apply() with lambda functions in Pandas - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to apply a lambda function that doubles each value in the 'score' column.

Pandas

df['double_score'] = df['score'].[1](lambda x: x * 2)

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

Afilter

Bmap

Capply

Dreduce

Attempts:

3 left

💡 Hint

Common Mistakes

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

Using filter or reduce, which are not pandas Series methods.

The apply() method lets you run a function on each element of a pandas Series. Here, it doubles each score.

2fill in blank

medium

Complete the code to create a new column 'passed' that is True if 'score' is 50 or more, else False.

Pandas

df['passed'] = df['score'].apply(lambda x: x [1] 50)

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A>=

B<=

C==

D!=

Attempts:

3 left

💡 Hint

Common Mistakes

Using '<=' which would mark scores less than or equal to 50 as passing.

Using '==' which only marks scores exactly 50 as passing.

The lambda checks if each score is greater than or equal to 50 to mark passing.

3fill in blank

hard

Fix the error in the lambda function to correctly add 10 to each value in 'score'.

Pandas

df['score_plus_10'] = df['score'].apply(lambda x: x [1] 10)

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A-

B/

C*

D+

Attempts:

3 left

💡 Hint

Common Mistakes

Using '-' which subtracts instead of adding.

Using '*' or '/' which multiply or divide.

The lambda should add 10 to each score, so the plus sign '+' is correct.

4fill in blank

hard

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

Pandas

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

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Alen(word)

B>

C<

Dword

Attempts:

3 left

💡 Hint

Common Mistakes

Using '<' instead of '>' which selects shorter words.

Using 'word' as the value instead of its length.

The dictionary comprehension maps each word to its length if the word length is greater than 3.

5fill in blank

hard

Fill all three blanks to create a dictionary with uppercase keys and values greater than 0.

Pandas

result = [1]: [2] for k, v in data.items() if v [3] 0}

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Ak.upper()

Bv

C>

Dk.lower()

Attempts:

3 left

💡 Hint

Common Mistakes

Using k.lower() instead of k.upper() for keys.

Using '<' instead of '>' in the condition.

The dictionary comprehension uses uppercase keys, keeps values, and filters for values greater than zero.