Aggregation-based features in Data Analysis Python - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to calculate the mean of the 'sales' column.

Data Analysis Python

mean_sales = df['sales'].[1]()

Drag options to blanks, or click blank then click option'

Asum

Bmax

Cmean

Dcount

Attempts:

3 left

💡 Hint

Common Mistakes

Using sum() instead of mean()

Using count() which counts entries, not averages

The mean() function calculates the average value of the 'sales' column.

2fill in blank

medium

Complete the code to group data by 'category' and calculate the sum of 'amount'.

Data Analysis Python

grouped = df.groupby('[1]')['amount'].sum()

Drag options to blanks, or click blank then click option'

Aregion

Bdate

Csales

Dcategory

Attempts:

3 left

💡 Hint

Common Mistakes

Grouping by wrong column like 'date' or 'region'

Not grouping at all before summing

Grouping by 'category' allows us to sum 'amount' values for each category.

3fill in blank

hard

Fix the error in the code to calculate the maximum 'score' per 'team'.

Data Analysis Python

max_scores = df.groupby('team')['[1]'].max()

Drag options to blanks, or click blank then click option'

Ascores

Bscore

CScore

Dteam

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'scores' or 'Score' which do not match the column name

Grouping by the wrong column

The column name is exactly 'score' in lowercase. Using the correct column name avoids errors.

4fill in blank

hard

Fill both blanks to create a dictionary of average 'price' for items with 'quantity' greater than 5.

Data Analysis Python

avg_price = {item: [1] for item, qty in data.items() if qty [2] 5}

Drag options to blanks, or click blank then click option'

Aprice_data[item]

B>

C<

Dquantity_data[item]

Attempts:

3 left

💡 Hint

Common Mistakes

Using wrong variable names for price or quantity

Using wrong comparison operator

We use price_data[item] to get the price for each item and filter items where quantity is greater than 5 using >.

5fill in blank

hard

Fill all three blanks to create a dictionary of total 'sales' for each 'region' where sales are above 1000.

Data Analysis Python

total_sales = { [1]: [2] for [3], sales in sales_data.items() if sales > 1000 }

Drag options to blanks, or click blank then click option'

Aregion

Bsales

Dregion_name

Attempts:

3 left

💡 Hint

Common Mistakes

Using different variable names inconsistently

Confusing keys and values in the dictionary comprehension

The dictionary keys are regions, so region is used for both key and loop variable. The values are sales amounts.

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