Python vs R vs Excel for analysis in Data Analysis Python - Hands-On Comparison

Python vs R vs Excel for analysis

📖 Scenario: You work in a small business that tracks sales data. You want to understand how to use Python, R, and Excel to analyze this data and find the best tool for your needs.

🎯 Goal: Build simple data analysis examples in Python, R, and Excel to compare how each tool handles data setup, filtering, and summarizing.

📋 What You'll Learn

Create a sales data dictionary in Python

Set a sales threshold variable

Filter sales above the threshold using a dictionary comprehension

Print the filtered sales dictionary

💡 Why This Matters

🌍 Real World

Businesses often need to analyze sales data to find top-performing months or products. Knowing how to filter data quickly helps make better decisions.

💼 Career

Data analysts and business users use Python, R, or Excel to clean and analyze data. Understanding these tools helps pick the right one for the task.

1

DATA SETUP: Create sales data dictionary

Create a dictionary called sales with these exact entries: 'January': 1500, 'February': 1800, 'March': 1200, 'April': 2200, 'May': 1700

Data Analysis Python

# Create the sales dictionary with exact month and sales values
# Your code here

Need a hint?

Use curly braces to create a dictionary with month names as keys and sales numbers as values.

2

CONFIGURATION: Set sales threshold

Create a variable called threshold and set it to 1600

Data Analysis Python

sales = {'January': 1500, 'February': 1800, 'March': 1200, 'April': 2200, 'May': 1700}
# Set the sales threshold to 1600
# Your code here

Need a hint?

Just assign the number 1600 to the variable named threshold.

3

CORE LOGIC: Filter sales above threshold

Use a dictionary comprehension to create a new dictionary called high_sales that contains only the months and sales from sales where the sales value is greater than threshold

Data Analysis Python

sales = {'January': 1500, 'February': 1800, 'March': 1200, 'April': 2200, 'May': 1700}
threshold = 1600
# Create high_sales dictionary with months having sales > threshold
# Your code here

Need a hint?

Use dictionary comprehension syntax: {key: value for key, value in dict.items() if condition}

4

OUTPUT: Print filtered sales

Print the high_sales dictionary

Data Analysis Python

sales = {'January': 1500, 'February': 1800, 'March': 1200, 'April': 2200, 'May': 1700}
threshold = 1600
high_sales = {month: amount for month, amount in sales.items() if amount > threshold}
# Print the high_sales dictionary
# Your code here

Need a hint?

Use print(high_sales) to show the filtered dictionary.