NumPy Tutorial for Beginners

A hands-on introduction to NumPy, the library that pretty much all numerical and data work in Python is built on. Each topic is a short Jupyter notebook with small examples you can run, change and break.

Everything has been updated for NumPy 2.x.

Contents

#	Notebook	What's in it
0	Data Types	dtypes, type codes, conversion, overflow, promotion, strings
1	Create an Array	array, arange, linspace, zeros/ones/full, eye, meshgrid
2	Inspect an Array	shape, size, memory use, strides
3	Sampling Methods	the Generator API, distributions, choice, shuffling, legacy API
4	Math Functions	what axis means, arithmetic, rounding, statistics, comparisons
5	Slicing & Indexing	slicing, boolean masks, fancy indexing, where, select
6	Sort an Array	sort, argsort, lexsort, partition, searchsorted
7	Manipulate an Array	reshape, transpose, flatten, adding/removing axes and elements
8	Combine & Split an Array	concatenate, stack, block, split, array_split
9	Set Operations	unique, intersection/union/difference, isin
10	Linear Algebra	@, norms, solve, lstsq, eigenvalues, QR, SVD
11	Broadcasting	the rules, practical uses, and common traps
12	Views and Copies	when changing one array changes another
13	Vectorization & Ufuncs	why loops are slow and how to get rid of them
14	Missing Values	NaN, inf, nan* functions, filling, masked arrays
15	Saving & Loading Arrays	.npy/.npz, memory mapping, CSV
16	Exercises	17 practice problems with solutions

If you'd rather read it all in one go, Complete Tutorial.ipynb has chapters 0 to 15 in a single notebook.

Suggested order

If you're completely new to NumPy, go through 0 to 5 in order, then read 11 (Broadcasting) and 12 (Views and Copies) before the rest. Those two explain most of the confusing behaviour you'll run into later. After that, pick whatever you need.

Running the notebooks

git clone https://github.com/eric496/numpy-tutorial.git
cd numpy-tutorial
pip install -r requirements.txt
jupyter lab

You can also open any notebook directly in Google Colab via File → Open notebook → GitHub and pasting the repo URL.

Most examples use a seeded random generator (np.random.default_rng(seed)), so you should see the same numbers as the saved outputs. The exception is the timing examples in notebook 13, which depend on your machine.

Upgrading from older NumPy?

The notebooks used to target NumPy 1.x. If you're following along with older code, these are the changes you're most likely to hit:

• np.string_ / np.unicode_ are gone, use np.bytes_ / np.str_
• np.in1d is gone, use np.isin
• np.random.seed + np.random.rand still work, but np.random.default_rng() is the recommended way
• Python scalars no longer upcast arrays (float32_array + 1.0 stays float32)

The NumPy 2.0 migration guide has the full list.

Contributing

Found a mistake or something that's unclear? Issues and pull requests are welcome.

If you edit a notebook, please re-run it top to bottom so the saved outputs match, and then regenerate the combined notebook:

python tools/build_complete.py

Further reading

• NumPy user guide
• NumPy: the absolute basics for beginners
• From Python to NumPy by Nicolas Rougier, a great next step once you're comfortable with the basics

License

MIT