Introduction to NumPy

How this NumPy track works

Python playground — lessons use execution_profile: server_script. Every snippet uses import numpy as np; install NumPy locally with pip install numpy if the runner lacks it.
Arrays first — creation, dtypes, shape, indexing, ufuncs, broadcasting, linear algebra, and performance—before Pandas labeled tables.
Prerequisites — finish Python basics and skim Data Science workflow. Statistics intuition from that track helps.
Pair with — Pandas for DataFrames, SciPy for scientific routines, and AI for ML context.

Run each lesson in the playground, tweak array values, and use MCQs to lock in ndarray vocabulary.

Install on your device (macOS, Linux, Windows)

Install Python 3.11+ locally for notebooks and frameworks; the on-site playground uses the dev runner when enabled.

macOS

brew install python@3.12 or install from python.org (check “Add to PATH” on installers).
Create a project folder: mkdir ~/python-practice && cd ~/python-practice.
python3 -m venv .venv && source .venv/bin/activate
pip install --upgrade pip

Linux

Debian/Ubuntu: sudo apt update && sudo apt install -y python3 python3-pip python3-venv
Fedora: sudo dnf install -y python3 python3-pip
python3 -m venv .venv && source .venv/bin/activate
pip install --upgrade pip

Windows

Install from python.org and enable Add python.exe to PATH.
Or: winget install Python.Python.3.12
PowerShell: py -3 -m venv .venv; .\.venv\Scripts\Activate.ps1
pip install --upgrade pip

Verify: python3 --version (or py --version on Windows) shows 3.11+.

Run code on this site (Backend & language playgrounds)

Clone or open this project locally; copy .env.example to .env.
Ensure LEARNING_RUNNER_ENABLED=true and LEARNING_RUNNER_URL=http://127.0.0.1:9999/v1/execute.
Terminal 1: php artisan serve (or composer run dev for Laravel + Vite + runner together).
Terminal 2: npm run runner — keep it running while you click Run on server.

In your venv: pip install numpy

NumPy provides the ndarray—a fast, homogeneous n-dimensional array—and vectorized operations that power Pandas, SciPy, and most Python ML stacks. This track teaches array thinking after Python basics and alongside Data Science workflow concepts.

Prerequisites and how this track works

Complete Python basics (lists, loops, functions) and skim Data Science intro for workflow context. Lessons run Python with execution_profile: server_script; NumPy is pre-installed in the playground—no pip install needed here.

What you will learn

Creating, shaping, and indexing ndarray objects
Dtypes, broadcasting, ufuncs, and axis-aware aggregations
Linear algebra, fancy indexing, and memory views
NaN handling, persistence (.npy), and vectorization habits
How NumPy connects to Pandas, Matplotlib, SciPy, and ML libraries

First run

import numpy as np
a = np.array([1, 2, 3, 4, 5])
print('NumPy version:', np.__version__)
print('array:', a)
print('shape:', a.shape, 'dtype:', a.dtype)

Why not plain Python lists?

Lists hold arbitrary objects and loop in Python bytecode. NumPy stores contiguous typed buffers and delegates math to optimized C—often 10–100× faster on large numeric data.

Important interview questions and answers

Q: Is NumPy required for data science?
A: Not strictly—but Pandas, scikit-learn, and most scientific Python assume NumPy arrays under the hood.
Q: What is an ndarray?
A: An n-dimensional homogeneous array: fixed dtype, shape tuple, and strided memory layout.

Self-check

Which two tracks should you finish before deep NumPy work?
What does ndarray stand for?

Challenge

First NumPy run in this track

Click Run with the default code.
Confirm the terminal shows array shape and dtype.
Change one element in the sample array and run again.

Done when: the terminal shows ndarray output and your edited value.

Tip: Run the playground challenge—every lesson assumes import numpy as np works.

Interview prep

Prerequisite?: Python basics (/python/intro) and data science workflow (/data-science/intro).
Core type?: ndarray—homogeneous n-dimensional array with shape and dtype.
Next track?: Pandas (/pandas/intro) for labeled tables; SciPy (/scipy/intro) for scientific algorithms.