import numpy as np

# Basic operations demonstration
a = np.array([10, 20, 30, 40])
b = np.array([1, 2, 3, 4])

print(f"Array a: {a}")
print(f"Array b: {b}")

print(f"Addition: {a + b}")
print(f"Subtraction: {a - b}")
print(f"Multiplication: {a * b}")
print(f"Division: {a / b}")

import numpy as np

# Addition and subtraction examples
prices = np.array([10.99, 25.50, 15.75, 8.25])
taxes = np.array([1.10, 2.55, 1.58, 0.83])
discounts = np.array([2.00, 5.00, 3.00, 1.00])

print(f"Prices: {prices}")
print(f"Taxes: {taxes}")
print(f"Discounts: {discounts}")

# Calculate final prices
with_tax = prices + taxes
with_discount = prices - discounts
final_price = (prices + taxes) - discounts

print(f"With tax: {with_tax}")
print(f"With discount: {with_discount}")
print(f"Final price: {final_price}")

# Operations with scalars
shipping = 5.99
total_with_shipping = final_price + shipping
print(f"Total with shipping: {total_with_shipping}")

import numpy as np

# Multiplication and division examples
quantities = np.array([2, 5, 3, 8])
unit_prices = np.array([12.50, 8.99, 15.25, 6.75])

print(f"Quantities: {quantities}")
print(f"Unit prices: {unit_prices}")

# Calculate totals
totals = quantities * unit_prices
print(f"Totals: {totals}")

# Apply bulk discount (divide by 2 for quantities > 5)
large_orders = quantities > 5
bulk_prices = np.where(large_orders, unit_prices / 2, unit_prices)
print(f"Bulk prices: {bulk_prices}")

# Division examples
print(f"Price per unit: {totals / quantities}")
print(f"Units per dollar: {quantities / unit_prices}")

# Floor division and modulo
numbers = np.array([17, 23, 31, 45])
divisor = 5
print(f"Floor division: {numbers // divisor}")
print(f"Remainder: {numbers % divisor}")

import numpy as np

# Power operations
base = np.array([2, 3, 4, 5])
print(f"Base numbers: {base}")

# Exponentiation
squared = base ** 2
cubed = base ** 3
custom_power = base ** 2.5

print(f"Squared: {squared}")
print(f"Cubed: {cubed}")
print(f"Power 2.5: {custom_power}")

# Root operations
numbers = np.array([4, 9, 16, 25])
print(f"Numbers: {numbers}")

square_roots = numbers ** 0.5  # Same as np.sqrt(numbers)
cube_roots = numbers ** (1/3)

print(f"Square roots: {square_roots}")
print(f"Cube roots: {cube_roots}")

# Using NumPy functions
print(f"np.sqrt(): {np.sqrt(numbers)}")
print(f"np.power(): {np.power(base, 3)}")

import numpy as np

# Scalar operations
scores = np.array([85, 92, 78, 96, 89])
print(f"Original scores: {scores}")

# Apply curve (add 5 points to everyone)
curved_scores = scores + 5
print(f"Curved scores: {curved_scores}")

# Apply percentage scaling (10% bonus)
bonus_scores = scores * 1.1
print(f"Bonus scores: {bonus_scores}")

# Calculate grade percentages
total_points = 100
percentages = scores / total_points * 100
print(f"Percentages: {percentages}")

# Temperature conversion example
celsius = np.array([0, 25, 37, 100])
fahrenheit = celsius * 9/5 + 32
print(f"Celsius: {celsius}")
print(f"Fahrenheit: {fahrenheit}")

import numpy as np

# Complex calculation example
print("📊 Monthly Budget Analysis")
print("=" * 25)

# Monthly expenses
rent = np.array([1200, 1200, 1200, 1250])     # Months 1-4
utilities = np.array([150, 180, 140, 165])
groceries = np.array([400, 450, 380, 420])
entertainment = np.array([200, 300, 150, 250])

print(f"Rent: {rent}")
print(f"Utilities: {utilities}")
print(f"Groceries: {groceries}")
print(f"Entertainment: {entertainment}")

# Calculate total expenses
total_expenses = rent + utilities + groceries + entertainment
print(f"Total expenses: {total_expenses}")

# Monthly income
income = np.array([3500, 3500, 3600, 3500])
print(f"Income: {income}")

# Calculate savings
savings = income - total_expenses
print(f"Savings: {savings}")

# Calculate savings rate
savings_rate = (savings / income) * 100
print(f"Savings rate: {savings_rate}%")

# Compound interest on savings (monthly)
interest_rate = 0.005  # 0.5% monthly
final_savings = savings * (1 + interest_rate)
print(f"Savings with interest: {final_savings}")

import numpy as np

# Comparison operations
test_scores = np.array([85, 92, 78, 96, 89, 74, 88])
passing_grade = 80

print(f"Test scores: {test_scores}")
print(f"Passing grade: {passing_grade}")

# Boolean comparisons
passed = test_scores >= passing_grade
failed = test_scores < passing_grade
excellent = test_scores >= 90

print(f"Passed: {passed}")
print(f"Failed: {failed}")
print(f"Excellent (≥90): {excellent}")

# Using comparisons for filtering
passing_scores = test_scores[passed]
failing_scores = test_scores[failed]

print(f"Passing scores: {passing_scores}")
print(f"Failing scores: {failing_scores}")

# Counting with boolean arrays
num_passed = np.sum(passed)  # True counts as 1
num_failed = np.sum(failed)
pass_rate = np.mean(passed) * 100  # Percentage

print(f"Students passed: {num_passed}")
print(f"Students failed: {num_failed}")
print(f"Pass rate: {pass_rate:.1f}%")

import numpy as np

# Division by zero handling
numerators = np.array([10, 20, 30, 40])
denominators = np.array([2, 0, 5, 0])

print(f"Numerators: {numerators}")
print(f"Denominators: {denominators}")

# Regular division (produces warnings and inf/nan)
with np.errstate(divide='ignore', invalid='ignore'):
    result = numerators / denominators
    print(f"Division result: {result}")

# Safe division using np.divide with where parameter
safe_result = np.divide(
    numerators, 
    denominators, 
    out=np.zeros_like(numerators, dtype=float), 
    where=denominators!=0
)
print(f"Safe division: {safe_result}")

# Using np.where for conditional operations
conditional_result = np.where(
    denominators != 0, 
    numerators / denominators, 
    -1  # Default value for division by zero
)
print(f"Conditional result: {conditional_result}")

import numpy as np

print("🎯 Practical Mathematical Operations")
print("=" * 35)

# Example 1: Stock portfolio analysis
stock_prices = np.array([150.25, 75.80, 234.50, 89.90])
shares_owned = np.array([10, 25, 5, 40])
purchase_prices = np.array([140.00, 80.00, 220.00, 85.00])

print(f"Current prices: {stock_prices}")
print(f"Shares owned: {shares_owned}")
print(f"Purchase prices: {purchase_prices}")

# Calculate portfolio value
current_value = stock_prices * shares_owned
original_value = purchase_prices * shares_owned
profit_loss = current_value - original_value

print(f"Current value: {current_value}")
print(f"Profit/Loss: {profit_loss}")
print(f"Total portfolio: ${np.sum(current_value):.2f}")
print(f"Total P/L: ${np.sum(profit_loss):.2f}")

# Example 2: Physics calculations
print(f"\n⚡ Physics: Kinetic Energy")
masses = np.array([2.5, 1.8, 3.2, 0.9])  # kg
velocities = np.array([10, 15, 8, 25])    # m/s

# Kinetic energy: KE = 0.5 * m * v²
kinetic_energy = 0.5 * masses * velocities ** 2
print(f"Masses: {masses} kg")
print(f"Velocities: {velocities} m/s")
print(f"Kinetic energies: {kinetic_energy} J")