NumPy Arrays

Working with NumPy Arrays

NumPy arrays are the foundation of numerical computing in Python. Understanding how to create, manipulate, and operate on arrays is essential for machine learning. Arrays are more efficient than Python lists for mathematical operations.

In this lesson, we'll dive deeper into array operations, indexing, slicing, and mathematical functions that make NumPy powerful for ML.

Array Creation Methods

NumPy provides many ways to create arrays beyond just converting lists:

array_creation.py
import numpy as np

# Zeros array
zeros = np.zeros((3, 4))
print("Zeros array (3x4):")
print(zeros)

# Ones array
ones = np.ones((2, 3))
print("\nOnes array (2x3):")
print(ones)

# Identity matrix
identity = np.eye(3)
print("\nIdentity matrix (3x3):")
print(identity)

# Array with range
range_arr = np.arange(0, 10, 2)  # Start, stop, step
print("\nRange array:", range_arr)

# Evenly spaced values
linspace = np.linspace(0, 1, 5)  # Start, stop, num_points
print("Linspace array:", linspace)

Array Indexing and Slicing

NumPy arrays support powerful indexing operations similar to Python lists, but with additional capabilities:

indexing_slicing.py
import numpy as np

arr = np.array([10, 20, 30, 40, 50, 60, 70, 80])

# Basic indexing
print("First element:", arr[0])
print("Last element:", arr[-1])

# Slicing
print("First 3:", arr[:3])
print("Last 3:", arr[-3:])
print("Middle:", arr[2:5])
print("Every other:", arr[::2])

# 2D array indexing
matrix = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
print("\nMatrix:")
print(matrix)
print("Element [1,2]:", matrix[1, 2])
print("First row:", matrix[0, :])
print("Second column:", matrix[:, 1])

Array Operations

NumPy enables vectorized operations that work on entire arrays efficiently:

array_math.py
import numpy as np

arr1 = np.array([1, 2, 3, 4])
arr2 = np.array([5, 6, 7, 8])

# Element-wise operations
print("Array 1:", arr1)
print("Array 2:", arr2)
print("Addition:", arr1 + arr2)
print("Multiplication:", arr1 * arr2)
print("Power:", arr1 ** 2)

# Mathematical functions
print("\nMath functions:")
print("Sum:", np.sum(arr1))
print("Mean:", np.mean(arr1))
print("Max:", np.max(arr1))
print("Min:", np.min(arr1))

# Dot product
print("\nDot product:", np.dot(arr1, arr2))

Reshaping Arrays

You can change the shape of arrays without changing the data:

reshaping.py
import numpy as np

arr = np.array([1, 2, 3, 4, 5, 6])
print("Original shape:", arr.shape)
print("Array:", arr)

# Reshape to 2D
reshaped_2d = arr.reshape(2, 3)
print("\nReshaped to (2, 3):")
print(reshaped_2d)

# Reshape to 3D
reshaped_3d = arr.reshape(2, 1, 3)
print("\nReshaped to (2, 1, 3):")
print(reshaped_3d)

# Flatten back to 1D
flattened = reshaped_2d.flatten()
print("\nFlattened:", flattened)

Boolean Indexing

NumPy allows you to select elements based on conditions:

boolean_indexing.py
import numpy as np

arr = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10])

# Select elements greater than 5
greater_than_5 = arr[arr > 5]
print("Elements > 5:", greater_than_5)

# Select even numbers
evens = arr[arr % 2 == 0]
print("Even numbers:", evens)

# Multiple conditions
between = arr[(arr > 3) & (arr < 8)]
print("Between 3 and 8:", between)

# Modify selected elements
arr[arr > 7] = 99
print("After modification:", arr)

💡 Key Concept

NumPy arrays enable vectorized operations that are much faster than Python loops. Instead of iterating through elements, operations happen on entire arrays at once, making ML computations efficient!

🎉

Lesson Complete!

Great work! Continue to the next lesson.