import pandas as pd
import numpy as np

# Create our student dataset
students_data = {
    'Name': ['Alice', 'Bob', 'Charlie', 'Diana', 'Eve', 'Frank', 'Grace', 'Henry', 'Iris', 'Jack'],
    'Grade': ['10th', '11th', '12th', '10th', '12th', '11th', '10th', '12th', '11th', '10th'],
    'Math_Score': [85, 76, 92, 88, 79, 84, 91, 87, 83, 90],
    'Science_Score': [78, 82, 89, 85, 88, 79, 86, 91, 87, 84],
    'English_Score': [92, 85, 88, 90, 86, 88, 94, 89, 91, 87],
    'Hours_Studied': [5, 8, 6, 7, 4, 9, 5, 8, 6, 7],
    'Extracurriculars': [2, 1, 3, 2, 1, 2, 4, 1, 3, 2]
}

df = pd.DataFrame(students_data)
print("Our Student Dataset:")
print(df)

# Question: How many students do we have?
print(f"Total number of students: {len(df)}")

# Question: What's the average score in each subject?
print("Average Scores:")
print(f"Math: {df['Math_Score'].mean():.1f}")
print(f"Science: {df['Science_Score'].mean():.1f}")
print(f"English: {df['English_Score'].mean():.1f}")

# Question: What's the range of study hours?
print(f"Study Hours - Min: {df['Hours_Studied'].min()}, Max: {df['Hours_Studied'].max()}")
print(f"Average study hours: {df['Hours_Studied'].mean():.1f}")

# Question: How many students are in each grade?
print("Students by Grade:")
print(df['Grade'].value_counts())

# Question: Do older students score better than younger students?
grade_performance = df.groupby('Grade')[['Math_Score', 'Science_Score', 'English_Score']].mean()
print("Average Scores by Grade:")
print(grade_performance.round(1))

# Question: Who studies the most hours by grade?
study_by_grade = df.groupby('Grade')['Hours_Studied'].mean()
print("Average Study Hours by Grade:")
print(study_by_grade.round(1))

# Question: Which subject has the highest scores?
subject_averages = {
    'Math': df['Math_Score'].mean(),
    'Science': df['Science_Score'].mean(), 
    'English': df['English_Score'].mean()
}

print("Subject Rankings (highest to lowest average):")
sorted_subjects = sorted(subject_averages.items(), key=lambda x: x[1], reverse=True)
for i, (subject, avg) in enumerate(sorted_subjects, 1):
    print(f"{i}. {subject}: {avg:.1f}")

# Question: Do students who study more hours get better grades?
# Let's calculate total score first
df['Total_Score'] = df['Math_Score'] + df['Science_Score'] + df['English_Score']

print("Study Hours vs. Total Scores:")
for _, student in df.iterrows():
    print(f"{student['Name']}: {student['Hours_Studied']} hours → {student['Total_Score']} total points")

# Question: Are students with more extracurriculars better at certain subjects?
print("Extracurriculars vs. English Scores (communication skills):")
for _, student in df.iterrows():
    print(f"{student['Name']}: {student['Extracurriculars']} activities → {student['English_Score']} English")

# Create some interesting comparisons
df['Average_Score'] = df[['Math_Score', 'Science_Score', 'English_Score']].mean(axis=1)

# Question: Who are the highest and lowest performers?
best_student = df.loc[df['Average_Score'].idxmax()]
struggling_student = df.loc[df['Average_Score'].idxmin()]

print("Highest Performer:")
print(f"{best_student['Name']} (Grade {best_student['Grade']}): {best_student['Average_Score']:.1f} average")
print(f"Studies {best_student['Hours_Studied']} hours/week")

print("\nNeeds More Support:")
print(f"{struggling_student['Name']} (Grade {struggling_student['Grade']}): {struggling_student['Average_Score']:.1f} average")
print(f"Studies {struggling_student['Hours_Studied']} hours/week")

# Question: Is there a "sweet spot" for extracurricular activities?
print("Extracurriculars vs Performance:")
activity_performance = df.groupby('Extracurriculars')['Average_Score'].mean()
for activities, avg_score in activity_performance.items():
    print(f"{activities} activities: {avg_score:.1f} average score")

# Check for outliers (unusually high or low values)
print("Checking for Unusual Values:")

# Are any scores impossible? (over 100 or under 0)
for subject in ['Math_Score', 'Science_Score', 'English_Score']:
    max_score = df[subject].max()
    min_score = df[subject].min()
    print(f"{subject}: Range {min_score} to {max_score}")
    if max_score > 100 or min_score < 0:
        print(f"  ⚠️  Warning: Unusual scores in {subject}")

# Are study hours realistic?
max_hours = df['Hours_Studied'].max()
if max_hours > 20:
    print(f"⚠️  Warning: {max_hours} study hours per week seems very high")
else:
    print(f"✓ Study hours look reasonable (max: {max_hours} hours/week)")

# New insights lead to new questions
print("New Questions to Investigate:")
print("\n1. Effect of Study Time:")
high_study = df[df['Hours_Studied'] >= 7]['Average_Score'].mean()
low_study = df[df['Hours_Studied'] < 7]['Average_Score'].mean()
print(f"   High study time (7+ hours): {high_study:.1f} average")
print(f"   Low study time (<7 hours): {low_study:.1f} average")
print(f"   Difference: {high_study - low_study:.1f} points")

print("2. Grade Level Performance:")
grade_12_avg = df[df['Grade'] == '12th']['Average_Score'].mean()
grade_10_avg = df[df['Grade'] == '10th']['Average_Score'].mean()
print(f"   12th grade average: {grade_12_avg:.1f}")
print(f"   10th grade average: {grade_10_avg:.1f}")
print(f"   Improvement from 10th to 12th: {grade_12_avg - grade_10_avg:.1f} points")

print("=" * 50)
print("STUDENT PERFORMANCE ANALYSIS REPORT")
print("=" * 50)

print(f"\nDataset Overview:")
print(f"• Total students: {len(df)}")
print(f"• Grade levels: {', '.join(sorted(df['Grade'].unique()))}")
print(f"• Subjects analyzed: Math, Science, English")

print(f"\nKey Findings:")
print(f"• Best subject overall: English ({df['English_Score'].mean():.1f} average)")
print(f"• Most challenging subject: Math ({df['Math_Score'].mean():.1f} average)")
print(f"• Study time range: {df['Hours_Studied'].min()}-{df['Hours_Studied'].max()} hours per week")

print(f"\nInteresting Patterns:")
correlation = df['Hours_Studied'].corr(df['Average_Score'])
if correlation > 0.5:
    print(f"• Strong positive link between study time and performance")
elif correlation > 0:
    print(f"• Weak positive link between study time and performance")
else:
    print(f"• No clear link between study time and performance")

print(f"\nQuestions for Further Investigation:")
print(f"• Why do some students perform well with less study time?")
print(f"• What teaching methods work best for different subjects?")
print(f"• How do extracurricular activities affect academic performance?")

# First, let's recreate the movie dataset:
movies_data = {
    'Title': ['The Matrix', 'Inception', 'Parasite', 'Toy Story', 'Pulp Fiction', 'The Dark Knight'],
    'Year': [1999, 2010, 2019, 1995, 1994, 2008],
    'Genre': ['Sci-Fi', 'Sci-Fi', 'Thriller', 'Animation', 'Crime', 'Action'],
    'Rating': [8.7, 8.8, 8.6, 8.3, 8.9, 9.0],
    'Duration': [136, 148, 132, 81, 154, 152]
}

movies_df = pd.DataFrame(movies_data)
print("Movie Dataset:")
print(movies_df)

# Your analysis code here...