Numerical Python (NumPy)

What is NumPy?

NumPy (Numerical Python) is the foundational scientific computing library for Python. At its core it provides the ndarray — an N-dimensional array data structure that stores homogeneous numerical data in a single contiguous block of memory. Unlike Python lists, which store pointers to objects scattered across the heap, NumPy arrays pack raw numerical values side-by-side, letting the CPU process many elements per clock cycle through hardware-level vectorization.

Put simply: NumPy lets you do math on entire arrays at once, without writing loops — and does it in C under the hood, making it 10–100× faster than pure Python.

Why Not Just Use Lists?

Python lists are flexible, but slow for math because they store pointers to objects. NumPy arrays store data in contiguous C-level memory blocks.

The Vectorization Difference

# Python List approach (SLOW - loops in Python)
squares = [x**2 for x in range(1000000)]

# NumPy approach (FAST - loops in C)
import numpy as np
arr = np.arange(1000000)
squares = arr**2  # Vectorized!

NumPy operations are:

• 10x to 100x faster than pure Python
• More readable (no loops)
• Memory efficient

Creating Arrays

np.array([1, 2, 3])          # From list
np.zeros((3, 4))             # 3x4 matrix of 0s
np.ones(5)                   # 1D array of 1s
np.arange(0, 1, 0.1)         # Like range() but with floats
np.linspace(0, 10, 50)       # 50 evenly spaced points