NumPy C-API#
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 than 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.
- Python types and C-structures
- System configuration
- Data type API
- Array API
- Array structure and data access
- Creating arrays
- Dealing with types
- Array flags
- ArrayMethod API
- API for calling array methods
- Functions
- Auxiliary data with object semantics
- Array iterators
- Broadcasting (multi-iterators)
- Neighborhood iterator
- Array scalars
- Data-type descriptors
- Data Type Promotion and Inspection
- Custom Data Types
- Conversion utilities
- Including and importing the C API
- Array iterator API
- ufunc API
- Generalized universal function API
- NpyString API
- NumPy core math library
- Datetime API
- C API deprecations
- Memory management in NumPy