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

Aggregation functions (sum, mean, std)

📖 Scenario: You work in a small store and have a list of daily sales amounts in dollars. You want to understand the total sales, the average sales per day, and how much the sales vary from day to day.

🎯 Goal: Build a simple Python program that calculates the total sales, average sales, and standard deviation of sales from a list of daily sales amounts.

📋 What You'll Learn

Create a list of daily sales amounts with exact values

Create a variable to hold the number of days

Calculate the sum, mean, and standard deviation of the sales using Python code

Print the results clearly

💡 Why This Matters

🌍 Real World

Stores and businesses often analyze daily sales to understand performance and plan inventory.

💼 Career

Data analysts and business analysts use aggregation functions like sum, mean, and standard deviation to summarize and interpret data.

1

Create the sales data list

Create a list called daily_sales with these exact values: 150, 200, 170, 220, 180.

Data Analysis Python

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

Need a hint?

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

2

Create a variable for the number of days

Create a variable called num_days that stores the length of the daily_sales list using the len() function.

Data Analysis Python

daily_sales = [150, 200, 170, 220, 180]
# Create num_days as the length of daily_sales
# Your code here

Need a hint?

Use len(daily_sales) to get the number of items in the list.

3

Calculate sum, mean, and standard deviation

Calculate the total sales as total_sales using the sum() function on daily_sales. Calculate the average sales as average_sales by dividing total_sales by num_days. Calculate the standard deviation as std_dev using the formula: square root of the average of squared differences from the mean. Use import math and math.sqrt() for the square root.

Data Analysis Python

daily_sales = [150, 200, 170, 220, 180]
num_days = len(daily_sales)
# Calculate total_sales, average_sales, and std_dev
# Your code here

Need a hint?

Use a list comprehension to calculate squared differences from the mean.

4

Print the results

Print the total_sales, average_sales, and std_dev variables each on a separate line with clear labels using print().

Data Analysis Python

import math

daily_sales = [150, 200, 170, 220, 180]
num_days = len(daily_sales)

total_sales = sum(daily_sales)
average_sales = total_sales / num_days

squared_diffs = [(sale - average_sales) ** 2 for sale in daily_sales]
variance = sum(squared_diffs) / num_days
std_dev = math.sqrt(variance)

# Print total_sales, average_sales, and std_dev with labels
# Your code here

Need a hint?

Use print(f"Label: {variable}") to show the results clearly.