The official NumPy documentation lives here.
Below is a curated collection of external resources. To contribute, see the end of this page.
There’s a ton of information about NumPy out there. If you are new, we’d strongly recommend these:
You may also want to check out the Goodreads list on the subject of “Python+SciPy.” Most books there are about the “SciPy ecosystem,” which has NumPy at its core.
Try these advanced resources for a better understanding of NumPy concepts like advanced indexing, splitting, stacking, linear algebra, and more.
If NumPy has been significant in your research, and you would like to acknowledge the project in your academic publication, please see this citation information.
To add to this collection, submit a recommendation via a pull request. Say why your recommendation deserves mention on this page and also which audience would benefit most.