Vectorized operations vs loops in Pandas - Interactive Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to create a new column 'double' by doubling the 'value' column using vectorized operations.

Pandas

df['double'] = df['value'] [1] 2

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A+

B/

C-

D*

Attempts:

3 left

💡 Hint

Common Mistakes

Using '+' instead of '*' will add 2 to each value, not double it.

Using '/' will divide values, not double them.

Multiplying the 'value' column by 2 doubles each element using vectorized operations.

2fill in blank

medium

Complete the code to calculate the mean of the 'score' column using a vectorized pandas method.

Pandas

mean_score = df['score'].[1]()

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Amean

Bsum

Ccount

Dmedian

Attempts:

3 left

💡 Hint

Common Mistakes

Using sum() returns total, not average.

Using count() returns number of entries, not average.

The mean() method calculates the average value of the 'score' column efficiently.

3fill in blank

hard

Fix the error in the loop that adds 10 to each element in the 'points' column and stores results in a list.

Pandas

results = []
for [1] in df['points']:
    results.append(x + 10)

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Ai

Bx

Cpoint

Dvalue

Attempts:

3 left

💡 Hint

Common Mistakes

Using a different variable name in the loop and inside the loop body causes a NameError.

Using 'i' or 'point' without updating the append statement.

The loop variable must match the variable used inside the loop. Here, x is used, so the loop should iterate with for x in df['points'].

4fill in blank

hard

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

Pandas

{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 the word itself as value instead of its length.

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

The dictionary comprehension maps each word to its length using len(word). The condition filters words with length greater than 3 using >.

5fill in blank

hard

Fill all three blanks to create a dictionary comprehension that maps uppercase words to their scores only if score is positive.

Pandas

result = { [1]: [2] for word, score in data.items() if score [3] 0 }

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Aword.upper()

Bscore

C>

Dword.lower()

Attempts:

3 left

💡 Hint

Common Mistakes

Using word.lower() instead of uppercase.

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

Mapping keys or values incorrectly.

The comprehension maps the uppercase version of each word (word.upper()) to its score (score) only if the score is greater than zero (> 0).