Introduction to NumPy: Arrays and Array Operations

NumPy (Numerical Python) is the foundational library for numerical computing in Python. It introduces the ndarray — a fast, memory-efficient array structure that performs mathematical operations on entire collections of numbers at once, instead of looping through them one by one as you would with a plain Python list.

Installing and Importing NumPy

# Install once from the terminal:
# pip install numpy

import numpy as np   # "np" is the universal, near-mandatory alias

Creating a NumPy Array

import numpy as np

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

Why Use NumPy Instead of a Python List?

Vectorized Operations — NumPy's Biggest Advantage

prices = np.array([100, 200, 300, 400])

# Apply a discount to EVERY element at once — no loop required
discounted = prices - 20
print(discounted)   # [ 80 180 280 380]

# Compare this to a plain list, which would require:
prices_list = [100, 200, 300, 400]
discounted_list = [p - 20 for p in prices_list]   # needs a loop or comprehension

Common Ways to Create Arrays

zeros = np.zeros(5)              # [0. 0. 0. 0. 0.]
ones = np.ones(4)                 # [1. 1. 1. 1.]
sequence = np.arange(0, 10, 2)    # [0 2 4 6 8] — like range(), but returns an array
even_split = np.linspace(0, 1, 5) # [0.   0.25 0.5  0.75 1.  ] — 5 evenly spaced values

Array Attributes

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

print(arr.shape)   # (2, 3) — 2 rows, 3 columns
print(arr.ndim)    # 2      — number of dimensions
print(arr.size)    # 6      — total number of elements
print(arr.dtype)   # int64  — the data type of every element

Element-Wise Arithmetic

a = np.array([1, 2, 3])
b = np.array([10, 20, 30])

print(a + b)   # [11 22 33]
print(a * b)   # [10 40 90]
print(b / a)   # [10. 10. 10.]
print(a ** 2)  # [1 4 9]

Every arithmetic operator works element-by-element automatically across the whole array — this is called vectorization, and it is the single biggest reason NumPy is so much faster than equivalent Python list code for numerical work.

Useful Aggregate Functions

scores = np.array([85, 90, 78, 92, 88])
print(np.mean(scores))   # 86.6
print(np.max(scores))    # 92

Interview tip: "Why is NumPy faster than a regular Python list for numerical operations?" is asked constantly — the core answer is that NumPy arrays store data in contiguous memory blocks of a single fixed type, and operations are implemented in pre-compiled C code, avoiding the overhead of Python's per-element type-checking that a list-based loop would incur.