.. _c-api: ########### Numpy C-API ########### .. sectionauthor:: Travis E. Oliphant | Beware of the man who won't be bothered with details. | --- *William Feather, Sr.* | The truth is out there. | --- *Chris Carter, The X Files* NumPy provides a C-API to enable users to extend the system and get access to the array object for use in other routines. The best way to truly understand the C-API is to read the source code. If you are unfamiliar with (C) source code, however, this can be a daunting experience at first. Be assured that the task becomes easier with practice, and you may be surprised at how simple the C-code can be to understand. Even if you don't think you can write C-code from scratch, it is much easier to understand and modify already-written source code then create it *de novo*. Python extensions are especially straightforward to understand because they all have a very similar structure. Admittedly, NumPy is not a trivial extension to Python, and may take a little more snooping to grasp. This is especially true because of the code-generation techniques, which simplify maintenance of very similar code, but can make the code a little less readable to beginners. Still, with a little persistence, the code can be opened to your understanding. It is my hope, that this guide to the C-API can assist in the process of becoming familiar with the compiled-level work that can be done with NumPy in order to squeeze that last bit of necessary speed out of your code. .. currentmodule:: numpy-c-api .. toctree:: :maxdepth: 2 c-api.types-and-structures c-api.config c-api.dtype c-api.array c-api.ufunc c-api.coremath