Inplace operations consideration in Pandas - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to drop missing values from the DataFrame inplace.

Pandas

df.dropna([1]=True)

Drag options to blanks, or click blank then click option'

Ainplace

Baxis

Csubset

Dhow

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'axis' instead of 'inplace' will not drop missing values inplace.

Forgetting to set inplace=True means changes are not saved to the original DataFrame.

Using inplace=True modifies the DataFrame directly without needing to assign it back.

2fill in blank

medium

Complete the code to fill missing values with 0 inplace.

Pandas

df.fillna([1]=0, inplace=True)

Drag options to blanks, or click blank then click option'

Aaxis

Bmethod

Climit

Dvalue

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'method' instead of 'value' will try to fill using a method like 'ffill'.

Not specifying 'value' will cause an error or unexpected behavior.

The value parameter specifies what to replace missing values with.

3fill in blank

hard

Fix the error in the code to sort the DataFrame by 'age' inplace.

Pandas

df.sort_values(by='age', [1]=True)

Drag options to blanks, or click blank then click option'

Ainplace

Bascending

Cignore_index

Daxis

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'ascending=True' only changes sort order but does not modify inplace.

Forgetting 'inplace=True' means the original DataFrame is unchanged.

To sort the DataFrame inplace, use inplace=True.

4fill in blank

hard

Fill both blanks to create a new DataFrame with duplicates removed without changing the original.

Pandas

new_df = df.drop_duplicates([1]=False, [2]=False)

Drag options to blanks, or click blank then click option'

Ainplace

Bkeep

Csubset

Dignore_index

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'inplace=True' modifies the original DataFrame, which is not desired here.

Not setting 'keep' properly may keep some duplicates.

Setting keep=False removes all duplicates, and ignore_index=False keeps original index.

5fill in blank

hard

Fill all three blanks to create a dictionary of word lengths for words longer than 3 characters.

Pandas

lengths = { [1]: [2] for [3] in words if len([3]) > 3 }

Drag options to blanks, or click blank then click option'

Aword

Blen(word)

Dw

Attempts:

3 left

💡 Hint

Common Mistakes

Using different variable names inconsistently causes errors.

Using the wrong expression for the value part.

This dictionary comprehension uses 'word' as the key and its length as the value for words longer than 3 characters.

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