NumPy Fundamentals

NumPy (Numerical Python) is the foundational library for numerical computing in Python. It provides a powerful N-dimensional array object, broadcasting functions, and tools for integrating C/C++ and Fortran code. Nearly every data science library in Python — Pandas, Matplotlib, Scikit-Learn, TensorFlow — is built on top of NumPy.

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Why NumPy?

Python lists are flexible but slow for numerical computation. NumPy arrays are:

Feature	Python List	NumPy Array
Speed	Slow (interpreted loops)	Fast (compiled C code)
Memory	High overhead per element	Compact, contiguous memory
Operations	Element-by-element loops required	Vectorised operations
Broadcasting	Not supported	Automatic shape matching
Type	Mixed types allowed	Homogeneous type

A NumPy operation on a million elements can be 100x faster than the equivalent Python loop.

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Creating Arrays

From Python Lists

import numpy as np

# 1D array
a = np.array([1, 2, 3, 4, 5])
print(a)         # [1 2 3 4 5]
print(type(a))   # <class 'numpy.ndarray'>