When to use apply vs vectorized operations in Pandas - Interactive Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to calculate the square of each number in the 'values' column using a vectorized operation.

Pandas

df['squared'] = df['values'][1]2

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A*

B**

C+

D//

Attempts:

3 left

💡 Hint

Common Mistakes

Using '+' instead of '**' will add 2 instead of squaring.

Using '*' will multiply the column by 2, not square.

Using ** applies the power operation element-wise on the column, which is vectorized and efficient.

2fill in blank

medium

Complete the code to apply a custom function that doubles each value in the 'values' column using the apply method.

Pandas

df['doubled'] = df['values'].[1](lambda x: x * 2)

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Aapply

Breduce

Cmap

Dfilter

Attempts:

3 left

💡 Hint

Common Mistakes

Using map which is similar but not the method asked here.

Using filter which is for filtering elements.

The apply method applies a function to each element in the Series.

3fill in blank

hard

Fix the error in the code to correctly apply a function that returns the length of each string in the 'names' column.

Pandas

df['name_length'] = df['names'].[1](len)

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Aapply

Bvectorize

Cmap

Dreduce

Attempts:

3 left

💡 Hint

Common Mistakes

Using vectorize which is a NumPy function, not a pandas Series method.

Using reduce which aggregates rather than applies element-wise.

apply correctly applies the len function to each string in the Series.

4fill in blank

hard

Fill both blanks to create a dictionary comprehension that includes words longer than 4 characters and maps them to their lengths.

Pandas

{word: [1] for word in words if len(word) [2] 4}

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Alen(word)

Bword

C>

D<

Attempts:

3 left

💡 Hint

Common Mistakes

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

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

The comprehension maps each word to its length if the word length is greater than 4.

5fill in blank

hard

Fill all three blanks to create a dictionary comprehension that maps uppercase words to their lengths only if the length is greater than 3.

Pandas

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

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Aword.upper()

Blen(word)

C>

Dword

Attempts:

3 left

💡 Hint

Common Mistakes

Using the original word as key instead of uppercase.

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

This comprehension creates keys as uppercase words and values as their lengths, filtering words longer than 3 characters.