Aggregation functions (sum, mean, count) in Data Analysis Python - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to calculate the total sales using sum.

Data Analysis Python

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

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

Amean

Bsum

Ccount

Dmax

Attempts:

3 left

💡 Hint

Common Mistakes

Using mean() instead of sum() which calculates average.

Using count() which counts entries, not sums values.

The sum() function adds all values in the 'sales' column to get the total sales.

2fill in blank

medium

Complete the code to find the average age using mean.

Data Analysis Python

average_age = df['age'].[1]()

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

Amin

Bsum

Ccount

Dmean

Attempts:

3 left

💡 Hint

Common Mistakes

Using sum() which adds values instead of averaging.

Using count() which counts entries, not averages.

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

3fill in blank

hard

Fix the error in the code to count the number of entries in the 'product' column.

Data Analysis Python

product_count = df['product'].[1]()

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

Acount

Bsum

Cmean

Dmax

Attempts:

3 left

💡 Hint

Common Mistakes

Using sum() which adds values instead of counting.

Using mean() which calculates average, not count.

The count() function counts the number of non-missing entries in the 'product' column.

4fill in blank

hard

Fill both blanks to create a dictionary with total and average sales.

Data Analysis Python

sales_summary = {'total': df['sales'].[1](), 'average': df['sales'].[2]()}

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

Asum

Bmean

Ccount

Dmax

Attempts:

3 left

💡 Hint

Common Mistakes

Mixing up sum and mean functions.

Using count instead of sum or mean.

Use sum() to get total sales and mean() to get average sales.

5fill in blank

hard

Fill all three blanks to create a dictionary with count, total, and average of 'quantity'.

Data Analysis Python

quantity_stats = {'count': df['quantity'].[1](), 'total': df['quantity'].[2](), 'average': df['quantity'].[3]()}

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

Amean

Bsum

Ccount

Dmin

Attempts:

3 left

💡 Hint

Common Mistakes

Using mean for count or sum.

Mixing order of functions.

Use count() to count entries, sum() for total quantity, and mean() for average quantity.

Practice

1. What does the sum() function do when applied to a column of numbers in a DataFrame?

easy

A. Counts how many numbers are in the column

B. Finds the middle value of the numbers

C. Adds all the numbers together to get a total

D. Sorts the numbers from smallest to largest

Solution

1. Step 1: Understand the purpose of sum()

   The sum() function adds all values in a column to get their total.

2. Step 2: Compare with other functions

   mean() finds average, count() counts entries, so they do different things.

3. Final Answer:

   Adds all the numbers together to get a total -> Option C

4. Quick Check:

   sum() = total of values [OK]

Hint: Sum adds values, mean averages, count counts entries [OK]

Common Mistakes:

• Confusing sum() with mean()
• Thinking sum() counts entries
• Assuming sum() sorts data

2. Which of the following is the correct way to calculate the average of a DataFrame column named sales using pandas?

easy

A. df.sales.sum() / df.sales.count()

B. df.mean('sales')

C. mean(df.sales)

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

Solution

1. Step 1: Identify correct pandas syntax for mean()

   In pandas, to get the mean of a column, use df['column'].mean().

2. Step 2: Check other options

   df.mean('sales') is invalid syntax; mean(df.sales) is not pandas method; df.sales.sum() / df.sales.count() works but is longer and less direct.

3. Final Answer:

   df['sales'].mean() -> Option D

4. Quick Check:

   Use df['column'].mean() for average [OK]

Hint: Use df['col'].mean() to get average quickly [OK]

Common Mistakes:

• Using mean() as a standalone function
• Passing column name as argument to df.mean()
• Dividing sum by count manually instead of mean()

3. Given the DataFrame df below:

import pandas as pd
data = {'score': [10, 20, 30, 40, 50]}
df = pd.DataFrame(data)

What is the output of df['score'].count()?

medium

A. 5

B. 150

C. 30

D. Error

Solution

1. Step 1: Understand what count() does on a column

   count() counts the number of non-missing entries in the column. Here, all 5 values are present.

2. Step 2: Count the entries in 'score'

   There are 5 numbers: 10, 20, 30, 40, 50, so count() returns 5.

3. Final Answer:

   5 -> Option A

4. Quick Check:

   count() = number of entries [OK]

Hint: count() returns number of non-empty entries [OK]

Common Mistakes:

• Confusing count() with sum()
• Expecting count() to return total value
• Thinking count() returns average

4. Consider this code snippet:

import pandas as pd
data = {'values': [5, None, 15, 20]}
df = pd.DataFrame(data)
result = df['values'].sum()

What is the value of result?

medium

A. 40

B. None

C. Error

D. 20

Solution

1. Step 1: Understand how sum() handles None values

   In pandas, sum() ignores None (missing) values and sums only the numbers.

2. Step 2: Calculate sum ignoring None

   Values are 5, None, 15, 20. Sum = 5 + 15 + 20 = 40.

3. Final Answer:

   40 -> Option A

4. Quick Check:

   sum() skips None and adds numbers [OK]

Hint: sum() ignores None/null values automatically [OK]

Common Mistakes:

• Expecting sum() to return None if any missing
• Getting error due to None value
• Counting None as zero explicitly

5. You have a DataFrame df with a column sales containing daily sales numbers. You want to find the average sales but only for days where sales were above zero. Which code correctly calculates this?

hard

A. df['sales'].mean()

B. df[df['sales'] > 0]['sales'].mean()

C. df['sales'].sum() / df['sales'].count()

D. df['sales'].count() / df['sales'].sum()

Solution

1. Step 1: Filter rows where sales > 0

   Use df[df['sales'] > 0] to keep only positive sales days.

2. Step 2: Calculate mean of filtered sales

   Apply ['sales'].mean() on filtered data to get average of positive sales only.

3. Final Answer:

   df[df['sales'] > 0]['sales'].mean() -> Option B

4. Quick Check:

   Filter then mean() for conditional average [OK]

Hint: Filter data first, then apply mean() for conditional average [OK]

Common Mistakes:

• Using mean() without filtering
• Dividing sum by count without filtering
• Swapping sum and count in division