Lesson 28: Introduction to NumPy for Numerical Computing

What Is NumPy?

NumPy (Numerical Python) is the foundation of scientific computing in Python. It provides fast, memory‑efficient arrays and mathematical functions optimized in C. Libraries like pandas, SciPy, scikit‑learn, and many others rely on NumPy.

Installing NumPy

pip install numpy

Creating Arrays

NumPy arrays are more efficient than Python lists.

import numpy as np

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

Array Types and Shapes

print(arr.dtype)   # data type
print(arr.shape)   # dimensions

Creating Special Arrays

np.zeros(5)
np.ones((2, 3))
np.arange(0, 10, 2)
np.linspace(0, 1, 5)

Reshaping Arrays

matrix = np.arange(9).reshape(3, 3)
print(matrix)

Element‑Wise Operations

arr = np.array([1, 2, 3])
print(arr * 2)
print(arr + 5)
print(arr ** 2)

Matrix Operations

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

print(a @ b)          # matrix multiplication
print(np.dot(a, b))   # same result

Statistical Functions

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

print(arr.mean())
print(arr.sum())
print(arr.std())

Indexing and Slicing

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

print(arr[0])      # first element
print(arr[1:4])    # slice

Boolean Masking

arr = np.array([1, 2, 3, 4, 5])
mask = arr > 3
print(arr[mask])   # [4 5]

Why NumPy Matters

• It is extremely fast for numerical operations.
• It supports vectorized operations that avoid Python loops.
• It is the backbone of data science and machine learning libraries.
• It handles matrices and linear algebra efficiently.

Next Steps

Now that you understand NumPy, you're ready to explore more advanced scientific tools in Lesson 29: Introduction to SciPy.

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