Introduction to NumPy

What is NumPy?

NumPy (Numerical Python) is the most important library for numerical computing in Python.

It provides:

• A fast, memory-efficient array object: ndarray
• Vectorized operations (operate on whole arrays without Python loops)
• Linear algebra, randomness, statistics, and more

NumPy is the foundation for many data tools:

• Pandas
• SciPy
• Scikit-learn
• Matplotlib

Why NumPy is fast

Python lists are flexible, but they’re not optimized for heavy numeric work.

NumPy arrays are fast because:

• They store data in contiguous memory blocks
• They have a fixed data type (dtype)
• Many operations run in optimized C code under the hood

diagram Why NumPy beats a Python loop mermaid

  flowchart LR
  A["Python list
(scattered objects)"] --> B["for loop in Python"]
  B --> C["Slow: type-checked
every single element"]
  D["NumPy ndarray
(contiguous memory, fixed dtype)"] --> E["Vectorized C loop"]
  E --> F["Fast: no per-element overhead"]

Contiguous memory plus a fixed dtype let NumPy hand the whole array to a C loop instead of looping element-by-element in Python.

Installing NumPy

With pip

command

pip install numpy

With conda

command

conda install numpy

Importing NumPy

The standard import alias is np:

import

import numpy as np

Check version:

version

import numpy as np
print(np.__version__)

NumPy arrays vs Python lists

Python list example

list

a = [1, 2, 3]
b = [4, 5, 6]
 
# This concatenates lists (not element-wise addition)
print(a + b)  # [1, 2, 3, 4, 5, 6]

NumPy array example

array

import numpy as np
 
a = np.array([1, 2, 3])
b = np.array([4, 5, 6])
 
# Element-wise addition
print(a + b)  # [5 7 9]

From the book

Wes McKinney benchmarks this directly in Python for Data Analysis: multiplying a NumPy array of one million integers by 2 takes about 715 microseconds, while the same operation on a plain Python list (with a list comprehension) takes about 49 milliseconds — NumPy is roughly 10 to 100x faster for large numeric workloads, and it uses less memory too.

Key concepts

1) ndarray

A NumPy array is called an n-dimensional array (ndarray). It can represent:

• 1D data (vector)
• 2D data (matrix)
• 3D+ data (tensors)

2) shape

The shape tells you the number of rows/columns (dimensions):

shape

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

3) dtype

dtype is the element type (int, float, etc.). Every value in an array shares the same dtype — that uniformity is exactly what makes vectorized math possible:

dtype

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

A tiny analytics-style example

Imagine you have daily sales and want quick math:

sales

import numpy as np
 
sales = np.array([100, 120, 90, 150, 130])
 
print("Total:", sales.sum())
print("Average:", sales.mean())
print("Max:", sales.max())
print("Min:", sales.min())

Common mistakes

Mistake 1: Mixing numbers and strings

NumPy will try to choose a single dtype. If you mix types, it may convert everything to strings.

Mistake 2: Using Python loops for large data

NumPy is built for vectorization. Prefer array operations over for loops.

Next

Continue to: NumPy Array Creation to learn all the ways to build arrays (from lists, zeros/ones, ranges, random data, and more).

🧪 Try It Yourself

Exercise 1 – Create a NumPy Array

pch.exerciseTag pch.exerciseTitle

Exercise 2 – Lists Concatenate, Arrays Add

pch.exerciseTag pch.exerciseTitle

Exercise 3 – Quick Descriptive Stats

pch.exerciseTag pch.exerciseTitle