transform() for group-level operations in Data Analysis Python - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

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

Data Analysis Python

df['mean_score'] = df.groupby('group')['score'].[1](lambda x: x.mean())

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

Atransform

Bapply

Caggregate

Dfilter

Attempts:

3 left

💡 Hint

Common Mistakes

Using apply returns a reduced result, not aligned with original DataFrame.

Using aggregate returns a smaller DataFrame, not suitable for adding new columns.

The transform method returns a Series with the same index as the original data, allowing group-level calculations to be assigned back to the original DataFrame.

2fill in blank

medium

Complete the code to create a new column with the z-score of 'score' within each group.

Data Analysis Python

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

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

Aapply

Bfilter

Ctransform

Daggregate

Attempts:

3 left

💡 Hint

Common Mistakes

Using apply returns a reduced result, not suitable for element-wise transformations.

Using filter removes rows, not suitable for creating new columns.

transform applies the function to each group and returns a Series aligned with the original DataFrame, perfect for creating new columns like z-scores.

3fill in blank

hard

Fix the error in the code to correctly calculate the cumulative sum of 'score' within each group.

Data Analysis Python

df['cum_sum'] = df.groupby('group')['score'].[1]('cumsum')

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

Atransform

Bapply

Caggregate

Dfilter

Attempts:

3 left

💡 Hint

Common Mistakes

Using apply with cumsum returns a Series but may not align properly for assignment.

Using aggregate returns a reduced DataFrame, not suitable for new columns.

The transform method correctly applies cumsum to each group and returns a Series aligned with the original DataFrame, allowing assignment to a new column.

4fill in blank

hard

Fill both blanks to create a new column with the rank of 'score' within each group in descending order.

Data Analysis Python

df['rank'] = df.groupby('group')['score'].[1](lambda x: x.rank(method='[2]', ascending=False))

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

Atransform

Bdense

Caggregate

Dfilter

Attempts:

3 left

💡 Hint

Common Mistakes

Using apply returns a reduced result, not aligned with original DataFrame.

Using method='average' instead of 'dense' changes the ranking style.

Using transform applies the rank function to each group and returns a Series aligned with the original DataFrame. The method='dense' option assigns ranks without gaps.

5fill in blank

hard

Fill all three blanks to create a new column with the normalized 'score' within each group, scaled between 0 and 1.

Data Analysis Python

df['normalized'] = df.groupby('group')['score'].[1](lambda x: (x - x.[2]()) / (x.[3]() - x.[2]()))

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

Atransform

Bmin

Cmax

Dapply

Attempts:

3 left

💡 Hint

Common Mistakes

Using apply instead of transform returns a reduced result, not suitable for new columns.

Swapping min and max functions changes the normalization calculation.

The transform method applies the normalization function to each group and returns a Series aligned with the original DataFrame. The min() and max() functions find the minimum and maximum values in each group for scaling.