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.

Practice

1. Why is it important to use Markdown cells in a Jupyter Notebook?

easy

A. To add titles and explanations that make the notebook easier to understand

B. To run code faster

C. To store large datasets

D. To change the notebook's theme

Solution

1. Step 1: Understand the role of Markdown cells

   Markdown cells are used to write text, titles, and explanations, not code.

2. Step 2: Identify the benefit of explanations

   Adding explanations helps others and yourself understand the notebook's purpose and steps.

3. Final Answer:

   To add titles and explanations that make the notebook easier to understand -> Option A

4. Quick Check:

   Markdown cells = explanations [OK]

Hint: Markdown = text and titles, not code [OK]

Common Mistakes:

• Thinking Markdown runs code
• Using Markdown to store data
• Ignoring explanations

2. Which of the following is the correct way to add a comment in a Python code cell in Jupyter Notebook?

easy

A. // This is a comment

B. # This is a comment

C.

D. /* This is a comment */

Solution

1. Step 1: Recall Python comment syntax

   Python uses the hash symbol (#) to start a comment.

2. Step 2: Identify correct comment style

   Only # This is a comment uses the correct Python comment syntax.

3. Final Answer:

   # This is a comment -> Option B

4. Quick Check:

   Python comments start with # [OK]

Hint: Python comments start with # [OK]

Common Mistakes:

• Using // which is for other languages
• Using HTML comment syntax
• Using C-style comments

3. Consider this Jupyter Notebook code sequence:

1. x = 5
2. y = x + 2
3. print(y)

What will be the output after running all cells from top to bottom?

medium

A. 2

B. 5

C. Error

D. 7

Solution

1. Step 1: Trace variable assignments

   x is assigned 5, then y is assigned x + 2, which is 7.

2. Step 2: Understand print output

   Printing y will output 7.

3. Final Answer:

   7 -> Option D

4. Quick Check:

   5 + 2 = 7 [OK]

Hint: Follow variable values step-by-step [OK]

Common Mistakes:

• Assuming print shows x instead of y
• Forgetting to run cells in order
• Expecting error without reason

4. You have a notebook with cells:

1. a = 10
2. print(a)
3. a = 20

If you run cells in order 1, 3, 2, what will happen?

medium

A. Prints 20

B. Prints 10

C. Error: variable a not defined

D. Prints nothing

Solution

1. Step 1: Analyze cell execution order

   Cell 1 sets a=10, cell 3 sets a=20, then cell 2 prints a.

2. Step 2: Determine printed value

   Since a was last set to 20 before printing, print(a) outputs 20.

3. Final Answer:

   Prints 20 -> Option A

4. Quick Check:

   Last assignment before print = 20 [OK]

Hint: Run cells top to bottom to avoid confusion [OK]

Common Mistakes:

• Assuming print shows old value
• Thinking print runs before assignment
• Expecting error due to order

5. You want to share a Jupyter Notebook with a colleague. Which practice ensures they see your results correctly?

hard

A. Delete all Markdown cells to reduce size

B. Only run the last cell before sharing

C. Run all cells from top to bottom before sharing

D. Share only the .py script exported from notebook

Solution

1. Step 1: Understand notebook execution state

   Running all cells top to bottom ensures outputs match code and order.

2. Step 2: Identify sharing best practice

   Sharing a fully run notebook avoids confusion and missing outputs.

3. Final Answer:

   Run all cells from top to bottom before sharing -> Option C

4. Quick Check:

   Run all cells = consistent notebook [OK]

Hint: Always run all cells before sharing [OK]

Common Mistakes:

• Sharing without running cells
• Deleting Markdown removes explanations
• Sharing only scripts loses notebook features