Doc/c-api/intro.rst
.. highlight:: c
.. _api-intro:
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 :dfn: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.
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".
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.
.. _api-includes:
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 :file:Python.h before any standard
headers are included.
It is recommended to always define PY_SSIZE_T_CLEAN before including
Python.h. See :ref:arg-parsing 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 :file:{prefix}/include/pythonversion/ and
:file:{exec_prefix}/include/pythonversion/, where :option:prefix <--prefix> and
:option:exec_prefix <--exec-prefix> are defined by the corresponding parameters to Python's
:program:configure script and version is
'%d.%d' % sys.version_info[:2]. On Windows, the headers are installed
in :file:{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
:option:prefix <--prefix> include the platform specific headers from
:option:exec_prefix <--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++.
.. _capi-system-includes:
:file:Python.h includes several standard header files.
C extensions should include the standard headers that they use,
and should not rely on these implicit includes.
The implicit includes are:
<assert.h><intrin.h> (on Windows)<inttypes.h><limits.h><math.h><stdarg.h><string.h><wchar.h><sys/types.h> (if present)The following are included for backwards compatibility, unless using
:ref:Limited API <limited-c-api> 3.13 or newer:
<ctype.h><unistd.h> (on POSIX)The following are included for backwards compatibility, unless using
:ref:Limited API <limited-c-api> 3.11 or newer:
<errno.h><stdio.h><stdlib.h>.. note::
Since Python may define some pre-processor definitions which affect the standard
headers on some systems, you must include :file:Python.h before any standard
headers are included.
Several useful macros are defined in the Python header files. Many are
defined closer to where they are useful (for example, :c:macro:Py_RETURN_NONE,
:c:macro:PyMODINIT_FUNC).
Others of a more general utility are defined here. This is not necessarily a
complete listing.
.. c:macro:: 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.
.. versionadded:: 3.13
.. c:macro:: Py_GETENV(s)
Like :samp:getenv({s}), but returns NULL if :option:-E was passed
on the command line (see :c:member:PyConfig.use_environment).
.. c:macro:: PyDoc_STRVAR(name, str)
Creates a variable with name name that can be used in docstrings.
If Python is built without docstrings (:option:--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 :samp:PyDoc_VAR({name}) = PyDoc_STR({str}).
.. c:macro:: PyDoc_STR(str)
Expands to the given input string, or an empty string
if docstrings are disabled (:option:--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}
};
.. c:macro:: PyDoc_VAR(name)
Declares a static character array variable with the given name.
Expands to :samp: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.");
The following macros are for common tasks not specific to Python.
.. c:macro:: 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; }.
.. versionadded:: 3.4
.. c:macro:: 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 :samp:__attribute__(({name)}) on a GCC compiler,
and expands to nothing on compilers that don't support GCC attributes.
Numeric utilities ^^^^^^^^^^^^^^^^^
.. c:macro:: 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 has
:c:macro:!INT_MIN value for :c:expr:int type), the behavior is
undefined.
Corresponds roughly to :samp:(({x}) < 0 ? -({x}) : ({x}))
.. versionadded:: 3.3
.. c:macro:: 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.
:c:macro:!Py_MAX corresponds roughly to
:samp:((({x}) > ({y})) ? ({x}) : ({y})).
.. versionadded:: 3.3
.. c:macro:: Py_ARITHMETIC_RIGHT_SHIFT(type, integer, positions)
Similar to :samp:{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.
.. versionchanged:: 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.
.. c:macro:: 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 an unsigned char.
Assertion utilities ^^^^^^^^^^^^^^^^^^^
.. c:macro:: Py_UNREACHABLE()
Use this when you have a code path that cannot be reached by design.
For example, in the default: clause in a switch statement for which
all possible values are covered in case statements. Use this in places
where you might be tempted to put an assert(0) or abort() 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
:c:func: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, :c:func:Py_FatalError can be used.
.. versionadded:: 3.7
.. c:macro:: 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
:samp:(({smaller}) {value})
(in C++, :samp:static_cast<{smaller}>({value}) will be used instead).
On debug builds (implying that :c:macro: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.
.. c: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 :samp:static_assert({cond}) on C23 and above.
For example::
Py_BUILD_ASSERT(sizeof(PyTime_t) == sizeof(int64_t));
.. versionadded:: 3.3
.. c:macro:: 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))
.. versionadded:: 3.3
Type size utilities ^^^^^^^^^^^^^^^^^^^
.. c:macro:: 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])
.. c:macro:: Py_MEMBER_SIZE(type, member)
Return the size of a structure (type) member in bytes.
Corresponds roughly to :samp:sizeof((({type} *)NULL)->{member}).
.. versionadded:: 3.6
Macro definition utilities ^^^^^^^^^^^^^^^^^^^^^^^^^^
.. c:macro:: Py_FORCE_EXPANSION(X)
This is equivalent to :samp:{X}, which is useful for token-pasting in
macros, as macro expansions in X are forcefully evaluated by the
preprocessor.
.. c:macro:: Py_STRINGIFY(x)
Convert x to a C string. For example, Py_STRINGIFY(123) returns
"123".
.. versionadded:: 3.4
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.
.. c:macro:: 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 :ref:built in debug mode <debug-build> (if the :c:macro:Py_DEBUG
macro is defined), the :c:macro:Py_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; }
.. versionadded:: 3.11
.. c:macro:: 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
:issue:33720).
Corresponds to the noinline attribute/specification on GCC and MSVC.
Usage::
Py_NO_INLINE static int random(void) { return 4; }
.. versionadded:: 3.11
.. c:macro:: 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);
.. versionchanged:: 3.8 MSVC support was added.
.. c:macro:: 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 :samp:static {type}.
.. c:macro:: Py_LOCAL_INLINE(type)
Equivalent to :c:macro:Py_LOCAL but additionally requests the function
be inlined.
.. c:macro:: 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"))).
.. c:macro:: 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.
.. c:macro:: 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.
.. c:macro:: 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.
.. c:macro:: 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.
The following :term:soft deprecated macros have been used to features that
have been standardized in C11 (or previous standards).
.. c:macro:: Py_ALIGNED(num)
On some GCC-like compilers, specify alignment to num bytes. This does nothing on other compilers.
Use the standard alignas specifier rather than this macro.
.. deprecated:: 3.15
The macro is :term:soft deprecated.
.. c:macro:: PY_FORMAT_SIZE_T
The :c:func:printf formatting modifier for :c:type:size_t.
Use "z" directly instead.
.. deprecated:: 3.15
The macro is :term:soft deprecated.
.. c:macro:: Py_LL(number) Py_ULL(number)
Use number as a long long or unsigned long long integer literal,
respectively.
Expands to number followed by LL or LLU, respectively, but will
expand to some compiler-specific suffixes on some older compilers.
Consider using the C99 standard suffixes LL and LLU directly.
.. deprecated:: 3.15
The macro is :term:soft deprecated.
.. c:macro:: PY_LONG_LONG PY_INT32_T PY_UINT32_T PY_INT64_T PY_UINT64_T
Aliases for the types :c:type:!long long, :c:type:!int32_t,
:c:type:!uint32_t. :c:type:!int64_t and :c:type:!uint64_t,
respectively.
Historically, these types needed compiler-specific extensions.
.. deprecated:: 3.15
These macros are :term:soft deprecated.
.. c:macro:: PY_LLONG_MIN PY_LLONG_MAX PY_ULLONG_MAX PY_SIZE_MAX
Aliases for the values :c:macro:!LLONG_MIN, :c:macro:!LLONG_MAX,
:c:macro:!ULLONG_MAX, and :c:macro:!SIZE_MAX, respectively.
Use these standard names instead.
The required header, <limits.h>,
:ref:is included <capi-system-includes> in Python.h.
.. deprecated:: 3.15
These macros are :term:soft deprecated.
.. c:macro:: Py_MEMCPY(dest, src, n)
This is a :term:soft deprecated alias to :c:func:!memcpy.
Use :c:func:!memcpy directly instead.
.. deprecated:: 3.14
The macro is :term:soft deprecated.
.. c:macro:: Py_UNICODE_SIZE
Size of the :c:type:!wchar_t type.
Use sizeof(wchar_t) or WCHAR_WIDTH/8 instead.
The required header for the latter, <limits.h>,
:ref:is included <capi-system-includes> in Python.h.
.. deprecated:: 3.15
The macro is :term:soft deprecated.
.. c:macro:: Py_UNICODE_WIDE
Defined if wchar_t can hold a Unicode character (UCS-4).
Use sizeof(wchar_t) >= 4 instead
.. deprecated:: 3.15
The macro is :term:soft deprecated.
.. c:macro:: Py_VA_COPY
This is a :term:soft deprecated alias to the C99-standard va_copy
function.
Historically, this would use a compiler-specific method to copy a va_list.
.. versionchanged:: 3.6
This is now an alias to va_copy.
.. deprecated:: 3.15
The macro is :term:soft deprecated.
.. _api-objects:
.. index:: pair: object; type
Most Python/C API functions have one or more arguments as well as a return value
of type :c:expr: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
:c:type:PyObject, only pointer variables of type :c:expr:PyObject* can be
declared. The sole exception are the type objects; since these must never be
deallocated, they are typically static :c:type:PyTypeObject objects.
All Python objects (even Python integers) have a :dfn:type and a
:dfn: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 :ref:types). 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.
.. _api-refcounts:
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 :term: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 :term: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.")
.. index:: single: Py_INCREF (C function) single: Py_DECREF (C function)
Reference counts are always manipulated explicitly. The normal way is
to use the macro :c:func:Py_INCREF to take a new reference to an
object (i.e. increment its reference count by one),
and :c:func:Py_DECREF to release that reference (i.e. decrement the
reference count by one). The :c:func: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 :term: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 :term: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 :c:func: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 :term:strong reference
(i.e. increment the reference count) of the object they return.
This leaves the caller with the responsibility to call :c:func:Py_DECREF when
they are done with the result; this soon becomes second nature.
.. _api-refcountdetails:
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 :c:func:Py_DECREF or :c:func: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
:term: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.
.. index:: single: PyList_SetItem (C function) single: PyTuple_SetItem (C function)
Few functions steal references; the two notable exceptions are
:c:func:PyList_SetItem and :c:func: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, :c:func:PyLong_FromLong returns a new reference which is immediately
stolen by :c:func:PyTuple_SetItem. When you want to keep using an object
although the reference to it will be stolen, use :c:func:Py_INCREF to grab
another reference before calling the reference-stealing function.
Incidentally, :c:func:PyTuple_SetItem is the only way to set tuple items;
:c:func:PySequence_SetItem and :c:func:PyObject_SetItem refuse to do this
since tuples are an immutable data type. You should only use
:c:func:PyTuple_SetItem for tuples that you are creating yourself.
Equivalent code for populating a list can be written using :c:func:PyList_New
and :c:func:PyList_SetItem.
However, in practice, you will rarely use these ways of creating and populating
a tuple or list. There's a generic function, :c:func:Py_BuildValue, that can
create most common objects from C values, directed by a :dfn: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 :c:func: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;
}
.. index:: single: set_all()
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 :c:func:PyObject_GetItem and :c:func: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 :c:func:PyList_GetItem, you
don't own the reference --- but if you obtain the same item from the same list
using :c:func:PySequence_GetItem (which happens to take exactly the same
arguments), you do own a reference to the returned object.
.. index:: single: PyList_GetItem (C function) single: PySequence_GetItem (C function)
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 :c:func:PyList_GetItem, and once
using :c:func: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;
}
.. index:: single: sum_list()
::
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; }
.. index:: single: sum_sequence()
.. _api-types:
There are few other data types that play a significant role in the Python/C
API; most are simple C types such as :c:expr:int, :c:expr:long,
:c:expr:double and :c:expr: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.
.. c:type:: Py_ssize_t
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 type :c:type:Py_ssize_t.
.. _api-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.
.. index:: single: PyErr_Occurred (C function)
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
:c:func:PyErr_Occurred. These exceptions are always explicitly documented.
.. index:: single: PyErr_SetString (C function) single: PyErr_Clear (C function)
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
:c:func: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:
:c:func:PyErr_SetString is the most common (though not the most general)
function to set the exception state, and :c:func: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 :keyword:try ...
:keyword: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.
.. index:: single: exc_info (in module sys)
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 :func: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.
.. index:: single: sum_sequence()
A simple example of detecting exceptions and passing them on is shown in the
:c:func:!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
.. index:: single: incr_item()
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 */
}
.. index:: single: incr_item()
.. index:: single: PyErr_ExceptionMatches (C function) single: PyErr_Clear (C function) single: Py_XDECREF (C function)
This example represents an endorsed use of the goto statement in C!
It illustrates the use of :c:func:PyErr_ExceptionMatches and
:c:func:PyErr_Clear to handle specific exceptions, and the use of
:c:func:Py_XDECREF to dispose of owned references that may be NULL (note the
'X' in the name; :c:func: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.
.. _api-embedding:
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.
.. index:: single: Py_Initialize (C function) pair: module; builtins pair: module; main pair: module; sys triple: module; search; path single: path (in module sys)
The basic initialization function is :c:func:Py_Initialize. This initializes
the table of loaded modules, and creates the fundamental modules
:mod:builtins, :mod:__main__, and :mod:sys. It also
initializes the module search path (sys.path).
:c:func: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
:c:member:PyConfig.argv and :c:member:PyConfig.parse_argv must be set: see
:ref:Python Initialization Configuration <init-config>.
On most systems (in particular, on Unix and Windows, although the details are
slightly different), :c:func: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 :file:lib/python{X.Y} relative to the parent directory
where the executable named :file:python is found on the shell command search
path (the environment variable :envvar:PATH).
For instance, if the Python executable is found in
:file:/usr/local/bin/python, it will assume that the libraries are in
:file:/usr/local/lib/python{X.Y}. (In fact, this particular path is also
the "fallback" location, used when no executable file named :file:python is
found along :envvar:PATH.) The user can override this behavior by setting the
environment variable :envvar:PYTHONHOME, or insert additional directories in
front of the standard path by setting :envvar:PYTHONPATH.
The embedding application can steer the search by setting
:c:member:PyConfig.program_name before calling
:c:func:Py_InitializeFromConfig. Note that
:envvar:PYTHONHOME still overrides this and :envvar:PYTHONPATH is still
inserted in front of the standard path.
.. index:: single: Py_IsInitialized (C function)
Sometimes, it is desirable to "uninitialize" Python. For instance, the
application may want to start over (make another call to
:c:func: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 :c:func:Py_FinalizeEx. The function :c:func: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 :c:func:Py_FinalizeEx
does not free all memory allocated by the Python interpreter, e.g. memory
allocated by extension modules currently cannot be released.
.. _api-debugging:
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
: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.
.. c:macro:: Py_DEBUG
Compiling the interpreter with the :c:macro:!Py_DEBUG macro defined produces
what is generally meant by :ref:a debug build of Python <debug-build>.
On Unix, :c:macro:!Py_DEBUG can be enabled by adding :option:--with-pydebug
to the :file:./configure command. This will also disable compiler optimization.
On Windows, selecting a debug build (e.g., by passing the :option:-d option to
:file:PCbuild/build.bat) automatically enables :c:macro:!Py_DEBUG.
Additionally, the presence of the not-Python-specific :c:macro:!_DEBUG macro,
when defined by the compiler, will also implicitly enable :c:macro:!Py_DEBUG.
In addition to the reference count debugging described below, extra checks are
performed. See :ref:Python Debug Build <debug-build> for more details.
Defining :c:macro:Py_TRACE_REFS enables reference tracing
(see the :option:configure --with-trace-refs option <--with-trace-refs>).
When defined, a circular doubly linked list of active objects is maintained by adding two extra
fields to every :c:type: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 :file:Misc/SpecialBuilds.txt in the Python source distribution
for more detailed information.
.. _c-api-tools:
The following third party tools offer both simpler and more sophisticated approaches to creating C, C++ and Rust extensions for Python:
Cython <https://cython.org/>_cffi <https://cffi.readthedocs.io>_HPy <https://hpyproject.org/>_nanobind <https://github.com/wjakob/nanobind>_ (C++)Numba <https://numba.pydata.org/>_pybind11 <https://pybind11.readthedocs.io/>_ (C++)PyO3 <https://pyo3.rs/>_ (Rust)SWIG <https://www.swig.org>_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.
.. seealso::
Python Packaging User Guide: Binary Extensions <https://packaging.python.org/guides/packaging-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.