import numpy as np

# Create your first NumPy array
numbers = np.array([1, 2, 3, 4, 5])
print("Your first NumPy array:")
print(numbers)
print(f"Type: {type(numbers)}")

# See the power - multiply entire array at once!
doubled = numbers * 2
print(f"Doubled: {doubled}")

# Mathematical operations are blazing fast
large_array = np.arange(1000000)  # 1 million numbers
result = np.sum(large_array)      # Sum them instantly
print(f"Sum of 1 million numbers: {result}")

import numpy as np
import time

# Python list approach
python_list = list(range(100000))
start_time = time.time()
python_result = [x * 2 for x in python_list]
python_time = time.time() - start_time

print(f"Python list time: {python_time:.4f} seconds")

# NumPy array approach
numpy_array = np.arange(100000)
start_time = time.time()
numpy_result = numpy_array * 2
numpy_time = time.time() - start_time

print(f"NumPy array time: {numpy_time:.4f} seconds")
print(f"NumPy is {python_time/numpy_time:.1f}x faster!")

import numpy as np

# Create arrays in different ways
simple_array = np.array([1, 2, 3, 4, 5])
zeros_array = np.zeros(5)
ones_array = np.ones(3)
range_array = np.arange(10)

print("Simple array:", simple_array)
print("Zeros array:", zeros_array)
print("Ones array:", ones_array)
print("Range array:", range_array)
print()

# 2D arrays (matrices)
matrix = np.array([[1, 2, 3], [4, 5, 6]])
print("2D array (matrix):")
print(matrix)
print(f"Shape: {matrix.shape}")
print()

# Mathematical operations
numbers = np.array([1, 4, 9, 16, 25])
square_roots = np.sqrt(numbers)
print(f"Numbers: {numbers}")
print(f"Square roots: {square_roots}")

import numpy as np

# Financial data analysis
stock_prices = np.array([100, 102, 98, 105, 103, 107, 104])
daily_returns = (stock_prices[1:] - stock_prices[:-1]) / stock_prices[:-1]
print("Stock prices:", stock_prices)
print("Daily returns:", daily_returns.round(3))
print(f"Average return: {np.mean(daily_returns):.3f}")
print()

# Scientific computation
angles = np.linspace(0, 2*np.pi, 5)  # 5 angles from 0 to 2π
sin_values = np.sin(angles)
cos_values = np.cos(angles)
print("Angles:", angles.round(2))
print("Sin values:", sin_values.round(2))
print("Cos values:", cos_values.round(2))
print()

# Data processing
sensor_data = np.array([23.1, 24.5, 22.8, 25.2, 23.9, 24.1, 23.7])
print("Sensor readings:", sensor_data)
print(f"Average: {np.mean(sensor_data):.1f}")
print(f"Standard deviation: {np.std(sensor_data):.1f}")
print(f"Max reading: {np.max(sensor_data)}")
print(f"Min reading: {np.min(sensor_data)}")