Introduction to NumPy

NumPy (Numerical Python) is the foundation of numerical computing in Python and is essential for machine learning. It provides powerful N-dimensional array objects and tools for working with these arrays efficiently.

Unlike Python lists, NumPy arrays are homogeneous (all elements have the same type) and are stored in contiguous memory, making operations much faster. This is crucial when working with large datasets in machine learning.

Why NumPy for Machine Learning?

NumPy is the backbone of most ML libraries because:

Speed: Operations are implemented in C, making them much faster than Python loops
Memory Efficiency: Arrays use less memory than Python lists
Vectorization: Perform operations on entire arrays without explicit loops
Integration: Works seamlessly with pandas, scikit-learn, and other ML libraries

Creating NumPy Arrays

The most common way to create arrays is using np.array(). You can create arrays from Python lists:

creating_arrays.py
# Create arrays from Python lists
import numpy as np

# 1D array (vector)
arr1d = np.array([1, 2, 3, 4, 5])
print("1D Array:", arr1d)
print("Shape:", arr1d.shape)  # (5,)
print("Dimensions:", arr1d.ndim)  # 1

# 2D array (matrix)
arr2d = np.array([[1, 2, 3], [4, 5, 6]])
print("\n2D Array:")
print(arr2d)
print("Shape:", arr2d.shape)  # (2, 3)
print("Dimensions:", arr2d.ndim)  # 2

# 3D array
arr3d = np.array([[[1, 2], [3, 4]], [[5, 6], [7, 8]]])
print("\n3D Array shape:", arr3d.shape)  # (2, 2, 2)

Array Operations

NumPy allows you to perform mathematical operations on entire arrays efficiently:

array_operations.py
# Element-wise operations
import numpy as np

arr = np.array([1, 2, 3, 4, 5])

# Arithmetic operations
print("Original:", arr)
print("Multiply by 2:", arr * 2)
print("Add 10:", arr + 10)
print("Square:", arr ** 2)

# Operations between arrays
arr1 = np.array([1, 2, 3])
arr2 = np.array([4, 5, 6])
print("\nArray 1:", arr1)
print("Array 2:", arr2)
print("Sum:", arr1 + arr2)
print("Product:", arr1 * arr2)
print("Dot product:", np.dot(arr1, arr2))

Useful Array Functions

NumPy provides many useful functions for array manipulation:

array_functions.py
# Common NumPy functions
import numpy as np

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

print("Array:", arr)
print("Sum:", np.sum(arr))
print("Mean:", np.mean(arr))
print("Max:", np.max(arr))
print("Min:", np.min(arr))
print("Standard deviation:", np.std(arr))

# Reshaping arrays
arr = np.array([1, 2, 3, 4, 5, 6])
print("\nOriginal shape:", arr.shape)
reshaped = arr.reshape(2, 3)
print("Reshaped to (2, 3):")
print(reshaped)

Array Indexing and Slicing

NumPy arrays support powerful indexing and slicing operations:

indexing.py
# Array indexing and slicing
import numpy as np

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

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

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

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

Creating Arrays with Built-in Functions

NumPy provides convenient functions to create arrays with specific patterns:

array_creation.py
# Creating arrays with NumPy functions
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)

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

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

# Random array
random_arr = np.random.rand(3, 3)  # Random values between 0 and 1
print("\nRandom array:")
print(random_arr)

💡 Key Takeaway

NumPy arrays are the foundation of machine learning in Python. Most ML libraries (pandas, scikit-learn, TensorFlow) use NumPy arrays internally. Mastering NumPy will make learning these libraries much easier!

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