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Deep Reads › CPython Internals › 7. C API and Extensions ›

71. Calling C From Python

71. Calling C From Python Calling C from Python means exposing native functions, objects, or libraries so Python code can invoke them as if they were ordinary Python APIs. This is the most common direction for native integration: Python code calls wrapper wrapper calls C function C returns native result wrapper converts result to Python object CPython supports several ways to do this: Mechanism Main use C extension module Fast,...
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Deep Reads › CPython Internals › 7. C API and Extensions ›

68. Limited API

68. Limited API The limited API is a restricted source-level subset of the CPython C API. It is selected at compile time and designed to let extension modules target the stable ABI. The stable ABI is the binary promise. The limited API is the programming surface used to reach that promise. extension source code ↓ #define Py_LIMITED_API ↓ limited API declarations ↓ stable ABI compatible binary ↓ abi3 wheel The...
cpython python internals c-api
Deep Reads › CPython Internals › 7. C API and Extensions ›

67. Stable ABI

67. Stable ABI The stable ABI is CPython’s binary compatibility interface for extension modules. It lets an extension module be compiled once and loaded by multiple CPython versions, as long as those versions support the requested ABI level. The normal CPython C API exposes many implementation details. An extension compiled against those details often needs a separate binary wheel for each Python version. The stable ABI reduces that burden by...
cpython python internals c-api
Deep Reads › CPython Internals › 7. C API and Extensions ›

66. Capsules

66. Capsules A capsule is a CPython object that stores an opaque C pointer. It allows extension modules to expose native pointers to other extension modules without making those pointers directly visible to normal Python code. Capsules are used when C code needs to share a function table, context pointer, type registry, library handle, allocator, or native resource across module boundaries. At Python level, a capsule usually appears as an...
cpython python internals c-api
Deep Reads › CPython Internals › 7. C API and Extensions ›

62. Reference Ownership Rules

62. Reference Ownership Rules Reference ownership is the core memory discipline of the Python C API. Every PyObject * points to a Python object whose lifetime is controlled by a reference count. Native extension code must follow ownership rules exactly. A single missing Py_DECREF can leak memory. A single extra Py_DECREF can free a live object and corrupt the interpreter. 62.1 Why Ownership Rules Exist CPython stores a reference count...
cpython python internals c-api memory
Deep Reads › CPython Internals › 7. C API and Extensions ›

69. Embedding Python

69. Embedding Python Embedding Python means starting and controlling the CPython runtime from a native application. Instead of Python importing a C extension, a C or C++ program initializes Python, executes Python code, exchanges objects with it, then shuts the interpreter down. The direction is reversed: extension module: Python process imports native code embedding: native process starts Python runtime Embedding is used when Python is the scripting layer inside a...
cpython python internals c-api
Deep Reads › CPython Internals › 10. Advanced Topics ›

96. Stable ABI Design Tradeoffs

96. Stable ABI Design Tradeoffs The CPython Stable ABI is a compatibility layer that allows extension modules compiled against one Python version to continue working across multiple later Python versions without recompilation. The Stable ABI exists to reduce binary compatibility problems in the Python extension ecosystem. Without a stable ABI: extension compiled for Python 3.x may fail on Python 3.y because internal structures, function layouts, macros, or object representations change....
cpython python internals c-api
Deep Reads › CPython Internals › 7. C API and Extensions ›

63. Creating Extension Modules

63. Creating Extension Modules An extension module is a native shared library loaded by CPython at runtime. It exposes functions, types, constants, and state implemented in C or C-compatible languages. From Python code, an extension module behaves like a normal module: import math import zlib import _sqlite3 Internally, these modules are compiled native binaries that integrate with the CPython runtime through the Python C API. Extension modules are one of...
cpython python internals c-api
Deep Reads › CPython Internals › 7. C API and Extensions ›

65. Buffer Protocol

65. Buffer Protocol The buffer protocol is CPython’s low-level interface for sharing raw memory between Python objects without copying. It allows one object to expose a contiguous or strided block of memory, and another object to read or write that memory through a common C structure. The protocol is used by objects such as: Object Buffer use bytes Read-only contiguous byte storage bytearray Mutable contiguous byte storage memoryview General Python-level...
cpython python internals c-api
Deep Reads › CPython Internals › 7. C API and Extensions ›

70. Calling Python From C

70. Calling Python From C Calling Python from C means using the CPython C API to invoke Python objects from native code. The callable may be a function, method, class, instance with __call__ , bound method, coroutine function, or any other object accepted by Python’s call protocol. This operation appears in both extension and embedding code. extension module: Python calls C C calls Python callback embedded application: C application starts...
cpython python internals c-api
Deep Reads › CPython Internals › 7. C API and Extensions ›

61. The Python C API

61. The Python C API The Python C API is the native programming interface exposed by CPython. It allows C programs to create Python objects, execute Python code, define extension modules, implement new object types, call Python functions, manipulate interpreter state, and embed the Python runtime inside larger applications. The C API is one of the central architectural features of CPython. It defines the boundary between the interpreter runtime and...
cpython python internals c-api
Deep Reads › CPython Internals › 7. C API and Extensions ›

64. Defining New Types

64. Defining New Types A CPython extension module can expose new Python types implemented in C. These types behave like normal Python classes from user code, but their memory layout, allocation, methods, attribute access, numeric operations, sequence operations, and deallocation logic are controlled by C structures and function pointers. A built-in type such as list , dict , int , or str is implemented this way. Extension modules can use...
cpython python internals c-api