.. highlightlang:: 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.

.. _api-includes:

Include Files

=============

All function, type and macro definitions needed to use the Python/C API are

included in your code by the following line::

   #include "Python.h"

This implies inclusion of the following standard headers: ``<stdio.h>``,

``<string.h>``, ``<errno.h>``, ``<limits.h>``, and ``<stdlib.h>`` (if

available).

.. warning::

   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.

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.

**Important:** 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 :envvar:`prefix` and

:envvar:`exec_prefix` are defined by the corresponding parameters to Python's

:program:`configure` script and *version* is ``sys.version[:3]``.  On Windows,

the headers are installed in :file:`{prefix}/include`, where :envvar:`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

:envvar:`prefix` include the platform specific headers from

:envvar:`exec_prefix`.

C++ users should note that though the API is defined entirely using C, the

header files do properly declare the entry points to be ``extern "C"``, so there

is no need to do anything special to use the API from C++.

.. _api-objects:

Objects, Types and Reference Counts

===================================

.. index:: object: type

Most Python/C API functions have one or more arguments as well as a return value

of type :ctype:`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

:ctype:`PyObject`, only pointer variables of type :ctype:`PyObject\*` can  be

declared.  The sole exception are the type objects; since these must never be

deallocated, they are typically static :ctype:`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:

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 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 an object's reference count  becomes zero, the object is deallocated.  If

it contains references to  other objects, their reference count is decremented.

Those other  objects may be deallocated in turn, if this decrement makes their

reference count become zero, 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()

   single: Py_DECREF()

Reference counts are always manipulated explicitly.  The normal way is  to use

the macro :cfunc:`Py_INCREF` to increment an object's reference count by one,

and :cfunc:`Py_DECREF` to decrement it by   one.  The :cfunc:`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 decrementing the

reference counts 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 increment an object's 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 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 incrementing its reference count. Some other operation might

conceivably remove the object from the list, decrementing its reference count

and possible 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 :cfunc:`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 increment the reference count of the object they return.

This leaves the caller with the responsibility to call :cfunc:`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 decref'ing it by calling :cfunc:`Py_DECREF` or :cfunc:`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 it 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()

   single: PyTuple_SetItem()

Few functions steal references; the two notable exceptions are

:cfunc:`PyList_SetItem` and :cfunc:`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, PyInt_FromLong(1L));

   PyTuple_SetItem(t, 1, PyInt_FromLong(2L));

   PyTuple_SetItem(t, 2, PyString_FromString("three"));

Here, :cfunc:`PyInt_FromLong` returns a new reference which is immediately

stolen by :cfunc:`PyTuple_SetItem`.  When you want to keep using an object

although the reference to it will be stolen, use :cfunc:`Py_INCREF` to grab

another reference before calling the reference-stealing function.

Incidentally, :cfunc:`PyTuple_SetItem` is the *only* way to set tuple items;

:cfunc:`PySequence_SetItem` and :cfunc:`PyObject_SetItem` refuse to do this

since tuples are an immutable data type.  You should only use

:cfunc:`PyTuple_SetItem` for tuples that you are creating yourself.

Equivalent code for populating a list can be written using :cfunc:`PyList_New`

and :cfunc:`PyList_SetItem`.

However, in practice, you will rarely use these ways of creating and populating

a tuple or list.  There's a generic function, :cfunc:`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 :cfunc:`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 reference

counts is much saner, since you don't have to increment a reference count so you

can give a 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)

   {

       int i, n;

       n = PyObject_Length(target);

       if (n < 0)

           return -1;

       for (i = 0; i < n; i++) {

           PyObject *index = PyInt_FromLong(i);

           if (!index)

               return -1;

           if (PyObject_SetItem(target, index, item) < 0)

               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 :cfunc:`PyObject_GetItem` and  :cfunc:`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 :cfunc:`PyList_GetItem`, you

don't own the reference --- but if you obtain the same item from the same list

using :cfunc:`PySequence_GetItem` (which happens to take exactly the same

arguments), you do own a reference to the returned object.

.. index::

   single: PyList_GetItem()

   single: PySequence_GetItem()

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  :cfunc:`PyList_GetItem`, and once

using :cfunc:`PySequence_GetItem`. ::

   long

   sum_list(PyObject *list)

   {

       int i, n;

       long total = 0;

       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 (!PyInt_Check(item)) continue; /* Skip non-integers */

           total += PyInt_AsLong(item);

       }

       return total;

   }

.. index:: single: sum_list()

::

   long

   sum_sequence(PyObject *sequence)

   {

       int i, n;

       long total = 0;

       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 (PyInt_Check(item))

               total += PyInt_AsLong(item);

           Py_DECREF(item); /* Discard reference ownership */

       }

       return total;

   }

.. index:: single: sum_sequence()

.. _api-types:

Types

-----

There are few other data types that play a significant role in  the Python/C

API; most are simple C types such as :ctype:`int`,  :ctype:`long`,

:ctype:`double` and :ctype:`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.

.. _api-exceptions:

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()

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 --- usually *NULL* or ``-1``.  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

:cfunc:`PyErr_Occurred`.

.. index::

   single: PyErr_SetString()

   single: PyErr_Clear()

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

:cfunc:`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:

:cfunc:`PyErr_SetString` is the most common (though not the most general)

function to set the exception state, and :cfunc:`PyErr_Clear` clears the

exception state.

.. index::

   single: exc_type (in module sys)

   single: exc_value (in module sys)

   single: exc_traceback (in module sys)

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   objects

``sys.exc_type``, ``sys.exc_value``, and ``sys.exc_traceback``; 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_type`` 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

:cfunc:`sum_sequence` example above.  It so happens that that 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 = PyInt_FromLong(0L);

           if (item == NULL)

               goto error;

       }

       const_one = PyInt_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()

   single: PyErr_Clear()

   single: Py_XDECREF()

This example represents an endorsed use of the ``goto`` statement  in C!

It illustrates the use of :cfunc:`PyErr_ExceptionMatches` and

:cfunc:`PyErr_Clear` to handle specific exceptions, and the use of

:cfunc:`Py_XDECREF` to dispose of owned references that may be *NULL* (note the

``'X'`` in the name; :cfunc:`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:

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.

.. index::

   single: Py_Initialize()

   module: __builtin__

   module: __main__

   module: sys

   module: exceptions

   triple: module; search; path

   single: path (in module sys)

The basic initialization function is :cfunc:`Py_Initialize`. This initializes

the table of loaded modules, and creates the fundamental modules

:mod:`__builtin__`, :mod:`__main__`, :mod:`sys`, and :mod:`exceptions`.  It also

initializes the module search path (``sys.path``).

.. index:: single: PySys_SetArgv()