import numpy as np

# Regular Python list
python_list = [1, 2, 3, 4, 5]
print("Python list:", python_list)
print("Type:", type(python_list))

# NumPy array
numpy_array = np.array([1, 2, 3, 4, 5])
print("NumPy array:", numpy_array)
print("Type:", type(numpy_array))

# The magic - operations on entire arrays!
result = numpy_array * 2
print("Multiplied by 2:", result)

import numpy as np

# Example: NumPy array that could be used anywhere in the ecosystem
data = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
print("Sample NumPy array:")
print(data)
print(f"Shape: {data.shape}")
print(f"Data type: {data.dtype}")

import numpy as np

# Demonstrate the performance difference concept
small_list = [1, 2, 3, 4, 5]
small_array = np.array([1, 2, 3, 4, 5])

print("Python list approach:")
python_result = [x + 10 for x in small_list]
print(f"Result: {python_result}")

print("\nNumPy array approach:")
numpy_result = small_array + 10
print(f"Result: {numpy_result}")

print("\n✅ Same results, but NumPy scales much better for large data!")

import numpy as np

# 1D array (vector) - like a list of numbers
vector = np.array([1, 2, 3, 4, 5])
print("1D array (vector):")
print(vector)
print(f"Shape: {vector.shape}")
print()

# 2D array (matrix) - like a table
matrix = np.array([[1, 2, 3], [4, 5, 6]])
print("2D array (matrix):")
print(matrix)
print(f"Shape: {matrix.shape}")  # (rows, columns)
print()

# 3D array (tensor) - like stacked matrices
tensor = np.array([[[1, 2], [3, 4]], [[5, 6], [7, 8]]])
print("3D array (tensor):")
print(tensor)
print(f"Shape: {tensor.shape}")  # (depth, rows, columns)

import numpy as np

# Mathematical operations
numbers = np.array([1, 4, 9, 16, 25])
print("Original numbers:", numbers)
print("Square roots:", np.sqrt(numbers))
print("Logarithms:", np.log(numbers))
print("Sine values:", np.sin(numbers))
print()

# Statistical operations
data = np.array([23, 45, 56, 78, 32, 67, 89, 12])
print("Data:", data)
print(f"Mean: {np.mean(data):.1f}")
print(f"Standard deviation: {np.std(data):.1f}")
print(f"Maximum: {np.max(data)}")
print(f"Minimum: {np.min(data)}")
print()

# Array operations
arr1 = np.array([1, 2, 3])
arr2 = np.array([4, 5, 6])
print("Array 1:", arr1)
print("Array 2:", arr2)
print("Addition:", arr1 + arr2)
print("Multiplication:", arr1 * arr2)
print("Dot product:", np.dot(arr1, arr2))

import numpy as np

# Key concepts demonstration
arr = np.array([[10, 20, 30], [40, 50, 60]])

print("Sample array:")
print(arr)
print()

# Shape - dimensions of the array
print(f"Shape: {arr.shape}")  # (2, 3) means 2 rows, 3 columns

# Size - total number of elements
print(f"Size: {arr.size}")   # 6 total elements

# Data type - type of elements
print(f"Data type: {arr.dtype}")  # int32 or int64

# Number of dimensions
print(f"Dimensions: {arr.ndim}")  # 2D array

# Memory usage
print(f"Memory usage: {arr.nbytes} bytes")