Named aggregation in Pandas - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of named aggregation with multiple functions

What is the output DataFrame after running this code with named aggregation?

Pandas

import pandas as pd

df = pd.DataFrame({
    'Team': ['A', 'A', 'B', 'B', 'C'],
    'Points': [10, 15, 10, 20, 30],
    'Assists': [5, 7, 8, 6, 10]
})

result = df.groupby('Team').agg(
    total_points=pd.NamedAgg(column='Points', aggfunc='sum'),
    max_assists=pd.NamedAgg(column='Assists', aggfunc='max')
)
print(result)

A {'Team': ['A', 'B', 'C'], 'total_points': [25, 30, 30], 'max_assists': [7, 8, 10]}

B

       total_points  max_assists
Team                            
A                25            7
B                30            8
C                30           10

C

Team
A    total_points    25
     max_assists      7
B    total_points    30
     max_assists      8
C    total_points    30
     max_assists     10
dtype: int64

D

       Points  Assists
Team                  
A          25       12
B          30       14
C          30       10

💡 Hint

Look at how named aggregation creates new columns with the given names.

The groupby aggregates sum of Points as total_points and max of Assists as max_assists for each Team. The output is a DataFrame indexed by Team with these two columns.

❓ data_output

intermediate

Number of rows in named aggregation result

Given this DataFrame and named aggregation, how many rows does the result have?

Pandas

import pandas as pd

df = pd.DataFrame({
    'Category': ['X', 'X', 'Y', 'Z', 'Z', 'Z'],
    'Value': [1, 2, 3, 4, 5, 6]
})

result = df.groupby('Category').agg(
    sum_value=pd.NamedAgg(column='Value', aggfunc='sum'),
    count_value=pd.NamedAgg(column='Value', aggfunc='count')
)
print(len(result))

A 3

B 6

C 2

D 1

💡 Hint

Count unique groups in the 'Category' column.

The groupby groups by unique categories: X, Y, Z, so 3 groups and 3 rows in result.

🔧 Debug

advanced

Identify the error in named aggregation syntax

What error does this code raise?

Pandas

import pandas as pd

df = pd.DataFrame({'A': [1,2,3], 'B': [4,5,6]})

result = df.groupby('A').agg(
    total_B=('B', 'sum'),
    mean_B='mean'
)
print(result)

A TypeError: named aggregation expects keyword arguments

B SyntaxError: invalid syntax

C KeyError: 'mean_B'

D No error, prints grouped DataFrame

💡 Hint

Check how named aggregation arguments are passed.

Named aggregation requires all aggregations as keyword arguments with pd.NamedAgg or tuples. Mixing tuple and string without keyword causes TypeError.

🚀 Application

advanced

Create a named aggregation to find min and max

Which option correctly uses named aggregation to find min and max of 'Score' grouped by 'Group'?

Pandas

import pandas as pd

df = pd.DataFrame({
    'Group': ['G1', 'G1', 'G2', 'G2'],
    'Score': [88, 92, 85, 90]
})

result = df.groupby('Group').agg(
    min_score=...,  # fill here
    max_score=...   # fill here
)
print(result)

A {'min_score': ('Score', 'min'), 'max_score': ('Score', 'max')}

B min_score=('Score', 'min'), max_score=('Score', 'max')

C min_score='min', max_score='max'

D min_score=pd.NamedAgg(column='Score', aggfunc='min'), max_score=pd.NamedAgg(column='Score', aggfunc='max')

💡 Hint

Named aggregation uses pd.NamedAgg or keyword tuples.

Option D correctly uses pd.NamedAgg with column and aggfunc keywords for named aggregation.

🧠 Conceptual

expert

Understanding output structure of named aggregation

After applying named aggregation with multiple functions on different columns, what is the structure of the resulting DataFrame?

A A MultiIndex DataFrame with original column names as first level and aggregation functions as second level.

B A Series with group keys as index and tuples of aggregated values as elements.

C A DataFrame indexed by group keys with columns named as specified in named aggregation, each column holding aggregated values.

D A DataFrame with original columns replaced by aggregated columns without group keys as index.

💡 Hint

Named aggregation creates new columns with given names and keeps group keys as index.

Named aggregation returns a DataFrame indexed by groups with columns named as specified, each column showing the aggregated result.