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.

Practice

1. What is the main purpose of aggregation-based features in data analysis?

easy

A. To summarize data by groups using functions like sum or mean

B. To visualize data using charts and graphs

C. To clean missing values from the dataset

D. To split data into training and testing sets

Solution

1. Step 1: Understand aggregation-based features

   Aggregation-based features summarize data by groups using functions like sum, mean, or count.

2. Step 2: Compare with other options

   Visualization, cleaning, and splitting data are different tasks not related to aggregation features.

3. Final Answer:

   To summarize data by groups using functions like sum or mean -> Option A

4. Quick Check:

   Aggregation = summary by groups [OK]

Hint: Aggregation means grouping and summarizing data [OK]

Common Mistakes:

• Confusing aggregation with visualization
• Thinking aggregation cleans data
• Mixing aggregation with data splitting

2. Which of the following is the correct syntax to calculate the mean of column 'sales' grouped by 'region' in pandas?

easy

A. df.mean('sales').groupby('region')

B. df['sales'].groupby('region').mean()

C. df.groupby('region')['sales'].mean()

D. df.groupby('sales')['region'].mean()

Solution

1. Step 1: Identify correct groupby syntax

   The correct syntax groups by 'region' then selects 'sales' column and applies mean: df.groupby('region')['sales'].mean()

2. Step 2: Check incorrect options

   df.mean('sales').groupby('region') reverses order, df['sales'].groupby('region').mean() tries to group on a column series, df.groupby('sales')['region'].mean() groups by 'sales' instead of 'region'.

3. Final Answer:

   df.groupby('region')['sales'].mean() -> Option C

4. Quick Check:

   Group by region, then mean sales [OK]

Hint: Use df.groupby('col')['col2'].agg_func() [OK]

Common Mistakes:

• Calling mean before groupby
• Grouping on wrong column
• Applying groupby on a Series instead of DataFrame

3. Given the DataFrame:

import pandas as pd
data = {'team': ['A', 'A', 'B', 'B', 'B'], 'points': [10, 15, 10, 5, 20]}
df = pd.DataFrame(data)
result = df.groupby('team').agg({'points': 'sum'})
print(result)

What is the output?

medium

A. team\nA 25\nB 10

B. team\nA 10\nB 20

C. team\nA 15\nB 10

D. team\nA 25\nB 35

Solution

1. Step 1: Group data by 'team'

   Teams are 'A' and 'B'. 'A' has points 10 and 15, 'B' has 10, 5, and 20.

2. Step 2: Sum points per team

   Sum for 'A' = 10 + 15 = 25; Sum for 'B' = 10 + 5 + 20 = 35.

3. Final Answer:

   team A 25 B 35 -> Option D

4. Quick Check:

   Sum points by team = 25 and 35 [OK]

Hint: Sum points after groupby to get totals per team [OK]

Common Mistakes:

• Adding only first value per group
• Confusing sum with mean
• Misreading group labels

4. What is wrong with this code snippet to get the average 'score' per 'class' in pandas?

df.groupby('class').score.agg('average')

medium

A. The groupby column 'class' is misspelled

B. The aggregation function name 'average' is invalid; should be 'mean'

C. The code should use double brackets: df.groupby(['class'])

D. The code is correct and will run without error

Solution

1. Step 1: Check aggregation function name

   'average' is not a valid pandas aggregation function; the correct function for average is 'mean'.

2. Step 2: Verify other parts

   Grouping by 'class' and accessing 'score' column is correct syntax; double brackets are optional here.

3. Final Answer:

   The aggregation function name 'average' is invalid; should be 'mean' -> Option B

4. Quick Check:

   Use 'mean' not 'average' for average calculation [OK]

Hint: Use 'mean' for average in agg() [OK]

Common Mistakes:

• Using 'average' instead of 'mean'
• Thinking double brackets are mandatory
• Assuming code runs without error

5. You have a sales DataFrame with columns 'store', 'product', and 'revenue'. You want to create a new feature showing the average revenue per store and product combined. Which pandas code correctly creates this aggregation-based feature and adds it as a new column 'avg_rev' to the original DataFrame?

hard

A. df['avg_rev'] = df.groupby(['store', 'product'])['revenue'].transform('mean')

B. df['avg_rev'] = df.groupby('store')['revenue'].mean()

C. df['avg_rev'] = df.groupby('product')['revenue'].sum()

D. df['avg_rev'] = df.groupby(['store', 'product']).agg({'revenue': 'mean'})

Solution

1. Step 1: Understand the goal

   We want average revenue per store and product, and add it as a new column matching original rows.

2. Step 2: Use groupby with transform

   Using groupby on ['store', 'product'] and transform('mean') returns a Series aligned with original DataFrame, perfect for new column.

3. Step 3: Check other options

   df['avg_rev'] = df.groupby('store')['revenue'].mean() groups only by store, not product; df['avg_rev'] = df.groupby('product')['revenue'].sum() sums revenue, not average; df['avg_rev'] = df.groupby(['store', 'product']).agg({'revenue': 'mean'}) returns a DataFrame, not a Series aligned for assignment.

4. Final Answer:

   df['avg_rev'] = df.groupby(['store', 'product'])['revenue'].transform('mean') -> Option A

5. Quick Check:

   Use transform to add group average as new column [OK]

Hint: Use transform for group stats as new column [OK]

Common Mistakes:

• Using agg() which returns DataFrame, not Series
• Grouping by only one column instead of both
• Using sum instead of mean