groupby() basics in Data Analysis Python - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of groupby() with sum aggregation

What is the output of this code snippet?

Data Analysis Python

import pandas as pd

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

A

       Points
Team          
A          15
B          5
C          20

B

       Points
Team          
A          10
B          10
C          20

C

       Points
Team          
A          25
B          15
C          20

D

       Points
Team          
A          25
B          10
C          20

💡 Hint

Sum adds all points for each team.

The groupby groups rows by 'Team'. Then sum adds 'Points' for each group. Team A has 10+15=25, B has 10+5=15, C has 20.

❓ data_output

intermediate

Number of groups created by groupby()

How many groups are created by this groupby operation?

Data Analysis Python

import pandas as pd

data = {'Category': ['X', 'Y', 'X', 'Z', 'Y', 'Z', 'Z'], 'Value': [1, 2, 3, 4, 5, 6, 7]}
df = pd.DataFrame(data)
groups = df.groupby('Category')
print(len(groups))

A 7

B 3

C 2

D 4

💡 Hint

Count unique categories in 'Category' column.

The unique categories are X, Y, and Z, so 3 groups are created.

🔧 Debug

advanced

Error raised by incorrect groupby syntax

What error does this code raise?

Data Analysis Python

import pandas as pd

data = {'Type': ['A', 'B', 'A'], 'Score': [5, 10, 15]}
df = pd.DataFrame(data)
result = df.groupby('Type', as_index=False).mean()
print(result)

# Now this incorrect code:
result2 = df.groupby(Type).sum()

A NameError: name 'Type' is not defined

B TypeError: groupby() got an unexpected keyword argument 'Type'

C AttributeError: 'DataFrame' object has no attribute 'Type'

D No error, prints sum grouped by 'Type'

💡 Hint

Check if 'Type' is quoted or not in groupby argument.

The second groupby uses Type without quotes, so Python looks for a variable named Type which is not defined, causing NameError.

🚀 Application

advanced

Using groupby() to find max value per group

Which option correctly finds the maximum 'Sales' value for each 'Region'?

Data Analysis Python

import pandas as pd

data = {'Region': ['East', 'West', 'East', 'West', 'North'], 'Sales': [200, 150, 300, 100, 250]}
df = pd.DataFrame(data)

A df.groupby('Region')['Sales'].max()

B df.groupby('Sales')['Region'].max()

C df.groupby('Region').max('Sales')

D df.groupby('Region').max()

💡 Hint

Group by 'Region' and select 'Sales' column before max.

Option A groups by 'Region' and selects 'Sales' column, then finds max per group. Option A returns max for all columns, which works but includes 'Region' column max which is meaningless here. Option A is invalid syntax. Option A groups by 'Sales' which is wrong.

🧠 Conceptual

expert

Understanding groupby() with multiple aggregation functions

Given this code, what is the shape (rows, columns) of the resulting DataFrame?

Data Analysis Python

import pandas as pd

data = {'Category': ['A', 'A', 'B', 'B', 'C'], 'Value': [1, 2, 3, 4, 5]}
df = pd.DataFrame(data)
result = df.groupby('Category').agg(['min', 'max'])

A (5, 2)

B (5, 1)

C (3, 1)

D (3, 2)

💡 Hint

Count unique groups and number of aggregation functions.

There are 3 unique categories (A, B, C). Two aggregation functions ('min' and 'max') are applied to 'Value'. So rows=3 groups, columns=2 aggregations.