Jupyter Notebook best practices in Data Analysis Python - Mini Project: Build & Apply

Jupyter Notebook Best Practices

📖 Scenario: You are working as a data analyst and want to create a clean, readable Jupyter Notebook to share your data analysis with your team.

🎯 Goal: Build a simple Jupyter Notebook structure that follows best practices: create data, add a configuration variable, apply a simple analysis, and display the results clearly.

📋 What You'll Learn

Create a dictionary called sales_data with exact monthly sales values

Add a variable called threshold to filter high sales months

Use a dictionary comprehension to create high_sales with months exceeding the threshold

Print the high_sales dictionary to show filtered results

💡 Why This Matters

🌍 Real World

Data analysts often use Jupyter Notebooks to explore and share data insights. Following best practices helps keep notebooks clean and easy to understand.

💼 Career

Knowing how to organize data, use configuration variables, apply filtering logic, and display results clearly is essential for data science and analytics roles.

1

DATA SETUP: Create the sales data dictionary

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

Data Analysis Python

# Create the sales_data dictionary with exact monthly sales values
# Your code here

Hint

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

2

CONFIGURATION: Add a sales threshold variable

Add a variable called threshold and set it to 1600 to filter months with sales above this value.

Data Analysis Python

sales_data = {'January': 1500, 'February': 1800, 'March': 1200, 'April': 2200, 'May': 1700}
# Add the threshold variable below
# Your code here

Hint

Just create a variable named threshold and assign the number 1600 to it.

3

CORE LOGIC: Filter months with high sales

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

Data Analysis Python

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

Hint

Use {month: sales for month, sales in sales_data.items() if sales > threshold} to filter the dictionary.

4

OUTPUT: Display the filtered high sales months

Print the high_sales dictionary to display the months with sales above the threshold.

Data Analysis Python

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

Hint

Use print(high_sales) to show the filtered dictionary.