map() for element-wise transformation in Pandas - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to apply the function to each element in the 'score' column using map().

Pandas

df['score'] = df['score'].[1](lambda x: x + 10)

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

Amap

Breduce

Cfilter

Dapply

Attempts:

3 left

💡 Hint

Common Mistakes

Using apply instead of map, which works differently on Series.

Using filter or reduce which are not suitable for element-wise transformation.

The map() function applies a given function to each element of the Series, transforming the data element-wise.

2fill in blank

medium

Complete the code to replace values in the 'grade' column using a mapping dictionary with map().

Pandas

grade_map = {'A': 4, 'B': 3, 'C': 2}
df['grade_num'] = df['grade'].[1](grade_map)

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

Amap

Bapply

Creplace

Dfilter

Attempts:

3 left

💡 Hint

Common Mistakes

Using replace instead of map, which also works but is different in behavior.

Using apply which does not directly accept a dictionary for mapping.

The map() function can take a dictionary to replace values element-wise in a Series.

3fill in blank

hard

Fix the error in the code to correctly map values in the 'status' column using map().

Pandas

status_map = {1: 'Active', 0: 'Inactive'}
df['status_text'] = df['status'].[1](status_map)

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

Aapply

Bfilter

Creplace

Dmap

Attempts:

3 left

💡 Hint

Common Mistakes

Using apply which does not accept a dictionary directly.

Using filter which is not for mapping values.

Using map() with a dictionary correctly replaces the numeric codes with text labels element-wise.

4fill in blank

hard

Fill both blanks to create a dictionary comprehension that maps words to their lengths, then apply it with map().

Pandas

length_map = {word: [1] for word in words if len(word) [2] 3}
result = df['text'].map(length_map)

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Alen(word)

B>

C<

Dword

Attempts:

3 left

💡 Hint

Common Mistakes

Using the word itself instead of its length in the dictionary values.

Using the wrong comparison operator in the condition.

The dictionary comprehension maps each word to its length if the word length is greater than 3. Then map() applies this mapping.

5fill in blank

hard

Fill all three blanks to create a dictionary comprehension mapping uppercase words to their lengths, then apply it with map().

Pandas

length_map = [1]: [2] for word in words if word [3] word.upper()
result = df['text'].map(length_map)

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

Aword.upper()

Blen(word)

C==

Dword

Attempts:

3 left

💡 Hint

Common Mistakes

Using word.upper() as the key instead of the original word.

Using incorrect comparison operators.

Using the wrong value for the dictionary.

The dictionary comprehension maps each uppercase word to its length. The condition checks if the word is uppercase by comparing it to its uppercase version.