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_ABS(x)

   Return the absolute value of "x".

   Added in version 3.3.

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.

   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), the "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; }

   Added in version 3.11.

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".

Py_DEPRECATED(version)

   Use this for deprecated declarations.  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_GETENV(s)

   Like "getenv(s)", but returns "NULL" if "-E" was passed on the
   command line (see "PyConfig.use_environment").

Py_MAX(x, y)

   Return the maximum value between "x" and "y".

   Added in version 3.3.

Py_MEMBER_SIZE(type, member)

   Return the size of a structure ("type") "member" in bytes.

   Added in version 3.6.

Py_MIN(x, y)

   Return the minimum value between "x" and "y".

   Added in version 3.3.

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).

   Usage:

      Py_NO_INLINE static int random(void) { return 4; }

   Added in version 3.11.

Py_STRINGIFY(x)

   Convert "x" to a C string.  E.g. "Py_STRINGIFY(123)" returns
   ""123"".

   Added in version 3.4.

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.

   A use for "Py_UNREACHABLE()" is following a call a function that
   never returns but that is not declared "_Py_NO_RETURN".

   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_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.

PyDoc_STRVAR(name, str)

   Creates a variable with name "name" that can be used in docstrings.
   If Python is built without docstrings, the value will be empty.

   Use "PyDoc_STRVAR" for docstrings to support building Python
   without docstrings, as specified in **PEP 7**.

   Example:

      PyDoc_STRVAR(pop_doc, "Remove and return the rightmost element.");

      static PyMethodDef deque_methods[] = {
          // ...
          {"pop", (PyCFunction)deque_pop, METH_NOARGS, pop_doc},
          // ...
      }

PyDoc_STR(str)

   Creates a docstring for the given input string or an empty string
   if docstrings are disabled.

   Use "PyDoc_STR" in specifying docstrings to support building Python
   without docstrings, as specified in **PEP 7**.

   Example:

      static PyMethodDef pysqlite_row_methods[] = {
          {"keys", (PyCFunction)pysqlite_row_keys, METH_NOARGS,
              PyDoc_STR("Returns the keys of the row.")},
          {NULL, NULL}
      };


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 type
   "Py_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/python*X.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/python*X.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:

* Cython

* cffi

* HPy

* nanobind (C++)

* Numba

* pybind11 (C++)

* PyO3 (Rust)

* SWIG

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.
