transform() for group-level operations in Pandas - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to calculate the mean of each group using transform.

Pandas

df['group_mean'] = df.groupby('group')['value'].[1](lambda x: x.mean())

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

Atransform

Bfilter

Cagg

Dapply

Attempts:

3 left

💡 Hint

Common Mistakes

Using apply() returns a reduced result, not matching the original DataFrame shape.

Using agg() returns aggregated results, not suitable for adding new columns with original shape.

The transform() function applies a function to each group and returns a result with the same shape as the original data, allowing group-level calculations to be assigned back to the DataFrame.

2fill in blank

medium

Complete the code to subtract the group mean from each value using transform.

Pandas

df['value_centered'] = df['value'] - df.groupby('group')['value'].[1](lambda x: x.mean())

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

Afilter

Btransform

Capply

Dagg

Attempts:

3 left

💡 Hint

Common Mistakes

Using agg() returns a smaller series, causing alignment errors.

Using apply() may return a series with different shape.

transform() returns a series aligned with the original data, so we can subtract the group mean from each value directly.

3fill in blank

hard

Fix the error in the code to correctly calculate the z-score within each group.

Pandas

df['z_score'] = df.groupby('group')['value'].[1](lambda x: (x - x.mean()) / x.std())

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

Aapply

Bagg

Ctransform

Dfilter

Attempts:

3 left

💡 Hint

Common Mistakes

Using agg() causes shape mismatch errors.

Using apply() may not preserve the original index alignment.

transform() is needed here because it returns a series with the same shape as the original, allowing element-wise operations like z-score calculation within groups.

4fill in blank

hard

Fill both blanks to create a new column with the max value per group and then subtract the min value per group.

Pandas

df['range'] = df.groupby('group')['value'].[1]('max') - df.groupby('group')['value'].[2]('min')

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

Atransform

Bapply

Cagg

Dfilter

Attempts:

3 left

💡 Hint

Common Mistakes

Using agg() returns aggregated results that cannot be subtracted element-wise.

Using apply() may cause misalignment.

Using transform() for both max and min returns series aligned with the original data, allowing element-wise subtraction to get the range per group.

5fill in blank

hard

Fill both blanks to create a dictionary comprehension that maps each word to its length only if the length is greater than 3.

Pandas

lengths = {word: {BLANK_2}} for word in words if {{BLANK_2}}

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

A:

Blen(word)

Clen(word) > 3

Dword

Attempts:

3 left

💡 Hint

Common Mistakes

Forgetting the colon between key and value.

Using the wrong condition or value expression.

The dictionary comprehension syntax requires a colon between key and value. The value is the length of the word, and the condition filters words longer than 3 characters.