GroupBy with pipe for chaining in Pandas - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of GroupBy with pipe chaining

What is the output of this code snippet using pandas GroupBy and pipe chaining?

Pandas

import pandas as pd

df = pd.DataFrame({
    'Team': ['A', 'A', 'B', 'B', 'C', 'C'],
    'Points': [10, 15, 10, 20, 5, 10],
    'Assists': [5, 7, 8, 6, 2, 3]
})

result = (df.groupby('Team')
            .pipe(lambda d: d['Points'].sum())
            .pipe(lambda s: s.sort_values(ascending=False)))

print(result)

A

Team
B    30
A    25
C    15
Name: Points, dtype: int64

B

Team
C    15
A    25
B    30
Name: Points, dtype: int64

C

Team
A    25
B    30
C    15
Name: Points, dtype: int64

D {'A': 25, 'B': 30, 'C': 15}

💡 Hint

Remember that pipe passes the object to the function and you can chain operations.

The code groups by 'Team', sums 'Points' per group, then sorts descending. So B=30, A=25, C=15.

❓ data_output

intermediate

Resulting DataFrame shape after GroupBy with pipe

After running this code, what is the shape of the resulting DataFrame?

Pandas

import pandas as pd

df = pd.DataFrame({
    'Category': ['X', 'X', 'Y', 'Y', 'Z'],
    'Value': [1, 2, 3, 4, 5],
    'Score': [10, 20, 30, 40, 50]
})

result = (df.groupby('Category')
            .pipe(lambda d: d.agg({'Value': 'sum', 'Score': 'mean'}))
            .reset_index())

print(result.shape)

A (3, 3)

B (3, 2)

C (5, 3)

D (5, 2)

💡 Hint

Check how many unique categories and how many aggregated columns.

There are 3 unique categories and 2 aggregated columns plus the reset index column, total 3 columns.

🔧 Debug

advanced

Identify the error in GroupBy pipe chaining

What error will this code raise when executed?

Pandas

import pandas as pd

df = pd.DataFrame({
    'Group': ['G1', 'G1', 'G2'],
    'Data': [10, 20, 30]
})

result = (df.groupby('Group')
            .pipe(lambda d: d['Data'].mean())
            .pipe(lambda s: s.reset_index(drop=True)))

print(result)

A AttributeError: 'float' object has no attribute 'reset_index'

B No error, prints a Series

C KeyError: 'Data'

D TypeError: 'DataFrameGroupBy' object is not subscriptable

💡 Hint

Check what the first pipe returns and what the second pipe tries to do.

No error occurs. The first pipe returns a Series of per-group means (G1: 15.0, G2: 30.0). The second pipe calls reset_index(drop=True) on the Series, which returns a new Series with RangeIndex.

🚀 Application

advanced

Using pipe to filter groups after aggregation

Which option produces a DataFrame with groups having sum of 'Sales' greater than 100 using pipe chaining?

Pandas

import pandas as pd

df = pd.DataFrame({
    'Store': ['S1', 'S1', 'S2', 'S2', 'S3'],
    'Sales': [50, 60, 40, 30, 120]
})

result = (df.groupby('Store')
            .pipe(lambda d: d['Sales'].sum())
            .pipe(lambda s: s[s > 100])
            .reset_index())

print(result)

A

  Store  Sales
0    S1    110
1    S2     70

B

  Store  Sales
0    S2     70
1    S3    120

C

  Store  Sales
0    S1    110
1    S3    120

D

  Store  Sales
0    S3    120

💡 Hint

Sum sales per store, then filter sums greater than 100.

Stores S1 and S3 have sales sums 110 and 120 respectively, both > 100.

🧠 Conceptual

expert

Why use pipe in GroupBy chaining?

What is the main advantage of using pipe in pandas GroupBy chaining?

A It automatically optimizes the GroupBy operation for faster computation.

B It converts the GroupBy object into a DataFrame immediately.

C It replaces the need for aggregation functions like sum or mean.

D It allows applying custom functions in the middle of method chains without breaking the flow.

💡 Hint

Think about how pipe helps keep code readable and chainable.

Pipe lets you insert any function into a chain, passing the current object as input, keeping code clean and readable.