GroupBy with transform for normalization in Pandas - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to calculate the mean of the 'score' column grouped by 'team'.

Pandas

mean_scores = df.groupby('team')['score'].[1]()

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

Asum

Bmax

Ccount

Dmean

Attempts:

3 left

💡 Hint

Common Mistakes

Using sum() instead of mean()

Using count() which counts entries, not averages

The mean() function calculates the average value of the 'score' for each group defined by 'team'.

2fill in blank

medium

Complete the code to normalize the 'score' column by subtracting the group mean using transform.

Pandas

df['score_normalized'] = df['score'] - df.groupby('team')['score'].[1]

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

Amean

Btransform('mean')

Csum

Dtransform('sum')

Attempts:

3 left

💡 Hint

Common Mistakes

Using mean() instead of transform('mean') which returns a reduced Series

Using sum() which sums values instead of averaging

The transform('mean') returns the mean for each group aligned with the original DataFrame, allowing element-wise subtraction.

3fill in blank

hard

Fix the error in the code to correctly normalize 'score' by group using transform.

Pandas

df['score_norm'] = df.groupby('team')['score'].[1]('mean')

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

Atransform

Bagg

Capply

Dmean

Attempts:

3 left

💡 Hint

Common Mistakes

Using mean('mean') which is invalid

Using apply which may change the shape of the result

The transform method applies the function and returns a Series aligned with the original DataFrame, which is needed for element-wise operations.

4fill in blank

hard

Fill both blanks to create a normalized score by subtracting the group mean and dividing by the group standard deviation.

Pandas

df['score_norm'] = (df['score'] - df.groupby('team')['score'].[1]) / df.groupby('team')['score'].[2]

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

Atransform('mean')

Bmean

Ctransform('std')

Dstd

Attempts:

3 left

💡 Hint

Common Mistakes

Using mean and std without transform, which returns reduced Series

Mixing transform with non-transform methods

Using transform('mean') and transform('std') returns Series aligned with the original DataFrame, allowing element-wise normalization.

5fill in blank

hard

Fill all three blanks to create a new column with z-score normalization of 'score' grouped by 'team' using transform.

Pandas

df['score_z'] = (df[[1]] - df.groupby([2])[[3]].transform('mean')) / df.groupby([2])[[3]].transform('std')

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

A'score'

B'team'

Cscore

Dteam

Attempts:

3 left

💡 Hint

Common Mistakes

Using variable names without quotes causing KeyError

Mixing string and non-string column names

The column to normalize is 'score' (string), and the grouping column is 'team' (string). Using strings is required for pandas indexing.