Aggregation functions (sum, mean, count) in Data Analysis Python - Mini Project: Build & Apply

Aggregation functions (sum, mean, count)

📖 Scenario: You work in a small store and want to analyze daily sales data to understand how much money you made, the average sale amount, and how many sales you had.

🎯 Goal: Build a simple Python program that uses aggregation functions sum, mean, and count to analyze sales data stored in a list.

📋 What You'll Learn

Create a list of sales amounts

Create a variable to count the number of sales

Calculate the total sales using sum

Calculate the average sale amount using mean

Print the total sales, average sale, and count of sales

💡 Why This Matters

🌍 Real World

Stores and businesses often analyze sales data to understand performance and make decisions.

💼 Career

Data analysts use aggregation functions like sum, mean, and count to summarize data quickly and clearly.

1

Create the sales data list

Create a list called sales with these exact values: 100, 250, 75, 300, 150.

Data Analysis Python

# Create the sales list with the exact values
# Your code here

Hint

Use square brackets [] to create a list and separate numbers with commas.

2

Count the number of sales

Create a variable called count_sales and set it to the number of items in the sales list using the len() function.

Data Analysis Python

sales = [100, 250, 75, 300, 150]
# Create count_sales using len(sales)
# Your code here

Hint

Use len(sales) to get the number of sales.

3

Calculate total and average sales

Create a variable called total_sales and set it to the sum of the sales list using the sum() function. Then create a variable called average_sale and set it to total_sales divided by count_sales.

Data Analysis Python

sales = [100, 250, 75, 300, 150]
count_sales = len(sales)
# Calculate total_sales using sum(sales)
# Calculate average_sale as total_sales / count_sales
# Your code here

Hint

Use sum(sales) to add all sales. Then divide by count_sales to get the average.

4

Print the results

Print the values of total_sales, average_sale, and count_sales each on a new line.

Data Analysis Python

sales = [100, 250, 75, 300, 150]
count_sales = len(sales)
total_sales = sum(sales)
average_sale = total_sales / count_sales
# Print total_sales, average_sale, and count_sales
# Your code here

Hint

Use three print() statements, one for each variable.

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