groupby() basics in Pandas - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of groupby sum aggregation

What is the output of this code?

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()

Pandas

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

  Team  Points
0    A      25
1    B      15
2    C      20

B

       Points
Team          
A          25
B          15
C          20

C

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

D

       Points
Team          
A          10
B          10
C          20

💡 Hint

Remember groupby groups rows by the 'Team' column and sums the 'Points' for each group.

The groupby('Team') groups rows by team names. Then sum() adds the Points in each group. So A has 10+15=25, B has 10+5=15, and C has 20.

❓ data_output

intermediate

Number of groups created by groupby

Given this DataFrame, how many groups will be created by grouping on 'Category' column?

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')

Pandas

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 3

B 7

C 4

D 2

💡 Hint

Count unique values in the 'Category' column.

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

🔧 Debug

advanced

Identify the error in groupby usage

What error does this code raise?

import pandas as pd

data = {'Name': ['Anna', 'Bob', 'Cara'], 'Score': [90, 80, 85]}
df = pd.DataFrame(data)
result = df.groupby('Age').mean()

Pandas

import pandas as pd

data = {'Name': ['Anna', 'Bob', 'Cara'], 'Score': [90, 80, 85]}
df = pd.DataFrame(data)
result = df.groupby('Age').mean()

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

B ValueError: No numeric types to aggregate

C KeyError: 'Age'

D AttributeError: 'DataFrame' object has no attribute 'mean'

💡 Hint

Check if the column used in groupby exists in the DataFrame.

The DataFrame has no 'Age' column, so groupby('Age') raises a KeyError.

🚀 Application

advanced

Calculate average sales per region

You have sales data:

import pandas as pd

data = {'Region': ['North', 'South', 'North', 'East', 'South', 'East'], 'Sales': [100, 200, 150, 300, 250, 350]}
df = pd.DataFrame(data)

Which code correctly calculates the average sales per region?

Pandas

import pandas as pd

data = {'Region': ['North', 'South', 'North', 'East', 'South', 'East'], 'Sales': [100, 200, 150, 300, 250, 350]}
df = pd.DataFrame(data)

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

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

C df.groupby('Region').sum()['Sales'] / df['Sales'].count()

D df.groupby('Region')['Sales'].mean()

💡 Hint

Group by 'Region' and then calculate mean of 'Sales'.

Option D groups by 'Region' and calculates mean sales correctly. Option D groups by sales values, which is wrong. Option D divides sum by total count, not per group. Option D counts rows per region, not average sales.

🧠 Conceptual

expert

Understanding groupby object behavior

Which statement about a pandas groupby object is TRUE?

A A groupby object is an iterable that yields (group_name, DataFrame) pairs.

B A groupby object immediately computes aggregation when created.

C A groupby object can be indexed like a DataFrame to get groups directly.

D A groupby object stores the original DataFrame as a list of groups.

💡 Hint

Think about what happens when you loop over a groupby object.

A groupby object is lazy and does not compute aggregations until asked. It is iterable and yields pairs of group name and group DataFrame. It cannot be indexed like a DataFrame. It does not store groups as a list but as a special object.