Inplace operations consideration in Pandas - Mini Project: Build & Apply

Inplace Operations Consideration with pandas

📖 Scenario: You work as a data analyst. You have a small sales dataset with product names and prices. You want to update the prices by applying a discount. You will learn how to do this with and without inplace operations in pandas.

🎯 Goal: Build a pandas DataFrame with product prices, create a discount rate variable, apply the discount to prices using inplace and non-inplace methods, and print the updated DataFrame.

📋 What You'll Learn

Create a pandas DataFrame called df with columns Product and Price with exact values

Create a variable discount with the value 0.1 (10% discount)

Apply the discount to the Price column using inplace operation

Print the updated DataFrame df

💡 Why This Matters

🌍 Real World

In real life, data analysts often need to update data values directly in their datasets. Understanding inplace operations helps save memory and write cleaner code.

💼 Career

Knowing how to use inplace operations in pandas is useful for data cleaning and transformation tasks in data science and analytics jobs.

1

Create the sales DataFrame

Import pandas as pd and create a DataFrame called df with these exact rows: Product as ['Pen', 'Notebook', 'Eraser'] and Price as [1.5, 3.0, 0.5].

Pandas

# Import pandas and create the DataFrame df with Product and Price columns
# Your code here

Need a hint?

Use pd.DataFrame with a dictionary for columns.

2

Create the discount variable

Create a variable called discount and set it to 0.1 to represent a 10% discount.

Pandas

import pandas as pd

df = pd.DataFrame({
    'Product': ['Pen', 'Notebook', 'Eraser'],
    'Price': [1.5, 3.0, 0.5]
})

# Create the discount variable
# Your code here

Need a hint?

Just assign 0.1 to discount.

3

Apply discount using inplace operation

Use the df['Price'].mul() method with 1 - discount and inplace=True to update the Price column directly.

Pandas

import pandas as pd

df = pd.DataFrame({
    'Product': ['Pen', 'Notebook', 'Eraser'],
    'Price': [1.5, 3.0, 0.5]
})

discount = 0.1

# Apply discount to Price column inplace
# Your code here

Need a hint?

Use the *= operator to update the column inplace.

4

Print the updated DataFrame

Print the DataFrame df to see the updated prices after the discount.

Pandas

import pandas as pd

df = pd.DataFrame({
    'Product': ['Pen', 'Notebook', 'Eraser'],
    'Price': [1.5, 3.0, 0.5]
})

discount = 0.1

df['Price'] *= (1 - discount)

# Print the updated DataFrame
# Your code here

Need a hint?

Use print(df) to show the DataFrame.