Day 3: Tasks & activities

A cartoon panda looking over a year’s worth of monthly class exams. The panda is doing great; A+! (Midjourney5)[https://www.midjourney.com/jobs/6b63c3ca-c64d-41b8-a791-7e4b2594c781?index=0]

Introduction

In this end-of-day activity, we’ll practice the concepts you learned today: control flows and pandas Series analysis. We’ll work with real student test score data to apply if/else statements, loops, and Series operations that you learned in today’s sessions.

Learning Objectives

By completing this exercise, you will practice:

1. Control Flow Concepts (from Sessions 3a & 3b):
   • Using if/elif/else statements for decision making
   • Using for loops to iterate through data
   • Combining control flows with data analysis
2. Series Operations (from Session 3c):
   • Creating Pandas Series with custom indices
   • Using Series statistical methods (.mean(), .median(), .max(), .min())
   • Basic Series indexing and slicing
   • Applying NumPy statistical functions to Series

Setup

Task 0.1: Import the necessary libraries

Import the pandas and numpy libraries that you learned about today.

Part 1: Creating and Analyzing Test Scores

We’ll work with monthly test scores for a student throughout the academic year.

Task 1.1: Create the Test Scores Series

Create a pandas Series using the following data: - Scores: [78, 85, 92, 88, 79, 83, 91, 87, 89, 94]
- Months: ['Sep', 'Oct', 'Nov', 'Dec', 'Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun']

Create a Series called scores where the months are the index and the scores are the values.

Task 1.2: Display the Series

Print your scores Series to see the data structure.

Task 2: Basic Statistical Analysis

Use the Series methods you learned today to calculate and print the following statistics:

1. Average score (using .mean())
2. Highest score (using .max())
   
3. Lowest score (using .min())
4. Median score (using .median())

Tip

Remember to use f-string formatting to make your output readable! For example: f"Average score: {average_score:.2f}"

Part 2: Applying Control Flows

Task 3: Performance Categories

Use if/elif/else statements to categorize the overall performance based on the average score:

• If average ≥ 90: “Excellent”
  
• If average ≥ 80: “Good”
• If average ≥ 70: “Satisfactory”
  
• Otherwise: “Needs Improvement”

Create a variable called performance and print the result with the average score.

For example, if the average score is 95, then you want your code to print:

Overall performance: Excellent (Average: 95.00)

Task 4: Finding Best and Worst Months

Use Series indexing to find: 1. The month with the highest score (hint: use .idxmax()) 2. The month with the lowest score (hint: use .idxmin())

Print the results in a clear format.

Conclusion

Excellent work! In just four focused tasks, you practiced the key concepts from Day 3:

✅ Pandas Series Fundamentals: - Created a Series with custom indices - Used statistical methods (.mean(), .max(), .min(), .median()) - Applied Series indexing methods (.idxmax(), .idxmin())

✅ Control Flow Application: - Used if/elif/else statements for data categorization - Combined conditional logic with data analysis

These core skills form the foundation for more advanced data analysis!

Reflection Questions

1. How did using Series methods compare to working with regular Python lists?
2. What other types of data could you analyze using these same techniques?
3. Which part of the exercise felt most challenging?

Optional Extension

If you finish early: Create your own Series with 5-7 data points (maybe daily temperatures for a week, or your favorite restaurant ratings) and apply the same analysis techniques you used today.

End Activity Session (Day 3)