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

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

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

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

Hint

Use print(df) to show the DataFrame.

Practice

1. What does setting inplace=True do in pandas methods?

easy

A. It modifies the original DataFrame directly without creating a copy.

B. It creates a new DataFrame and leaves the original unchanged.

C. It saves the DataFrame to a file automatically.

D. It prints the DataFrame to the console.

Solution

1. Step 1: Understand inplace parameter meaning

   Setting inplace=True tells pandas to change the original data directly.

2. Step 2: Compare inplace with default behavior

   By default, pandas methods return a new object and keep the original unchanged.

3. Final Answer:

   It modifies the original DataFrame directly without creating a copy. -> Option A

4. Quick Check:

   inplace=True means direct change [OK]

Hint: Inplace=True means change original data directly [OK]

Common Mistakes:

• Thinking inplace=True creates a new copy
• Confusing inplace with saving to file
• Assuming inplace prints output

2. Which of the following is the correct syntax to drop a column named 'Age' from DataFrame df inplace?

easy

A. df.drop('Age', axis=1)

B. df.drop('Age', axis=1, inplace=True)

C. df.drop('Age', inplace=True)

D. df.drop('Age', axis=0, inplace=True)

Solution

1. Step 1: Identify correct axis for dropping columns

   Columns are dropped with axis=1, rows with axis=0.

2. Step 2: Check syntax for inplace drop

   To drop inplace, use inplace=True along with correct axis.

3. Final Answer:

   df.drop('Age', axis=1, inplace=True) -> Option B

4. Quick Check:

   Drop column inplace needs axis=1 and inplace=True [OK]

Hint: Drop columns inplace needs axis=1 and inplace=True [OK]

Common Mistakes:

• Omitting axis=1 when dropping columns
• Using inplace without axis parameter
• Setting axis=0 to drop columns

3. What will be the output of this code?

import pandas as pd
df = pd.DataFrame({'A': [1, 2], 'B': [3, 4]})
df.drop('A', axis=1, inplace=True)
print(df)

medium

A. DataFrame with only column 'B'

B. Empty DataFrame

C. DataFrame with columns 'A' and 'B'

D. Error because inplace=True is invalid

Solution

1. Step 1: Understand drop with inplace=True

   Using inplace=True removes column 'A' directly from df.

2. Step 2: Check remaining columns after drop

   Only column 'B' remains in the DataFrame after dropping 'A'.

3. Final Answer:

   DataFrame with only column 'B' -> Option A

4. Quick Check:

   Drop column inplace removes it from original [OK]

Hint: Inplace drop removes column from original DataFrame [OK]

Common Mistakes:

• Expecting original DataFrame unchanged
• Thinking both columns remain
• Assuming error with inplace=True

4. Identify the error in this code snippet:

import pandas as pd
df = pd.DataFrame({'X': [5, 6], 'Y': [7, 8]})
df.sort_values('X', inplace=True)
print(df.sort_values('Y'))

medium

A. The first sort_values call should not use inplace=True.

B. The second print statement ignores inplace and returns a sorted copy.

C. There is no error; code runs correctly.

D. sort_values does not support inplace parameter.

Solution

1. Step 1: Analyze first sort_values call

   df.sort_values('X', inplace=True) correctly sorts the DataFrame inplace by column 'X'.

2. Step 2: Analyze second sort_values call

   print(df.sort_values('Y')) correctly prints a new DataFrame sorted by 'Y' without modifying the original.

3. Final Answer:

   There is no error; code runs correctly. -> Option C

4. Quick Check:

   Both sort_values calls are valid; code executes successfully [OK]

Hint: sort_values supports inplace=True; without it returns a copy [OK]

Common Mistakes:

• Thinking sort_values does not support inplace
• Assuming second sort modifies original df
• Believing first sort inplace is wrong

5. You want to remove rows with missing values from DataFrame df and save memory by not creating a copy. Which code is best?

hard

A. df.dropna(copy=False)

B. df = df.dropna()

C. df.dropna(inplace=False)

D. df.dropna(inplace=True)

Solution

1. Step 1: Understand dropna behavior

   By default, dropna() returns a new DataFrame without missing rows.

2. Step 2: Use inplace to avoid copy

   Setting inplace=True modifies df directly, saving memory and time.

3. Final Answer:

   df.dropna(inplace=True) -> Option D

4. Quick Check:

   dropna with inplace=True modifies original without copy [OK]

Hint: Use inplace=True to avoid copying when dropping rows [OK]

Common Mistakes:

• Assigning back when inplace=True is used
• Using copy=False which is invalid parameter
• Not using inplace and wasting memory