Day 3: Tasks & activities

Control Flows and Series Analysis

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

Let’s import the libraries we learned about today:

Code 
import pandas as pd
import numpy as np

Part 1: Creating and Analyzing Test Scores

Let’s work with a realistic dataset of monthly test scores for a student throughout the academic year.

Task 1: Create the Test Scores Series

Create a pandas Series with the following monthly test scores:

Code 
# Monthly test scores (September through June)
scores_data = [78, 85, 92, 88, 79, 83, 91, 87, 89, 94]
months = ['Sep', 'Oct', 'Nov', 'Dec', 'Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun']

# Create the Series
scores = pd.Series(scores_data, index=months)
print("Monthly Test Scores:")
print(scores)
Monthly Test Scores:
Sep    78
Oct    85
Nov    92
Dec    88
Jan    79
Feb    83
Mar    91
Apr    87
May    89
Jun    94
dtype: int64

Task 2: Basic Statistical Analysis

Using the Series methods you learned today, calculate:

Code 
# Calculate statistics using Series methods
average_score = scores.mean()
highest_score = scores.max()
lowest_score = scores.min()
median_score = scores.median()

print(f"Average score: {average_score:.2f}")
print(f"Highest score: {highest_score}")
print(f"Lowest score: {lowest_score}")
print(f"Median score: {median_score:.2f}")
Average score: 86.60
Highest score: 94
Lowest score: 78
Median score: 87.50

Task 3: Applying Control Flows

Now let’s use the control flow concepts from today to analyze the scores:

3a. Performance Categories

Use if/elif/else statements to categorize the average score:

Code 
# Categorize performance based on average score
if average_score >= 90:
    performance = "Excellent"
elif average_score >= 80:
    performance = "Good"
elif average_score >= 70:
    performance = "Satisfactory"
else:
    performance = "Needs Improvement"

print(f"Overall performance: {performance} (Average: {average_score:.2f})")
Overall performance: Good (Average: 86.60)

3b. Month-by-Month Analysis

Use a for loop to analyze each month’s performance:

Code 
print("\nMonth-by-Month Performance Analysis:")
print("=" * 40)

for month in scores.index:
    score = scores[month]
    
    # Use if/else to provide feedback for each month
    if score >= 90:
        feedback = "Outstanding!"
    elif score >= 85:
        feedback = "Great work!"
    elif score >= 80:
        feedback = "Good job!"
    elif score >= 75:
        feedback = "Solid effort!"
    else:
        feedback = "Room for improvement"
    
    print(f"{month}: {score} - {feedback}")

Month-by-Month Performance Analysis:
========================================
Sep: 78 - Solid effort!
Oct: 85 - Great work!
Nov: 92 - Outstanding!
Dec: 88 - Great work!
Jan: 79 - Solid effort!
Feb: 83 - Good job!
Mar: 91 - Outstanding!
Apr: 87 - Great work!
May: 89 - Great work!
Jun: 94 - Outstanding!

Part 2: Series Operations and Comparisons

Task 4: Finding Specific Months

Use basic Series indexing to explore the data:

Code 
# Find specific months using indexing (concepts from Session 3c)
print("Score in December:", scores['Dec'])
print("Score in May:", scores['May'])

# Using basic slicing (taught in Session 3c)
print("\nFirst three months:")
print(scores[:3])

print("\nLast three months:")
print(scores[-3:])
Score in December: 88
Score in May: 89

First three months:
Sep    78
Oct    85
Nov    92
dtype: int64

Last three months:
Apr    87
May    89
Jun    94
dtype: int64

Task 5: Comparing Performance Periods

Let’s analyze different parts of the year using basic slicing:

Code 
# Fall semester (first 4 months)
fall_scores = scores[:4]
fall_average = fall_scores.mean()

# Spring semester (last 6 months) 
spring_scores = scores[4:]
spring_average = spring_scores.mean()

print(f"Fall semester average: {fall_average:.2f}")
print(f"Spring semester average: {spring_average:.2f}")

# Use control flow to compare performance
if spring_average > fall_average:
    improvement = spring_average - fall_average
    print(f"Improvement in spring: +{improvement:.2f} points!")
elif fall_average > spring_average:
    decline = fall_average - spring_average
    print(f"Performance declined in spring: -{decline:.2f} points")
else:
    print("Performance remained consistent between semesters")
Fall semester average: 85.75
Spring semester average: 87.17
Improvement in spring: +1.42 points!

Part 3: Advanced Analysis with NumPy Functions

Task 6: Using NumPy Statistical Functions

Apply NumPy functions to our Series (as learned in Session 3c):

Code 
# NumPy statistical functions work on Pandas Series!
np_mean = np.mean(scores)
np_std = np.std(scores)
np_sum = np.sum(scores)

print(f"Using NumPy functions:")
print(f"Mean: {np_mean:.2f}")
print(f"Standard deviation: {np_std:.2f}")
print(f"Total points: {np_sum}")

# Determine consistency using standard deviation
if np_std < 5:
    consistency = "Very consistent"
elif np_std < 8:
    consistency = "Moderately consistent"
else:
    consistency = "Inconsistent"

print(f"Performance consistency: {consistency} (std dev: {np_std:.2f})")
Using NumPy functions:
Mean: 86.60
Standard deviation: 5.08
Total points: 866
Performance consistency: Moderately consistent (std dev: 5.08)

Part 4: Challenge - Combining All Concepts

Task 7: Comprehensive Analysis

Combine control flows, Series operations, and NumPy functions:

Code 
print("Comprehensive Performance Report")
print("=" * 35)

# Count months above/below average using a for loop and control flow
above_average_count = 0
below_average_count = 0

print(f"Overall average: {average_score:.2f}")
print("\nMonthly performance relative to average:")

for month in scores.index:
    score = scores[month]
    if score > average_score:
        status = "Above average"
        above_average_count += 1
    elif score < average_score:
        status = "Below average"
        below_average_count += 1
    else:
        status = "At average"
    
    difference = score - average_score
    print(f"{month}: {score} ({status}, {difference:+.2f})")

print(f"\nSummary:")
print(f"Months above average: {above_average_count}")
print(f"Months below average: {below_average_count}")

# Final recommendation using control flow
if above_average_count > below_average_count:
    print("Recommendation: Strong performance overall! Keep up the good work.")
elif below_average_count > above_average_count:
    print("Recommendation: Focus on consistency. Consider study habit adjustments.")
else:
    print("Recommendation: Balanced performance. Work on achieving more peak months.")
Comprehensive Performance Report
===================================
Overall average: 86.60

Monthly performance relative to average:
Sep: 78 (Below average, -8.60)
Oct: 85 (Below average, -1.60)
Nov: 92 (Above average, +5.40)
Dec: 88 (Above average, +1.40)
Jan: 79 (Below average, -7.60)
Feb: 83 (Below average, -3.60)
Mar: 91 (Above average, +4.40)
Apr: 87 (Above average, +0.40)
May: 89 (Above average, +2.40)
Jun: 94 (Above average, +7.40)

Summary:
Months above average: 6
Months below average: 4
Recommendation: Strong performance overall! Keep up the good work.

Conclusion

In this activity, you practiced:

βœ… Control Flow Concepts: - if/elif/else statements for categorizing and comparing data - for loops for iterating through Series data - Combining conditions with data analysis

βœ… Pandas Series Operations: - Creating Series with custom indices - Statistical methods (.mean(), .max(), .min(), .median()) - Basic indexing (series['key']) and slicing (series[:3])

βœ… NumPy Integration: - Using NumPy statistical functions on Pandas Series - Understanding how NumPy and Pandas work together

These are the foundational skills that will support all your future data science work. Great job applying today’s concepts!

Additional Practice (Optional)

Try creating your own Series with different data (maybe daily temperatures, stock prices, or sports scores) and apply the same analysis techniques you used today.

End Activity Session (Day 3)