Split-apply-combine mental model in Pandas - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of groupby and aggregation

What is the output of this code snippet using pandas split-apply-combine?

Pandas

import pandas as pd

data = pd.DataFrame({
    'Team': ['A', 'A', 'B', 'B', 'C'],
    'Points': [10, 15, 10, 5, 20]
})
result = data.groupby('Team')['Points'].sum()
print(result)

A

Team
A    25
B    15
C    20
Name: Points, dtype: int64

B

Team
A    10
B    10
C    20
Name: Points, dtype: int64

C

Team
A    15
B    5
C    20
Name: Points, dtype: int64

D

Team
A    25
B    15
C    20
Name: Team, dtype: int64

💡 Hint

Think about what sum() does after grouping by 'Team'.

The code groups data by 'Team' and sums the 'Points' for each group. Team A has 10+15=25, B has 10+5=15, and C has 20.

❓ data_output

intermediate

Resulting DataFrame after applying mean aggregation

Given the DataFrame below, what is the resulting DataFrame after applying groupby and mean aggregation on 'Category'?

Pandas

import pandas as pd

data = pd.DataFrame({
    'Category': ['X', 'X', 'Y', 'Y', 'Z'],
    'Value': [4, 6, 8, 2, 10]
})
result = data.groupby('Category', as_index=False).mean()
print(result)

A

  Category  Value
0        X    10
1        Y    10
2        Z    10

B

  Category  Value
0        X    5.0
1        Y    5.0
2        Z   10.0

C

Category  Value
X         5.0
Y         5.0
Z        10.0

D

Category  Value
X         4
Y         8
Z        10

💡 Hint

Remember that mean averages the values per group.

Grouping by 'Category' and taking mean averages the 'Value' column per category. X: (4+6)/2=5, Y: (8+2)/2=5, Z: 10.

🔧 Debug

advanced

Identify the error in groupby aggregation code

What error does this code raise when run?

Pandas

import pandas as pd

data = pd.DataFrame({
    'Group': ['G1', 'G1', 'G2'],
    'Score': [10, 20, 30]
})
result = data.groupby('Group').agg({'Score': 'sum', 'Age': 'mean'})
print(result)

A No error, prints sum and mean

B TypeError: unsupported operand type(s) for +: 'int' and 'str'

C KeyError: 'Age'

D ValueError: No numeric types to aggregate

💡 Hint

Check if all columns in agg dictionary exist in DataFrame.

The DataFrame has no 'Age' column, so trying to aggregate 'Age' causes a KeyError.

🚀 Application

advanced

Calculate median per group and filter groups

You have a DataFrame with sales data. Which code correctly calculates the median sales per 'Store' and returns only stores with median sales above 100?

Pandas

import pandas as pd

data = pd.DataFrame({
    'Store': ['S1', 'S1', 'S2', 'S2', 'S3'],
    'Sales': [120, 80, 150, 200, 90]
})

A

result = data.groupby('Store')['Sales'].median()
filtered = result[result > 100]
print(filtered)

B

result = data.groupby('Store').median()['Sales']
filtered = result[result > 100]
print(filtered)

C

result = data.groupby('Store')['Sales'].mean()
filtered = result[result > 100]
print(filtered)

D

result = data.groupby('Store')['Sales'].sum()
filtered = result[result > 100]
print(filtered)

💡 Hint

Median is the middle value, not the average or sum.

Option A correctly groups by 'Store', calculates median sales, then filters stores with median > 100.

🧠 Conceptual

expert

Understanding split-apply-combine with custom aggregation

Which option best describes what happens in the split-apply-combine process when using a custom aggregation function in pandas groupby?

A Data is split into groups, combined immediately, and then the custom function is applied to the combined data.

B Data is combined first, then the custom function is applied to the entire dataset, and finally split into groups.

C The custom function is applied to the entire dataset without splitting, then results are grouped.

D Data is split into groups, the custom function is applied to each group independently, then the results are combined into a new DataFrame or Series.

💡 Hint

Think about the order: split, apply, then combine.

Split-apply-combine means data is split into groups, the function is applied to each group, then results are combined.