', 'Age': 25}, {'Name': 'Bob', 'Age': 30}, {'Name': 'Cody', 'Age': 22}]

C. [{'Name': 'Bob', 'Age': 30}, {'Name': 'Cody', 'Age': 22}]

D. [{'Name': 'Anna', 'Age': 25}, {'Name': 'Bob', 'Age': 30}]

Solution

1. Step 1: Understand subset parameter

   Using subset=['Name'] means duplicates are checked only on the 'Name' column.

2. Step 2: Identify duplicates by 'Name'

   'Anna' appears twice; only the first occurrence is kept, others dropped.

3. Final Answer:

   [{'Name': 'Anna', 'Age': 25}, {'Name': 'Bob', 'Age': 30}, {'Name': 'Cody', 'Age': 22}] -> Option A

4. Quick Check:

   Duplicates dropped by 'Name' column [OK]

Hint: Use subset to check duplicates on specific columns [OK]

Common Mistakes:

• Not using subset and dropping all duplicates
• Keeping all duplicates instead of first
• Confusing which rows are dropped

4. What is wrong with this code snippet to remove duplicates from DataFrame df?

df.drop_duplicates(inplace=True)

After running it, duplicates still appear in df. Why?

medium

A. The DataFrame has no duplicates to remove

B. drop_duplicates() does not modify the original DataFrame even with inplace=True

C. The DataFrame was not reassigned and inplace=True is ignored

D. The inplace parameter is misspelled

Solution

1. Step 1: Check inplace parameter usage

   inplace=True modifies the original DataFrame, so no reassignment needed.

2. Step 2: Consider DataFrame content

   If duplicates still appear, likely the DataFrame had no duplicates to remove.

3. Final Answer:

   The DataFrame has no duplicates to remove -> Option A

4. Quick Check:

   inplace=True modifies original; no duplicates means no change [OK]

Hint: Check if DataFrame actually has duplicates before drop_duplicates [OK]

Common Mistakes:

• Assuming inplace=True does nothing
• Thinking drop_duplicates always changes data
• Not verifying if duplicates exist

5. You have a DataFrame df with columns ['ID', 'Name', 'Score']. Some rows have duplicate ID values but different Score. How do you keep only the row with the highest Score for each ID?

hard

A. Use df.drop_duplicates(subset=['Score'])

B. Use df.drop_duplicates(subset=['ID']) without sorting

C. Use df.drop_duplicates(keep='last') without sorting

D. Use df.sort_values('Score', ascending=False).drop_duplicates(subset=['ID'])

Solution

1. Step 1: Sort DataFrame by Score descending

   Sorting by 'Score' descending puts highest scores first for each ID.

2. Step 2: Drop duplicates by ID keeping first

   After sorting, dropping duplicates by 'ID' keeps the first (highest score) row per ID.

3. Final Answer:

   df.sort_values('Score', ascending=False).drop_duplicates(subset=['ID']) -> Option D

4. Quick Check:

   Sort then drop duplicates keeps highest score per ID [OK]

Hint: Sort by score descending before drop_duplicates to keep max [OK]

Common Mistakes:

• Not sorting before dropping duplicates
• Dropping duplicates on wrong column
• Using keep='last' without sorting