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 that 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.

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).

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 /include/pythonversion/ and /include/pythonversion/, where and are defined by the corresponding parameters to Python’s configure script and version is sys.version[:3]. On Windows, the headers are installed in /include, where 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 <python/Python.h>; this will break on multi-platform builds since the platform independent headers under include the platform specific headers from .

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 Python Reference Manual). 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 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.”)

Reference counts are always manipulated explicitly. The normal way is to use the macro to increment an object’s reference count by one, and to decrement it by one. The 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(long) >= sizeof(char*)). 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 , 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 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. Note that we talk of owning references, never of owning objects; objects are always shared! When a function owns a reference, it has to dispose of it properly — either by passing ownership on (usually to its caller) or by calling or . 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. Few functions steal references; the two notable exceptions are and , 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"));

Incidentally, is the only way to set tuple items; and refuse to do this since tuples are an immutable data type. You should only use for tuples that you are creating yourself.

Equivalent code for populating a list can be written using and . Such code can also use ; this illustrates the difference between the two (the extra calls):

PyObject *l, *x;

l = PyList_New(3);
x = PyInt_FromLong(1L);
PySequence_SetItem(l, 0, x); Py_DECREF(x);
x = PyInt_FromLong(2L);
PySequence_SetItem(l, 1, x); Py_DECREF(x);
x = PyString_FromString("three");
PySequence_SetItem(l, 2, x); Py_DECREF(x);

You might find it strange that the “recommended” approach takes more code. However, in practice, you will rarely use these ways of creating and populating a tuple or list. There’s a generic function, , 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 *t, *l;

t = Py_BuildValue("(iis)", 1, 2, "three");
l = Py_BuildValue("[iis]", 1, 2, "three");

It is much more common to use 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++) {
        if (PyObject_SetItem(target, i, item) < 0)
            return -1;
    }
    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 referece 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 and , always return a new reference (i.e., 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 (i.e., the type of 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 , you don’t own the reference — but if you obtain the same item from the same list using (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 , and once using .

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;
}

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;
}

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.

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 — 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 .

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 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: is the most common (though not the most general) function to set the exception state, and 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

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 …  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.

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 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
    return 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 = 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 */
}

This example represents an endorsed use of the statement in C! It illustrates the use of and to handle specific exceptions, and the use of to dispose of owned references that may be NULL (note the X in the name; 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 . This initializes the table of loaded modules, and creates the fundamental modules __builtin__, __main__ and sys. It also initializes the module search path (sys.path).

does not set the “script argument list” (sys.argv). If this variable is needed by Python code that will be executed later, it must be set explicitly with a call to PySys_SetArgv(argc, argv) subsequent to the call to .

On most systems (in particular, on Unix and Windows, although the details are slightly different), 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 relative to the parent directory where the executable named python is found on the shell command search path (the environment variable ).

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. (In fact, this particular path is also the “fallback” location, used when no executable file named python is found along .) The user can override this behavior by setting the environment variable , or insert additional directories in front of the standard path by setting .

The embedding application can steer the search by calling Py_SetProgramName(file) before calling . Note that still overrides this and is still inserted in front of the standard path. An application that requires total control has to provide its own implementation of , , , and (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 ) or the application is simply done with its use of Python and wants to free all memory allocated by Python. This can be accomplished by calling . The function returns true if Python is currently in the initialized state. More information about these functions is given in a later chapter.

The Very High Level Layer#

The functions in this chapter will let you execute Python source code given in a file or a buffer, but they will not let you interact in a more detailed way with the interpreter.

Several of these functions accept a start symbol from the grammar as a parameter. The available start symbols are , , and . These are described following the functions which accept them as parameters.

Note also that several of these functions take FILE* parameters. On particular issue which needs to be handled carefully is that the FILE structure for different C libraries can be different and incompatible. Under Windows (at least), it is possible for dynamically linked extensions to actually use different libraries, so care should be taken that FILE* parameters are only passed to these functions if it is certain that they were created by the same library that the Python runtime is using.

PyRun_AnyFile(FILE *fp, char *filename)#

If fp refers to a file associated with an interactive device (console or terminal input or Unix pseudo-terminal), return the value of , otherwise return the result of . If filename is NULL, this function uses "???" as the filename.

PyRun_SimpleString(char *command)#

Executes the Python source code from command in the __main__ module. If __main__ does not already exist, it is created. Returns 0 on success or -1 if an exception was raised. If there was an error, there is no way to get the exception information.

PyRun_SimpleFile(FILE *fp, char *filename)#

Similar to , but the Python source code is read from fp instead of an in-memory string. filename should be the name of the file.

PyRun_InteractiveOne(FILE *fp, char *filename)#

Read and execute a single statement from a file associated with an interactive device. If filename is NULL, "???" is used instead. The user will be prompted using sys.ps1 and sys.ps2. Returns 0 when the input was executed successfully, -1 if there was an exception, or an error code from the errcode.h include file distributed as part of Python in case of a parse error. (Note that errcode.h is not included by Python.h, so must be included specifically if needed.)

PyRun_InteractiveLoop(FILE *fp, char *filename)#

Read and execute statements from a file associated with an interactive device until EOF is reached. If filename is NULL, "???" is used instead. The user will be prompted using sys.ps1 and sys.ps2. Returns 0 at EOF.

PyParser_SimpleParseString(char *str, int start)#

Parse Python source code from str using the start token start. The result can be used to create a code object which can be evaluated efficiently. This is useful if a code fragment must be evaluated many times.

PyParser_SimpleParseFile(FILE *fp, char *filename, int start)#

Similar to , but the Python source code is read from fp instead of an in-memory string. filename should be the name of the file.

PyRun_String(char *str, int start, PyObject *globals, PyObject *locals)#

Execute Python source code from str in the context specified by the dictionaries globals and locals. The parameter start specifies the start token that should be used to parse the source code.

Returns the result of executing the code as a Python object, or NULL if an exception was raised.

PyRun_File(FILE *fp, char *filename, int start, PyObject *globals, PyObject *locals)#

Similar to , but the Python source code is read from fp instead of an in-memory string. filename should be the name of the file.

Py_CompileString(char *str, char *filename, int start)#

Parse and compile the Python source code in str, returning the resulting code object. The start token is given by start; this can be used to constrain the code which can be compiled and should be , , or . The filename specified by filename is used to construct the code object and may appear in tracebacks or SyntaxError exception messages. This returns NULL if the code cannot be parsed or compiled.

Py_eval_input#

The start symbol from the Python grammar for isolated expressions; for use with .

Py_file_input#

The start symbol from the Python grammar for sequences of statements as read from a file or other source; for use with . This is the symbol to use when compiling arbitrarily long Python source code.

Py_single_input#

The start symbol from the Python grammar for a single statement; for use with . This is the symbol used for the interactive interpreter loop.

Reference Counting#

The macros in this section are used for managing reference counts of Python objects.

Py_INCREF(PyObject *o)#

Increment the reference count for object o. The object must not be NULL; if you aren’t sure that it isn’t NULL, use .

Py_XINCREF(PyObject *o)#

Increment the reference count for object o. The object may be NULL, in which case the macro has no effect.

Py_DECREF(PyObject *o)#

Decrement the reference count for object o. The object must not be NULL; if you aren’t sure that it isn’t NULL, use . If the reference count reaches zero, the object’s type’s deallocation function (which must not be NULL) is invoked.

Warning: The deallocation function can cause arbitrary Python code to be invoked (e.g. when a class instance with a __del__() method is deallocated). While exceptions in such code are not propagated, the executed code has free access to all Python global variables. This means that any object that is reachable from a global variable should be in a consistent state before is invoked. For example, code to delete an object from a list should copy a reference to the deleted object in a temporary variable, update the list data structure, and then call for the temporary variable.

Py_XDECREF(PyObject *o)#

Decrement the reference count for object o. The object may be NULL, in which case the macro has no effect; otherwise the effect is the same as for , and the same warning applies.

The following functions or macros are only for use within the interpreter core: , , , as well as the global variable .

Exception Handling#

The functions described in this chapter will let you handle and raise Python exceptions. It is important to understand some of the basics of Python exception handling. It works somewhat like the Unix variable: there is a global indicator (per thread) of the last error that occurred. Most functions don’t clear this on success, but will set it to indicate the cause of the error on failure. Most functions also return an error indicator, usually NULL if they are supposed to return a pointer, or -1 if they return an integer (exception: the functions return 1 for success and 0 for failure). When a function must fail because some function it called failed, it generally doesn’t set the error indicator; the function it called already set it.

The error indicator consists of three Python objects corresponding to

the Python variables sys.exc_type, sys.exc_value and sys.exc_traceback. API functions exist to interact with the error indicator in various ways. There is a separate error indicator for each thread.

PyErr_Print()#

Print a standard traceback to sys.stderr and clear the error indicator. Call this function only when the error indicator is set. (Otherwise it will cause a fatal error!)

PyErr_Occurred()#

Test whether the error indicator is set. If set, return the exception type (the first argument to the last call to one of the functions or to ). If not set, return NULL. You do not own a reference to the return value, so you do not need to it. Note: Do not compare the return value to a specific exception; use instead, shown below. (The comparison could easily fail since the exception may be an instance instead of a class, in the case of a class exception, or it may the a subclass of the expected exception.)

PyErr_ExceptionMatches(PyObject *exc)#

Equivalent to PyErr_GivenExceptionMatches(PyErr_Occurred(), exc). This should only be called when an exception is actually set; a memory access violation will occur if no exception has been raised.

PyErr_GivenExceptionMatches(PyObject *given, PyObject *exc)#

Return true if the given exception matches the exception in exc. If exc is a class object, this also returns true when given is an instance of a subclass. If exc is a tuple, all exceptions in the tuple (and recursively in subtuples) are searched for a match. If given is NULL, a memory access violation will occur.

PyErr_NormalizeException(PyObjectexc, PyObjectval, PyObject**tb)#

Under certain circumstances, the values returned by below can be “unnormalized”, meaning that *exc is a class object but *val is not an instance of the same class. This function can be used to instantiate the class in that case. If the values are already normalized, nothing happens. The delayed normalization is implemented to improve performance.

PyErr_Clear()#

Clear the error indicator. If the error indicator is not set, there is no effect.

PyErr_Fetch(PyObject **ptype, PyObject **pvalue, PyObject **ptraceback)#

Retrieve the error indicator into three variables whose addresses are passed. If the error indicator is not set, set all three variables to NULL. If it is set, it will be cleared and you own a reference to each object retrieved. The value and traceback object may be NULL even when the type object is not. Note: This function is normally only used by code that needs to handle exceptions or by code that needs to save and restore the error indicator temporarily.

PyErr_Restore(PyObject *type, PyObject *value, PyObject *traceback)#

Set the error indicator from the three objects. If the error indicator is already set, it is cleared first. If the objects are NULL, the error indicator is cleared. Do not pass a NULL type and non-NULL value or traceback. The exception type should be a string or class; if it is a class, the value should be an instance of that class. Do not pass an invalid exception type or value. (Violating these rules will cause subtle problems later.) This call takes away a reference to each object, i.e. you must own a reference to each object before the call and after the call you no longer own these references. (If you don’t understand this, don’t use this function. I warned you.) Note: This function is normally only used by code that needs to save and restore the error indicator temporarily.

PyErr_SetString(PyObject *type, char *message)#

This is the most common way to set the error indicator. The first argument specifies the exception type; it is normally one of the standard exceptions, e.g. . You need not increment its reference count. The second argument is an error message; it is converted to a string object.

PyErr_SetObject(PyObject *type, PyObject *value)#

This function is similar to but lets you specify an arbitrary Python object for the “value” of the exception. You need not increment its reference count.

PyErr_Format(PyObject *exception, const char *format, )#

This function sets the error indicator. exception should be a Python object. fmt should be a string, containing format codes, similar to . The width.precision before a format code is parsed, but the width part is ignored.

An unrecognized format character causes all the rest of the format string to be copied as-is to the result string, and any extra arguments discarded.

A new reference is returned, which is owned by the caller.

PyErr_SetNone(PyObject *type)#

This is a shorthand for PyErr_SetObject(type, Py_None).

PyErr_BadArgument()#

This is a shorthand for PyErr_SetString(PyExc_TypeError, message), where message indicates that a built-in operation was invoked with an illegal argument. It is mostly for internal use.

PyErr_NoMemory()#

This is a shorthand for PyErr_SetNone(PyExc_MemoryError); it returns NULL so an object allocation function can write return PyErr_NoMemory(); when it runs out of memory.

PyErr_SetFromErrno(PyObject *type)#

This is a convenience function to raise an exception when a C library function has returned an error and set the C variable . It constructs a tuple object whose first item is the integer value and whose second item is the corresponding error message (gotten from ), and then calls PyErr_SetObject(type, object). On Unix, when the value is , indicating an interrupted system call, this calls , and if that set the error indicator, leaves it set to that. The function always returns NULL, so a wrapper function around a system call can write return PyErr_SetFromErrno(); when the system call returns an error.

PyErr_BadInternalCall()#

This is a shorthand for PyErr_SetString(PyExc_TypeError, message), where message indicates that an internal operation (e.g. a Python/C API function) was invoked with an illegal argument. It is mostly for internal use.

PyErr_CheckSignals()#

This function interacts with Python’s signal handling. It checks whether a signal has been sent to the processes and if so, invokes the corresponding signal handler. If the signal module is supported, this can invoke a signal handler written in Python. In all cases, the default effect for is to raise the

KeyboardInterrupt exception. If an exception is raised the error indicator is set and the function returns 1; otherwise the function returns 0. The error indicator may or may not be cleared if it was previously set.

PyErr_SetInterrupt()#

This function is obsolete. It simulates the effect of a signal arriving — the next time is called,

KeyboardInterrupt will be raised. It may be called without holding the interpreter lock.

PyErr_NewException(char *name, PyObject *base, PyObject *dict)#

This utility function creates and returns a new exception object. The name argument must be the name of the new exception, a C string of the form module.class. The base and dict arguments are normally NULL. This creates a class object derived from the root for all exceptions, the built-in name Exception (accessible in C as ). The __module__ attribute of the new class is set to the first part (up to the last dot) of the name argument, and the class name is set to the last part (after the last dot). The base argument can be used to specify an alternate base class. The dict argument can be used to specify a dictionary of class variables and methods.

PyErr_WriteUnraisable(PyObject *obj)#

This utility function prints a warning message to sys.stderr when an exception has been set but it is impossible for the interpreter to actually raise the exception. It is used, for example, when an exception occurs in an __del__ method.

The function is called with a single argument obj that identifies where the context in which the unraisable exception occurred. The repr of obj will be printed in the warning message.

Standard Exceptions#

All standard Python exceptions are available as global variables whose names are PyExc_ followed by the Python exception name. These have the type PyObject*; they are all class objects. For completeness, here are all the variables:

Notes:

(1)
This is a base class for other standard exceptions.

(2)
Only defined on Windows; protect code that uses this by testing that the preprocessor macro MS_WINDOWS is defined.

Deprecation of String Exceptions#

All exceptions built into Python or provided in the standard library are derived from Exception.

String exceptions are still supported in the interpreter to allow existing code to run unmodified, but this will also change in a future release.

Utilities#

The functions in this chapter perform various utility tasks, such as parsing function arguments and constructing Python values from C values.

OS Utilities#

Py_FdIsInteractive(FILE *fp, char *filename)#

Return true (nonzero) if the standard I/O file fp with name filename is deemed interactive. This is the case for files for which isatty(fileno(fp)) is true. If the global flag is true, this function also returns true if the name pointer is NULL or if the name is equal to one of the strings ’<stdin>’ or ’???’.

PyOS_GetLastModificationTime(char *filename)#

Return the time of last modification of the file filename. The result is encoded in the same way as the timestamp returned by the standard C library function .

PyOS_AfterFork()#

Function to update some internal state after a process fork; this should be called in the new process if the Python interpreter will continue to be used. If a new executable is loaded into the new process, this function does not need to be called.

PyOS_CheckStack()#

Return true when the interpreter runs out of stack space. This is a reliable check, but is only available when USE_STACKCHECK is defined (currently on Windows using the Microsoft Visual C++ compiler and on the Macintosh). USE_CHECKSTACK will be defined automatically; you should never change the definition in your own code.

PyOS_getsig(int i)#

Return the current signal handler for signal i. This is a thin wrapper around either or . Do not call those functions directly! PyOS_sighandler_t is a typedef alias for void (*)(int).

PyOS_setsig(int i, PyOS_sighandler_t h)#

Set the signal handler for signal i to be h; return the old signal handler. This is a thin wrapper around either or . Do not call those functions directly! PyOS_sighandler_t is a typedef alias for void (*)(int).

Process Control#

Py_FatalError(char *message)#

Print a fatal error message and kill the process. No cleanup is performed. This function should only be invoked when a condition is detected that would make it dangerous to continue using the Python interpreter; e.g., when the object administration appears to be corrupted. On Unix, the standard C library function is called which will attempt to produce a core file.

Py_Exit(int status)#

Exit the current process. This calls and then calls the standard C library function exit(status).

Py_AtExit(void (*func) ())#

Register a cleanup function to be called by . The cleanup function will be called with no arguments and should return no value. At most 32 cleanup functions can be registered. When the registration is successful, returns 0; on failure, it returns -1. The cleanup function registered last is called first. Each cleanup function will be called at most once. Since Python’s internal finallization will have completed before the cleanup function, no Python APIs should be called by func.

Importing Modules#

PyImport_ImportModule(char *name)#

This is a simplified interface to below, leaving the globals and locals arguments set to NULL. When the name argument contains a dot (i.e., when it specifies a submodule of a package), the fromlist argument is set to the list [’*’] so that the return value is the named module rather than the top-level package containing it as would otherwise be the case. (Unfortunately, this has an additional side effect when name in fact specifies a subpackage instead of a submodule: the submodules specified in the package’s __all__ variable are

loaded.) Return a new reference to the imported module, or NULL with an exception set on failure (the module may still be created in this case — examine sys.modules to find out).

PyImport_ImportModuleEx(char *name, PyObject *globals, PyObject *locals, PyObject *fromlist)#

Import a module. This is best described by referring to the built-in Python function __import__(), as the standard __import__() function calls this function directly.

The return value is a new reference to the imported module or top-level package, or NULL with an exception set on failure (the module may still be created in this case). Like for __import__(), the return value when a submodule of a package was requested is normally the top-level package, unless a non-empty fromlist was given.

PyImport_Import(PyObject *name)#

This is a higher-level interface that calls the current “import hook function”. It invokes the __import__() function from the __builtins__ of the current globals. This means that the import is done using whatever import hooks are installed in the current environment, e.g. by rexec or ihooks.

PyImport_ReloadModule(PyObject *m)#

Reload a module. This is best described by referring to the built-in Python function reload(), as the standard reload() function calls this function directly. Return a new reference to the reloaded module, or NULL with an exception set on failure (the module still exists in this case).

PyImport_AddModule(char *name)#

Return the module object corresponding to a module name. The name argument may be of the form package.module). First check the modules dictionary if there’s one there, and if not, create a new one and insert in in the modules dictionary. Warning: this function does not load or import the module; if the module wasn’t already loaded, you will get an empty module object. Use or one of its variants to import a module. Return NULL with an exception set on failure.

PyImport_ExecCodeModule(char *name, PyObject *co)#

Given a module name (possibly of the form package.module) and a code object read from a Python bytecode file or obtained from the built-in function compile(), load the module. Return a new reference to the module object, or NULL with an exception set if an error occurred (the module may still be created in this case). (This function would reload the module if it was already imported.)

PyImport_GetMagicNumber()#

Return the magic number for Python bytecode files (a.k.a. .pyc and .pyo files). The magic number should be present in the first four bytes of the bytecode file, in little-endian byte order.

PyImport_GetModuleDict()#

Return the dictionary used for the module administration (a.k.a. sys.modules). Note that this is a per-interpreter variable.

_PyImport_Init()#

Initialize the import mechanism. For internal use only.

PyImport_Cleanup()#

Empty the module table. For internal use only.

_PyImport_Fini()#

Finalize the import mechanism. For internal use only.

_PyImport_FindExtension(char *, char *)#

For internal use only.

_PyImport_FixupExtension(char *, char *)#

For internal use only.

PyImport_ImportFrozenModule(char *name)#

Load a frozen module named name. Return 1 for success, 0 if the module is not found, and -1 with an exception set if the initialization failed. To access the imported module on a successful load, use . (Note the misnomer — this function would reload the module if it was already imported.)

[#

_frozen]struct _frozen This is the structure type definition for frozen module descriptors, as generated by the freeze utility (see Tools/freeze/ in the Python source distribution). Its definition, found in Include/import.h, is:

struct _frozen {
    char *name;
    unsigned char *code;
    int size;
};

PyImport_FrozenModules#

This pointer is initialized to point to an array of struct _frozen records, terminated by one whose members are all NULL or zero. When a frozen module is imported, it is searched in this table. Third-party code could play tricks with this to provide a dynamically created collection of frozen modules.

PyImport_AppendInittab(char *name, void (*initfunc)(void))#

Add a single module to the existing table of built-in modules. This is a convenience wrapper around , returning -1 if the table could not be extended. The new module can be imported by the name name, and uses the function initfunc as the initialization function called on the first attempted import. This should be called before .

[#

_inittab]struct _inittab Structure describing a single entry in the list of built-in modules. Each of these structures gives the name and initialization function for a module built into the interpreter. Programs which embed Python may use an array of these structures in conjunction with to provide additional built-in modules. The structure is defined in Include/import.h as:

struct _inittab {
    char *name;
    void (*initfunc)(void);
};

PyImport_ExtendInittab(struct _inittab *newtab)#

Add a collection of modules to the table of built-in modules. The newtab array must end with a sentinel entry which contains NULL for the name field; failure to provide the sentinel value can result in a memory fault. Returns 0 on success or -1 if insufficient memory could be allocated to extend the internal table. In the event of failure, no modules are added to the internal table. This should be called before .

Abstract Objects Layer#

The functions in this chapter interact with Python objects regardless of their type, or with wide classes of object types (e.g. all numerical types, or all sequence types). When used on object types for which they do not apply, they will raise a Python exception.

Object Protocol#

PyObject_Print(PyObject *o, FILE *fp, int flags)#

Print an object o, on file fp. Returns -1 on error. The flags argument is used to enable certain printing options. The only option currently supported is ; if given, the str() of the object is written instead of the repr().

PyObject_HasAttrString(PyObject *o, char *attr_name)#

Returns 1 if o has the attribute attr_name, and 0 otherwise. This is equivalent to the Python expression hasattr(o, attr_name). This function always succeeds.

PyObject_GetAttrString(PyObject *o, char *attr_name)#

Retrieve an attribute named attr_name from object o. Returns the attribute value on success, or NULL on failure. This is the equivalent of the Python expression o.attr_name.

PyObject_HasAttr(PyObject *o, PyObject *attr_name)#

Returns 1 if o has the attribute attr_name, and 0 otherwise. This is equivalent to the Python expression hasattr(o, attr_name). This function always succeeds.

PyObject_GetAttr(PyObject *o, PyObject *attr_name)#

Retrieve an attribute named attr_name from object o. Returns the attribute value on success, or NULL on failure. This is the equivalent of the Python expression o.attr_name.

PyObject_SetAttrString(PyObject *o, char *attr_name, PyObject *v)#

Set the value of the attribute named attr_name, for object o, to the value v. Returns -1 on failure. This is the equivalent of the Python statement o.attr_name=v.

PyObject_SetAttr(PyObject *o, PyObject *attr_name, PyObject *v)#

Set the value of the attribute named attr_name, for object o, to the value v. Returns -1 on failure. This is the equivalent of the Python statement o.attr_name=v.

PyObject_DelAttrString(PyObject *o, char *attr_name)#

Delete attribute named attr_name, for object o. Returns -1 on failure. This is the equivalent of the Python statement: del o.attr_name.

PyObject_DelAttr(PyObject *o, PyObject *attr_name)#

Delete attribute named attr_name, for object o. Returns -1 on failure. This is the equivalent of the Python statement del o.attr_name.

PyObject_Cmp(PyObject *o1, PyObject *o2, int *result)#

Compare the values of o1 and o2 using a routine provided by o1, if one exists, otherwise with a routine provided by o2. The result of the comparison is returned in result. Returns -1 on failure. This is the equivalent of the Python statement result = cmp(o1, o2).

PyObject_Compare(PyObject *o1, PyObject *o2)#

Compare the values of o1 and o2 using a routine provided by o1, if one exists, otherwise with a routine provided by o2. Returns the result of the comparison on success. On error, the value returned is undefined; use to detect an error. This is equivalent to the Python expression cmp(o1, o2).

PyObject_Repr(PyObject *o)#

Compute a string representation of object o. Returns the string representation on success, NULL on failure. This is the equivalent of the Python expression repr(o). Called by the repr() built-in function and by reverse quotes.

PyObject_Str(PyObject *o)#

Compute a string representation of object o. Returns the string representation on success, NULL on failure. This is the equivalent of the Python expression str(o). Called by the str() built-in function and by the statement.

PyCallable_Check(PyObject *o)#

Determine if the object o is callable. Return 1 if the object is callable and 0 otherwise. This function always succeeds.

PyObject_CallObject(PyObject *callable_object, PyObject *args)#

Call a callable Python object callable_object, with arguments given by the tuple args. If no arguments are needed, then args may be NULL. Returns the result of the call on success, or NULL on failure. This is the equivalent of the Python expression apply(o, args).

PyObject_CallFunction(PyObject *callable_object, char *format, …)#

Call a callable Python object callable_object, with a variable number of C arguments. The C arguments are described using a style format string. The format may be NULL, indicating that no arguments are provided. Returns the result of the call on success, or NULL on failure. This is the equivalent of the Python expression apply(o, args).

PyObject_CallMethod(PyObject *o, char *m, char *format, …)#

Call the method named m of object o with a variable number of C arguments. The C arguments are described by a format string. The format may be NULL, indicating that no arguments are provided. Returns the result of the call on success, or NULL on failure. This is the equivalent of the Python expression o.method(args). Note that special method names, such as __add__(), __getitem__(), and so on are not supported. The specific abstract-object routines for these must be used.

PyObject_Hash(PyObject *o)#

Compute and return the hash value of an object o. On failure, return -1. This is the equivalent of the Python expression hash(o).

PyObject_IsTrue(PyObject *o)#

Returns 1 if the object o is considered to be true, and 0 otherwise. This is equivalent to the Python expression not not o. This function always succeeds.

PyObject_Type(PyObject *o)#

On success, returns a type object corresponding to the object type of object o. On failure, returns NULL. This is equivalent to the Python expression type(o).

PyObject_Length(PyObject *o)#

Return the length of object o. If the object o provides both sequence and mapping protocols, the sequence length is returned. On error, -1 is returned. This is the equivalent to the Python expression len(o).

PyObject_GetItem(PyObject *o, PyObject *key)#

Return element of o corresponding to the object key or NULL on failure. This is the equivalent of the Python expression o[key].

PyObject_SetItem(PyObject *o, PyObject *key, PyObject *v)#

Map the object key to the value v. Returns -1 on failure. This is the equivalent of the Python statement o[key] = v.

PyObject_DelItem(PyObject *o, PyObject *key)#

Delete the mapping for key from o. Returns -1 on failure. This is the equivalent of the Python statement del o[key].

PyObject_AsFileDescriptor(PyObject *o)#

Derives a file-descriptor from a Python object. If the object is an integer or long integer, its value is returned. If not, the object’s fileno() method is called if it exists; the method must return an integer or long integer, which is returned as the file descriptor value. Returns -1 on failure.

Number Protocol#

PyNumber_Check(PyObject *o)#

Returns 1 if the object o provides numeric protocols, and false otherwise. This function always succeeds.

PyNumber_Add(PyObject *o1, PyObject *o2)#

Returns the result of adding o1 and o2, or NULL on failure. This is the equivalent of the Python expression o1+o2.

PyNumber_Subtract(PyObject *o1, PyObject *o2)#

Returns the result of subtracting o2 from o1, or NULL on failure. This is the equivalent of the Python expression o1-o2.

PyNumber_Multiply(PyObject *o1, PyObject *o2)#

Returns the result of multiplying o1 and o2, or NULL on failure. This is the equivalent of the Python expression o1*o2.

PyNumber_Divide(PyObject *o1, PyObject *o2)#

Returns the result of dividing o1 by o2, or NULL on failure. This is the equivalent of the Python expression o1/o2.

PyNumber_Remainder(PyObject *o1, PyObject *o2)#

Returns the remainder of dividing o1 by o2, or NULL on failure. This is the equivalent of the Python expression o1 % o2.

PyNumber_Divmod(PyObject *o1, PyObject *o2)#

See the built-in function divmod(). Returns NULL on failure. This is the equivalent of the Python expression divmod(o1, o2).

PyNumber_Power(PyObject *o1, PyObject *o2, PyObject *o3)#

See the built-in function pow(). Returns NULL on failure. This is the equivalent of the Python expression pow(o1, o2, o3), where o3 is optional. If o3 is to be ignored, pass in its place (passing NULL for o3 would cause an illegal memory access).

PyNumber_Negative(PyObject *o)#

Returns the negation of o on success, or NULL on failure. This is the equivalent of the Python expression -o.

PyNumber_Positive(PyObject *o)#

Returns o on success, or NULL on failure. This is the equivalent of the Python expression +o.

PyNumber_Absolute(PyObject *o)#

Returns the absolute value of o, or NULL on failure. This is the equivalent of the Python expression abs(o).

PyNumber_Invert(PyObject *o)#

Returns the bitwise negation of o on success, or NULL on failure. This is the equivalent of the Python expression o.

PyNumber_Lshift(PyObject *o1, PyObject *o2)#

Returns the result of left shifting o1 by o2 on success, or NULL on failure. This is the equivalent of the Python expression o1<<o2.

PyNumber_Rshift(PyObject *o1, PyObject *o2)#

Returns the result of right shifting o1 by o2 on success, or NULL on failure. This is the equivalent of the Python expression o1>>o2.

PyNumber_And(PyObject *o1, PyObject *o2)#

Returns the “bitwise and” of o2 and o2 on success and NULL on failure. This is the equivalent of the Python expression o1&o2.

PyNumber_Xor(PyObject *o1, PyObject *o2)#

Returns the “bitwise exclusive or” of o1 by o2 on success, or NULL on failure. This is the equivalent of the Python expression o1 ^o2.

PyNumber_Or(PyObject *o1, PyObject *o2)#

Returns the “bitwise or” of o1 and o2 on success, or NULL on failure. This is the equivalent of the Python expression o1|o2.

PyNumber_InPlaceAdd(PyObject *o1, PyObject *o2)#

Returns the result of adding o1 and o2, or NULL on failure. The operation is done in-place when o1 supports it. This is the equivalent of the Python expression o1+=o2.

PyNumber_InPlaceSubtract(PyObject *o1, PyObject *o2)#

Returns the result of subtracting o2 from o1, or NULL on failure. The operation is done in-place when o1 supports it. This is the equivalent of the Python expression o1-=o2.

PyNumber_InPlaceMultiply(PyObject *o1, PyObject *o2)#

Returns the result of multiplying o1 and o2, or NULL on failure. The operation is done in-place when o1 supports it. This is the equivalent of the Python expression o1*=o2.

PyNumber_InPlaceDivide(PyObject *o1, PyObject *o2)#

Returns the result of dividing o1 by o2, or NULL on failure. The operation is done in-place when o1 supports it. This is the equivalent of the Python expression o1/=o2.

PyNumber_InPlaceRemainder(PyObject *o1, PyObject *o2)#

Returns the remainder of dividing o1 by o2, or NULL on failure. The operation is done in-place when o1 supports it. This is the equivalent of the Python expression o1%=o2.

PyNumber_InPlacePower(PyObject *o1, PyObject *o2, PyObject *o3)#

See the built-in function pow(). Returns NULL on failure. The operation is done in-place when o1 supports it. This is the equivalent of the Python expression o1**=o2 when o3 is , or an in-place variant of pow(o1, o2, o3) otherwise. If o3 is to be ignored, pass in its place (passing NULL for o3 would cause an illegal memory access).

PyNumber_InPlaceLshift(PyObject *o1, PyObject *o2)#

Returns the result of left shifting o1 by o2 on success, or NULL on failure. The operation is done in-place when o1 supports it. This is the equivalent of the Python expression o1<<=o2.

PyNumber_InPlaceRshift(PyObject *o1, PyObject *o2)#

Returns the result of right shifting o1 by o2 on success, or NULL on failure. The operation is done in-place when o1 supports it. This is the equivalent of the Python expression o1>>=o2.

PyNumber_InPlaceAnd(PyObject *o1, PyObject *o2)#

Returns the “bitwise and” of o2 and o2 on success and NULL on failure. The operation is done in-place when o1 supports it. This is the equivalent of the Python expression o1&=o2.

PyNumber_InPlaceXor(PyObject *o1, PyObject *o2)#

Returns the “bitwise exclusive or” of o1 by o2 on success, or NULL on failure. The operation is done in-place when o1 supports it. This is the equivalent of the Python expression o1=̂o2.

PyNumber_InPlaceOr(PyObject *o1, PyObject *o2)#

Returns the “bitwise or” of o1 and o2 on success, or NULL on failure. The operation is done in-place when o1 supports it. This is the equivalent of the Python expression o1|=o2.

PyNumber_Coerce(PyObject **p1, PyObject **p2)#

This function takes the addresses of two variables of type PyObject*. If the objects pointed to by *p1 and *p2 have the same type, increment their reference count and return 0 (success). If the objects can be converted to a common numeric type, replace *p1 and *p2 by their converted value (with ’new’ reference counts), and return 0. If no conversion is possible, or if some other error occurs, return -1 (failure) and don’t increment the reference counts. The call PyNumber_Coerce(&o1, &o2) is equivalent to the Python statement o1, o2 = coerce(o1, o2).

PyNumber_Int(PyObject *o)#

Returns the o converted to an integer object on success, or NULL on failure. This is the equivalent of the Python expression int(o).

PyNumber_Long(PyObject *o)#

Returns the o converted to a long integer object on success, or NULL on failure. This is the equivalent of the Python expression long(o).

PyNumber_Float(PyObject *o)#

Returns the o converted to a float object on success, or NULL on failure. This is the equivalent of the Python expression float(o).

Sequence Protocol#

PySequence_Check(PyObject *o)#

Return 1 if the object provides sequence protocol, and 0 otherwise. This function always succeeds.

PySequence_Length(PyObject *o)#

Returns the number of objects in sequence o on success, and -1 on failure. For objects that do not provide sequence protocol, this is equivalent to the Python expression len(o).

PySequence_Concat(PyObject *o1, PyObject *o2)#

Return the concatenation of o1 and o2 on success, and NULL on failure. This is the equivalent of the Python expression o1+o2.

PySequence_Repeat(PyObject *o, int count)#

Return the result of repeating sequence object o count times, or NULL on failure. This is the equivalent of the Python expression o*count.

PySequence_InPlaceConcat(PyObject *o1, PyObject *o2)#

Return the concatenation of o1 and o2 on success, and NULL on failure. The operation is done in-place when o1 supports it. This is the equivalent of the Python expression o1+=o2.

PySequence_InPlaceRepeat(PyObject *o, int count)#

Return the result of repeating sequence object o count times, or NULL on failure. The operation is done in-place when o supports it. This is the equivalent of the Python expression o*=count.

PySequence_GetItem(PyObject *o, int i)#

Return the ith element of o, or NULL on failure. This is the equivalent of the Python expression o[i].

PySequence_GetSlice(PyObject *o, int i1, int i2)#

Return the slice of sequence object o between i1 and i2, or NULL on failure. This is the equivalent of the Python expression o[i1:i2].

PySequence_SetItem(PyObject *o, int i, PyObject *v)#

Assign object v to the ith element of o. Returns -1 on failure. This is the equivalent of the Python statement o[i] = v.

PySequence_DelItem(PyObject *o, int i)#

Delete the ith element of object v. Returns -1 on failure. This is the equivalent of the Python statement del o[i].

PySequence_SetSlice(PyObject *o, int i1, int i2, PyObject *v)#

Assign the sequence object v to the slice in sequence object o from i1 to i2. This is the equivalent of the Python statement o[i1:i2] = v.

PySequence_DelSlice(PyObject *o, int i1, int i2)#

Delete the slice in sequence object o from i1 to i2. Returns -1 on failure. This is the equivalent of the Python statement del o[i1:i2].

PySequence_Tuple(PyObject *o)#

Returns the o as a tuple on success, and NULL on failure. This is equivalent to the Python expression tuple(o).

PySequence_Count(PyObject *o, PyObject *value)#

Return the number of occurrences of value in o, that is, return the number of keys for which o[key] == value. On failure, return -1. This is equivalent to the Python expression o.count(value).

PySequence_Contains(PyObject *o, PyObject *value)#

Determine if o contains value. If an item in o is equal to value, return 1, otherwise return 0. On error, return -1. This is equivalent to the Python expression valueino.

PySequence_Index(PyObject *o, PyObject *value)#

Return the first index i for which o[i] == value. On error, return -1. This is equivalent to the Python expression o.index(value).

PySequence_List(PyObject *o)#

Return a list object with the same contents as the arbitrary sequence o. The returned list is guaranteed to be new.

PySequence_Tuple(PyObject *o)#

Return a tuple object with the same contents as the arbitrary sequence o. If o is a tuple, a new reference will be returned, otherwise a tuple will be constructed with the appropriate contents.

PySequence_Fast(PyObject *o, const char *m)#

Returns the sequence o as a tuple, unless it is already a tuple or list, in which case o is returned. Use to access the members of the result. Returns NULL on failure. If the object is not a sequence, raises TypeError with m as the message text.

PySequence_Fast_GET_ITEM(PyObject *o, int i)#

Return the ith element of o, assuming that o was returned by , and that i is within bounds. The caller is expected to get the length of the sequence by calling on o, since lists and tuples are guaranteed to always return their true length.

Mapping Protocol#

PyMapping_Check(PyObject *o)#

Return 1 if the object provides mapping protocol, and 0 otherwise. This function always succeeds.

PyMapping_Length(PyObject *o)#

Returns the number of keys in object o on success, and -1 on failure. For objects that do not provide mapping protocol, this is equivalent to the Python expression len(o).

PyMapping_DelItemString(PyObject *o, char *key)#

Remove the mapping for object key from the object o. Return -1 on failure. This is equivalent to the Python statement del o[key].

PyMapping_DelItem(PyObject *o, PyObject *key)#

Remove the mapping for object key from the object o. Return -1 on failure. This is equivalent to the Python statement del o[key].

PyMapping_HasKeyString(PyObject *o, char *key)#

On success, return 1 if the mapping object has the key key and 0 otherwise. This is equivalent to the Python expression o.has_key(key). This function always succeeds.

PyMapping_HasKey(PyObject *o, PyObject *key)#

Return 1 if the mapping object has the key key and 0 otherwise. This is equivalent to the Python expression o.has_key(key). This function always succeeds.

PyMapping_Keys(PyObject *o)#

On success, return a list of the keys in object o. On failure, return NULL. This is equivalent to the Python expression o.keys().

PyMapping_Values(PyObject *o)#

On success, return a list of the values in object o. On failure, return NULL. This is equivalent to the Python expression o.values().

PyMapping_Items(PyObject *o)#

On success, return a list of the items in object o, where each item is a tuple containing a key-value pair. On failure, return NULL. This is equivalent to the Python expression o.items().

PyMapping_GetItemString(PyObject *o, char *key)#

Return element of o corresponding to the object key or NULL on failure. This is the equivalent of the Python expression o[key].

PyMapping_SetItemString(PyObject *o, char *key, PyObject *v)#

Map the object key to the value v in object o. Returns -1 on failure. This is the equivalent of the Python statement o[key] = v.

Concrete Objects Layer#

The functions in this chapter are specific to certain Python object types. Passing them an object of the wrong type is not a good idea; if you receive an object from a Python program and you are not sure that it has the right type, you must perform a type check first; for example. to check that an object is a dictionary, use . The chapter is structured like the “family tree” of Python object types.

Fundamental Objects#

This section describes Python type objects and the singleton object None.

Type Objects#

PyTypeObject#

The C structure of the objects used to describe built-in types.

PyType_Type#

This is the type object for type objects; it is the same object as types.TypeType in the Python layer.

PyType_Check(PyObject *o)#

Returns true is the object o is a type object.

PyType_HasFeature(PyObject *o, int feature)#

Returns true if the type object o sets the feature feature. Type features are denoted by single bit flags. The only defined feature flag is , described in section.

The None Object#

Note that the PyTypeObject for None is not directly exposed in the Python/C API. Since None is a singleton, testing for object identity (using == in C) is sufficient. There is no function for the same reason.

Py_None#

The Python None object, denoting lack of value. This object has no methods.

Sequence Objects#

Generic operations on sequence objects were discussed in the previous chapter; this section deals with the specific kinds of sequence objects that are intrinsic to the Python language.

String Objects#

PyStringObject#

This subtype of PyObject represents a Python string object.

PyString_Type#

This instance of PyTypeObject represents the Python string type; it is the same object as types.TypeType in the Python layer..

PyString_Check(PyObject *o)#

Returns true if the object o is a string object.

PyString_FromString(const char *v)#

Returns a new string object with the value v on success, and NULL on failure.

PyString_FromStringAndSize(const char *v, int len)#

Returns a new string object with the value v and length len on success, and NULL on failure. If v is NULL, the contents of the string are uninitialized.

PyString_Size(PyObject *string)#

Returns the length of the string in string object string.

PyString_GET_SIZE(PyObject *string)#

Macro form of but without error checking.

PyString_AsString(PyObject *string)#

Returns a null-terminated representation of the contents of string. The pointer refers to the internal buffer of string, not a copy. The data must not be modified in any way. It must not be de-allocated.

PyString_AS_STRING(PyObject *string)#

Macro form of but without error checking.

PyString_AsStringAndSize(PyObject *obj, char **buffer, int *length)#

Returns a null-terminated representation of the contents of the object obj through the output variables buffer and length.

The function accepts both string and Unicode objects as input. For Unicode objects it returns the default encoded version of the object. If length is set to NULL, the resulting buffer may not contain null characters; if it does, the function returns -1 and a TypeError is raised.

The buffer refers to an internal string buffer of obj, not a copy. The data must not be modified in any way. It must not be de-allocated.

PyString_Concat(PyObject **string, PyObject *newpart)#

Creates a new string object in *string containing the contents of newpart appended to string; the caller will own the new reference. The reference to the old value of string will be stolen. If the new string cannot be created, the old reference to string will still be discarded and the value of *string will be set to NULL; the appropriate exception will be set.

PyString_ConcatAndDel(PyObject **string, PyObject *newpart)#

Creates a new string object in *string containing the contents of newpart appended to string. This version decrements the reference count of newpart.

_PyString_Resize(PyObject **string, int newsize)#

A way to resize a string object even though it is “immutable”. Only use this to build up a brand new string object; don’t use this if the string may already be known in other parts of the code.

PyString_Format(PyObject *format, PyObject *args)#

Returns a new string object from format and args. Analogous to format % args. The args argument must be a tuple.

PyString_InternInPlace(PyObject **string)#

Intern the argument *string in place. The argument must be the address of a pointer variable pointing to a Python string object. If there is an existing interned string that is the same as *string, it sets *string to it (decrementing the reference count of the old string object and incrementing the reference count of the interned string object), otherwise it leaves *string alone and interns it (incrementing its reference count). (Clarification: even though there is a lot of talk about reference counts, think of this function as reference-count-neutral; you own the object after the call if and only if you owned it before the call.)

PyString_InternFromString(const char *v)#

A combination of and , returning either a new string object that has been interned, or a new (“owned”) reference to an earlier interned string object with the same value.

PyString_Decode(const char *s, int size, const char *encoding, const char *errors)#

Create a string object by decoding size bytes of the encoded buffer s. encoding and errors have the same meaning as the parameters of the same name in the unicode() builtin function. The codec to be used is looked up using the Python codec registry. Returns NULL in case an exception was raised by the codec.

PyString_Encode(const Py_UNICODE *s, int size, const char *encoding, const char *errors)#

Encodes the Py_UNICODE buffer of the given size and returns a Python string object. encoding and errors have the same meaning as the parameters of the same name in the string .encode() method. The codec to be used is looked up using the Python codec registry. Returns NULL in case an exception was raised by the codec.

PyString_AsEncodedString(PyObject *unicode, const char *encoding, const char *errors)#

Encodes a string object and returns the result as Python string object. encoding and errors have the same meaning as the parameters of the same name in the string .encode() method. The codec to be used is looked up using the Python codec registry. Returns NULL in case an exception was raised by the codec.

Unicode Objects#

These are the basic Unicode object types used for the Unicode implementation in Python:

Py_UNICODE#

This type represents a 16-bit unsigned storage type which is used by Python internally as basis for holding Unicode ordinals. On platforms where wchar_t is available and also has 16-bits, Py_UNICODE is a typedef alias for wchar_t to enhance native platform compatibility. On all other platforms, Py_UNICODE is a typedef alias for unsigned short.

PyUnicodeObject#

This subtype of PyObject represents a Python Unicode object.

PyUnicode_Type#

This instance of PyTypeObject represents the Python Unicode type.

The following APIs are really C macros and can be used to do fast checks and to access internal read-only data of Unicode objects:

PyUnicode_Check(PyObject *o)#

Returns true if the object o is a Unicode object.

PyUnicode_GET_SIZE(PyObject *o)#

Returns the size of the object. o has to be a PyUnicodeObject (not checked).

PyUnicode_GET_DATA_SIZE(PyObject *o)#

Returns the size of the object’s internal buffer in bytes. o has to be a PyUnicodeObject (not checked).

PyUnicode_AS_UNICODE(PyObject *o)#

Returns a pointer to the internal Py_UNICODE buffer of the object. o has to be a PyUnicodeObject (not checked).

PyUnicode_AS_DATA(PyObject *o)#

Returns a (const char *) pointer to the internal buffer of the object. o has to be a PyUnicodeObject (not checked).

Unicode provides many different character properties. The most often needed ones are available through these macros which are mapped to C functions depending on the Python configuration.

Py_UNICODE_ISSPACE(Py_UNICODE ch)#

Returns 1/0 depending on whether ch is a whitespace character.

Py_UNICODE_ISLOWER(Py_UNICODE ch)#

Returns 1/0 depending on whether ch is a lowercase character.

Py_UNICODE_ISUPPER(Py_UNICODE ch)#

Returns 1/0 depending on whether ch is an uppercase character.

Py_UNICODE_ISTITLE(Py_UNICODE ch)#

Returns 1/0 depending on whether ch is a titlecase character.

Py_UNICODE_ISLINEBREAK(Py_UNICODE ch)#

Returns 1/0 depending on whether ch is a linebreak character.

Py_UNICODE_ISDECIMAL(Py_UNICODE ch)#

Returns 1/0 depending on whether ch is a decimal character.

Py_UNICODE_ISDIGIT(Py_UNICODE ch)#

Returns 1/0 depending on whether ch is a digit character.

Py_UNICODE_ISNUMERIC(Py_UNICODE ch)#

Returns 1/0 depending on whether ch is a numeric character.

Py_UNICODE_ISALPHA(Py_UNICODE ch)#

Returns 1/0 depending on whether ch is an alphabetic character.

Py_UNICODE_ISALNUM(Py_UNICODE ch)#

Returns 1/0 depending on whether ch is an alphanumeric character.

These APIs can be used for fast direct character conversions:

Py_UNICODE_TOLOWER(Py_UNICODE ch)#

Returns the character ch converted to lower case.

Py_UNICODE_TOUPPER(Py_UNICODE ch)#

Returns the character ch converted to upper case.

Py_UNICODE_TOTITLE(Py_UNICODE ch)#

Returns the character ch converted to title case.

Py_UNICODE_TODECIMAL(Py_UNICODE ch)#

Returns the character ch converted to a decimal positive integer. Returns -1 in case this is not possible. Does not raise exceptions.

Py_UNICODE_TODIGIT(Py_UNICODE ch)#

Returns the character ch converted to a single digit integer. Returns -1 in case this is not possible. Does not raise exceptions.

Py_UNICODE_TONUMERIC(Py_UNICODE ch)#

Returns the character ch converted to a (positive) double. Returns -1.0 in case this is not possible. Does not raise exceptions.

To create Unicode objects and access their basic sequence properties, use these APIs:

PyUnicode_FromUnicode(const Py_UNICODE *u, int size)#

Create a Unicode Object from the Py_UNICODE buffer u of the given size. u may be NULL which causes the contents to be undefined. It is the user’s responsibility to fill in the needed data. The buffer is copied into the new object.

PyUnicode_AsUnicode(PyObject *unicode)#

Return a read-only pointer to the Unicode object’s internal Py_UNICODE buffer.

PyUnicode_GetSize(PyObject *unicode)#

Return the length of the Unicode object.

PyUnicode_FromEncodedObject(PyObject *obj, const char *encoding, const char *errors)#

Coerce an encoded object obj to an Unicode object and return a reference with incremented refcount.

Coercion is done in the following way:

1. Unicode objects are passed back as-is with incremented refcount. Note: these cannot be decoded; passing a non-NULL value for encoding will result in a TypeError.

2. String and other char buffer compatible objects are decoded according to the given encoding and using the error handling defined by errors. Both can be NULL to have the interface use the default values (see the next section for details).

3. All other objects cause an exception.

The API returns NULL in case of an error. The caller is responsible for decref’ing the returned objects.

PyUnicode_FromObject(PyObject *obj)#

Shortcut for PyUnicode_FromEncodedObject(obj, NULL, “strict”) which is used throughout the interpreter whenever coercion to Unicode is needed.

If the platform supports wchar_t and provides a header file wchar.h, Python can interface directly to this type using the following functions. Support is optimized if Python’s own Py_UNICODE type is identical to the system’s wchar_t.

PyUnicode_FromWideChar(const wchar_t *w, int size)#

Create a Unicode Object from the whcar_t buffer w of the given size. Returns NULL on failure.

PyUnicode_AsWideChar(PyUnicodeObject *unicode, wchar_t *w, int size)#

Copies the Unicode Object contents into the whcar_t buffer w. At most size whcar_t characters are copied. Returns the number of whcar_t characters copied or -1 in case of an error.

Builtin Codecs#

Python provides a set of builtin codecs which are written in C for speed. All of these codecs are directly usable via the following functions.

Many of the following APIs take two arguments encoding and errors. These parameters encoding and errors have the same semantics as the ones of the builtin unicode() Unicode object constructor.

Setting encoding to NULL causes the default encoding to be used which is UTF-8.

Error handling is set by errors which may also be set to NULL meaning to use the default handling defined for the codec. Default error handling for all builtin codecs is “strict” (ValueErrors are raised).

The codecs all use a similar interface. Only deviation from the following generic ones are documented for simplicity.

These are the generic codec APIs:

PyUnicode_Decode(const char *s, int size, const char *encoding, const char *errors)#

Create a Unicode object by decoding size bytes of the encoded string s. encoding and errors have the same meaning as the parameters of the same name in the unicode() builtin function. The codec to be used is looked up using the Python codec registry. Returns NULL in case an exception was raised by the codec.

PyUnicode_Encode(const Py_UNICODE *s, int size, const char *encoding, const char *errors)#

Encodes the Py_UNICODE buffer of the given size and returns a Python string object. encoding and errors have the same meaning as the parameters of the same name in the Unicode .encode() method. The codec to be used is looked up using the Python codec registry. Returns NULL in case an exception was raised by the codec.

PyUnicode_AsEncodedString(PyObject *unicode, const char *encoding, const char *errors)#

Encodes a Unicode object and returns the result as Python string object. encoding and errors have the same meaning as the parameters of the same name in the Unicode .encode() method. The codec to be used is looked up using the Python codec registry. Returns NULL in case an exception was raised by the codec.

These are the UTF-8 codec APIs:

PyUnicode_DecodeUTF8(const char *s, int size, const char *errors)#

Creates a Unicode object by decoding size bytes of the UTF-8 encoded string s. Returns NULL in case an exception was raised by the codec.

PyUnicode_EncodeUTF8(const Py_UNICODE *s, int size, const char *errors)#

Encodes the Py_UNICODE buffer of the given size using UTF-8 and returns a Python string object. Returns NULL in case an exception was raised by the codec.

PyUnicode_AsUTF8String(PyObject *unicode)#

Encodes a Unicode objects using UTF-8 and returns the result as Python string object. Error handling is “strict”. Returns NULL in case an exception was raised by the codec.

These are the UTF-16 codec APIs:

PyUnicode_DecodeUTF16(const char *s, int size, const char *errors, int *byteorder)#

Decodes length bytes from a UTF-16 encoded buffer string and returns the corresponding Unicode object.

errors (if non-NULL) defines the error handling. It defaults to “strict”.

If byteorder is non-NULL, the decoder starts decoding using the given byte order:

   *byteorder == -1: little endian
   *byteorder == 0:  native order
   *byteorder == 1:  big endian

and then switches according to all byte order marks (BOM) it finds in the input data. BOM marks are not copied into the resulting Unicode string. After completion, *byteorder is set to the current byte order at the end of input data.

If byteorder is NULL, the codec starts in native order mode.

Returns NULL in case an exception was raised by the codec.

PyUnicode_EncodeUTF16(const Py_UNICODE *s, int size, const char *errors, int byteorder)#

Returns a Python string object holding the UTF-16 encoded value of the Unicode data in s.

If byteorder is not 0, output is written according to the following byte order:

   byteorder == -1: little endian
   byteorder == 0:  native byte order (writes a BOM mark)
   byteorder == 1:  big endian

If byteorder is 0, the output string will always start with the Unicode BOM mark (U+FEFF). In the other two modes, no BOM mark is prepended.

Note that Py_UNICODE data is being interpreted as UTF-16 reduced to UCS-2. This trick makes it possible to add full UTF-16 capabilities at a later point without comprimising the APIs.

Returns NULL in case an exception was raised by the codec.

PyUnicode_AsUTF16String(PyObject *unicode)#

Returns a Python string using the UTF-16 encoding in native byte order. The string always starts with a BOM mark. Error handling is “strict”. Returns NULL in case an exception was raised by the codec.

These are the “Unicode Esacpe” codec APIs:

PyUnicode_DecodeUnicodeEscape(const char *s, int size, const char *errors)#

Creates a Unicode object by decoding size bytes of the Unicode-Esacpe encoded string s. Returns NULL in case an exception was raised by the codec.

PyUnicode_EncodeUnicodeEscape(const Py_UNICODE *s, int size, const char *errors)#

Encodes the Py_UNICODE buffer of the given size using Unicode-Escape and returns a Python string object. Returns NULL in case an exception was raised by the codec.

PyUnicode_AsUnicodeEscapeString(PyObject *unicode)#

Encodes a Unicode objects using Unicode-Escape and returns the result as Python string object. Error handling is “strict”. Returns NULL in case an exception was raised by the codec.

These are the “Raw Unicode Esacpe” codec APIs:

PyUnicode_DecodeRawUnicodeEscape(const char *s, int size, const char *errors)#

Creates a Unicode object by decoding size bytes of the Raw-Unicode-Esacpe encoded string s. Returns NULL in case an exception was raised by the codec.

PyUnicode_EncodeRawUnicodeEscape(const Py_UNICODE *s, int size, const char *errors)#

Encodes the Py_UNICODE buffer of the given size using Raw-Unicode-Escape and returns a Python string object. Returns NULL in case an exception was raised by the codec.

PyUnicode_AsRawUnicodeEscapeString(PyObject *unicode)#

Encodes a Unicode objects using Raw-Unicode-Escape and returns the result as Python string object. Error handling is “strict”. Returns NULL in case an exception was raised by the codec.

These are the Latin-1 codec APIs:

Latin-1 corresponds to the first 256 Unicode ordinals and only these are accepted by the codecs during encoding.

PyUnicode_DecodeLatin1(const char *s, int size, const char *errors)#

Creates a Unicode object by decoding size bytes of the Latin-1 encoded string s. Returns NULL in case an exception was raised by the codec.

PyUnicode_EncodeLatin1(const Py_UNICODE *s, int size, const char *errors)#

Encodes the Py_UNICODE buffer of the given size using Latin-1 and returns a Python string object. Returns NULL in case an exception was raised by the codec.

PyUnicode_AsLatin1String(PyObject *unicode)#

Encodes a Unicode objects using Latin-1 and returns the result as Python string object. Error handling is “strict”. Returns NULL in case an exception was raised by the codec.

These are the ASCII codec APIs. Only 7-bit ASCII data is accepted. All other codes generate errors.

PyUnicode_DecodeASCII(const char *s, int size, const char *errors)#

Creates a Unicode object by decoding size bytes of the ASCII encoded string s. Returns NULL in case an exception was raised by the codec.

PyUnicode_EncodeASCII(const Py_UNICODE *s, int size, const char *errors)#

Encodes the Py_UNICODE buffer of the given size using ASCII and returns a Python string object. Returns NULL in case an exception was raised by the codec.

PyUnicode_AsASCIIString(PyObject *unicode)#

Encodes a Unicode objects using ASCII and returns the result as Python string object. Error handling is “strict”. Returns NULL in case an exception was raised by the codec.

These are the mapping codec APIs:

This codec is special in that it can be used to implement many different codecs (and this is in fact what was done to obtain most of the standard codecs included in the encodings package). The codec uses mapping to encode and decode characters.

Decoding mappings must map single string characters to single Unicode characters, integers (which are then interpreted as Unicode ordinals) or None (meaning “undefined mapping” and causing an error).

Encoding mappings must map single Unicode characters to single string characters, integers (which are then interpreted as Latin-1 ordinals) or None (meaning “undefined mapping” and causing an error).

The mapping objects provided must only support the getitem mapping interface.

If a character lookup fails with a LookupError, the character is copied as-is meaning that its ordinal value will be interpreted as Unicode or Latin-1 ordinal resp. Because of this, mappings only need to contain those mappings which map characters to different code points.

PyUnicode_DecodeCharmap(const char *s, int size, PyObject *mapping, const char *errors)#

Creates a Unicode object by decoding size bytes of the encoded string s using the given mapping object. Returns NULL in case an exception was raised by the codec.

PyUnicode_EncodeCharmap(const Py_UNICODE *s, int size, PyObject *mapping, const char *errors)#

Encodes the Py_UNICODE buffer of the given size using the given mapping object and returns a Python string object. Returns NULL in case an exception was raised by the codec.

PyUnicode_AsCharmapString(PyObject *unicode, PyObject *mapping)#

Encodes a Unicode objects using the given mapping object and returns the result as Python string object. Error handling is “strict”. Returns NULL in case an exception was raised by the codec.

The following codec API is special in that maps Unicode to Unicode.

PyUnicode_TranslateCharmap(const Py_UNICODE *s, int size, PyObject *table, const char *errors)#

Translates a Py_UNICODE buffer of the given length by applying a character mapping table to it and returns the resulting Unicode object. Returns NULL when an exception was raised by the codec.

The mapping table must map Unicode ordinal integers to Unicode ordinal integers or None (causing deletion of the character).

Mapping tables must only provide the getitem interface, e.g. dictionaries or sequences. Unmapped character ordinals (ones which cause a LookupError) are left untouched and are copied as-is.

These are the MBCS codec APIs. They are currently only available on Windows and use the Win32 MBCS converters to implement the conversions. Note that MBCS (or DBCS) is a class of encodings, not just one. The target encoding is defined by the user settings on the machine running the codec.

PyUnicode_DecodeMBCS(const char *s, int size, const char *errors)#

Creates a Unicode object by decoding size bytes of the MBCS encoded string s. Returns NULL in case an exception was raised by the codec.

PyUnicode_EncodeMBCS(const Py_UNICODE *s, int size, const char *errors)#

Encodes the Py_UNICODE buffer of the given size using MBCS and returns a Python string object. Returns NULL in case an exception was raised by the codec.

PyUnicode_AsMBCSString(PyObject *unicode)#

Encodes a Unicode objects using MBCS and returns the result as Python string object. Error handling is “strict”. Returns NULL in case an exception was raised by the codec.

Methods and Slot Functions#

The following APIs are capable of handling Unicode objects and strings on input (we refer to them as strings in the descriptions) and return Unicode objects or integers as apporpriate.

They all return NULL or -1 in case an exception occurrs.

PyUnicode_Concat(PyObject *left, PyObject *right)#

Concat two strings giving a new Unicode string.

PyUnicode_Split(PyObject *s, PyObject *sep, int maxsplit)#

Split a string giving a list of Unicode strings.

If sep is NULL, splitting will be done at all whitespace substrings. Otherwise, splits occur at the given separator.

At most maxsplit splits will be done. If negative, no limit is set.

Separators are not included in the resulting list.

PyUnicode_Splitlines(PyObject *s, int maxsplit)#

Split a Unicode string at line breaks, returning a list of Unicode strings. CRLF is considered to be one line break. The Line break characters are not included in the resulting strings.

PyUnicode_Translate(PyObject *str, PyObject *table, const char *errors)#

Translate a string by applying a character mapping table to it and return the resulting Unicode object.

The mapping table must map Unicode ordinal integers to Unicode ordinal integers or None (causing deletion of the character).

Mapping tables must only provide the getitem interface, e.g. dictionaries or sequences. Unmapped character ordinals (ones which cause a LookupError) are left untouched and are copied as-is.

errors has the usual meaning for codecs. It may be NULL which indicates to use the default error handling.

PyUnicode_Join(PyObject *separator, PyObject *seq)#

Join a sequence of strings using the given separator and return the resulting Unicode string.

PyUnicode_Tailmatch(PyObject *str, PyObject *substr, int start, int end, int direction)#

Return 1 if substr matches str[start:end] at the given tail end (direction == -1 means to do a prefix match, direction == 1 a suffix match), 0 otherwise.

PyUnicode_Find(PyObject *str, PyObject *substr, int start, int end, int direction)#

Return the first position of substr in str[start:end] using the given direction (direction == 1 means to do a forward search, direction == -1 a backward search), 0 otherwise.

PyUnicode_Count(PyObject *str, PyObject *substr, int start, int end)#

Count the number of occurrences of substr in str[start:end]

PyUnicode_Replace(PyObject *str, PyObject *substr, PyObject *replstr, int maxcount)#

Replace at most maxcount occurrences of substr in str with replstr and return the resulting Unicode object. maxcount == -1 means: replace all occurrences.

PyUnicode_Compare(PyObject *left, PyObject *right)#

Compare two strings and return -1, 0, 1 for less than, equal, greater than resp.

PyUnicode_Format(PyObject *format, PyObject *args)#

Returns a new string object from format and args; this is analogous to format % args. The args argument must be a tuple.

PyUnicode_Contains(PyObject *container, PyObject *element)#

Checks whether element is contained in container and returns true or false accordingly.

element has to coerce to a one element Unicode string. -1 is returned in case of an error.

Buffer Objects#

Python objects implemented in C can export a group of functions called the “buffer interface.” These functions can be used by an object to expose its data in a raw, byte-oriented format. Clients of the object can use the buffer interface to access the object data directly, without needing to copy it first.

Two examples of objects that support the buffer interface are strings and arrays. The string object exposes the character contents in the buffer interface’s byte-oriented form. An array can also expose its contents, but it should be noted that array elements may be multi-byte values.

An example user of the buffer interface is the file object’s write() method. Any object that can export a series of bytes through the buffer interface can be written to a file. There are a number of format codes to that operate against an object’s buffer interface, returning data from the target object.

More information on the buffer interface is provided in the section “Buffer Object Structures” (section), under the description for PyBufferProcs.

A “buffer object” is defined in the bufferobject.h header (included by Python.h). These objects look very similar to string objects at the Python programming level: they support slicing, indexing, concatenation, and some other standard string operations. However, their data can come from one of two sources: from a block of memory, or from another object which exports the buffer interface.

Buffer objects are useful as a way to expose the data from another object’s buffer interface to the Python programmer. They can also be used as a zero-copy slicing mechanism. Using their ability to reference a block of memory, it is possible to expose any data to the Python programmer quite easily. The memory could be a large, constant array in a C extension, it could be a raw block of memory for manipulation before passing to an operating system library, or it could be used to pass around structured data in its native, in-memory format.

PyBufferObject#

This subtype of PyObject represents a buffer object.

PyBuffer_Type#

The instance of PyTypeObject which represents the Python buffer type; it is the same object as types.BufferType in the Python layer..

Py_END_OF_BUFFER#

This constant may be passed as the size parameter to or . It indicates that the new PyBufferObject should refer to base object from the specified offset to the end of its exported buffer. Using this enables the caller to avoid querying the base object for its length.

PyBuffer_Check(PyObject *p)#

Return true if the argument has type .

PyBuffer_FromObject(PyObject *base, int offset, int size)#

Return a new read-only buffer object. This raises TypeError if base doesn’t support the read-only buffer protocol or doesn’t provide exactly one buffer segment, or it raises ValueError if offset is less than zero. The buffer will hold a reference to the base object, and the buffer’s contents will refer to the base object’s buffer interface, starting as position offset and extending for size bytes. If size is , then the new buffer’s contents extend to the length of the base object’s exported buffer data.

PyBuffer_FromReadWriteObject(PyObject *base, int offset, int size)#

Return a new writable buffer object. Parameters and exceptions are similar to those for . If the base object does not export the writeable buffer protocol, then TypeError is raised.

PyBuffer_FromMemory(void *ptr, int size)#

Return a new read-only buffer object that reads from a specified location in memory, with a specified size. The caller is responsible for ensuring that the memory buffer, passed in as ptr, is not deallocated while the returned buffer object exists. Raises ValueError if size is less than zero. Note that may not be passed for the size parameter; ValueError will be raised in that case.

PyBuffer_FromReadWriteMemory(void *ptr, int size)#

Similar to , but the returned buffer is writable.

PyBuffer_New(int size)#

Returns a new writable buffer object that maintains its own memory buffer of size bytes. ValueError is returned if size is not zero or positive.

Tuple Objects#

PyTupleObject#

This subtype of PyObject represents a Python tuple object.

PyTuple_Type#

This instance of PyTypeObject represents the Python tuple type; it is the same object as types.TupleType in the Python layer..

PyTuple_Check(PyObject *p)#

Return true if the argument is a tuple object.

PyTuple_New(int len)#

Return a new tuple object of size len, or NULL on failure.

PyTuple_Size(PyTupleObject *p)#

Takes a pointer to a tuple object, and returns the size of that tuple.

PyTuple_GetItem(PyTupleObject *p, int pos)#

Returns the object at position pos in the tuple pointed to by p. If pos is out of bounds, returns NULL and sets an IndexError exception.

PyTuple_GET_ITEM(PyTupleObject *p, int pos)#

Does the same, but does no checking of its arguments.

PyTuple_GetSlice(PyTupleObject *p, int low, int high)#

Takes a slice of the tuple pointed to by p from low to high and returns it as a new tuple.

PyTuple_SetItem(PyObject *p, int pos, PyObject *o)#

Inserts a reference to object o at position pos of the tuple pointed to by p. It returns 0 on success. Note: This function “steals” a reference to o.

PyTuple_SET_ITEM(PyObject *p, int pos, PyObject *o)#

Does the same, but does no error checking, and should only be used to fill in brand new tuples. Note: This function “steals” a reference to o.

_PyTuple_Resize(PyTupleObject *p, int newsize, int last_is_sticky)#

Can be used to resize a tuple. newsize will be the new length of the tuple. Because tuples are supposed to be immutable, this should only be used if there is only one reference to the object. Do not use this if the tuple may already be known to some other part of the code. The tuple will always grow or shrink at the end. The last_is_sticky flag is not used and should always be false. Think of this as destroying the old tuple and creating a new one, only more efficiently. Returns 0 on success and -1 on failure (in which case a MemoryError or SystemError will be raised).

List Objects#

PyListObject#

This subtype of PyObject represents a Python list object.

PyList_Type#

This instance of PyTypeObject represents the Python list type. This is the same object as types.ListType.

PyList_Check(PyObject *p)#

Returns true if its argument is a PyListObject.

PyList_New(int len)#

Returns a new list of length len on success, or NULL on failure.

PyList_Size(PyObject *list)#

Returns the length of the list object in list; this is equivalent to len(list) on a list object.

PyList_GET_SIZE(PyObject *list)#

Macro form of without error checking.

PyList_GetItem(PyObject *list, int index)#

Returns the object at position pos in the list pointed to by p. If pos is out of bounds, returns NULL and sets an IndexError exception.

PyList_GET_ITEM(PyObject *list, int i)#

Macro form of without error checking.

PyList_SetItem(PyObject *list, int index, PyObject *item)#

Sets the item at index index in list to item. Note: This function “steals” a reference to item.

PyList_SET_ITEM(PyObject *list, int i, PyObject *o)#

Macro form of without error checking. Note: This function “steals” a reference to item.

PyList_Insert(PyObject *list, int index, PyObject *item)#

Inserts the item item into list list in front of index index. Returns 0 if successful; returns -1 and raises an exception if unsuccessful. Analogous to list.insert(index, item).

PyList_Append(PyObject *list, PyObject *item)#

Appends the object item at the end of list list. Returns 0 if successful; returns -1 and sets an exception if unsuccessful. Analogous to list.append(item).

PyList_GetSlice(PyObject *list, int low, int high)#

Returns a list of the objects in list containing the objects between low and high. Returns NULL and sets an exception if unsuccessful. Analogous to list[low:high].

PyList_SetSlice(PyObject *list, int low, int high, PyObject *itemlist)#

Sets the slice of list between low and high to the contents of itemlist. Analogous to list[low:high] = itemlist. Returns 0 on success, -1 on failure.

PyList_Sort(PyObject *list)#

Sorts the items of list in place. Returns 0 on success, -1 on failure. This is equivalent to list.sort().

PyList_Reverse(PyObject *list)#

Reverses the items of list in place. Returns 0 on success, -1 on failure. This is the equivalent of list.reverse().

PyList_AsTuple(PyObject *list)#

Returns a new tuple object containing the contents of list; equivalent to tuple(list).

Mapping Objects#

Dictionary Objects#

PyDictObject#

This subtype of PyObject represents a Python dictionary object.

PyDict_Type#

This instance of PyTypeObject represents the Python dictionary type. This is exposed to Python programs as types.DictType and types.DictionaryType.

PyDict_Check(PyObject *p)#

Returns true if its argument is a PyDictObject.

PyDict_New()#

Returns a new empty dictionary, or NULL on failure.

PyDict_Clear(PyObject *p)#

Empties an existing dictionary of all key-value pairs.

PyDict_Copy(PyObject *p)#

Returns a new dictionary that contains the same key-value pairs as p. Empties an existing dictionary of all key-value pairs.

PyDict_SetItem(PyObject *p, PyObject *key, PyObject *val)#

Inserts value into the dictionary with a key of key. key must be hashable; if it isn’t, TypeError will be raised.

PyDict_SetItemString(PyDictObject *p, char *key, PyObject *val)#

Inserts value into the dictionary using key as a key. key should be a char*. The key object is created using PyString_FromString(key).

PyDict_DelItem(PyObject *p, PyObject *key)#

Removes the entry in dictionary p with key key. key must be hashable; if it isn’t, TypeError is raised.

PyDict_DelItemString(PyObject *p, char *key)#

Removes the entry in dictionary p which has a key specified by the string key.

PyDict_GetItem(PyObject *p, PyObject *key)#

Returns the object from dictionary p which has a key key. Returns NULL if the key key is not present, but without setting an exception.

PyDict_GetItemString(PyObject *p, char *key)#

This is the same as , but key is specified as a char*, rather than a PyObject*.

PyDict_Items(PyObject *p)#

Returns a PyListObject containing all the items from the dictionary, as in the dictinoary method items() (see the Python Library Reference).

PyDict_Keys(PyObject *p)#

Returns a PyListObject containing all the keys from the dictionary, as in the dictionary method keys() (see the Python Library Reference).

PyDict_Values(PyObject *p)#

Returns a PyListObject containing all the values from the dictionary p, as in the dictionary method values() (see the Python Library Reference).

PyDict_Size(PyObject *p)#

Returns the number of items in the dictionary. This is equivalent to len(p) on a dictionary.

PyDict_Next(PyDictObject *p, int *ppos, PyObject **pkey, PyObject **pvalue)#

Numeric Objects#

Plain Integer Objects#

PyIntObject#

This subtype of PyObject represents a Python integer object.

PyInt_Type#

This instance of PyTypeObject represents the Python plain integer type. This is the same object as types.IntType.

PyInt_Check(PyObject* o)#

Returns true if o is of type .

PyInt_FromLong(long ival)#

Creates a new integer object with a value of ival.

The current implementation keeps an array of integer objects for all integers between -1 and 100, when you create an int in that range you actually just get back a reference to the existing object. So it should be possible to change the value of 1. I suspect the behaviour of Python in this case is undefined. :-)

PyInt_AsLong(PyObject *io)#

Will first attempt to cast the object to a PyIntObject, if it is not already one, and then return its value.

PyInt_AS_LONG(PyObject *io)#

Returns the value of the object io. No error checking is performed.

PyInt_GetMax()#

Returns the system’s idea of the largest integer it can handle (, as defined in the system header files).

Long Integer Objects#

PyLongObject#

This subtype of PyObject represents a Python long integer object.

PyLong_Type#

This instance of PyTypeObject represents the Python long integer type. This is the same object as types.LongType.

PyLong_Check(PyObject *p)#

Returns true if its argument is a PyLongObject.

PyLong_FromLong(long v)#

Returns a new PyLongObject object from v, or NULL on failure.

PyLong_FromUnsignedLong(unsigned long v)#

Returns a new PyLongObject object from a C unsigned long, or NULL on failure.

PyLong_FromDouble(double v)#

Returns a new PyLongObject object from the integer part of v, or NULL on failure.

PyLong_AsLong(PyObject *pylong)#

Returns a C long representation of the contents of pylong. If pylong is greater than , an OverflowError is raised.

PyLong_AsUnsignedLong(PyObject *pylong)#

Returns a C unsigned long representation of the contents of pylong. If pylong is greater than , an OverflowError is raised.

PyLong_AsDouble(PyObject *pylong)#

Returns a C double representation of the contents of pylong.

PyLong_FromString(char *str, char **pend, int base)#

Return a new PyLongObject based on the string value in str, which is interpreted according to the radix in base. If pend is non-NULL, *pend will point to the first character in str which follows the representation of the number. If base is 0, the radix will be determined base on the leading characters of str: if str starts with ’0x’ or ’0X’, radix 16 will be used; if str starts with ’0’, radix 8 will be used; otherwise radix 10 will be used. If base is not 0, it must be between 2 and 36, inclusive. Leading spaces are ignored. If there are no digits, ValueError will be raised.

Floating Point Objects#

PyFloatObject#

This subtype of PyObject represents a Python floating point object.

PyFloat_Type#

This instance of PyTypeObject represents the Python floating point type. This is the same object as types.FloatType.

PyFloat_Check(PyObject *p)#

Returns true if its argument is a PyFloatObject.

PyFloat_FromDouble(double v)#

Creates a PyFloatObject object from v, or NULL on failure.

PyFloat_AsDouble(PyObject *pyfloat)#

Returns a C double representation of the contents of pyfloat.

PyFloat_AS_DOUBLE(PyObject *pyfloat)#

Returns a C double representation of the contents of pyfloat, but without error checking.

Complex Number Objects#

Python’s complex number objects are implemented as two distinct types when viewed from the C API: one is the Python object exposed to Python programs, and the other is a C structure which represents the actual complex number value. The API provides functions for working with both.

Complex Numbers as C Structures#

Note that the functions which accept these structures as parameters and return them as results do so by value rather than dereferencing them through pointers. This is consistent throughout the API.

Py_complex#

The C structure which corresponds to the value portion of a Python complex number object. Most of the functions for dealing with complex number objects use structures of this type as input or output values, as appropriate. It is defined as:

typedef struct {
   double real;
   double imag;
} Py_complex;

_Py_c_sum(Py_complex left, Py_complex right)#

Return the sum of two complex numbers, using the C Py_complex representation.

_Py_c_diff(Py_complex left, Py_complex right)#

Return the difference between two complex numbers, using the C Py_complex representation.

_Py_c_neg(Py_complex complex)#

Return the negation of the complex number complex, using the C Py_complex representation.

_Py_c_prod(Py_complex left, Py_complex right)#

Return the product of two complex numbers, using the C Py_complex representation.

_Py_c_quot(Py_complex dividend, Py_complex divisor)#

Return the quotient of two complex numbers, using the C Py_complex representation.

_Py_c_pow(Py_complex num, Py_complex exp)#

Return the exponentiation of num by exp, using the C Py_complex representation.

Complex Numbers as Python Objects#

PyComplexObject#

This subtype of PyObject represents a Python complex number object.

PyComplex_Type#

This instance of PyTypeObject represents the Python complex number type.

PyComplex_Check(PyObject *p)#

Returns true if its argument is a PyComplexObject.

PyComplex_FromCComplex(Py_complex v)#

Create a new Python complex number object from a C Py_complex value.

PyComplex_FromDoubles(double real, double imag)#

Returns a new PyComplexObject object from real and imag.

PyComplex_RealAsDouble(PyObject *op)#

Returns the real part of op as a C double.

PyComplex_ImagAsDouble(PyObject *op)#

Returns the imaginary part of op as a C double.

PyComplex_AsCComplex(PyObject *op)#

Returns the Py_complex value of the complex number op.

Other Objects#

File Objects#

Python’s built-in file objects are implemented entirely on the FILE* support from the C standard library. This is an implementation detail and may change in future releases of Python.

PyFileObject#

This subtype of PyObject represents a Python file object.

PyFile_Type#

This instance of PyTypeObject represents the Python file type. This is exposed to Python programs as types.FileType.

PyFile_Check(PyObject *p)#

Returns true if its argument is a PyFileObject.

PyFile_FromString(char *filename, char *mode)#

On success, returns a new file object that is opened on the file given by filename, with a file mode given by mode, where mode has the same semantics as the standard C routine . On failure, returns NULL.

PyFile_FromFile(FILE *fp, char *name, char *mode, int (close)(FILE))#

Creates a new PyFileObject from the already-open standard C file pointer, fp. The function close will be called when the file should be closed. Returns NULL on failure.

PyFile_AsFile(PyFileObject *p)#

Returns the file object associated with p as a FILE*.

PyFile_GetLine(PyObject *p, int n)#

Equivalent to p.readline(), this function reads one line from the object p. p may be a file object or any object with a readline() method. If n is 0, exactly one line is read, regardless of the length of the line. If n is greater than 0, no more than n bytes will be read from the file; a partial line can be returned. In both cases, an empty string is returned if the end of the file is reached immediately. If n is less than 0, however, one line is read regardless of length, but EOFError is raised if the end of the file is reached immediately.

PyFile_Name(PyObject *p)#

Returns the name of the file specified by p as a string object.

PyFile_SetBufSize(PyFileObject *p, int n)#

Available on systems with only. This should only be called immediately after file object creation.

PyFile_SoftSpace(PyObject *p, int newflag)#

This function exists for internal use by the interpreter. Sets the softspace attribute of p to newflag and returns the previous value. p does not have to be a file object for this function to work properly; any object is supported (thought its only interesting if the softspace attribute can be set). This function clears any errors, and will return 0 as the previous value if the attribute either does not exist or if there were errors in retrieving it. There is no way to detect errors from this function, but doing so should not be needed.

PyFile_WriteObject(PyObject *obj, PyFileObject *p, int flags)#

Writes object obj to file object p. The only supported flag for flags is ; if given, the str() of the object is written instead of the repr(). Returns 0 on success or -1 on failure; the appropriate exception will be set.

PyFile_WriteString(char *s, PyFileObject *p, int flags)#

Writes string s to file object p. Returns 0 on success or -1 on failure; the appropriate exception will be set.

Module Objects#

There are only a few functions special to module objects.

PyModule_Type#

This instance of PyTypeObject represents the Python module type. This is exposed to Python programs as types.ModuleType.

PyModule_Check(PyObject *p)#

Returns true if its argument is a module object.

PyModule_New(char *name)#

Return a new module object with the __name__ attribute set to name. Only the module’s __doc__ and __name__ attributes are filled in; the caller is responsible for providing a __file__ attribute.

PyModule_GetDict(PyObject *module)#

Return the dictionary object that implements module’s namespace; this object is the same as the __dict__ attribute of the module object. This function never fails.

PyModule_GetName(PyObject *module)#

Return module’s __name__ value. If the module does not provide one, or if it is not a string, SystemError is raised and NULL is returned.

PyModule_GetFilename(PyObject *module)#

Return the name of the file from which module was loaded using module’s __file__ attribute. If this is not defined, or if it is not a string, raise SystemError and return NULL.

PyModule_AddObject(PyObject *module, char *name, PyObject *value)#

Add an object to module as name. This is a convenience function which can be used from the module’s initialization function. This steals a reference to value. Returns -1 on error, 0 on success. New in version 2.0.

PyModule_AddIntConstant(PyObject *module, char *name, int value)#

Add an integer constant to module as name. This convenience function can be used from the module’s initialization function. Returns -1 on error, 0 on success. New in version 2.0.

PyModule_AddStringConstant(PyObject *module, char *name, char *value)#

Add a string constant to module as name. This convenience function can be used from the module’s initialization function. The string value must be null-terminated. Returns -1 on error, 0 on success. New in version 2.0.

CObjects#

Refer to Extending and Embedding the Python Interpreter, section 1.12 (“Providing a C API for an Extension Module”), for more information on using these objects.

PyCObject#

This subtype of PyObject represents an opaque value, useful for C extension modules who need to pass an opaque value (as a void* pointer) through Python code to other C code. It is often used to make a C function pointer defined in one module available to other modules, so the regular import mechanism can be used to access C APIs defined in dynamically loaded modules.

PyCObject_Check(PyObject *p)#

Returns true if its argument is a PyCObject.

PyCObject_FromVoidPtr(void* cobj, void (*destr)(void *))#

Creates a PyCObject from the void *cobj. The destr function will be called when the object is reclaimed, unless it is NULL.

PyCObject_FromVoidPtrAndDesc(void* cobj, void* desc, void (*destr)(void *, void *) )#

Creates a PyCObject from the void *cobj. The destr function will be called when the object is reclaimed. The desc argument can be used to pass extra callback data for the destructor function.

PyCObject_AsVoidPtr(PyObject* self)#

Returns the object void * that the PyCObject self was created with.

PyCObject_GetDesc(PyObject* self)#

Returns the description void * that the PyCObject self was created with.

Initialization, Finalization, and Threads#

Py_Initialize()#

Initialize the Python interpreter. In an application embedding Python, this should be called before using any other Python/C API functions; with the exception of , , , and . This initializes the table of loaded modules (sys.modules), and creates the fundamental modules __builtin__, __main__ and sys. It also initializes the module search path (sys.path). It does not set sys.argv; use for that. This is a no-op when called for a second time (without calling first). There is no return value; it is a fatal error if the initialization fails.

Py_IsInitialized()#

Return true (nonzero) when the Python interpreter has been initialized, false (zero) if not. After is called, this returns false until is called again.

Py_Finalize()#

Undo all initializations made by and subsequent use of Python/C API functions, and destroy all sub-interpreters (see below) that were created and not yet destroyed since the last call to . Ideally, this frees all memory allocated by the Python interpreter. This is a no-op when called for a second time (without calling again first). There is no return value; errors during finalization are ignored.

This function is provided for a number of reasons. An embedding application might want to restart Python without having to restart the application itself. An application that has loaded the Python interpreter from a dynamically loadable library (or DLL) might want to free all memory allocated by Python before unloading the DLL. During a hunt for memory leaks in an application a developer might want to free all memory allocated by Python before exiting from the application.

Bugs and caveats: The destruction of modules and objects in modules is done in random order; this may cause destructors (__del__() methods) to fail when they depend on other objects (even functions) or modules. Dynamically loaded extension modules loaded by Python are not unloaded. Small amounts of memory allocated by the Python interpreter may not be freed (if you find a leak, please report it). Memory tied up in circular references between objects is not freed. Some memory allocated by extension modules may not be freed. Some extension may not work properly if their initialization routine is called more than once; this can happen if an applcation calls and more than once.

Py_NewInterpreter()#

Create a new sub-interpreter. This is an (almost) totally separate environment for the execution of Python code. In particular, the new interpreter has separate, independent versions of all imported modules, including the fundamental modules __builtin__, __main__ and sys. The table of loaded modules (sys.modules) and the module search path (sys.path) are also separate. The new environment has no sys.argv variable. It has new standard I/O stream file objects sys.stdin, sys.stdout and sys.stderr (however these refer to the same underlying FILE structures in the C library).

The return value points to the first thread state created in the new sub-interpreter. This thread state is made the current thread state. Note that no actual thread is created; see the discussion of thread states below. If creation of the new interpreter is unsuccessful, NULL is returned; no exception is set since the exception state is stored in the current thread state and there may not be a current thread state. (Like all other Python/C API functions, the global interpreter lock must be held before calling this function and is still held when it returns; however, unlike most other Python/C API functions, there needn’t be a current thread state on entry.)

Extension modules are shared between (sub-)interpreters as follows: the first time a particular extension is imported, it is initialized normally, and a (shallow) copy of its module’s dictionary is squirreled away. When the same extension is imported by another (sub-)interpreter, a new module is initialized and filled with the contents of this copy; the extension’s init function is not called. Note that this is different from what happens when an extension is imported after the interpreter has been completely re-initialized by calling and ; in that case, the extension’s initmodule function is called again.

Bugs and caveats: Because sub-interpreters (and the main interpreter) are part of the same process, the insulation between them isn’t perfect — for example, using low-level file operations like

os.close() they can (accidentally or maliciously) affect each other’s open files. Because of the way extensions are shared between (sub-)interpreters, some extensions may not work properly; this is especially likely when the extension makes use of (static) global variables, or when the extension manipulates its module’s dictionary after its initialization. It is possible to insert objects created in one sub-interpreter into a namespace of another sub-interpreter; this should be done with great care to avoid sharing user-defined functions, methods, instances or classes between sub-interpreters, since import operations executed by such objects may affect the wrong (sub-)interpreter’s dictionary of loaded modules. (XXX This is a hard-to-fix bug that will be addressed in a future release.)

Py_EndInterpreter(PyThreadState *tstate)#

Destroy the (sub-)interpreter represented by the given thread state. The given thread state must be the current thread state. See the discussion of thread states below. When the call returns, the current thread state is NULL. All thread states associated with this interpreted are destroyed. (The global interpreter lock must be held before calling this function and is still held when it returns.) will destroy all sub-interpreters that haven’t been explicitly destroyed at that point.

Py_SetProgramName(char *name)#

This function should be called before is called for the first time, if it is called at all. It tells the interpreter the value of the argv[0] argument to the function of the program. This is used by and some other functions below to find the Python run-time libraries relative to the interpreter executable. The default value is ’python’. The argument should point to a zero-terminated character string in static storage whose contents will not change for the duration of the program’s execution. No code in the Python interpreter will change the contents of this storage.

Py_GetProgramName()#

Return the program name set with , or the default. The returned string points into static storage; the caller should not modify its value.

Py_GetPrefix()#

Return the prefix for installed platform-independent files. This is derived through a number of complicated rules from the program name set with and some environment variables; for example, if the program name is ’/usr/local/bin/python’, the prefix is ’/usr/local’. The returned string points into static storage; the caller should not modify its value. This corresponds to the variable in the top-level Makefile and the --prefix argument to the configure script at build time. The value is available to Python code as sys.prefix. It is only useful on Unix. See also the next function.

Py_GetExecPrefix()#

Return the exec-prefix for installed platform-dependent files. This is derived through a number of complicated rules from the program name set with and some environment variables; for example, if the program name is ’/usr/local/bin/python’, the exec-prefix is ’/usr/local’. The returned string points into static storage; the caller should not modify its value. This corresponds to the variable in the top-level Makefile and the --exec-prefix argument to the configure script at build time. The value is available to Python code as sys.exec_prefix. It is only useful on Unix.

Background: The exec-prefix differs from the prefix when platform dependent files (such as executables and shared libraries) are installed in a different directory tree. In a typical installation, platform dependent files may be installed in the /usr/local/plat subtree while platform independent may be installed in /usr/local.

Generally speaking, a platform is a combination of hardware and software families, e.g. Sparc machines running the Solaris 2.x operating system are considered the same platform, but Intel machines running Solaris 2.x are another platform, and Intel machines running Linux are yet another platform. Different major revisions of the same operating system generally also form different platforms. Non-Unix operating systems are a different story; the installation strategies on those systems are so different that the prefix and exec-prefix are meaningless, and set to the empty string. Note that compiled Python bytecode files are platform independent (but not independent from the Python version by which they were compiled!).

System administrators will know how to configure the mount or automount programs to share /usr/local between platforms while having /usr/local/plat be a different filesystem for each platform.

Py_GetProgramFullPath()#

Return the full program name of the Python executable; this is computed as a side-effect of deriving the default module search path from the program name (set by above). The returned string points into static storage; the caller should not modify its value. The value is available to Python code as sys.executable.

Py_GetPath()#

Return the default module search path; this is computed from the program name (set by above) and some environment variables. The returned string consists of a series of directory names separated by a platform dependent delimiter character. The delimiter character is : on Unix, ; on DOS/Windows, and n (the ASCII newline character) on Macintosh. The returned string points into static storage; the caller should not modify its value. The value is available to Python code as the list sys.path, which may be modified to change the future search path for loaded modules.

Py_GetVersion()#

Return the version of this Python interpreter. This is a string that looks something like

"1.5 (#67, Dec 31 1997, 22:34:28) [GCC 2.7.2.2]"

The first word (up to the first space character) is the current Python version; the first three characters are the major and minor version separated by a period. The returned string points into static storage; the caller should not modify its value. The value is available to Python code as the list sys.version.

Py_GetPlatform()#

Return the platform identifier for the current platform. On Unix, this is formed from the “official” name of the operating system, converted to lower case, followed by the major revision number; e.g., for Solaris 2.x, which is also known as SunOS 5.x, the value is ’sunos5’. On Macintosh, it is ’mac’. On Windows, it is ’win’. The returned string points into static storage; the caller should not modify its value. The value is available to Python code as sys.platform.

Py_GetCopyright()#

Return the official copyright string for the current Python version, for example

’Copyright 1991-1995 Stichting Mathematisch Centrum, Amsterdam’

The returned string points into static storage; the caller should not modify its value. The value is available to Python code as the list sys.copyright.

Py_GetCompiler()#

Return an indication of the compiler used to build the current Python version, in square brackets, for example:

"[GCC 2.7.2.2]"

The returned string points into static storage; the caller should not modify its value. The value is available to Python code as part of the variable sys.version.

Py_GetBuildInfo()#

Return information about the sequence number and build date and time of the current Python interpreter instance, for example

"#67, Aug  1 1997, 22:34:28"

The returned string points into static storage; the caller should not modify its value. The value is available to Python code as part of the variable sys.version.

PySys_SetArgv(int argc, char **argv)#

Set sys.argv based on argc and argv. These parameters are similar to those passed to the program’s function with the difference that the first entry should refer to the script file to be executed rather than the executable hosting the Python interpreter. If there isn’t a script that will be run, the first entry in argv can be an empty string. If this function fails to initialize sys.argv, a fatal condition is signalled using .

Thread State and the Global Interpreter Lock#

The Python interpreter is not fully thread safe. In order to support multi-threaded Python programs, there’s a global lock that must be held by the current thread before it can safely access Python objects. Without the lock, even the simplest operations could cause problems in a multi-threaded program: for example, when two threads simultaneously increment the reference count of the same object, the reference count could end up being incremented only once instead of twice.

Therefore, the rule exists that only the thread that has acquired the global interpreter lock may operate on Python objects or call Python/C API functions. In order to support multi-threaded Python programs, the interpreter regularly releases and reacquires the lock — by default, every ten bytecode instructions (this can be changed with

sys.setcheckinterval()). The lock is also released and reacquired around potentially blocking I/O operations like reading or writing a file, so that other threads can run while the thread that requests the I/O is waiting for the I/O operation to complete.

The Python interpreter needs to keep some bookkeeping information separate per thread — for this it uses a data structure called PyThreadState. This is new in Python 1.5; in earlier versions, such state was stored in global variables, and switching threads could cause problems. In particular, exception handling is now thread safe, when the application uses

sys.exc_info() to access the exception last raised in the current thread.

There’s one global variable left, however: the pointer to the current PyThreadState structure. While most thread packages have a way to store “per-thread global data,” Python’s internal platform independent thread abstraction doesn’t support this yet. Therefore, the current thread state must be manipulated explicitly.

This is easy enough in most cases. Most code manipulating the global interpreter lock has the following simple structure:

Save the thread state in a local variable.
Release the interpreter lock.
...Do some blocking I/O operation...
Reacquire the interpreter lock.
Restore the thread state from the local variable.

This is so common that a pair of macros exists to simplify it:

Py_BEGIN_ALLOW_THREADS
...Do some blocking I/O operation...
Py_END_ALLOW_THREADS

The Py_BEGIN_ALLOW_THREADS macro opens a new block and declares a hidden local variable; the Py_END_ALLOW_THREADS macro closes the block. Another advantage of using these two macros is that when Python is compiled without thread support, they are defined empty, thus saving the thread state and lock manipulations.

When thread support is enabled, the block above expands to the following code:

    PyThreadState *_save;

    _save = PyEval_SaveThread();
    ...Do some blocking I/O operation...
    PyEval_RestoreThread(_save);

Using even lower level primitives, we can get roughly the same effect as follows:

    PyThreadState *_save;

    _save = PyThreadState_Swap(NULL);
    PyEval_ReleaseLock();
    ...Do some blocking I/O operation...
    PyEval_AcquireLock();
    PyThreadState_Swap(_save);

There are some subtle differences; in particular, saves and restores the value of the global variable , since the lock manipulation does not guarantee that is left alone. Also, when thread support is disabled, and don’t manipulate the lock; in this case, and are not available. This is done so that dynamically loaded extensions compiled with thread support enabled can be loaded by an interpreter that was compiled with disabled thread support.

The global interpreter lock is used to protect the pointer to the current thread state. When releasing the lock and saving the thread state, the current thread state pointer must be retrieved before the lock is released (since another thread could immediately acquire the lock and store its own thread state in the global variable). Conversely, when acquiring the lock and restoring the thread state, the lock must be acquired before storing the thread state pointer.

Why am I going on with so much detail about this? Because when threads are created from C, they don’t have the global interpreter lock, nor is there a thread state data structure for them. Such threads must bootstrap themselves into existence, by first creating a thread state data structure, then acquiring the lock, and finally storing their thread state pointer, before they can start using the Python/C API. When they are done, they should reset the thread state pointer, release the lock, and finally free their thread state data structure.

When creating a thread data structure, you need to provide an interpreter state data structure. The interpreter state data structure hold global data that is shared by all threads in an interpreter, for example the module administration (sys.modules). Depending on your needs, you can either create a new interpreter state data structure, or share the interpreter state data structure used by the Python main thread (to access the latter, you must obtain the thread state and access its interp member; this must be done by a thread that is created by Python or by the main thread after Python is initialized).

PyInterpreterState#

This data structure represents the state shared by a number of cooperating threads. Threads belonging to the same interpreter share their module administration and a few other internal items. There are no public members in this structure.

Threads belonging to different interpreters initially share nothing, except process state like available memory, open file descriptors and such. The global interpreter lock is also shared by all threads, regardless of to which interpreter they belong.

PyThreadState#

This data structure represents the state of a single thread. The only public data member is PyInterpreterState *interp, which points to this thread’s interpreter state.

PyEval_InitThreads()#

Initialize and acquire the global interpreter lock. It should be called in the main thread before creating a second thread or engaging in any other thread operations such as or PyEval_ReleaseThread(tstate). It is not needed before calling or .

This is a no-op when called for a second time. It is safe to call this function before calling .

When only the main thread exists, no lock operations are needed. This is a common situation (most Python programs do not use threads), and the lock operations slow the interpreter down a bit. Therefore, the lock is not created initially. This situation is equivalent to having acquired the lock: when there is only a single thread, all object accesses are safe. Therefore, when this function initializes the lock, it also acquires it. Before the Python thread module creates a new thread, knowing that either it has the lock or the lock hasn’t been created yet, it calls . When this call returns, it is guaranteed that the lock has been created and that it has acquired it.

It is not safe to call this function when it is unknown which thread (if any) currently has the global interpreter lock.

This function is not available when thread support is disabled at compile time.

PyEval_AcquireLock()#

Acquire the global interpreter lock. The lock must have been created earlier. If this thread already has the lock, a deadlock ensues. This function is not available when thread support is disabled at compile time.

PyEval_ReleaseLock()#

Release the global interpreter lock. The lock must have been created earlier. This function is not available when thread support is disabled at compile time.

PyEval_AcquireThread(PyThreadState *tstate)#

Acquire the global interpreter lock and then set the current thread state to tstate, which should not be NULL. The lock must have been created earlier. If this thread already has the lock, deadlock ensues. This function is not available when thread support is disabled at compile time.

PyEval_ReleaseThread(PyThreadState *tstate)#

Reset the current thread state to NULL and release the global interpreter lock. The lock must have been created earlier and must be held by the current thread. The tstate argument, which must not be NULL, is only used to check that it represents the current thread state — if it isn’t, a fatal error is reported. This function is not available when thread support is disabled at compile time.

PyEval_SaveThread()#

Release the interpreter lock (if it has been created and thread support is enabled) and reset the thread state to NULL, returning the previous thread state (which is not NULL). If the lock has been created, the current thread must have acquired it. (This function is available even when thread support is disabled at compile time.)

PyEval_RestoreThread(PyThreadState *tstate)#

Acquire the interpreter lock (if it has been created and thread support is enabled) and set the thread state to tstate, which must not be NULL. If the lock has been created, the current thread must not have acquired it, otherwise deadlock ensues. (This function is available even when thread support is disabled at compile time.)

The following macros are normally used without a trailing semicolon; look for example usage in the Python source distribution.

Py_BEGIN_ALLOW_THREADS#

This macro expands to { PyThreadState *_save; _save = PyEval_SaveThread();. Note that it contains an opening brace; it must be matched with a following Py_END_ALLOW_THREADS macro. See above for further discussion of this macro. It is a no-op when thread support is disabled at compile time.

Py_END_ALLOW_THREADS#

This macro expands to PyEval_RestoreThread(_save); }. Note that it contains a closing brace; it must be matched with an earlier Py_BEGIN_ALLOW_THREADS macro. See above for further discussion of this macro. It is a no-op when thread support is disabled at compile time.

Py_BEGIN_BLOCK_THREADS#

This macro expands to PyEval_RestoreThread(_save); i.e. it is equivalent to Py_END_ALLOW_THREADS without the closing brace. It is a no-op when thread support is disabled at compile time.

Py_BEGIN_UNBLOCK_THREADS#

This macro expands to _save = PyEval_SaveThread(); i.e. it is equivalent to Py_BEGIN_ALLOW_THREADS without the opening brace and variable declaration. It is a no-op when thread support is disabled at compile time.

All of the following functions are only available when thread support is enabled at compile time, and must be called only when the interpreter lock has been created.

PyInterpreterState_New()#

Create a new interpreter state object. The interpreter lock need not be held, but may be held if it is necessary to serialize calls to this function.

PyInterpreterState_Clear(PyInterpreterState *interp)#

Reset all information in an interpreter state object. The interpreter lock must be held.

PyInterpreterState_Delete(PyInterpreterState *interp)#

Destroy an interpreter state object. The interpreter lock need not be held. The interpreter state must have been reset with a previous call to .

PyThreadState_New(PyInterpreterState *interp)#

Create a new thread state object belonging to the given interpreter object. The interpreter lock need not be held, but may be held if it is necessary to serialize calls to this function.

PyThreadState_Clear(PyThreadState *tstate)#

Reset all information in a thread state object. The interpreter lock must be held.

PyThreadState_Delete(PyThreadState *tstate)#

Destroy a thread state object. The interpreter lock need not be held. The thread state must have been reset with a previous call to .

PyThreadState_Get()#

Return the current thread state. The interpreter lock must be held. When the current thread state is NULL, this issues a fatal error (so that the caller needn’t check for NULL).

PyThreadState_Swap(PyThreadState *tstate)#

Swap the current thread state with the thread state given by the argument tstate, which may be NULL. The interpreter lock must be held.

Memory Management#

Overview#

Memory management in Python involves a private heap containing all Python objects and data structures. The management of this private heap is ensured internally by the Python memory manager. The Python memory manager has different components which deal with various dynamic storage management aspects, like sharing, segmentation, preallocation or caching.

At the lowest level, a raw memory allocator ensures that there is enough room in the private heap for storing all Python-related data by interacting with the memory manager of the operating system. On top of the raw memory allocator, several object-specific allocators operate on the same heap and implement distinct memory management policies adapted to the peculiarities of every object type. For example, integer objects are managed differently within the heap than strings, tuples or dictionaries because integers imply different storage requirements and speed/space tradeoffs. The Python memory manager thus delegates some of the work to the object-specific allocators, but ensures that the latter operate within the bounds of the private heap.

It is important to understand that the management of the Python heap is performed by the interpreter itself and that the user has no control on it, even if she regularly manipulates object pointers to memory blocks inside that heap. The allocation of heap space for Python objects and other internal buffers is performed on demand by the Python memory manager through the Python/C API functions listed in this document.

To avoid memory corruption, extension writers should never try to operate on Python objects with the functions exported by the C library: , , and . This will result in mixed calls between the C allocator and the Python memory manager with fatal consequences, because they implement different algorithms and operate on different heaps. However, one may safely allocate and release memory blocks with the C library allocator for individual purposes, as shown in the following example:

    PyObject *res;
    char *buf = (char *) malloc(BUFSIZ); /* for I/O */

    if (buf == NULL)
        return PyErr_NoMemory();
    ...Do some I/O operation involving buf...
    res = PyString_FromString(buf);
    free(buf); /* malloc'ed */
    return res;

In this example, the memory request for the I/O buffer is handled by the C library allocator. The Python memory manager is involved only in the allocation of the string object returned as a result.

In most situations, however, it is recommended to allocate memory from the Python heap specifically because the latter is under control of the Python memory manager. For example, this is required when the interpreter is extended with new object types written in C. Another reason for using the Python heap is the desire to inform the Python memory manager about the memory needs of the extension module. Even when the requested memory is used exclusively for internal, highly-specific purposes, delegating all memory requests to the Python memory manager causes the interpreter to have a more accurate image of its memory footprint as a whole. Consequently, under certain circumstances, the Python memory manager may or may not trigger appropriate actions, like garbage collection, memory compaction or other preventive procedures. Note that by using the C library allocator as shown in the previous example, the allocated memory for the I/O buffer escapes completely the Python memory manager.

Memory Interface#

The following function sets, modeled after the ANSI C standard, are available for allocating and releasing memory from the Python heap:

PyMem_Malloc(size_t n)#

Allocates n bytes and returns a pointer of type void* to the allocated memory, or NULL if the request fails. Requesting zero bytes returns a non-NULL pointer.

PyMem_Realloc(void *p, size_t n)#

Resizes the memory block pointed to by p to n bytes. The contents will be unchanged to the minimum of the old and the new sizes. If p is NULL, the call is equivalent to ; if n is equal to zero, the memory block is resized but is not freed, and the returned pointer is non-NULL. Unless p is NULL, it must have been returned by a previous call to or .

PyMem_Free(void *p)#

Frees the memory block pointed to by p, which must have been returned by a previous call to or . Otherwise, or if has been called before, undefined behaviour occurs. If p is NULL, no operation is performed.

The following type-oriented macros are provided for convenience. Note that TYPE refers to any C type.

PyMem_New(TYPE, size_t n)#

Same as , but allocates (n * sizeof(TYPE)) bytes of memory. Returns a pointer cast to TYPE*.

PyMem_Resize(void *p, TYPE, size_t n)#

Same as , but the memory block is resized to (n * sizeof(TYPE)) bytes. Returns a pointer cast to TYPE*.

PyMem_Del(void *p)#

Same as .

In addition, the following macro sets are provided for calling the Python memory allocator directly, without involving the C API functions listed above. However, note that their use does not preserve binary compatibility accross Python versions and is therefore deprecated in extension modules.

, , .

, , .

Examples#

Here is the example from section, rewritten so that the I/O buffer is allocated from the Python heap by using the first function set:

    PyObject *res;
    char *buf = (char *) PyMem_Malloc(BUFSIZ); /* for I/O */

    if (buf == NULL)
        return PyErr_NoMemory();
    /* ...Do some I/O operation involving buf... */
    res = PyString_FromString(buf);
    PyMem_Free(buf); /* allocated with PyMem_Malloc */
    return res;

The same code using the type-oriented function set:

    PyObject *res;
    char *buf = PyMem_New(char, BUFSIZ); /* for I/O */

    if (buf == NULL)
        return PyErr_NoMemory();
    /* ...Do some I/O operation involving buf... */
    res = PyString_FromString(buf);
    PyMem_Del(buf); /* allocated with PyMem_New */
    return res;

Note that in the two examples above, the buffer is always manipulated via functions belonging to the same set. Indeed, it is required to use the same memory API family for a given memory block, so that the risk of mixing different allocators is reduced to a minimum. The following code sequence contains two errors, one of which is labeled as fatal because it mixes two different allocators operating on different heaps.

char *buf1 = PyMem_New(char, BUFSIZ);
char *buf2 = (char *) malloc(BUFSIZ);
char *buf3 = (char *) PyMem_Malloc(BUFSIZ);
...
PyMem_Del(buf3);  /* Wrong -- should be PyMem_Free() */
free(buf2);       /* Right -- allocated via malloc() */
free(buf1);       /* Fatal -- should be PyMem_Del()  */

In addition to the functions aimed at handling raw memory blocks from the Python heap, objects in Python are allocated and released with , and , or with their corresponding macros , and .

These will be explained in the next chapter on defining and implementing new object types in C.

Defining New Object Types#

_PyObject_New(PyTypeObject *type)#

_PyObject_NewVar(PyTypeObject *type, int size)#

_PyObject_Del(PyObject *op)#

PyObject_Init(PyObject *op, PyTypeObject *type)#

PyObject_InitVar(PyVarObject *op, PyTypeObject *type, int size)#

PyObject_New(TYPE, PyTypeObject *type)#

PyObject_NewVar(TYPE, PyTypeObject *type, int size)#

PyObject_Del(PyObject *op)#

PyObject_NEW(TYPE, PyTypeObject *type)#

PyObject_NEW_VAR(TYPE, PyTypeObject *type, int size)#

PyObject_DEL(PyObject *op)#

Py_InitModule (!!!)

PyArg_ParseTupleAndKeywords, PyArg_ParseTuple, PyArg_Parse

Py_BuildValue

DL_IMPORT

Py*_Check

_Py_NoneStruct

Common Object Structures#

PyObject, PyVarObject

PyObject_HEAD, PyObject_HEAD_INIT, PyObject_VAR_HEAD

Typedefs: unaryfunc, binaryfunc, ternaryfunc, inquiry, coercion, intargfunc, intintargfunc, intobjargproc, intintobjargproc, objobjargproc, destructor, printfunc, getattrfunc, getattrofunc, setattrfunc, setattrofunc, cmpfunc, reprfunc, hashfunc

PyCFunction#

Type of the functions used to implement most Python callables in C.

PyMethodDef#

Structure used to describe a method of an extension type. This structure has four fields:

Py_FindMethod(PyMethodDef[] table, PyObject *ob, char *name)#

Return a bound method object for an extension type implemented in C. This function also handles the special attribute __methods__, returning a list of all the method names defined in table.

Mapping Object Structures#

PyMappingMethods#

Structure used to hold pointers to the functions used to implement the mapping protocol for an extension type.

Number Object Structures#

PyNumberMethods#

Structure used to hold pointers to the functions an extension type uses to implement the number protocol.

Sequence Object Structures#

PySequenceMethods#

Structure used to hold pointers to the functions which an object uses to implement the sequence protocol.

Buffer Object Structures#

The buffer interface exports a model where an object can expose its internal data as a set of chunks of data, where each chunk is specified as a pointer/length pair. These chunks are called segments and are presumed to be non-contiguous in memory.

If an object does not export the buffer interface, then its tp_as_buffer member in the PyTypeObject structure should be NULL. Otherwise, the tp_as_buffer will point to a PyBufferProcs structure.

Note: It is very important that your PyTypeObject structure uses Py_TPFLAGS_DEFAULT for the value of the tp_flags member rather than 0. This tells the Python runtime that your PyBufferProcs structure contains the bf_getcharbuffer slot. Older versions of Python did not have this member, so a new Python interpreter using an old extension needs to be able to test for its presence before using it.

PyBufferProcs#

Structure used to hold the function pointers which define an implementation of the buffer protocol.

The first slot is bf_getreadbuffer, of type getreadbufferproc. If this slot is NULL, then the object does not support reading from the internal data. This is non-sensical, so implementors should fill this in, but callers should test that the slot contains a non-NULL value.

The next slot is bf_getwritebuffer having type getwritebufferproc. This slot may be NULL if the object does not allow writing into its returned buffers.

The third slot is bf_getsegcount, with type getsegcountproc. This slot must not be NULL and is used to inform the caller how many segments the object contains. Simple objects such as PyString_Type and PyBuffer_Type objects contain a single segment.

The last slot is bf_getcharbuffer, of type getcharbufferproc. This slot will only be present if the Py_TPFLAGS_HAVE_GETCHARBUFFER flag is present in the tp_flags field of the object’s PyTypeObject. Before using this slot, the caller should test whether it is present by using the function. If present, it may be NULL, indicating that the object’s contents cannot be used as 8-bit characters. The slot function may also raise an error if the object’s contents cannot be interpreted as 8-bit characters. For example, if the object is an array which is configured to hold floating point values, an exception may be raised if a caller attempts to use bf_getcharbuffer to fetch a sequence of 8-bit characters. This notion of exporting the internal buffers as “text” is used to distinguish between objects that are binary in nature, and those which have character-based content.

Note: The current policy seems to state that these characters may be multi-byte characters. This implies that a buffer size of N does not mean there are N characters present.

Py_TPFLAGS_HAVE_GETCHARBUFFER#

Flag bit set in the type structure to indicate that the bf_getcharbuffer slot is known. This being set does not indicate that the object supports the buffer interface or that the bf_getcharbuffer slot is non-NULL.

[#

getreadbufferproc]int (getreadbufferproc) (PyObject self, int segment, void **ptrptr) Return a pointer to a readable segment of the buffer. This function is allowed to raise an exception, in which case it must return -1. The segment which is passed must be zero or positive, and strictly less than the number of segments returned by the bf_getsegcount slot function. On success, returns 0 and sets *ptrptr to a pointer to the buffer memory.

[#

getwritebufferproc]int (getwritebufferproc) (PyObject self, int segment, void **ptrptr) Return a pointer to a writable memory buffer in *ptrptr; the memory buffer must correspond to buffer segment segment. Must return -1 and set an exception on error. TypeError should be raised if the object only supports read-only buffers, and SystemError should be raised when segment specifies a segment that doesn’t exist.

[#

getsegcountproc]int (*getsegcountproc) (PyObject self, int lenp) Return the number of memory segments which comprise the buffer. If lenp is not NULL, the implementation must report the sum of the sizes (in bytes) of all segments in *lenp. The function cannot fail.

[#

getcharbufferproc]int (*getcharbufferproc) (PyObject *self, int segment, const char **ptrptr)

Reporting Bugs#