Aggregation-based features in Data Analysis Python - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of group aggregation with multiple functions

What is the output of the following code snippet that groups data by 'Category' and calculates the mean and max of 'Value'?

Data Analysis Python

import pandas as pd

data = pd.DataFrame({
    'Category': ['A', 'A', 'B', 'B', 'C'],
    'Value': [10, 20, 10, 30, 40]
})

result = data.groupby('Category')['Value'].agg(['mean', 'max']).reset_index()
print(result)

A [{'Category': 'A', 'mean': 15.0, 'max': 20}, {'Category': 'B', 'mean': 20.0, 'max': 30}, {'Category': 'C', 'mean': 40.0, 'max': 40}]

B [{'Category': 'A', 'mean': 10.0, 'max': 20}, {'Category': 'B', 'mean': 20.0, 'max': 30}, {'Category': 'C', 'mean': 40.0, 'max': 40}]

C [{'Category': 'A', 'mean': 15.0, 'max': 10}, {'Category': 'B', 'mean': 20.0, 'max': 30}, {'Category': 'C', 'mean': 40.0, 'max': 40}]

D [{'Category': 'A', 'mean': 15.0, 'max': 20}, {'Category': 'B', 'mean': 20.0, 'max': 10}, {'Category': 'C', 'mean': 40.0, 'max': 40}]

💡 Hint

Remember that mean is the average and max is the highest value in each group.

The group 'A' has values 10 and 20, so mean is (10+20)/2=15 and max is 20. Group 'B' has 10 and 30, mean is 20 and max is 30. Group 'C' has one value 40, so mean and max are both 40.

❓ data_output

intermediate

Number of unique users per product

Given a DataFrame with user purchases, what is the number of unique users who bought each product?

Data Analysis Python

import pandas as pd

data = pd.DataFrame({
    'User': ['Alice', 'Bob', 'Alice', 'David', 'Bob', 'Eve'],
    'Product': ['X', 'X', 'Y', 'Y', 'X', 'Z']
})

result = data.groupby('Product')['User'].nunique().reset_index(name='UniqueUsers')
print(result)

A [{'Product': 'X', 'UniqueUsers': 2}, {'Product': 'Y', 'UniqueUsers': 3}, {'Product': 'Z', 'UniqueUsers': 1}]

B [{'Product': 'X', 'UniqueUsers': 3}, {'Product': 'Y', 'UniqueUsers': 2}, {'Product': 'Z', 'UniqueUsers': 1}]

C [{'Product': 'X', 'UniqueUsers': 2}, {'Product': 'Y', 'UniqueUsers': 2}, {'Product': 'Z', 'UniqueUsers': 1}]

D [{'Product': 'X', 'UniqueUsers': 3}, {'Product': 'Y', 'UniqueUsers': 3}, {'Product': 'Z', 'UniqueUsers': 1}]

💡 Hint

Count distinct users per product.

Product 'X' was bought by Alice and Bob (2 unique users). Product 'Y' was bought by Alice and David (2 unique users). Product 'Z' was bought by Eve (1 unique user).

❓ visualization

advanced

Visualizing average sales per region

Which plot correctly shows the average sales per region from the given data?

Data Analysis Python

import pandas as pd
import matplotlib.pyplot as plt

data = pd.DataFrame({
    'Region': ['North', 'South', 'East', 'West', 'North', 'South'],
    'Sales': [100, 150, 200, 130, 120, 170]
})
avg_sales = data.groupby('Region')['Sales'].mean().reset_index()

plt.bar(avg_sales['Region'], avg_sales['Sales'])
plt.title('Average Sales per Region')
plt.xlabel('Region')
plt.ylabel('Average Sales')
plt.show()

A Bar chart with regions on x-axis and average sales on y-axis, bars heights: North=110, South=160, East=200, West=130

B Line chart with regions on x-axis and average sales on y-axis, points: North=110, South=160, East=200, West=130

C Pie chart showing percentage sales per region with values: North=220, South=320, East=200, West=130

D Scatter plot with sales values plotted against region names

💡 Hint

Look for a bar chart showing average values per category.

The code groups sales by region and calculates mean, then plots a bar chart with those averages. The heights correspond to the average sales per region.

🔧 Debug

advanced

Identify the error in aggregation code

What error will the following code raise when executed?

Data Analysis Python

import pandas as pd

data = pd.DataFrame({
    'Category': ['A', 'B', 'A'],
    'Value': [10, 20, 30]
})

result = data.groupby('Category')['Value'].agg('sum', 'mean')
print(result)

A No error, outputs sum and mean aggregation

B TypeError: agg() takes from 1 to 2 positional arguments but 3 were given

C KeyError: 'mean'

D ValueError: Function names must be passed as a list or tuple

💡 Hint

Check how multiple aggregation functions are passed to agg().

agg() expects a single function or a list/tuple of functions. Passing multiple positional arguments causes a TypeError.

🚀 Application

expert

Calculate weighted average feature per group

You have a DataFrame with 'Group', 'Value', and 'Weight' columns. Which code correctly calculates the weighted average of 'Value' per 'Group' using 'Weight' as weights?

Data Analysis Python

import pandas as pd

data = pd.DataFrame({
    'Group': ['X', 'X', 'Y', 'Y', 'Y'],
    'Value': [10, 20, 30, 40, 50],
    'Weight': [1, 3, 2, 1, 1]
})

A data.groupby('Group').agg({'Value': 'mean'}).reset_index()

B data.groupby('Group').apply(lambda x: x['Value'].mean() * x['Weight'].mean()).reset_index(name='WeightedAvg')

C data.groupby('Group').agg({'Value': lambda x: (x * data['Weight']).sum() / data['Weight'].sum()}).reset_index()

D data.groupby('Group').apply(lambda x: (x['Value'] * x['Weight']).sum() / x['Weight'].sum()).reset_index(name='WeightedAvg')

💡 Hint

Weighted average is sum of value*weight divided by sum of weights per group.

Option D correctly calculates weighted average by multiplying 'Value' and 'Weight' per group, summing, then dividing by sum of weights per group. Option D incorrectly uses full data weights, not group weights. Option D is simple mean, ignoring weights. Option D multiplies means, which is incorrect.