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.