Introduction

Introduction¶
The Application Programmer’s Interface to Python gives C and C++ programmers access to the Python interpreter at a variety of levels. The API is equally usable from C++, but for brevity it is generally referred to as the Python/C API. There are two fundamentally different reasons for using the Python/C API. The first reason is to write extension modules for specific purposes; these are C modules that extend the Python interpreter. This is probably the most common use. The second reason is to use Python as a component in a larger application; this technique is generally referred to as embedding Python in an application.
Writing an extension module is a relatively well-understood process, where a “cookbook” approach works well. There are several tools that automate the process to some extent. While people have embedded Python in other applications since its early existence, the process of embedding Python is less straightforward than writing an extension.
Many API functions are useful independent of whether you’re embedding or extending Python; moreover, most applications that embed Python will need to provide a custom extension as well, so it’s probably a good idea to become familiar with writing an extension before attempting to embed Python in a real application.
Language version compatibility¶
Python’s C API is compatible with C11 and C++11 versions of C and C++.
This is a lower limit: the C API does not require features from later C/C++ versions. You do not need to enable your compiler’s “c11 mode”.
Coding standards¶
If you’re writing C code for inclusion in CPython, you must follow the guidelines and standards defined in PEP 7. These guidelines apply regardless of the version of Python you are contributing to. Following these conventions is not necessary for your own third party extension modules, unless you eventually expect to contribute them to Python.
Include Files¶
All function, type and macro definitions needed to use the Python/C API are included in your code by the following line:
#define PY_SSIZE_T_CLEAN
#include <Python.h>
This implies inclusion of the following standard headers: <stdio.h>
,
<string.h>
, <errno.h>
, <limits.h>
, <assert.h>
and <stdlib.h>
(if available).
Note
Since Python may define some pre-processor definitions which affect the standard
headers on some systems, you must include Python.h
before any standard
headers are included.
It is recommended to always define PY_SSIZE_T_CLEAN
before including
Python.h
. See Parsing arguments and building values for a description of this macro.
All user visible names defined by Python.h (except those defined by the included
standard headers) have one of the prefixes Py
or _Py
. Names beginning
with _Py
are for internal use by the Python implementation and should not be
used by extension writers. Structure member names do not have a reserved prefix.
Note
User code should never define names that begin with Py
or _Py
. This
confuses the reader, and jeopardizes the portability of the user code to
future Python versions, which may define additional names beginning with one
of these prefixes.
The header files are typically installed with Python. On Unix, these are
located in the directories prefix/include/pythonversion/
and
exec_prefix/include/pythonversion/
, where prefix
and
exec_prefix
are defined by the corresponding parameters to Python’s
configure script and version is
'%d.%d' % sys.version_info[:2]
. On Windows, the headers are installed
in prefix/include
, where prefix
is the installation
directory specified to the installer.
To include the headers, place both directories (if different) on your compiler’s
search path for includes. Do not place the parent directories on the search
path and then use #include <pythonX.Y/Python.h>
; this will break on
multi-platform builds since the platform independent headers under
prefix
include the platform specific headers from
exec_prefix
.
C++ users should note that although the API is defined entirely using C, the
header files properly declare the entry points to be extern "C"
. As a result,
there is no need to do anything special to use the API from C++.
Useful macros¶
Several useful macros are defined in the Python header files. Many are
defined closer to where they are useful (for example, Py_RETURN_NONE
,
PyMODINIT_FUNC
).
Others of a more general utility are defined here. This is not necessarily a
complete listing.
-
Py_CAN_START_THREADS¶
If this macro is defined, then the current system is able to start threads.
Currently, all systems supported by CPython (per PEP 11), with the exception of some WebAssembly platforms, support starting threads.
Added in version 3.13.
-
Py_GETENV(s)¶
Like
getenv(s)
, but returnsNULL
if-E
was passed on the command line (seePyConfig.use_environment
).
Docstring macros¶
-
PyDoc_STRVAR(name, str)¶
Creates a variable with name name that can be used in docstrings. If Python is built without docstrings (
--without-doc-strings
), the value will be an empty string.Example:
PyDoc_STRVAR(pop_doc, "Remove and return the rightmost element."); static PyMethodDef deque_methods[] = { // ... {"pop", (PyCFunction)deque_pop, METH_NOARGS, pop_doc}, // ... }
Expands to
PyDoc_VAR(name) = PyDoc_STR(str)
.
-
PyDoc_STR(str)¶
Expands to the given input string, or an empty string if docstrings are disabled (
--without-doc-strings
).Example:
static PyMethodDef pysqlite_row_methods[] = { {"keys", (PyCFunction)pysqlite_row_keys, METH_NOARGS, PyDoc_STR("Returns the keys of the row.")}, {NULL, NULL} };
-
PyDoc_VAR(name)¶
Declares a static character array variable with the given name. Expands to
static const char name[]
For example:
PyDoc_VAR(python_doc) = PyDoc_STR( "A genus of constricting snakes in the Pythonidae family native " "to the tropics and subtropics of the Eastern Hemisphere.");
General utility macros¶
The following macros are for common tasks not specific to Python.
-
Py_UNUSED(arg)¶
Use this for unused arguments in a function definition to silence compiler warnings. Example:
int func(int a, int Py_UNUSED(b)) { return a; }
.Added in version 3.4.
-
Py_GCC_ATTRIBUTE(name)¶
Use a GCC attribute name, hiding it from compilers that don’t support GCC attributes (such as MSVC).
This expands to
__attribute__((name))
on a GCC compiler, and expands to nothing on compilers that don’t support GCC attributes.
Numeric utilities¶
-
Py_ABS(x)¶
Return the absolute value of
x
.The argument may be evaluated more than once. Consequently, do not pass an expression with side-effects directly to this macro.
If the result cannot be represented (for example, if
x
hasINT_MIN
value for int type), the behavior is undefined.Corresponds roughly to
((x) < 0 ? -(x) : (x))
Added in version 3.3.
-
Py_MAX(x, y)¶
-
Py_MIN(x, y)¶
Return the larger or smaller of the arguments, respectively.
Any arguments may be evaluated more than once. Consequently, do not pass an expression with side-effects directly to this macro.
Py_MAX
corresponds roughly to(((x) > (y)) ? (x) : (y))
.Added in version 3.3.
-
Py_ARITHMETIC_RIGHT_SHIFT(type, integer, positions)¶
Similar to
integer >> positions
, but forces sign extension, as the C standard does not define whether a right-shift of a signed integer will perform sign extension or a zero-fill.integer should be any signed integer type. positions is the number of positions to shift to the right.
Both integer and positions can be evaluated more than once; consequently, avoid directly passing a function call or some other operation with side-effects to this macro. Instead, store the result as a variable and then pass it.
type is unused and only kept for backwards compatibility. Historically, type was used to cast integer.
Changed in version 3.1: This macro is now valid for all signed integer types, not just those for which
unsigned type
is legal. As a result, type is no longer used.
-
Py_CHARMASK(c)¶
Argument must be a character or an integer in the range [-128, 127] or [0, 255]. This macro returns
c
cast to anunsigned char
.
Assertion utilities¶
-
Py_UNREACHABLE()¶
Use this when you have a code path that cannot be reached by design. For example, in the
default:
clause in aswitch
statement for which all possible values are covered incase
statements. Use this in places where you might be tempted to put anassert(0)
orabort()
call.In release mode, the macro helps the compiler to optimize the code, and avoids a warning about unreachable code. For example, the macro is implemented with
__builtin_unreachable()
on GCC in release mode.In debug mode, and on unsupported compilers, the macro expands to a call to
Py_FatalError()
.A use for
Py_UNREACHABLE()
is following a call to a function that never returns but that is not declared_Noreturn
.If a code path is very unlikely code but can be reached under exceptional case, this macro must not be used. For example, under low memory condition or if a system call returns a value out of the expected range. In this case, it’s better to report the error to the caller. If the error cannot be reported to caller,
Py_FatalError()
can be used.Added in version 3.7.
-
Py_SAFE_DOWNCAST(value, larger, smaller)¶
Cast value to type smaller from type larger, validating that no information was lost.
On release builds of Python, this is roughly equivalent to
((smaller) value)
(in C++,static_cast<smaller>(value)
will be used instead).On debug builds (implying that
Py_DEBUG
is defined), this asserts that no information was lost with the cast from larger to smaller.value, larger, and smaller may all be evaluated more than once in the expression; consequently, do not pass an expression with side-effects directly to this macro.
-
Py_BUILD_ASSERT(cond)¶
Asserts a compile-time condition cond, as a statement. The build will fail if the condition is false or cannot be evaluated at compile time.
Corresponds roughly to
static_assert(cond)
on C23 and above.For example:
Py_BUILD_ASSERT(sizeof(PyTime_t) == sizeof(int64_t));
Added in version 3.3.
-
Py_BUILD_ASSERT_EXPR(cond)¶
Asserts a compile-time condition cond, as an expression that evaluates to
0
. The build will fail if the condition is false or cannot be evaluated at compile time.For example:
#define foo_to_char(foo) \ ((char *)(foo) + Py_BUILD_ASSERT_EXPR(offsetof(struct foo, string) == 0))
Added in version 3.3.
Type size utilities¶
-
Py_ARRAY_LENGTH(array)¶
Compute the length of a statically allocated C array at compile time.
The array argument must be a C array with a size known at compile time. Passing an array with an unknown size, such as a heap-allocated array, will result in a compilation error on some compilers, or otherwise produce incorrect results.
This is roughly equivalent to:
sizeof(array) / sizeof((array)[0])
-
Py_MEMBER_SIZE(type, member)¶
Return the size of a structure (type) member in bytes.
Corresponds roughly to
sizeof(((type *)NULL)->member)
.Added in version 3.6.
Macro definition utilities¶
-
Py_FORCE_EXPANSION(X)¶
This is equivalent to
X
, which is useful for token-pasting in macros, as macro expansions in X are forcefully evaluated by the preprocessor.
-
Py_STRINGIFY(x)¶
Convert
x
to a C string. For example,Py_STRINGIFY(123)
returns"123"
.Added in version 3.4.
Declaration utilities¶
The following macros can be used in declarations. They are most useful for defining the C API itself, and have limited use for extension authors. Most of them expand to compiler-specific spellings of common extensions to the C language.
-
Py_ALWAYS_INLINE¶
Ask the compiler to always inline a static inline function. The compiler can ignore it and decide to not inline the function.
Corresponds to
always_inline
attribute in GCC and__forceinline
in MSVC.It can be used to inline performance critical static inline functions when building Python in debug mode with function inlining disabled. For example, MSC disables function inlining when building in debug mode.
Marking blindly a static inline function with Py_ALWAYS_INLINE can result in worse performances (due to increased code size for example). The compiler is usually smarter than the developer for the cost/benefit analysis.
If Python is built in debug mode (if the
Py_DEBUG
macro is defined), thePy_ALWAYS_INLINE
macro does nothing.It must be specified before the function return type. Usage:
static inline Py_ALWAYS_INLINE int random(void) { return 4; }
Added in version 3.11.
-
Py_NO_INLINE¶
Disable inlining on a function. For example, it reduces the C stack consumption: useful on LTO+PGO builds which heavily inline code (see bpo-33720).
Corresponds to the
noinline
attribute/specification on GCC and MSVC.Usage:
Py_NO_INLINE static int random(void) { return 4; }
Added in version 3.11.
-
Py_DEPRECATED(version)¶
Use this to declare APIs that were deprecated in a specific CPython version. The macro must be placed before the symbol name.
Example:
Py_DEPRECATED(3.8) PyAPI_FUNC(int) Py_OldFunction(void);
Changed in version 3.8: MSVC support was added.
-
Py_LOCAL(type)¶
Declare a function returning the specified type using a fast-calling qualifier for functions that are local to the current file. Semantically, this is equivalent to
static type
.
-
Py_LOCAL_SYMBOL¶
Macro used to declare a symbol as local to the shared library (hidden). On supported platforms, it ensures the symbol is not exported.
On compatible versions of GCC/Clang, it expands to
__attribute__((visibility("hidden")))
.
-
Py_EXPORTED_SYMBOL¶
Macro used to declare a symbol (function or data) as exported. On Windows, this expands to
__declspec(dllexport)
. On compatible versions of GCC/Clang, it expands to__attribute__((visibility("default")))
. This macro is for defining the C API itself; extension modules should not use it.
-
Py_IMPORTED_SYMBOL¶
Macro used to declare a symbol as imported. On Windows, this expands to
__declspec(dllimport)
. This macro is for defining the C API itself; extension modules should not use it.
-
PyAPI_FUNC(type)¶
Macro used by CPython to declare a function as part of the C API. Its expansion depends on the platform and build configuration. This macro is intended for defining CPython’s C API itself; extension modules should not use it for their own symbols.
-
PyAPI_DATA(type)¶
Macro used by CPython to declare a public global variable as part of the C API. Its expansion depends on the platform and build configuration. This macro is intended for defining CPython’s C API itself; extension modules should not use it for their own symbols.
Outdated macros¶
The following macros have been used to features that have been standardized in C11.
-
Py_ALIGNED(num)¶
Specify alignment to num bytes on compilers that support it.
Consider using the C11 standard
_Alignas
specifier over this macro.
-
Py_LL(number)¶
-
Py_ULL(number)¶
Use number as a
long long
orunsigned long long
integer literal, respectively.Expands to number followed by
LL
orLLU
, respectively, but will expand to some compiler-specific suffixes on some older compilers.Consider using the C99 standard suffixes
LL
andLLU
directly.
-
Py_MEMCPY(dest, src, n)¶
This is a soft deprecated alias to
memcpy()
. Usememcpy()
directly instead.Deprecated since version 3.14: The macro is soft deprecated.
-
Py_VA_COPY¶
This is a soft deprecated alias to the C99-standard
va_copy
function.Historically, this would use a compiler-specific method to copy a
va_list
.Changed in version 3.6: This is now an alias to
va_copy
.
Objects, Types and Reference Counts¶
Most Python/C API functions have one or more arguments as well as a return value
of type PyObject*. This type is a pointer to an opaque data type
representing an arbitrary Python object. Since all Python object types are
treated the same way by the Python language in most situations (e.g.,
assignments, scope rules, and argument passing), it is only fitting that they
should be represented by a single C type. Almost all Python objects live on the
heap: you never declare an automatic or static variable of type
PyObject
, only pointer variables of type PyObject* can be
declared. The sole exception are the type objects; since these must never be
deallocated, they are typically static PyTypeObject
objects.
All Python objects (even Python integers) have a type and a
reference count. An object’s type determines what kind of object it is
(e.g., an integer, a list, or a user-defined function; there are many more as
explained in The standard type hierarchy). For each of the well-known types there is a macro
to check whether an object is of that type; for instance, PyList_Check(a)
is
true if (and only if) the object pointed to by a is a Python list.
Reference Counts¶
The reference count is important because today’s computers have a finite (and often severely limited) memory size; it counts how many different places there are that have a strong reference to an object. Such a place could be another object, or a global (or static) C variable, or a local variable in some C function. When the last strong reference to an object is released (i.e. its reference count becomes zero), the object is deallocated. If it contains references to other objects, those references are released. Those other objects may be deallocated in turn, if there are no more references to them, and so on. (There’s an obvious problem with objects that reference each other here; for now, the solution is “don’t do that.”)
Reference counts are always manipulated explicitly. The normal way is
to use the macro Py_INCREF()
to take a new reference to an
object (i.e. increment its reference count by one),
and Py_DECREF()
to release that reference (i.e. decrement the
reference count by one). The Py_DECREF()
macro
is considerably more complex than the incref one, since it must check whether
the reference count becomes zero and then cause the object’s deallocator to be
called. The deallocator is a function pointer contained in the object’s type
structure. The type-specific deallocator takes care of releasing references
for other objects contained in the object if this is a compound
object type, such as a list, as well as performing any additional finalization
that’s needed. There’s no chance that the reference count can overflow; at
least as many bits are used to hold the reference count as there are distinct
memory locations in virtual memory (assuming sizeof(Py_ssize_t) >= sizeof(void*)
).
Thus, the reference count increment is a simple operation.
It is not necessary to hold a strong reference (i.e. increment the reference count) for every local variable that contains a pointer to an object. In theory, the object’s reference count goes up by one when the variable is made to point to it and it goes down by one when the variable goes out of scope. However, these two cancel each other out, so at the end the reference count hasn’t changed. The only real reason to use the reference count is to prevent the object from being deallocated as long as our variable is pointing to it. If we know that there is at least one other reference to the object that lives at least as long as our variable, there is no need to take a new strong reference (i.e. increment the reference count) temporarily. An important situation where this arises is in objects that are passed as arguments to C functions in an extension module that are called from Python; the call mechanism guarantees to hold a reference to every argument for the duration of the call.
However, a common pitfall is to extract an object from a list and hold on to it
for a while without taking a new reference. Some other operation might
conceivably remove the object from the list, releasing that reference,
and possibly deallocating it. The real danger is that innocent-looking
operations may invoke arbitrary Python code which could do this; there is a code
path which allows control to flow back to the user from a Py_DECREF()
, so
almost any operation is potentially dangerous.
A safe approach is to always use the generic operations (functions whose name
begins with PyObject_
, PyNumber_
, PySequence_
or PyMapping_
).
These operations always create a new strong reference
(i.e. increment the reference count) of the object they return.
This leaves the caller with the responsibility to call Py_DECREF()
when
they are done with the result; this soon becomes second nature.
Reference Count Details¶
The reference count behavior of functions in the Python/C API is best explained
in terms of ownership of references. Ownership pertains to references, never
to objects (objects are not owned: they are always shared). “Owning a
reference” means being responsible for calling Py_DECREF on it when the
reference is no longer needed. Ownership can also be transferred, meaning that
the code that receives ownership of the reference then becomes responsible for
eventually releasing it by calling Py_DECREF()
or Py_XDECREF()
when it’s no longer needed—or passing on this responsibility (usually to its
caller). When a function passes ownership of a reference on to its caller, the
caller is said to receive a new reference. When no ownership is transferred,
the caller is said to borrow the reference. Nothing needs to be done for a
borrowed reference.
Conversely, when a calling function passes in a reference to an object, there are two possibilities: the function steals a reference to the object, or it does not. Stealing a reference means that when you pass a reference to a function, that function assumes that it now owns that reference, and you are not responsible for it any longer.
Few functions steal references; the two notable exceptions are
PyList_SetItem()
and PyTuple_SetItem()
, which steal a reference
to the item (but not to the tuple or list into which the item is put!). These
functions were designed to steal a reference because of a common idiom for
populating a tuple or list with newly created objects; for example, the code to
create the tuple (1, 2, "three")
could look like this (forgetting about
error handling for the moment; a better way to code this is shown below):
PyObject *t;
t = PyTuple_New(3);
PyTuple_SetItem(t, 0, PyLong_FromLong(1L));
PyTuple_SetItem(t, 1, PyLong_FromLong(2L));
PyTuple_SetItem(t, 2, PyUnicode_FromString("three"));
Here, PyLong_FromLong()
returns a new reference which is immediately
stolen by PyTuple_SetItem()
. When you want to keep using an object
although the reference to it will be stolen, use Py_INCREF()
to grab
another reference before calling the reference-stealing function.
Incidentally, PyTuple_SetItem()
is the only way to set tuple items;
PySequence_SetItem()
and PyObject_SetItem()
refuse to do this
since tuples are an immutable data type. You should only use
PyTuple_SetItem()
for tuples that you are creating yourself.
Equivalent code for populating a list can be written using PyList_New()
and PyList_SetItem()
.
However, in practice, you will rarely use these ways of creating and populating
a tuple or list. There’s a generic function, Py_BuildValue()
, that can
create most common objects from C values, directed by a format string.
For example, the above two blocks of code could be replaced by the following
(which also takes care of the error checking):
PyObject *tuple, *list;
tuple = Py_BuildValue("(iis)", 1, 2, "three");
list = Py_BuildValue("[iis]", 1, 2, "three");
It is much more common to use PyObject_SetItem()
and friends with items
whose references you are only borrowing, like arguments that were passed in to
the function you are writing. In that case, their behaviour regarding references
is much saner, since you don’t have to take a new reference just so you
can give that reference away (“have it be stolen”). For example, this function
sets all items of a list (actually, any mutable sequence) to a given item:
int
set_all(PyObject *target, PyObject *item)
{
Py_ssize_t i, n;
n = PyObject_Length(target);
if (n < 0)
return -1;
for (i = 0; i < n; i++) {
PyObject *index = PyLong_FromSsize_t(i);
if (!index)
return -1;
if (PyObject_SetItem(target, index, item) < 0) {
Py_DECREF(index);
return -1;
}
Py_DECREF(index);
}
return 0;
}
The situation is slightly different for function return values. While passing
a reference to most functions does not change your ownership responsibilities
for that reference, many functions that return a reference to an object give
you ownership of the reference. The reason is simple: in many cases, the
returned object is created on the fly, and the reference you get is the only
reference to the object. Therefore, the generic functions that return object
references, like PyObject_GetItem()
and PySequence_GetItem()
,
always return a new reference (the caller becomes the owner of the reference).
It is important to realize that whether you own a reference returned by a
function depends on which function you call only — the plumage (the type of
the object passed as an argument to the function) doesn’t enter into it!
Thus, if you extract an item from a list using PyList_GetItem()
, you
don’t own the reference — but if you obtain the same item from the same list
using PySequence_GetItem()
(which happens to take exactly the same
arguments), you do own a reference to the returned object.
Here is an example of how you could write a function that computes the sum of
the items in a list of integers; once using PyList_GetItem()
, and once
using PySequence_GetItem()
.
long
sum_list(PyObject *list)
{
Py_ssize_t i, n;
long total = 0, value;
PyObject *item;
n = PyList_Size(list);
if (n < 0)
return -1; /* Not a list */
for (i = 0; i < n; i++) {
item = PyList_GetItem(list, i); /* Can't fail */
if (!PyLong_Check(item)) continue; /* Skip non-integers */
value = PyLong_AsLong(item);
if (value == -1 && PyErr_Occurred())
/* Integer too big to fit in a C long, bail out */
return -1;
total += value;
}
return total;
}
long
sum_sequence(PyObject *sequence)
{
Py_ssize_t i, n;
long total = 0, value;
PyObject *item;
n = PySequence_Length(sequence);
if (n < 0)
return -1; /* Has no length */
for (i = 0; i < n; i++) {
item = PySequence_GetItem(sequence, i);
if (item == NULL)
return -1; /* Not a sequence, or other failure */
if (PyLong_Check(item)) {
value = PyLong_AsLong(item);
Py_DECREF(item);
if (value == -1 && PyErr_Occurred())
/* Integer too big to fit in a C long, bail out */
return -1;
total += value;
}
else {
Py_DECREF(item); /* Discard reference ownership */
}
}
return total;
}
Types¶
There are few other data types that play a significant role in the Python/C API; most are simple C types such as int, long, double and char*. A few structure types are used to describe static tables used to list the functions exported by a module or the data attributes of a new object type, and another is used to describe the value of a complex number. These will be discussed together with the functions that use them.
-
type Py_ssize_t¶
- Part of the Stable ABI.
A signed integral type such that
sizeof(Py_ssize_t) == sizeof(size_t)
. C99 doesn’t define such a thing directly (size_t is an unsigned integral type). See PEP 353 for details.PY_SSIZE_T_MAX
is the largest positive value of typePy_ssize_t
.
Exceptions¶
The Python programmer only needs to deal with exceptions if specific error handling is required; unhandled exceptions are automatically propagated to the caller, then to the caller’s caller, and so on, until they reach the top-level interpreter, where they are reported to the user accompanied by a stack traceback.
For C programmers, however, error checking always has to be explicit. All
functions in the Python/C API can raise exceptions, unless an explicit claim is
made otherwise in a function’s documentation. In general, when a function
encounters an error, it sets an exception, discards any object references that
it owns, and returns an error indicator. If not documented otherwise, this
indicator is either NULL
or -1
, depending on the function’s return type.
A few functions return a Boolean true/false result, with false indicating an
error. Very few functions return no explicit error indicator or have an
ambiguous return value, and require explicit testing for errors with
PyErr_Occurred()
. These exceptions are always explicitly documented.
Exception state is maintained in per-thread storage (this is equivalent to
using global storage in an unthreaded application). A thread can be in one of
two states: an exception has occurred, or not. The function
PyErr_Occurred()
can be used to check for this: it returns a borrowed
reference to the exception type object when an exception has occurred, and
NULL
otherwise. There are a number of functions to set the exception state:
PyErr_SetString()
is the most common (though not the most general)
function to set the exception state, and PyErr_Clear()
clears the
exception state.
The full exception state consists of three objects (all of which can be
NULL
): the exception type, the corresponding exception value, and the
traceback. These have the same meanings as the Python result of
sys.exc_info()
; however, they are not the same: the Python objects represent
the last exception being handled by a Python try
…
except
statement, while the C level exception state only exists while
an exception is being passed on between C functions until it reaches the Python
bytecode interpreter’s main loop, which takes care of transferring it to
sys.exc_info()
and friends.
Note that starting with Python 1.5, the preferred, thread-safe way to access the
exception state from Python code is to call the function sys.exc_info()
,
which returns the per-thread exception state for Python code. Also, the
semantics of both ways to access the exception state have changed so that a
function which catches an exception will save and restore its thread’s exception
state so as to preserve the exception state of its caller. This prevents common
bugs in exception handling code caused by an innocent-looking function
overwriting the exception being handled; it also reduces the often unwanted
lifetime extension for objects that are referenced by the stack frames in the
traceback.
As a general principle, a function that calls another function to perform some task should check whether the called function raised an exception, and if so, pass the exception state on to its caller. It should discard any object references that it owns, and return an error indicator, but it should not set another exception — that would overwrite the exception that was just raised, and lose important information about the exact cause of the error.
A simple example of detecting exceptions and passing them on is shown in the
sum_sequence()
example above. It so happens that this example doesn’t
need to clean up any owned references when it detects an error. The following
example function shows some error cleanup. First, to remind you why you like
Python, we show the equivalent Python code:
def incr_item(dict, key):
try:
item = dict[key]
except KeyError:
item = 0
dict[key] = item + 1
Here is the corresponding C code, in all its glory:
int
incr_item(PyObject *dict, PyObject *key)
{
/* Objects all initialized to NULL for Py_XDECREF */
PyObject *item = NULL, *const_one = NULL, *incremented_item = NULL;
int rv = -1; /* Return value initialized to -1 (failure) */
item = PyObject_GetItem(dict, key);
if (item == NULL) {
/* Handle KeyError only: */
if (!PyErr_ExceptionMatches(PyExc_KeyError))
goto error;
/* Clear the error and use zero: */
PyErr_Clear();
item = PyLong_FromLong(0L);
if (item == NULL)
goto error;
}
const_one = PyLong_FromLong(1L);
if (const_one == NULL)
goto error;
incremented_item = PyNumber_Add(item, const_one);
if (incremented_item == NULL)
goto error;
if (PyObject_SetItem(dict, key, incremented_item) < 0)
goto error;
rv = 0; /* Success */
/* Continue with cleanup code */
error:
/* Cleanup code, shared by success and failure path */
/* Use Py_XDECREF() to ignore NULL references */
Py_XDECREF(item);
Py_XDECREF(const_one);
Py_XDECREF(incremented_item);
return rv; /* -1 for error, 0 for success */
}
This example represents an endorsed use of the goto
statement in C!
It illustrates the use of PyErr_ExceptionMatches()
and
PyErr_Clear()
to handle specific exceptions, and the use of
Py_XDECREF()
to dispose of owned references that may be NULL
(note the
'X'
in the name; Py_DECREF()
would crash when confronted with a
NULL
reference). It is important that the variables used to hold owned
references are initialized to NULL
for this to work; likewise, the proposed
return value is initialized to -1
(failure) and only set to success after
the final call made is successful.
Embedding Python¶
The one important task that only embedders (as opposed to extension writers) of the Python interpreter have to worry about is the initialization, and possibly the finalization, of the Python interpreter. Most functionality of the interpreter can only be used after the interpreter has been initialized.
The basic initialization function is Py_Initialize()
. This initializes
the table of loaded modules, and creates the fundamental modules
builtins
, __main__
, and sys
. It also
initializes the module search path (sys.path
).
Py_Initialize()
does not set the “script argument list” (sys.argv
).
If this variable is needed by Python code that will be executed later, setting
PyConfig.argv
and PyConfig.parse_argv
must be set: see
Python Initialization Configuration.
On most systems (in particular, on Unix and Windows, although the details are
slightly different), Py_Initialize()
calculates the module search path
based upon its best guess for the location of the standard Python interpreter
executable, assuming that the Python library is found in a fixed location
relative to the Python interpreter executable. In particular, it looks for a
directory named lib/pythonX.Y
relative to the parent directory
where the executable named python
is found on the shell command search
path (the environment variable PATH
).
For instance, if the Python executable is found in
/usr/local/bin/python
, it will assume that the libraries are in
/usr/local/lib/pythonX.Y
. (In fact, this particular path is also
the “fallback” location, used when no executable file named python
is
found along PATH
.) The user can override this behavior by setting the
environment variable PYTHONHOME
, or insert additional directories in
front of the standard path by setting PYTHONPATH
.
The embedding application can steer the search by setting
PyConfig.program_name
before calling
Py_InitializeFromConfig()
. Note that
PYTHONHOME
still overrides this and PYTHONPATH
is still
inserted in front of the standard path. An application that requires total
control has to provide its own implementation of Py_GetPath()
,
Py_GetPrefix()
, Py_GetExecPrefix()
, and
Py_GetProgramFullPath()
(all defined in Modules/getpath.c
).
Sometimes, it is desirable to “uninitialize” Python. For instance, the
application may want to start over (make another call to
Py_Initialize()
) or the application is simply done with its use of
Python and wants to free memory allocated by Python. This can be accomplished
by calling Py_FinalizeEx()
. The function Py_IsInitialized()
returns
true if Python is currently in the initialized state. More information about
these functions is given in a later chapter. Notice that Py_FinalizeEx()
does not free all memory allocated by the Python interpreter, e.g. memory
allocated by extension modules currently cannot be released.
Debugging Builds¶
Python can be built with several macros to enable extra checks of the interpreter and extension modules. These checks tend to add a large amount of overhead to the runtime so they are not enabled by default.
A full list of the various types of debugging builds is in the file
Misc/SpecialBuilds.txt
in the Python source distribution. Builds are
available that support tracing of reference counts, debugging the memory
allocator, or low-level profiling of the main interpreter loop. Only the most
frequently used builds will be described in the remainder of this section.
-
Py_DEBUG¶
Compiling the interpreter with the Py_DEBUG
macro defined produces
what is generally meant by a debug build of Python.
Py_DEBUG
is enabled in the Unix build by adding
--with-pydebug
to the ./configure
command.
It is also implied by the presence of the
not-Python-specific _DEBUG
macro. When Py_DEBUG
is enabled
in the Unix build, compiler optimization is disabled.
In addition to the reference count debugging described below, extra checks are performed, see Python Debug Build.
Defining Py_TRACE_REFS
enables reference tracing
(see the configure --with-trace-refs option
).
When defined, a circular doubly linked list of active objects is maintained by adding two extra
fields to every PyObject
. Total allocations are tracked as well. Upon
exit, all existing references are printed. (In interactive mode this happens
after every statement run by the interpreter.)
Please refer to Misc/SpecialBuilds.txt
in the Python source distribution
for more detailed information.
Recommended third party tools¶
The following third party tools offer both simpler and more sophisticated approaches to creating C, C++ and Rust extensions for Python:
Using tools such as these can help avoid writing code that is tightly bound to a particular version of CPython, avoid reference counting errors, and focus more on your own code than on using the CPython API. In general, new versions of Python can be supported by updating the tool, and your code will often use newer and more efficient APIs automatically. Some tools also support compiling for other implementations of Python from a single set of sources.
These projects are not supported by the same people who maintain Python, and issues need to be raised with the projects directly. Remember to check that the project is still maintained and supported, as the list above may become outdated.
See also
- Python Packaging User Guide: Binary Extensions
The Python Packaging User Guide not only covers several available tools that simplify the creation of binary extensions, but also discusses the various reasons why creating an extension module may be desirable in the first place.


— The

fcntl
— The fcntl
and ioctl
system calls¶
This module performs file and I/O control on file descriptors. It is an
interface to the fcntl()
and ioctl()
Unix routines.
See the fcntl(2) and ioctl(2) Unix manual pages
for full details.
Availability: Unix, not WASI.
All functions in this module take a file descriptor fd as their first
argument. This can be an integer file descriptor, such as returned by
sys.stdin.fileno()
, or an io.IOBase
object, such as sys.stdin
itself, which provides a fileno()
that returns a genuine file
descriptor.
Changed in version 3.3: Operations in this module used to raise an IOError
where they now
raise an OSError
.
Changed in version 3.8: The fcntl
module now contains F_ADD_SEALS
, F_GET_SEALS
, and
F_SEAL_*
constants for sealing of os.memfd_create()
file
descriptors.
Changed in version 3.9: On macOS, the fcntl
module exposes the F_GETPATH
constant,
which obtains the path of a file from a file descriptor.
On Linux(>=3.15), the fcntl
module exposes the F_OFD_GETLK
,
F_OFD_SETLK
and F_OFD_SETLKW
constants, which are used when working
with open file description locks.
Changed in version 3.10: On Linux >= 2.6.11, the fcntl
module exposes the F_GETPIPE_SZ
and
F_SETPIPE_SZ
constants, which allow to check and modify a pipe’s size
respectively.
Changed in version 3.11: On FreeBSD, the fcntl
module exposes the F_DUP2FD
and
F_DUP2FD_CLOEXEC
constants, which allow to duplicate a file descriptor,
the latter setting FD_CLOEXEC
flag in addition.
Changed in version 3.12: On Linux >= 4.5, the fcntl
module exposes the FICLONE
and
FICLONERANGE
constants, which allow to share some data of one file with
another file by reflinking on some filesystems (e.g., btrfs, OCFS2, and
XFS). This behavior is commonly referred to as “copy-on-write”.
Changed in version 3.13: On Linux >= 2.6.32, the fcntl
module exposes the
F_GETOWN_EX
, F_SETOWN_EX
, F_OWNER_TID
, F_OWNER_PID
, F_OWNER_PGRP
constants, which allow to direct I/O availability signals
to a specific thread, process, or process group.
On Linux >= 4.13, the fcntl
module exposes the
F_GET_RW_HINT
, F_SET_RW_HINT
, F_GET_FILE_RW_HINT
,
F_SET_FILE_RW_HINT
, and RWH_WRITE_LIFE_*
constants, which allow
to inform the kernel about the relative expected lifetime of writes on
a given inode or via a particular open file description.
On Linux >= 5.1 and NetBSD, the fcntl
module exposes the
F_SEAL_FUTURE_WRITE
constant for use with F_ADD_SEALS
and
F_GET_SEALS
operations.
On FreeBSD, the fcntl
module exposes the F_READAHEAD
, F_ISUNIONSTACK
, and F_KINFO
constants.
On macOS and FreeBSD, the fcntl
module exposes the F_RDAHEAD
constant.
On NetBSD and AIX, the fcntl
module exposes the F_CLOSEM
constant.
On NetBSD, the fcntl
module exposes the F_MAXFD
constant.
On macOS and NetBSD, the fcntl
module exposes the F_GETNOSIGPIPE
and F_SETNOSIGPIPE
constant.
Changed in version 3.14: On Linux >= 6.1, the fcntl
module exposes the F_DUPFD_QUERY
to query a file descriptor pointing to the same file.
The module defines the following functions:
- fcntl.fcntl(fd, cmd, arg=0, /)¶
Perform the operation cmd on file descriptor fd (file objects providing a
fileno()
method are accepted as well). The values used for cmd are operating system dependent, and are available as constants in thefcntl
module, using the same names as used in the relevant C header files. The argument arg can either be an integer value, a bytes-like object, or a string. The type and size of arg must match the type and size of the argument of the operation as specified in the relevant C documentation.When arg is an integer, the function returns the integer return value of the C
fcntl()
call.When the argument is bytes-like object, it represents a binary structure, for example, created by
struct.pack()
. A string value is encoded to binary using the UTF-8 encoding. The binary data is copied to a buffer whose address is passed to the Cfcntl()
call. The return value after a successful call is the contents of the buffer, converted to abytes
object. The length of the returned object will be the same as the length of the arg argument. This is limited to 1024 bytes.If the
fcntl()
call fails, anOSError
is raised.Note
If the type or the size of arg does not match the type or size of the argument of the operation (for example, if an integer is passed when a pointer is expected, or the information returned in the buffer by the operating system is larger than 1024 bytes), this is most likely to result in a segmentation violation or a more subtle data corruption.
Raises an auditing event
fcntl.fcntl
with argumentsfd
,cmd
,arg
.Changed in version 3.14: Add support of arbitrary bytes-like objects, not only
bytes
.
- fcntl.ioctl(fd, request, arg=0, mutate_flag=True, /)¶
This function is identical to the
fcntl()
function, except that the argument handling is even more complicated.The request parameter is limited to values that can fit in 32-bits or 64-bits, depending on the platform. Additional constants of interest for use as the request argument can be found in the
termios
module, under the same names as used in the relevant C header files.The parameter arg can be an integer, a bytes-like object, or a string. The type and size of arg must match the type and size of the argument of the operation as specified in the relevant C documentation.
If arg does not support the read-write buffer interface or the mutate_flag is false, behavior is as for the
fcntl()
function.If arg supports the read-write buffer interface (like
bytearray
) and mutate_flag is true (the default), then the buffer is (in effect) passed to the underlyingioctl()
system call, the latter’s return code is passed back to the calling Python, and the buffer’s new contents reflect the action of theioctl()
. This is a slight simplification, because if the supplied buffer is less than 1024 bytes long it is first copied into a static buffer 1024 bytes long which is then passed toioctl()
and copied back into the supplied buffer.If the
ioctl()
call fails, anOSError
exception is raised.Note
If the type or size of arg does not match the type or size of the operation’s argument (for example, if an integer is passed when a pointer is expected, or the information returned in the buffer by the operating system is larger than 1024 bytes, or the size of the mutable bytes-like object is too small), this is most likely to result in a segmentation violation or a more subtle data corruption.
An example:
>>> import array, fcntl, struct, termios, os >>> os.getpgrp() 13341 >>> struct.unpack('h', fcntl.ioctl(0, termios.TIOCGPGRP, " "))[0] 13341 >>> buf = array.array('h', [0]) >>> fcntl.ioctl(0, termios.TIOCGPGRP, buf, 1) 0 >>> buf array('h', [13341])
Raises an auditing event
fcntl.ioctl
with argumentsfd
,request
,arg
.Changed in version 3.14: The GIL is always released during a system call. System calls failing with EINTR are automatically retried.
- fcntl.flock(fd, operation, /)¶
Perform the lock operation operation on file descriptor fd (file objects providing a
fileno()
method are accepted as well). See the Unix manual flock(2) for details. (On some systems, this function is emulated usingfcntl()
.)If the
flock()
call fails, anOSError
exception is raised.Raises an auditing event
fcntl.flock
with argumentsfd
,operation
.
- fcntl.lockf(fd, cmd, len=0, start=0, whence=0, /)¶
This is essentially a wrapper around the
fcntl()
locking calls. fd is the file descriptor (file objects providing afileno()
method are accepted as well) of the file to lock or unlock, and cmd is one of the following values:- fcntl.LOCK_UN¶
Release an existing lock.
- fcntl.LOCK_SH¶
Acquire a shared lock.
- fcntl.LOCK_EX¶
Acquire an exclusive lock.
- fcntl.LOCK_NB¶
Bitwise OR with any of the other three
LOCK_*
constants to make the request non-blocking.
If
LOCK_NB
is used and the lock cannot be acquired, anOSError
will be raised and the exception will have an errno attribute set toEACCES
orEAGAIN
(depending on the operating system; for portability, check for both values). On at least some systems,LOCK_EX
can only be used if the file descriptor refers to a file opened for writing.len is the number of bytes to lock, start is the byte offset at which the lock starts, relative to whence, and whence is as with
io.IOBase.seek()
, specifically:0
– relative to the start of the file (os.SEEK_SET
)1
– relative to the current buffer position (os.SEEK_CUR
)2
– relative to the end of the file (os.SEEK_END
)
The default for start is 0, which means to start at the beginning of the file. The default for len is 0 which means to lock to the end of the file. The default for whence is also 0.
Raises an auditing event
fcntl.lockf
with argumentsfd
,cmd
,len
,start
,whence
.
Examples (all on a SVR4 compliant system):
import struct, fcntl, os
f = open(...)
rv = fcntl.fcntl(f, fcntl.F_SETFL, os.O_NDELAY)
lockdata = struct.pack('hhllhh', fcntl.F_WRLCK, 0, 0, 0, 0, 0)
rv = fcntl.fcntl(f, fcntl.F_SETLKW, lockdata)
Note that in the first example the return value variable rv will hold an
integer value; in the second example it will hold a bytes
object. The
structure lay-out for the lockdata variable is system dependent — therefore
using the flock()
call may be better.


— SMTP Server

smtpd
— SMTP Server¶
Deprecated since version 3.6, removed in version 3.12.
This module is no longer part of the Python standard library. It was removed in Python 3.12 after being deprecated in Python 3.6. The removal was decided in PEP 594.
A possible replacement is the third-party aiosmtpd library. This library is not maintained or supported by the Python core team.
The last version of Python that provided the smtpd
module was
Python 3.11.


Utilities

Utilities¶
The functions in this chapter perform various utility tasks, ranging from helping C code be more portable across platforms, using Python modules from C, and parsing function arguments and constructing Python values from C values.
- Operating System Utilities
- System Functions
- Process Control
- Importing Modules
- Data marshalling support
- Parsing arguments and building values
- String conversion and formatting
- Character classification and conversion
- PyHash API
- Reflection
- Codec registry and support functions
- PyTime C API
- Support for Perf Maps