transform() for group-level operations in Pandas - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of group transform with mean subtraction

What is the output of the following code snippet?

Pandas

import pandas as pd

df = pd.DataFrame({'Team': ['A', 'A', 'B', 'B'], 'Points': [10, 20, 30, 40]})
df['AdjPoints'] = df.groupby('Team')['Points'].transform(lambda x: x - x.mean())
print(df)

A

  Team  Points  AdjPoints
0    A      10       -5.0
1    A      20        5.0
2    B      30       -5.0
3    B      40        5.0

B

  Team  Points  AdjPoints
0    A      10       10.0
1    A      20       20.0
2    B      30       30.0
3    B      40       40.0

C

  Team  Points  AdjPoints
0    A      10       15.0
1    A      20       15.0
2    B      30       35.0
3    B      40       35.0

D

  Team  Points  AdjPoints
0    A      10       -10.0
1    A      20       10.0
2    B      30       -30.0
3    B      40       30.0

💡 Hint

Think about what subtracting the group mean from each value does.

The transform applies the lambda function to each group. For each group, it subtracts the mean of that group's Points from each Points value, centering the data around zero within each group.

❓ data_output

intermediate

Number of items after transform with duplicated values

Given the code below, how many rows does the resulting DataFrame have?

Pandas

import pandas as pd

df = pd.DataFrame({'Category': ['X', 'X', 'Y', 'Y', 'Y'], 'Value': [1, 2, 3, 4, 5]})
df['Rank'] = df.groupby('Category')['Value'].transform('rank')
print(len(df))

A 5

B 3

C 2

D 1

💡 Hint

transform keeps the original DataFrame shape.

The transform method returns a Series with the same length as the original DataFrame, so the number of rows remains unchanged.

🔧 Debug

advanced

Identify the error in transform usage

What error does the following code raise?

Pandas

import pandas as pd

df = pd.DataFrame({'Group': ['G1', 'G1', 'G2'], 'Score': [10, 20, 30]})
df['Scaled'] = df.groupby('Group')['Score'].transform(lambda x: x / x.sum())
print(df)

A TypeError: unsupported operand type(s) for /: 'int' and 'method'

B No error, outputs scaled scores per group

C AttributeError: 'SeriesGroupBy' object has no attribute 'sum'

D ValueError: Length of values does not match length of index

💡 Hint

Check if the lambda function uses correct pandas methods.

The code correctly divides each score by the sum of scores in its group. The transform returns a Series aligned with the original DataFrame, so no error occurs.

🚀 Application

advanced

Using transform to fill missing values with group mean

You have a DataFrame with missing values in the 'Sales' column. Which code snippet correctly fills missing 'Sales' values with the mean sales of their group in 'Region'?

Pandas

import pandas as pd
import numpy as np

df = pd.DataFrame({'Region': ['East', 'East', 'West', 'West'], 'Sales': [100, np.nan, 200, np.nan]})

A df['Sales'] = df['Sales'].fillna(df.groupby('Region')['Sales'].mean())

B df['Sales'] = df.groupby('Region')['Sales'].transform('mean')

C df['Sales'] = df.groupby('Region')['Sales'].apply(lambda x: x.fillna(x.mean()))

D df['Sales'] = df.groupby('Region')['Sales'].transform(lambda x: x.fillna(x.mean()))

💡 Hint

transform keeps the original index and shape, apply may change it.

Option D uses transform with a lambda that fills missing values with the group mean, preserving the DataFrame shape and index alignment. Option D tries to fillna with a Series that does not align properly. Option D uses apply which may change the index. Option D replaces all values with the group mean, not just missing ones.

🧠 Conceptual

expert

Why use transform instead of apply for group-level operations?

Which statement best explains why transform is preferred over apply for group-level operations that return a Series with the same shape as the original DataFrame?

A apply always returns a DataFrame, transform always returns a scalar.

B apply is faster than transform but cannot handle group operations.

C transform returns a Series aligned with the original DataFrame, preserving index and shape, while apply may return aggregated or differently shaped results.

D transform can only be used with numeric data, apply works with all data types.

💡 Hint

Think about the shape and alignment of the output.

transform returns a Series with the same length as the original DataFrame, aligned by index, which is useful for adding group-level calculations back to the original data. apply can return aggregated results or different shapes, making it less suitable for this use case.

Practice

1. What does the transform() function do when used on a grouped pandas DataFrame?

easy

A. Applies a function to each group and returns a result with the same size as the original data

B. Aggregates each group into a single summary value

C. Filters out groups based on a condition

D. Sorts the groups in ascending order

Solution

1. Step 1: Understand the purpose of transform()

   transform() applies a function to each group but returns a result with the same number of rows as the original DataFrame.

2. Step 2: Compare with other group operations

   Unlike aggregation which reduces groups to single values, transform() keeps the original data size, allowing new columns with group-level info.

3. Final Answer:

   Applies a function to each group and returns a result with the same size as the original data -> Option A

4. Quick Check:

   transform() = same size output [OK]

Hint: Remember: transform keeps original rows, aggregation reduces them [OK]

Common Mistakes:

• Confusing transform with aggregation
• Thinking transform filters groups
• Assuming transform changes group order

2. Which of the following is the correct syntax to use transform() to calculate the mean of each group in a DataFrame df grouped by column 'Category'?

easy

A. df.groupby('Category').mean().transform()

B. df.groupby('Category').transform('mean')

C. df.transform('mean').groupby('Category')

D. df.groupby('Category').apply(transform('mean'))

Solution

1. Step 1: Identify correct method chaining

   First group by 'Category' then call transform('mean') to apply mean to each group and keep original size.

2. Step 2: Check other options for syntax errors

   df.transform('mean').groupby('Category') calls transform before grouping, which is invalid. df.groupby('Category').mean().transform() calls mean before transform, which is wrong order. df.groupby('Category').apply(transform('mean')) misuses apply with transform.

3. Final Answer:

   df.groupby('Category').transform('mean') -> Option B

4. Quick Check:

   Group first, then transform [OK]

Hint: Group first, then call transform with function name [OK]

Common Mistakes:

• Calling transform before groupby
• Using apply incorrectly with transform
• Calling aggregation before transform

3. Given the DataFrame df below:

  Category  Value
0        A      5
1        A      7
2        B      3
3        B      9
4        B      6

What is the output of df.groupby('Category')['Value'].transform('max')?

medium

A. [6, 6, 9, 9, 9]

B. [5, 7, 3, 6, 9]

C. [7, 5, 9, 3, 6]

D. [7, 7, 9, 9, 9]

Solution

1. Step 1: Group by 'Category' and find max per group

   Group A has values [5,7], max is 7; Group B has [3,9,6], max is 9.

2. Step 2: Apply transform to assign max to each row in group

   Rows in group A get 7, rows in group B get 9, preserving original row order.

3. Final Answer:

   [7, 7, 9, 9, 9] -> Option D

4. Quick Check:

   Max per group repeated for each row [OK]

Hint: transform repeats group result for each row [OK]

Common Mistakes:

• Confusing transform output with aggregation output
• Mixing up max values per group
• Not preserving original row order

4. Consider this code snippet:

df = pd.DataFrame({'Group': ['X', 'X', 'Y'], 'Score': [10, 20, 30]})
df['MeanScore'] = df.groupby('Group')['Score'].transform(mean)

What is the error in this code?

medium

A. The function name 'mean' should be a string: 'mean'

B. Group column name is incorrect

C. transform() cannot be used with mean function

D. DataFrame creation syntax is wrong

Solution

1. Step 1: Check function argument in transform

   The function mean is passed without quotes, but transform expects a string or a callable.

2. Step 2: Correct usage

   Passing 'mean' as a string fixes the error: transform('mean').

3. Final Answer:

   The function name 'mean' should be a string: 'mean' -> Option A

4. Quick Check:

   Function names in transform must be strings or callables [OK]

Hint: Use quotes around function names in transform [OK]

Common Mistakes:

• Passing function name without quotes
• Assuming transform can't use built-in functions
• Misnaming DataFrame columns

5. You have a DataFrame df with columns 'Team' and 'Points'. You want to add a new column 'PointsDiff' that shows the difference between each player's points and the average points of their team. Which code correctly uses transform() to achieve this?

hard

A. df['PointsDiff'] = df.groupby('Team')['Points'].mean() - df['Points']

B. df['PointsDiff'] = df.groupby('Team')['Points'].transform(lambda x: x.mean())

C. df['PointsDiff'] = df['Points'] - df.groupby('Team')['Points'].transform('mean')

D. df['PointsDiff'] = df.groupby('Team')['Points'].apply(lambda x: x.mean())

Solution

1. Step 1: Calculate team average points with transform

   Use transform('mean') to get average points per team for each row, preserving size.

2. Step 2: Subtract team average from each player's points

   Subtract the transformed mean from the original 'Points' column to get difference per player.

3. Step 3: Check other options

   df['PointsDiff'] = df.groupby('Team')['Points'].mean() - df['Points']: df.groupby('Team')['Points'].mean() returns smaller Series, size mismatch. df['PointsDiff'] = df.groupby('Team')['Points'].transform(lambda x: x.mean()): transform(lambda x: x.mean()) sets PointsDiff to repeated group means, not differences. df['PointsDiff'] = df.groupby('Team')['Points'].apply(lambda x: x.mean()): apply(lambda x: x.mean()) returns smaller Series of means, misalignment.

4. Final Answer:

   df['PointsDiff'] = df['Points'] - df.groupby('Team')['Points'].transform('mean') -> Option C

5. Quick Check:

   Subtract group mean using transform for aligned output [OK]

Hint: Subtract transform('mean') result from original column [OK]

Common Mistakes:

• Using apply instead of transform causing misalignment
• Not subtracting in correct order
• Confusing transform with aggregation