Object Protocol

Object Protocol¶
-
PyObject *Py_GetConstant(unsigned int constant_id)¶
- Part of the Stable ABI since version 3.13.
Get a strong reference to a constant.
Set an exception and return
NULL
if constant_id is invalid.constant_id must be one of these constant identifiers:
Constant Identifier
Value
Returned object
-
Py_CONSTANT_NONE¶
0
-
Py_CONSTANT_FALSE¶
1
-
Py_CONSTANT_TRUE¶
2
-
Py_CONSTANT_ELLIPSIS¶
3
-
Py_CONSTANT_NOT_IMPLEMENTED¶
4
-
Py_CONSTANT_ZERO¶
5
0
-
Py_CONSTANT_ONE¶
6
1
-
Py_CONSTANT_EMPTY_STR¶
7
''
-
Py_CONSTANT_EMPTY_BYTES¶
8
b''
-
Py_CONSTANT_EMPTY_TUPLE¶
9
()
Numeric values are only given for projects which cannot use the constant identifiers.
Added in version 3.13.
CPython implementation detail: In CPython, all of these constants are immortal.
-
Py_CONSTANT_NONE¶
-
PyObject *Py_GetConstantBorrowed(unsigned int constant_id)¶
- Part of the Stable ABI since version 3.13.
Similar to
Py_GetConstant()
, but return a borrowed reference.This function is primarily intended for backwards compatibility: using
Py_GetConstant()
is recommended for new code.The reference is borrowed from the interpreter, and is valid until the interpreter finalization.
Added in version 3.13.
-
PyObject *Py_NotImplemented¶
The
NotImplemented
singleton, used to signal that an operation is not implemented for the given type combination.
-
Py_RETURN_NOTIMPLEMENTED¶
Properly handle returning
Py_NotImplemented
from within a C function (that is, create a new strong reference toNotImplemented
and return it).
-
Py_PRINT_RAW¶
Flag to be used with multiple functions that print the object (like
PyObject_Print()
andPyFile_WriteObject()
). If passed, these functions use thestr()
of the object instead of therepr()
.
-
int 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 isPy_PRINT_RAW
; if given, thestr()
of the object is written instead of therepr()
.
-
int PyObject_HasAttrWithError(PyObject *o, PyObject *attr_name)¶
- Part of the Stable ABI since version 3.13.
Returns
1
if o has the attribute attr_name, and0
otherwise. This is equivalent to the Python expressionhasattr(o, attr_name)
. On failure, return-1
.Added in version 3.13.
-
int PyObject_HasAttrStringWithError(PyObject *o, const char *attr_name)¶
- Part of the Stable ABI since version 3.13.
This is the same as
PyObject_HasAttrWithError()
, but attr_name is specified as a const char* UTF-8 encoded bytes string, rather than a PyObject*.Added in version 3.13.
-
int PyObject_HasAttr(PyObject *o, PyObject *attr_name)¶
- Part of the Stable ABI.
Returns
1
if o has the attribute attr_name, and0
otherwise. This function always succeeds.Note
Exceptions that occur when this calls
__getattr__()
and__getattribute__()
methods aren’t propagated, but instead given tosys.unraisablehook()
. For proper error handling, usePyObject_HasAttrWithError()
,PyObject_GetOptionalAttr()
orPyObject_GetAttr()
instead.
-
int PyObject_HasAttrString(PyObject *o, const char *attr_name)¶
- Part of the Stable ABI.
This is the same as
PyObject_HasAttr()
, but attr_name is specified as a const char* UTF-8 encoded bytes string, rather than a PyObject*.Note
Exceptions that occur when this calls
__getattr__()
and__getattribute__()
methods or while creating the temporarystr
object are silently ignored. For proper error handling, usePyObject_HasAttrStringWithError()
,PyObject_GetOptionalAttrString()
orPyObject_GetAttrString()
instead.
-
PyObject *PyObject_GetAttr(PyObject *o, PyObject *attr_name)¶
- Return value: New reference. Part of the Stable ABI.
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 expressiono.attr_name
.If the missing attribute should not be treated as a failure, you can use
PyObject_GetOptionalAttr()
instead.
-
PyObject *PyObject_GetAttrString(PyObject *o, const char *attr_name)¶
- Return value: New reference. Part of the Stable ABI.
This is the same as
PyObject_GetAttr()
, but attr_name is specified as a const char* UTF-8 encoded bytes string, rather than a PyObject*.If the missing attribute should not be treated as a failure, you can use
PyObject_GetOptionalAttrString()
instead.
-
int PyObject_GetOptionalAttr(PyObject *obj, PyObject *attr_name, PyObject **result);¶
- Part of the Stable ABI since version 3.13.
Variant of
PyObject_GetAttr()
which doesn’t raiseAttributeError
if the attribute is not found.If the attribute is found, return
1
and set *result to a new strong reference to the attribute. If the attribute is not found, return0
and set *result toNULL
; theAttributeError
is silenced. If an error other thanAttributeError
is raised, return-1
and set *result toNULL
.Added in version 3.13.
-
int PyObject_GetOptionalAttrString(PyObject *obj, const char *attr_name, PyObject **result);¶
- Part of the Stable ABI since version 3.13.
This is the same as
PyObject_GetOptionalAttr()
, but attr_name is specified as a const char* UTF-8 encoded bytes string, rather than a PyObject*.Added in version 3.13.
-
PyObject *PyObject_GenericGetAttr(PyObject *o, PyObject *name)¶
- Return value: New reference. Part of the Stable ABI.
Generic attribute getter function that is meant to be put into a type object’s
tp_getattro
slot. It looks for a descriptor in the dictionary of classes in the object’s MRO as well as an attribute in the object’s__dict__
(if present). As outlined in Implementing Descriptors, data descriptors take preference over instance attributes, while non-data descriptors don’t. Otherwise, anAttributeError
is raised.
-
int PyObject_SetAttr(PyObject *o, PyObject *attr_name, PyObject *v)¶
- Part of the Stable ABI.
Set the value of the attribute named attr_name, for object o, to the value v. Raise an exception and return
-1
on failure; return0
on success. This is the equivalent of the Python statemento.attr_name = v
.If v is
NULL
, the attribute is deleted. This behaviour is deprecated in favour of usingPyObject_DelAttr()
, but there are currently no plans to remove it.
-
int PyObject_SetAttrString(PyObject *o, const char *attr_name, PyObject *v)¶
- Part of the Stable ABI.
This is the same as
PyObject_SetAttr()
, but attr_name is specified as a const char* UTF-8 encoded bytes string, rather than a PyObject*.If v is
NULL
, the attribute is deleted, but this feature is deprecated in favour of usingPyObject_DelAttrString()
.The number of different attribute names passed to this function should be kept small, usually by using a statically allocated string as attr_name. For attribute names that aren’t known at compile time, prefer calling
PyUnicode_FromString()
andPyObject_SetAttr()
directly. For more details, seePyUnicode_InternFromString()
, which may be used internally to create a key object.
-
int PyObject_GenericSetAttr(PyObject *o, PyObject *name, PyObject *value)¶
- Part of the Stable ABI.
Generic attribute setter and deleter function that is meant to be put into a type object’s
tp_setattro
slot. It looks for a data descriptor in the dictionary of classes in the object’s MRO, and if found it takes preference over setting or deleting the attribute in the instance dictionary. Otherwise, the attribute is set or deleted in the object’s__dict__
(if present). On success,0
is returned, otherwise anAttributeError
is raised and-1
is returned.
-
int PyObject_DelAttr(PyObject *o, PyObject *attr_name)¶
- Part of the Stable ABI since version 3.13.
Delete attribute named attr_name, for object o. Returns
-1
on failure. This is the equivalent of the Python statementdel o.attr_name
.
-
int PyObject_DelAttrString(PyObject *o, const char *attr_name)¶
- Part of the Stable ABI since version 3.13.
This is the same as
PyObject_DelAttr()
, but attr_name is specified as a const char* UTF-8 encoded bytes string, rather than a PyObject*.The number of different attribute names passed to this function should be kept small, usually by using a statically allocated string as attr_name. For attribute names that aren’t known at compile time, prefer calling
PyUnicode_FromString()
andPyObject_DelAttr()
directly. For more details, seePyUnicode_InternFromString()
, which may be used internally to create a key object for lookup.
-
PyObject *PyObject_GenericGetDict(PyObject *o, void *context)¶
- Return value: New reference. Part of the Stable ABI since version 3.10.
A generic implementation for the getter of a
__dict__
descriptor. It creates the dictionary if necessary.This function may also be called to get the
__dict__
of the object o. PassNULL
for context when calling it. Since this function may need to allocate memory for the dictionary, it may be more efficient to callPyObject_GetAttr()
when accessing an attribute on the object.On failure, returns
NULL
with an exception set.Added in version 3.3.
-
int PyObject_GenericSetDict(PyObject *o, PyObject *value, void *context)¶
- Part of the Stable ABI since version 3.7.
A generic implementation for the setter of a
__dict__
descriptor. This implementation does not allow the dictionary to be deleted.Added in version 3.3.
-
PyObject **_PyObject_GetDictPtr(PyObject *obj)¶
Return a pointer to
__dict__
of the object obj. If there is no__dict__
, returnNULL
without setting an exception.This function may need to allocate memory for the dictionary, so it may be more efficient to call
PyObject_GetAttr()
when accessing an attribute on the object.
-
PyObject *PyObject_RichCompare(PyObject *o1, PyObject *o2, int opid)¶
- Return value: New reference. Part of the Stable ABI.
Compare the values of o1 and o2 using the operation specified by opid, which must be one of
Py_LT
,Py_LE
,Py_EQ
,Py_NE
,Py_GT
, orPy_GE
, corresponding to<
,<=
,==
,!=
,>
, or>=
respectively. This is the equivalent of the Python expressiono1 op o2
, whereop
is the operator corresponding to opid. Returns the value of the comparison on success, orNULL
on failure.
-
int PyObject_RichCompareBool(PyObject *o1, PyObject *o2, int opid)¶
- Part of the Stable ABI.
Compare the values of o1 and o2 using the operation specified by opid, like
PyObject_RichCompare()
, but returns-1
on error,0
if the result is false,1
otherwise.
Note
If o1 and o2 are the same object, PyObject_RichCompareBool()
will always return 1
for Py_EQ
and 0
for Py_NE
.
-
PyObject *PyObject_Format(PyObject *obj, PyObject *format_spec)¶
- Part of the Stable ABI.
Format obj using format_spec. This is equivalent to the Python expression
format(obj, format_spec)
.format_spec may be
NULL
. In this case the call is equivalent toformat(obj)
. Returns the formatted string on success,NULL
on failure.
-
PyObject *PyObject_Repr(PyObject *o)¶
- Return value: New reference. Part of the Stable ABI.
Compute a string representation of object o. Returns the string representation on success,
NULL
on failure. This is the equivalent of the Python expressionrepr(o)
. Called by therepr()
built-in function.Changed in version 3.4: This function now includes a debug assertion to help ensure that it does not silently discard an active exception.
-
PyObject *PyObject_ASCII(PyObject *o)¶
- Return value: New reference. Part of the Stable ABI.
As
PyObject_Repr()
, compute a string representation of object o, but escape the non-ASCII characters in the string returned byPyObject_Repr()
with\x
,\u
or\U
escapes. This generates a string similar to that returned byPyObject_Repr()
in Python 2. Called by theascii()
built-in function.
-
PyObject *PyObject_Str(PyObject *o)¶
- Return value: New reference. Part of the Stable ABI.
Compute a string representation of object o. Returns the string representation on success,
NULL
on failure. This is the equivalent of the Python expressionstr(o)
. Called by thestr()
built-in function and, therefore, by theprint()
function.Changed in version 3.4: This function now includes a debug assertion to help ensure that it does not silently discard an active exception.
-
PyObject *PyObject_Bytes(PyObject *o)¶
- Return value: New reference. Part of the Stable ABI.
Compute a bytes representation of object o.
NULL
is returned on failure and a bytes object on success. This is equivalent to the Python expressionbytes(o)
, when o is not an integer. Unlikebytes(o)
, a TypeError is raised when o is an integer instead of a zero-initialized bytes object.
-
int PyObject_IsSubclass(PyObject *derived, PyObject *cls)¶
- Part of the Stable ABI.
Return
1
if the class derived is identical to or derived from the class cls, otherwise return0
. In case of an error, return-1
.If cls is a tuple, the check will be done against every entry in cls. The result will be
1
when at least one of the checks returns1
, otherwise it will be0
.If cls has a
__subclasscheck__()
method, it will be called to determine the subclass status as described in PEP 3119. Otherwise, derived is a subclass of cls if it is a direct or indirect subclass, i.e. contained incls.__mro__
.Normally only class objects, i.e. instances of
type
or a derived class, are considered classes. However, objects can override this by having a__bases__
attribute (which must be a tuple of base classes).
-
int PyObject_IsInstance(PyObject *inst, PyObject *cls)¶
- Part of the Stable ABI.
Return
1
if inst is an instance of the class cls or a subclass of cls, or0
if not. On error, returns-1
and sets an exception.If cls is a tuple, the check will be done against every entry in cls. The result will be
1
when at least one of the checks returns1
, otherwise it will be0
.If cls has a
__instancecheck__()
method, it will be called to determine the subclass status as described in PEP 3119. Otherwise, inst is an instance of cls if its class is a subclass of cls.An instance inst can override what is considered its class by having a
__class__
attribute.An object cls can override if it is considered a class, and what its base classes are, by having a
__bases__
attribute (which must be a tuple of base classes).
-
Py_hash_t PyObject_Hash(PyObject *o)¶
- Part of the Stable ABI.
Compute and return the hash value of an object o. On failure, return
-1
. This is the equivalent of the Python expressionhash(o)
.Changed in version 3.2: The return type is now Py_hash_t. This is a signed integer the same size as
Py_ssize_t
.
-
Py_hash_t PyObject_HashNotImplemented(PyObject *o)¶
- Part of the Stable ABI.
Set a
TypeError
indicating thattype(o)
is not hashable and return-1
. This function receives special treatment when stored in atp_hash
slot, allowing a type to explicitly indicate to the interpreter that it is not hashable.
-
int PyObject_IsTrue(PyObject *o)¶
- Part of the Stable ABI.
Returns
1
if the object o is considered to be true, and0
otherwise. This is equivalent to the Python expressionnot not o
. On failure, return-1
.
-
int PyObject_Not(PyObject *o)¶
- Part of the Stable ABI.
Returns
0
if the object o is considered to be true, and1
otherwise. This is equivalent to the Python expressionnot o
. On failure, return-1
.
-
PyObject *PyObject_Type(PyObject *o)¶
- Return value: New reference. Part of the Stable ABI.
When o is non-
NULL
, returns a type object corresponding to the object type of object o. On failure, raisesSystemError
and returnsNULL
. This is equivalent to the Python expressiontype(o)
. This function creates a new strong reference to the return value. There’s really no reason to use this function instead of thePy_TYPE()
function, which returns a pointer of type PyTypeObject*, except when a new strong reference is needed.
-
int PyObject_TypeCheck(PyObject *o, PyTypeObject *type)¶
Return non-zero if the object o is of type type or a subtype of type, and
0
otherwise. Both parameters must be non-NULL
.
-
Py_ssize_t PyObject_Size(PyObject *o)¶
-
Py_ssize_t PyObject_Length(PyObject *o)¶
- Part of the Stable ABI.
Return the length of object o. If the object o provides either the sequence and mapping protocols, the sequence length is returned. On error,
-1
is returned. This is the equivalent to the Python expressionlen(o)
.
-
Py_ssize_t PyObject_LengthHint(PyObject *o, Py_ssize_t defaultvalue)¶
Return an estimated length for the object o. First try to return its actual length, then an estimate using
__length_hint__()
, and finally return the default value. On error return-1
. This is the equivalent to the Python expressionoperator.length_hint(o, defaultvalue)
.Added in version 3.4.
-
PyObject *PyObject_GetItem(PyObject *o, PyObject *key)¶
- Return value: New reference. Part of the Stable ABI.
Return element of o corresponding to the object key or
NULL
on failure. This is the equivalent of the Python expressiono[key]
.
-
int PyObject_SetItem(PyObject *o, PyObject *key, PyObject *v)¶
- Part of the Stable ABI.
Map the object key to the value v. Raise an exception and return
-1
on failure; return0
on success. This is the equivalent of the Python statemento[key] = v
. This function does not steal a reference to v.
-
int PyObject_DelItem(PyObject *o, PyObject *key)¶
- Part of the Stable ABI.
Remove the mapping for the object key from the object o. Return
-1
on failure. This is equivalent to the Python statementdel o[key]
.
-
int PyObject_DelItemString(PyObject *o, const char *key)¶
- Part of the Stable ABI.
This is the same as
PyObject_DelItem()
, but key is specified as a const char* UTF-8 encoded bytes string, rather than a PyObject*.
-
PyObject *PyObject_Dir(PyObject *o)¶
- Return value: New reference. Part of the Stable ABI.
This is equivalent to the Python expression
dir(o)
, returning a (possibly empty) list of strings appropriate for the object argument, orNULL
if there was an error. If the argument isNULL
, this is like the Pythondir()
, returning the names of the current locals; in this case, if no execution frame is active thenNULL
is returned butPyErr_Occurred()
will return false.
-
PyObject *PyObject_GetIter(PyObject *o)¶
- Return value: New reference. Part of the Stable ABI.
This is equivalent to the Python expression
iter(o)
. It returns a new iterator for the object argument, or the object itself if the object is already an iterator. RaisesTypeError
and returnsNULL
if the object cannot be iterated.
-
PyObject *PyObject_SelfIter(PyObject *obj)¶
- Return value: New reference. Part of the Stable ABI.
This is equivalent to the Python
__iter__(self): return self
method. It is intended for iterator types, to be used in thePyTypeObject.tp_iter
slot.
-
PyObject *PyObject_GetAIter(PyObject *o)¶
- Return value: New reference. Part of the Stable ABI since version 3.10.
This is the equivalent to the Python expression
aiter(o)
. Takes anAsyncIterable
object and returns anAsyncIterator
for it. This is typically a new iterator but if the argument is anAsyncIterator
, this returns itself. RaisesTypeError
and returnsNULL
if the object cannot be iterated.Added in version 3.10.
-
void *PyObject_GetTypeData(PyObject *o, PyTypeObject *cls)¶
- Part of the Stable ABI since version 3.12.
Get a pointer to subclass-specific data reserved for cls.
The object o must be an instance of cls, and cls must have been created using negative
PyType_Spec.basicsize
. Python does not check this.On error, set an exception and return
NULL
.Added in version 3.12.
-
Py_ssize_t PyType_GetTypeDataSize(PyTypeObject *cls)¶
- Part of the Stable ABI since version 3.12.
Return the size of the instance memory space reserved for cls, i.e. the size of the memory
PyObject_GetTypeData()
returns.This may be larger than requested using
-PyType_Spec.basicsize
; it is safe to use this larger size (e.g. withmemset()
).The type cls must have been created using negative
PyType_Spec.basicsize
. Python does not check this.On error, set an exception and return a negative value.
Added in version 3.12.
-
void *PyObject_GetItemData(PyObject *o)¶
Get a pointer to per-item data for a class with
Py_TPFLAGS_ITEMS_AT_END
.On error, set an exception and return
NULL
.TypeError
is raised if o does not havePy_TPFLAGS_ITEMS_AT_END
set.Added in version 3.12.
-
int PyObject_VisitManagedDict(PyObject *obj, visitproc visit, void *arg)¶
Visit the managed dictionary of obj.
This function must only be called in a traverse function of the type which has the
Py_TPFLAGS_MANAGED_DICT
flag set.Added in version 3.13.
-
void PyObject_ClearManagedDict(PyObject *obj)¶
Clear the managed dictionary of obj.
This function must only be called in a clear function of the type which has the
Py_TPFLAGS_MANAGED_DICT
flag set.Added in version 3.13.
-
int PyUnstable_Object_EnableDeferredRefcount(PyObject *obj)¶
- This is Unstable API. It may change without warning in minor releases.
Enable deferred reference counting on obj, if supported by the runtime. In the free-threaded build, this allows the interpreter to avoid reference count adjustments to obj, which may improve multi-threaded performance. The tradeoff is that obj will only be deallocated by the tracing garbage collector, and not when the interpreter no longer has any references to it.
This function returns
1
if deferred reference counting is enabled on obj, and0
if deferred reference counting is not supported or if the hint was ignored by the interpreter, such as when deferred reference counting is already enabled on obj. This function is thread-safe, and cannot fail.This function does nothing on builds with the GIL enabled, which do not support deferred reference counting. This also does nothing if obj is not an object tracked by the garbage collector (see
gc.is_tracked()
andPyObject_GC_IsTracked()
).This function is intended to be used soon after obj is created, by the code that creates it, such as in the object’s
tp_new
slot.Added in version 3.14.
-
int PyUnstable_Object_IsUniqueReferencedTemporary(PyObject *obj)¶
- This is Unstable API. It may change without warning in minor releases.
Check if obj is a unique temporary object. Returns
1
if obj is known to be a unique temporary object, and0
otherwise. This function cannot fail, but the check is conservative, and may return0
in some cases even if obj is a unique temporary object.If an object is a unique temporary, it is guaranteed that the current code has the only reference to the object. For arguments to C functions, this should be used instead of checking if the reference count is
1
. Starting with Python 3.14, the interpreter internally avoids some reference count modifications when loading objects onto the operands stack by borrowing references when possible, which means that a reference count of1
by itself does not guarantee that a function argument uniquely referenced.In the example below,
my_func
is called with a unique temporary object as its argument:my_func([1, 2, 3])
In the example below,
my_func
is not called with a unique temporary object as its argument, even if its refcount is1
:my_list = [1, 2, 3] my_func(my_list)
See also the function
Py_REFCNT()
.Added in version 3.14.
-
int PyUnstable_IsImmortal(PyObject *obj)¶
- This is Unstable API. It may change without warning in minor releases.
This function returns non-zero if obj is immortal, and zero otherwise. This function cannot fail.
Note
Objects that are immortal in one CPython version are not guaranteed to be immortal in another.
Added in version 3.14.
-
int PyUnstable_TryIncRef(PyObject *obj)¶
- This is Unstable API. It may change without warning in minor releases.
Increments the reference count of obj if it is not zero. Returns
1
if the object’s reference count was successfully incremented. Otherwise, this function returns0
.PyUnstable_EnableTryIncRef()
must have been called earlier on obj or this function may spuriously return0
in the free-threaded build.This function is logically equivalent to the following C code, except that it behaves atomically in the free-threaded build:
if (Py_REFCNT(op) > 0) { Py_INCREF(op); return 1; } return 0;
This is intended as a building block for managing weak references without the overhead of a Python weak reference object.
Typically, correct use of this function requires support from obj’s deallocator (
tp_dealloc
). For example, the following sketch could be adapted to implement a “weakmap” that works like aWeakValueDictionary
for a specific type:PyMutex mutex; PyObject * add_entry(weakmap_key_type *key, PyObject *value) { PyUnstable_EnableTryIncRef(value); weakmap_type weakmap = ...; PyMutex_Lock(&mutex); weakmap_add_entry(weakmap, key, value); PyMutex_Unlock(&mutex); Py_RETURN_NONE; } PyObject * get_value(weakmap_key_type *key) { weakmap_type weakmap = ...; PyMutex_Lock(&mutex); PyObject *result = weakmap_find(weakmap, key); if (PyUnstable_TryIncRef(result)) { // `result` is safe to use PyMutex_Unlock(&mutex); return result; } // if we get here, `result` is starting to be garbage-collected, // but has not been removed from the weakmap yet PyMutex_Unlock(&mutex); return NULL; } // tp_dealloc function for weakmap values void value_dealloc(PyObject *value) { weakmap_type weakmap = ...; PyMutex_Lock(&mutex); weakmap_remove_value(weakmap, value); ... PyMutex_Unlock(&mutex); }
Added in version 3.14.
-
void PyUnstable_EnableTryIncRef(PyObject *obj)¶
- This is Unstable API. It may change without warning in minor releases.
Enables subsequent uses of
PyUnstable_TryIncRef()
on obj. The caller must hold a strong reference to obj when calling this.Added in version 3.14.
-
int PyUnstable_Object_IsUniquelyReferenced(PyObject *op)¶
- This is Unstable API. It may change without warning in minor releases.
Determine if op only has one reference.
On GIL-enabled builds, this function is equivalent to Py_REFCNT(op) == 1.
On a free-threaded build, this checks if op’s reference count is equal to one and additionally checks if op is only used by this thread. Py_REFCNT(op) == 1 is not thread-safe on free-threaded builds; prefer this function.
The caller must hold an attached thread state, despite the fact that this function doesn’t call into the Python interpreter. This function cannot fail.
Added in version 3.14.


What’s New in Python 2.0

What’s New in Python 2.0¶
- Author:
A.M. Kuchling and Moshe Zadka
Introduction¶
A new release of Python, version 2.0, was released on October 16, 2000. This article covers the exciting new features in 2.0, highlights some other useful changes, and points out a few incompatible changes that may require rewriting code.
Python’s development never completely stops between releases, and a steady flow of bug fixes and improvements are always being submitted. A host of minor fixes, a few optimizations, additional docstrings, and better error messages went into 2.0; to list them all would be impossible, but they’re certainly significant. Consult the publicly available CVS logs if you want to see the full list. This progress is due to the five developers working for PythonLabs are now getting paid to spend their days fixing bugs, and also due to the improved communication resulting from moving to SourceForge.
What About Python 1.6?¶
Python 1.6 can be thought of as the Contractual Obligations Python release. After the core development team left CNRI in May 2000, CNRI requested that a 1.6 release be created, containing all the work on Python that had been performed at CNRI. Python 1.6 therefore represents the state of the CVS tree as of May 2000, with the most significant new feature being Unicode support. Development continued after May, of course, so the 1.6 tree received a few fixes to ensure that it’s forward-compatible with Python 2.0. 1.6 is therefore part of Python’s evolution, and not a side branch.
So, should you take much interest in Python 1.6? Probably not. The 1.6final and 2.0beta1 releases were made on the same day (September 5, 2000), the plan being to finalize Python 2.0 within a month or so. If you have applications to maintain, there seems little point in breaking things by moving to 1.6, fixing them, and then having another round of breakage within a month by moving to 2.0; you’re better off just going straight to 2.0. Most of the really interesting features described in this document are only in 2.0, because a lot of work was done between May and September.
New Development Process¶
The most important change in Python 2.0 may not be to the code at all, but to how Python is developed: in May 2000 the Python developers began using the tools made available by SourceForge for storing source code, tracking bug reports, and managing the queue of patch submissions. To report bugs or submit patches for Python 2.0, use the bug tracking and patch manager tools available from Python’s project page, located at https://sourceforge.net/projects/python/.
The most important of the services now hosted at SourceForge is the Python CVS tree, the version-controlled repository containing the source code for Python. Previously, there were roughly 7 or so people who had write access to the CVS tree, and all patches had to be inspected and checked in by one of the people on this short list. Obviously, this wasn’t very scalable. By moving the CVS tree to SourceForge, it became possible to grant write access to more people; as of September 2000 there were 27 people able to check in changes, a fourfold increase. This makes possible large-scale changes that wouldn’t be attempted if they’d have to be filtered through the small group of core developers. For example, one day Peter Schneider-Kamp took it into his head to drop K&R C compatibility and convert the C source for Python to ANSI C. After getting approval on the python-dev mailing list, he launched into a flurry of checkins that lasted about a week, other developers joined in to help, and the job was done. If there were only 5 people with write access, probably that task would have been viewed as “nice, but not worth the time and effort needed” and it would never have gotten done.
The shift to using SourceForge’s services has resulted in a remarkable increase in the speed of development. Patches now get submitted, commented on, revised by people other than the original submitter, and bounced back and forth between people until the patch is deemed worth checking in. Bugs are tracked in one central location and can be assigned to a specific person for fixing, and we can count the number of open bugs to measure progress. This didn’t come without a cost: developers now have more e-mail to deal with, more mailing lists to follow, and special tools had to be written for the new environment. For example, SourceForge sends default patch and bug notification e-mail messages that are completely unhelpful, so Ka-Ping Yee wrote an HTML screen-scraper that sends more useful messages.
The ease of adding code caused a few initial growing pains, such as code was checked in before it was ready or without getting clear agreement from the developer group. The approval process that has emerged is somewhat similar to that used by the Apache group. Developers can vote +1, +0, -0, or -1 on a patch; +1 and -1 denote acceptance or rejection, while +0 and -0 mean the developer is mostly indifferent to the change, though with a slight positive or negative slant. The most significant change from the Apache model is that the voting is essentially advisory, letting Guido van Rossum, who has Benevolent Dictator For Life status, know what the general opinion is. He can still ignore the result of a vote, and approve or reject a change even if the community disagrees with him.
Producing an actual patch is the last step in adding a new feature, and is usually easy compared to the earlier task of coming up with a good design. Discussions of new features can often explode into lengthy mailing list threads, making the discussion hard to follow, and no one can read every posting to python-dev. Therefore, a relatively formal process has been set up to write Python Enhancement Proposals (PEPs), modelled on the internet RFC process. PEPs are draft documents that describe a proposed new feature, and are continually revised until the community reaches a consensus, either accepting or rejecting the proposal. Quoting from the introduction to PEP 1, “PEP Purpose and Guidelines”:
PEP stands for Python Enhancement Proposal. A PEP is a design document providing information to the Python community, or describing a new feature for Python. The PEP should provide a concise technical specification of the feature and a rationale for the feature.
We intend PEPs to be the primary mechanisms for proposing new features, for collecting community input on an issue, and for documenting the design decisions that have gone into Python. The PEP author is responsible for building consensus within the community and documenting dissenting opinions.
Read the rest of PEP 1 for the details of the PEP editorial process, style, and format. PEPs are kept in the Python CVS tree on SourceForge, though they’re not part of the Python 2.0 distribution, and are also available in HTML form from https://peps.python.org/. As of September 2000, there are 25 PEPs, ranging from PEP 201, “Lockstep Iteration”, to PEP 225, “Elementwise/Objectwise Operators”.
Unicode¶
The largest new feature in Python 2.0 is a new fundamental data type: Unicode strings. Unicode uses 16-bit numbers to represent characters instead of the 8-bit number used by ASCII, meaning that 65,536 distinct characters can be supported.
The final interface for Unicode support was arrived at through countless often-stormy discussions on the python-dev mailing list, and mostly implemented by Marc-André Lemburg, based on a Unicode string type implementation by Fredrik Lundh. A detailed explanation of the interface was written up as PEP 100, “Python Unicode Integration”. This article will simply cover the most significant points about the Unicode interfaces.
In Python source code, Unicode strings are written as u"string"
. Arbitrary
Unicode characters can be written using a new escape sequence, \uHHHH
, where
HHHH is a 4-digit hexadecimal number from 0000 to FFFF. The existing
\xHH
escape sequence can also be used, and octal escapes can be used for
characters up to U+01FF, which is represented by \777
.
Unicode strings, just like regular strings, are an immutable sequence type.
They can be indexed and sliced, but not modified in place. Unicode strings have
an encode( [encoding] )
method that returns an 8-bit string in the desired
encoding. Encodings are named by strings, such as 'ascii'
, 'utf-8'
,
'iso-8859-1'
, or whatever. A codec API is defined for implementing and
registering new encodings that are then available throughout a Python program.
If an encoding isn’t specified, the default encoding is usually 7-bit ASCII,
though it can be changed for your Python installation by calling the
sys.setdefaultencoding(encoding)
function in a customized version of
site.py
.
Combining 8-bit and Unicode strings always coerces to Unicode, using the default
ASCII encoding; the result of 'a' + u'bc'
is u'abc'
.
New built-in functions have been added, and existing built-ins modified to support Unicode:
unichr(ch)
returns a Unicode string 1 character long, containing the character ch.ord(u)
, where u is a 1-character regular or Unicode string, returns the number of the character as an integer.unicode(string [, encoding] [, errors] )
creates a Unicode string from an 8-bit string.encoding
is a string naming the encoding to use. Theerrors
parameter specifies the treatment of characters that are invalid for the current encoding; passing'strict'
as the value causes an exception to be raised on any encoding error, while'ignore'
causes errors to be silently ignored and'replace'
uses U+FFFD, the official replacement character, in case of any problems.The
exec
statement, and various built-ins such aseval()
,getattr()
, andsetattr()
will also accept Unicode strings as well as regular strings. (It’s possible that the process of fixing this missed some built-ins; if you find a built-in function that accepts strings but doesn’t accept Unicode strings at all, please report it as a bug.)
A new module, unicodedata
, provides an interface to Unicode character
properties. For example, unicodedata.category(u'A')
returns the 2-character
string ‘Lu’, the ‘L’ denoting it’s a letter, and ‘u’ meaning that it’s
uppercase. unicodedata.bidirectional(u'\u0660')
returns ‘AN’, meaning that
U+0660 is an Arabic number.
The codecs
module contains functions to look up existing encodings and
register new ones. Unless you want to implement a new encoding, you’ll most
often use the codecs.lookup(encoding)
function, which returns a
4-element tuple: (encode_func, decode_func, stream_reader, stream_writer)
.
encode_func is a function that takes a Unicode string, and returns a 2-tuple
(string, length)
. string is an 8-bit string containing a portion (perhaps all) of the Unicode string converted into the given encoding, and length tells you how much of the Unicode string was converted.decode_func is the opposite of encode_func, taking an 8-bit string and returning a 2-tuple
(ustring, length)
, consisting of the resulting Unicode string ustring and the integer length telling how much of the 8-bit string was consumed.stream_reader is a class that supports decoding input from a stream. stream_reader(file_obj) returns an object that supports the
read()
,readline()
, andreadlines()
methods. These methods will all translate from the given encoding and return Unicode strings.stream_writer, similarly, is a class that supports encoding output to a stream. stream_writer(file_obj) returns an object that supports the
write()
andwritelines()
methods. These methods expect Unicode strings, translating them to the given encoding on output.
For example, the following code writes a Unicode string into a file, encoding it as UTF-8:
import codecs
unistr = u'\u0660\u2000ab ...'
(UTF8_encode, UTF8_decode,
UTF8_streamreader, UTF8_streamwriter) = codecs.lookup('UTF-8')
output = UTF8_streamwriter( open( '/tmp/output', 'wb') )
output.write( unistr )
output.close()
The following code would then read UTF-8 input from the file:
input = UTF8_streamreader( open( '/tmp/output', 'rb') )
print repr(input.read())
input.close()
Unicode-aware regular expressions are available through the re
module,
which has a new underlying implementation called SRE written by Fredrik Lundh of
Secret Labs AB.
A -U
command line option was added which causes the Python compiler to
interpret all string literals as Unicode string literals. This is intended to be
used in testing and future-proofing your Python code, since some future version
of Python may drop support for 8-bit strings and provide only Unicode strings.
List Comprehensions¶
Lists are a workhorse data type in Python, and many programs manipulate a list at some point. Two common operations on lists are to loop over them, and either pick out the elements that meet a certain criterion, or apply some function to each element. For example, given a list of strings, you might want to pull out all the strings containing a given substring, or strip off trailing whitespace from each line.
The existing map()
and filter()
functions can be used for this
purpose, but they require a function as one of their arguments. This is fine if
there’s an existing built-in function that can be passed directly, but if there
isn’t, you have to create a little function to do the required work, and
Python’s scoping rules make the result ugly if the little function needs
additional information. Take the first example in the previous paragraph,
finding all the strings in the list containing a given substring. You could
write the following to do it:
# Given the list L, make a list of all strings
# containing the substring S.
sublist = filter( lambda s, substring=S:
string.find(s, substring) != -1,
L)
Because of Python’s scoping rules, a default argument is used so that the
anonymous function created by the lambda
expression knows what
substring is being searched for. List comprehensions make this cleaner:
sublist = [ s for s in L if string.find(s, S) != -1 ]
List comprehensions have the form:
[ expression for expr in sequence1
for expr2 in sequence2 ...
for exprN in sequenceN
if condition ]
The for
…in
clauses contain the sequences to be
iterated over. The sequences do not have to be the same length, because they
are not iterated over in parallel, but from left to right; this is explained
more clearly in the following paragraphs. The elements of the generated list
will be the successive values of expression. The final if
clause
is optional; if present, expression is only evaluated and added to the result
if condition is true.
To make the semantics very clear, a list comprehension is equivalent to the following Python code:
for expr1 in sequence1:
for expr2 in sequence2:
...
for exprN in sequenceN:
if (condition):
# Append the value of
# the expression to the
# resulting list.
This means that when there are multiple for
…in
clauses, the resulting list will be equal to the product of the lengths of all
the sequences. If you have two lists of length 3, the output list is 9 elements
long:
seq1 = 'abc'
seq2 = (1,2,3)
>>> [ (x,y) for x in seq1 for y in seq2]
[('a', 1), ('a', 2), ('a', 3), ('b', 1), ('b', 2), ('b', 3), ('c', 1),
('c', 2), ('c', 3)]
To avoid introducing an ambiguity into Python’s grammar, if expression is creating a tuple, it must be surrounded with parentheses. The first list comprehension below is a syntax error, while the second one is correct:
# Syntax error
[ x,y for x in seq1 for y in seq2]
# Correct
[ (x,y) for x in seq1 for y in seq2]
The idea of list comprehensions originally comes from the functional programming language Haskell (https://www.haskell.org). Greg Ewing argued most effectively for adding them to Python and wrote the initial list comprehension patch, which was then discussed for a seemingly endless time on the python-dev mailing list and kept up-to-date by Skip Montanaro.
Augmented Assignment¶
Augmented assignment operators, another long-requested feature, have been added
to Python 2.0. Augmented assignment operators include +=
, -=
, *=
,
and so forth. For example, the statement a += 2
increments the value of the
variable a
by 2, equivalent to the slightly lengthier a = a + 2
.
The full list of supported assignment operators is +=
, -=
, *=
,
/=
, %=
, **=
, &=
, |=
, ^=
, >>=
, and <<=
. Python
classes can override the augmented assignment operators by defining methods
named __iadd__()
, __isub__()
, etc. For example, the following
Number
class stores a number and supports using += to create a new
instance with an incremented value.
class Number:
def __init__(self, value):
self.value = value
def __iadd__(self, increment):
return Number( self.value + increment)
n = Number(5)
n += 3
print n.value
The __iadd__()
special method is called with the value of the increment,
and should return a new instance with an appropriately modified value; this
return value is bound as the new value of the variable on the left-hand side.
Augmented assignment operators were first introduced in the C programming language, and most C-derived languages, such as awk, C++, Java, Perl, and PHP also support them. The augmented assignment patch was implemented by Thomas Wouters.
String Methods¶
Until now string-manipulation functionality was in the string
module,
which was usually a front-end for the strop
module written in C. The
addition of Unicode posed a difficulty for the strop
module, because the
functions would all need to be rewritten in order to accept either 8-bit or
Unicode strings. For functions such as string.replace()
, which takes 3
string arguments, that means eight possible permutations, and correspondingly
complicated code.
Instead, Python 2.0 pushes the problem onto the string type, making string manipulation functionality available through methods on both 8-bit strings and Unicode strings.
>>> 'andrew'.capitalize()
'Andrew'
>>> 'hostname'.replace('os', 'linux')
'hlinuxtname'
>>> 'moshe'.find('sh')
2
One thing that hasn’t changed, a noteworthy April Fools’ joke notwithstanding, is that Python strings are immutable. Thus, the string methods return new strings, and do not modify the string on which they operate.
The old string
module is still around for backwards compatibility, but it
mostly acts as a front-end to the new string methods.
Two methods which have no parallel in pre-2.0 versions, although they did exist
in JPython for quite some time, are startswith()
and endswith()
.
s.startswith(t)
is equivalent to s[:len(t)] == t
, while
s.endswith(t)
is equivalent to s[-len(t):] == t
.
One other method which deserves special mention is join()
. The
join()
method of a string receives one parameter, a sequence of strings,
and is equivalent to the string.join()
function from the old string
module, with the arguments reversed. In other words, s.join(seq)
is
equivalent to the old string.join(seq, s)
.
Garbage Collection of Cycles¶
The C implementation of Python uses reference counting to implement garbage collection. Every Python object maintains a count of the number of references pointing to itself, and adjusts the count as references are created or destroyed. Once the reference count reaches zero, the object is no longer accessible, since you need to have a reference to an object to access it, and if the count is zero, no references exist any longer.
Reference counting has some pleasant properties: it’s easy to understand and implement, and the resulting implementation is portable, fairly fast, and reacts well with other libraries that implement their own memory handling schemes. The major problem with reference counting is that it sometimes doesn’t realise that objects are no longer accessible, resulting in a memory leak. This happens when there are cycles of references.
Consider the simplest possible cycle, a class instance which has a reference to itself:
instance = SomeClass()
instance.myself = instance
After the above two lines of code have been executed, the reference count of
instance
is 2; one reference is from the variable named 'instance'
, and
the other is from the myself
attribute of the instance.
If the next line of code is del instance
, what happens? The reference count
of instance
is decreased by 1, so it has a reference count of 1; the
reference in the myself
attribute still exists. Yet the instance is no
longer accessible through Python code, and it could be deleted. Several objects
can participate in a cycle if they have references to each other, causing all of
the objects to be leaked.
Python 2.0 fixes this problem by periodically executing a cycle detection
algorithm which looks for inaccessible cycles and deletes the objects involved.
A new gc
module provides functions to perform a garbage collection,
obtain debugging statistics, and tuning the collector’s parameters.
Running the cycle detection algorithm takes some time, and therefore will result
in some additional overhead. It is hoped that after we’ve gotten experience
with the cycle collection from using 2.0, Python 2.1 will be able to minimize
the overhead with careful tuning. It’s not yet obvious how much performance is
lost, because benchmarking this is tricky and depends crucially on how often the
program creates and destroys objects. The detection of cycles can be disabled
when Python is compiled, if you can’t afford even a tiny speed penalty or
suspect that the cycle collection is buggy, by specifying the
--without-cycle-gc
switch when running the configure
script.
Several people tackled this problem and contributed to a solution. An early implementation of the cycle detection approach was written by Toby Kelsey. The current algorithm was suggested by Eric Tiedemann during a visit to CNRI, and Guido van Rossum and Neil Schemenauer wrote two different implementations, which were later integrated by Neil. Lots of other people offered suggestions along the way; the March 2000 archives of the python-dev mailing list contain most of the relevant discussion, especially in the threads titled “Reference cycle collection for Python” and “Finalization again”.
Other Core Changes¶
Various minor changes have been made to Python’s syntax and built-in functions. None of the changes are very far-reaching, but they’re handy conveniences.
Minor Language Changes¶
A new syntax makes it more convenient to call a given function with a tuple of
arguments and/or a dictionary of keyword arguments. In Python 1.5 and earlier,
you’d use the apply()
built-in function: apply(f, args, kw)
calls the
function f()
with the argument tuple args and the keyword arguments in
the dictionary kw. apply()
is the same in 2.0, but thanks to a patch
from Greg Ewing, f(*args, **kw)
is a shorter and clearer way to achieve the
same effect. This syntax is symmetrical with the syntax for defining
functions:
def f(*args, **kw):
# args is a tuple of positional args,
# kw is a dictionary of keyword args
...
The print
statement can now have its output directed to a file-like
object by following the print
with >> file
, similar to the
redirection operator in Unix shells. Previously you’d either have to use the
write()
method of the file-like object, which lacks the convenience and
simplicity of print
, or you could assign a new value to
sys.stdout
and then restore the old value. For sending output to standard
error, it’s much easier to write this:
print >> sys.stderr, "Warning: action field not supplied"
Modules can now be renamed on importing them, using the syntax import module
as name
or from module import name as othername
. The patch was submitted
by Thomas Wouters.
A new format style is available when using the %
operator; ‘%r’ will insert
the repr()
of its argument. This was also added from symmetry
considerations, this time for symmetry with the existing ‘%s’ format style,
which inserts the str()
of its argument. For example, '%r %s' % ('abc',
'abc')
returns a string containing 'abc' abc
.
Previously there was no way to implement a class that overrode Python’s built-in
in
operator and implemented a custom version. obj in seq
returns
true if obj is present in the sequence seq; Python computes this by simply
trying every index of the sequence until either obj is found or an
IndexError
is encountered. Moshe Zadka contributed a patch which adds a
__contains__()
magic method for providing a custom implementation for
in
. Additionally, new built-in objects written in C can define what
in
means for them via a new slot in the sequence protocol.
Earlier versions of Python used a recursive algorithm for deleting objects. Deeply nested data structures could cause the interpreter to fill up the C stack and crash; Christian Tismer rewrote the deletion logic to fix this problem. On a related note, comparing recursive objects recursed infinitely and crashed; Jeremy Hylton rewrote the code to no longer crash, producing a useful result instead. For example, after this code:
a = []
b = []
a.append(a)
b.append(b)
The comparison a==b
returns true, because the two recursive data structures
are isomorphic. See the thread “trashcan and PR#7” in the April 2000 archives of
the python-dev mailing list for the discussion leading up to this
implementation, and some useful relevant links. Note that comparisons can now
also raise exceptions. In earlier versions of Python, a comparison operation
such as cmp(a,b)
would always produce an answer, even if a user-defined
__cmp__()
method encountered an error, since the resulting exception would
simply be silently swallowed.
Work has been done on porting Python to 64-bit Windows on the Itanium processor,
mostly by Trent Mick of ActiveState. (Confusingly, sys.platform
is still
'win32'
on Win64 because it seems that for ease of porting, MS Visual C++
treats code as 32 bit on Itanium.) PythonWin also supports Windows CE; see the
Python CE page at https://pythonce.sourceforge.net/ for more information.
Another new platform is Darwin/MacOS X; initial support for it is in Python 2.0. Dynamic loading works, if you specify “configure –with-dyld –with-suffix=.x”. Consult the README in the Python source distribution for more instructions.
An attempt has been made to alleviate one of Python’s warts, the often-confusing
NameError
exception when code refers to a local variable before the
variable has been assigned a value. For example, the following code raises an
exception on the print
statement in both 1.5.2 and 2.0; in 1.5.2 a
NameError
exception is raised, while 2.0 raises a new
UnboundLocalError
exception. UnboundLocalError
is a subclass of
NameError
, so any existing code that expects NameError
to be
raised should still work.
def f():
print "i=",i
i = i + 1
f()
Two new exceptions, TabError
and IndentationError
, have been
introduced. They’re both subclasses of SyntaxError
, and are raised when
Python code is found to be improperly indented.
Changes to Built-in Functions¶
A new built-in, zip(seq1, seq2, ...)
, has been added. zip()
returns a list of tuples where each tuple contains the i-th element from each of
the argument sequences. The difference between zip()
and map(None,
seq1, seq2)
is that map()
pads the sequences with None
if the
sequences aren’t all of the same length, while zip()
truncates the
returned list to the length of the shortest argument sequence.
The int()
and long()
functions now accept an optional “base”
parameter when the first argument is a string. int('123', 10)
returns 123,
while int('123', 16)
returns 291. int(123, 16)
raises a
TypeError
exception with the message “can’t convert non-string with
explicit base”.
A new variable holding more detailed version information has been added to the
sys
module. sys.version_info
is a tuple (major, minor, micro,
level, serial)
For example, in a hypothetical 2.0.1beta1, sys.version_info
would be (2, 0, 1, 'beta', 1)
. level is a string such as "alpha"
,
"beta"
, or "final"
for a final release.
Dictionaries have an odd new method, setdefault(key, default)
, which
behaves similarly to the existing get()
method. However, if the key is
missing, setdefault()
both returns the value of default as get()
would do, and also inserts it into the dictionary as the value for key. Thus,
the following lines of code:
if dict.has_key( key ): return dict[key]
else:
dict[key] = []
return dict[key]
can be reduced to a single return dict.setdefault(key, [])
statement.
The interpreter sets a maximum recursion depth in order to catch runaway
recursion before filling the C stack and causing a core dump or GPF..
Previously this limit was fixed when you compiled Python, but in 2.0 the maximum
recursion depth can be read and modified using sys.getrecursionlimit()
and
sys.setrecursionlimit()
. The default value is 1000, and a rough maximum
value for a given platform can be found by running a new script,
Misc/find_recursionlimit.py
.
Porting to 2.0¶
New Python releases try hard to be compatible with previous releases, and the record has been pretty good. However, some changes are considered useful enough, usually because they fix initial design decisions that turned out to be actively mistaken, that breaking backward compatibility can’t always be avoided. This section lists the changes in Python 2.0 that may cause old Python code to break.
The change which will probably break the most code is tightening up the
arguments accepted by some methods. Some methods would take multiple arguments
and treat them as a tuple, particularly various list methods such as
append()
and insert()
.
In earlier versions of Python, if L
is
a list, L.append( 1,2 )
appends the tuple (1,2)
to the list. In Python
2.0 this causes a TypeError
exception to be raised, with the message:
‘append requires exactly 1 argument; 2 given’. The fix is to simply add an
extra set of parentheses to pass both values as a tuple: L.append( (1,2) )
.
The earlier versions of these methods were more forgiving because they used an
old function in Python’s C interface to parse their arguments; 2.0 modernizes
them to use PyArg_ParseTuple()
, the current argument parsing function,
which provides more helpful error messages and treats multi-argument calls as
errors. If you absolutely must use 2.0 but can’t fix your code, you can edit
Objects/listobject.c
and define the preprocessor symbol
NO_STRICT_LIST_APPEND
to preserve the old behaviour; this isn’t recommended.
Some of the functions in the socket
module are still forgiving in this
way. For example, socket.connect( ('hostname', 25) )
is the correct
form, passing a tuple representing an IP address, but socket.connect('hostname', 25)
also works. socket.connect_ex
and socket.bind
are similarly easy-going. 2.0alpha1 tightened these functions up, but because
the documentation actually used the erroneous multiple argument form, many
people wrote code which would break with the stricter checking. GvR backed out
the changes in the face of public reaction, so for the socket
module, the
documentation was fixed and the multiple argument form is simply marked as
deprecated; it will be tightened up again in a future Python version.
The \x
escape in string literals now takes exactly 2 hex digits. Previously
it would consume all the hex digits following the ‘x’ and take the lowest 8 bits
of the result, so \x123456
was equivalent to \x56
.
The AttributeError
and NameError
exceptions have a more friendly
error message, whose text will be something like 'Spam' instance has no
attribute 'eggs'
or name 'eggs' is not defined
. Previously the error
message was just the missing attribute name eggs
, and code written to take
advantage of this fact will break in 2.0.
Some work has been done to make integers and long integers a bit more
interchangeable. In 1.5.2, large-file support was added for Solaris, to allow
reading files larger than 2 GiB; this made the tell()
method of file
objects return a long integer instead of a regular integer. Some code would
subtract two file offsets and attempt to use the result to multiply a sequence
or slice a string, but this raised a TypeError
. In 2.0, long integers
can be used to multiply or slice a sequence, and it’ll behave as you’d
intuitively expect it to; 3L * 'abc'
produces ‘abcabcabc’, and
(0,1,2,3)[2L:4L]
produces (2,3). Long integers can also be used in various
contexts where previously only integers were accepted, such as in the
seek()
method of file objects, and in the formats supported by the %
operator (%d
, %i
, %x
, etc.). For example, "%d" % 2L**64
will
produce the string 18446744073709551616
.
The subtlest long integer change of all is that the str()
of a long
integer no longer has a trailing ‘L’ character, though repr()
still
includes it. The ‘L’ annoyed many people who wanted to print long integers that
looked just like regular integers, since they had to go out of their way to chop
off the character. This is no longer a problem in 2.0, but code which does
str(longval)[:-1]
and assumes the ‘L’ is there, will now lose the final
digit.
Taking the repr()
of a float now uses a different formatting precision
than str()
. repr()
uses %.17g
format string for C’s
sprintf()
, while str()
uses %.12g
as before. The effect is that
repr()
may occasionally show more decimal places than str()
, for
certain numbers. For example, the number 8.1 can’t be represented exactly in
binary, so repr(8.1)
is '8.0999999999999996'
, while str(8.1) is
'8.1'
.
The -X
command-line option, which turned all standard exceptions into
strings instead of classes, has been removed; the standard exceptions will now
always be classes. The exceptions
module containing the standard
exceptions was translated from Python to a built-in C module, written by Barry
Warsaw and Fredrik Lundh.
Extending/Embedding Changes¶
Some of the changes are under the covers, and will only be apparent to people writing C extension modules or embedding a Python interpreter in a larger application. If you aren’t dealing with Python’s C API, you can safely skip this section.
The version number of the Python C API was incremented, so C extensions compiled for 1.5.2 must be recompiled in order to work with 2.0. On Windows, it’s not possible for Python 2.0 to import a third party extension built for Python 1.5.x due to how Windows DLLs work, so Python will raise an exception and the import will fail.
Users of Jim Fulton’s ExtensionClass module will be pleased to find out that
hooks have been added so that ExtensionClasses are now supported by
isinstance()
and issubclass()
. This means you no longer have to
remember to write code such as if type(obj) == myExtensionClass
, but can use
the more natural if isinstance(obj, myExtensionClass)
.
The Python/importdl.c
file, which was a mass of #ifdefs to support
dynamic loading on many different platforms, was cleaned up and reorganised by
Greg Stein. importdl.c
is now quite small, and platform-specific code
has been moved into a bunch of Python/dynload_*.c
files. Another
cleanup: there were also a number of my*.h
files in the Include/
directory that held various portability hacks; they’ve been merged into a single
file, Include/pyport.h
.
Vladimir Marangozov’s long-awaited malloc restructuring was completed, to make
it easy to have the Python interpreter use a custom allocator instead of C’s
standard malloc()
. For documentation, read the comments in
Include/pymem.h
and Include/objimpl.h
. For the lengthy
discussions during which the interface was hammered out, see the web archives of
the ‘patches’ and ‘python-dev’ lists at python.org.
Recent versions of the GUSI development environment for MacOS support POSIX
threads. Therefore, Python’s POSIX threading support now works on the
Macintosh. Threading support using the user-space GNU pth
library was also
contributed.
Threading support on Windows was enhanced, too. Windows supports thread locks that use kernel objects only in case of contention; in the common case when there’s no contention, they use simpler functions which are an order of magnitude faster. A threaded version of Python 1.5.2 on NT is twice as slow as an unthreaded version; with the 2.0 changes, the difference is only 10%. These improvements were contributed by Yakov Markovitch.
Python 2.0’s source now uses only ANSI C prototypes, so compiling Python now requires an ANSI C compiler, and can no longer be done using a compiler that only supports K&R C.
Previously the Python virtual machine used 16-bit numbers in its bytecode,
limiting the size of source files. In particular, this affected the maximum
size of literal lists and dictionaries in Python source; occasionally people who
are generating Python code would run into this limit. A patch by Charles G.
Waldman raises the limit from 2**16
to 2**32
.
Three new convenience functions intended for adding constants to a module’s
dictionary at module initialization time were added: PyModule_AddObject()
,
PyModule_AddIntConstant()
, and PyModule_AddStringConstant()
. Each
of these functions takes a module object, a null-terminated C string containing
the name to be added, and a third argument for the value to be assigned to the
name. This third argument is, respectively, a Python object, a C long, or a C
string.
A wrapper API was added for Unix-style signal handlers. PyOS_getsig()
gets
a signal handler and PyOS_setsig()
will set a new handler.
Distutils: Making Modules Easy to Install¶
Before Python 2.0, installing modules was a tedious affair – there was no way to figure out automatically where Python is installed, or what compiler options to use for extension modules. Software authors had to go through an arduous ritual of editing Makefiles and configuration files, which only really work on Unix and leave Windows and MacOS unsupported. Python users faced wildly differing installation instructions which varied between different extension packages, which made administering a Python installation something of a chore.
The SIG for distribution utilities, shepherded by Greg Ward, has created the
Distutils, a system to make package installation much easier. They form the
distutils
package, a new part of Python’s standard library. In the best
case, installing a Python module from source will require the same steps: first
you simply mean unpack the tarball or zip archive, and the run “python
setup.py install
”. The platform will be automatically detected, the compiler
will be recognized, C extension modules will be compiled, and the distribution
installed into the proper directory. Optional command-line arguments provide
more control over the installation process, the distutils package offers many
places to override defaults – separating the build from the install, building
or installing in non-default directories, and more.
In order to use the Distutils, you need to write a setup.py
script. For
the simple case, when the software contains only .py files, a minimal
setup.py
can be just a few lines long:
from distutils.core import setup
setup (name = "foo", version = "1.0",
py_modules = ["module1", "module2"])
The setup.py
file isn’t much more complicated if the software consists
of a few packages:
from distutils.core import setup
setup (name = "foo", version = "1.0",
packages = ["package", "package.subpackage"])
A C extension can be the most complicated case; here’s an example taken from the PyXML package:
from distutils.core import setup, Extension
expat_extension = Extension('xml.parsers.pyexpat',
define_macros = [('XML_NS', None)],
include_dirs = [ 'extensions/expat/xmltok',
'extensions/expat/xmlparse' ],
sources = [ 'extensions/pyexpat.c',
'extensions/expat/xmltok/xmltok.c',
'extensions/expat/xmltok/xmlrole.c', ]
)
setup (name = "PyXML", version = "0.5.4",
ext_modules =[ expat_extension ] )
The Distutils can also take care of creating source and binary distributions.
The “sdist” command, run by “python setup.py sdist
’, builds a source
distribution such as foo-1.0.tar.gz
. Adding new commands isn’t
difficult, “bdist_rpm” and “bdist_wininst” commands have already been
contributed to create an RPM distribution and a Windows installer for the
software, respectively. Commands to create other distribution formats such as
Debian packages and Solaris .pkg
files are in various stages of
development.
All this is documented in a new manual, Distributing Python Modules, that joins the basic set of Python documentation.
XML Modules¶
Python 1.5.2 included a simple XML parser in the form of the xmllib
module, contributed by Sjoerd Mullender. Since 1.5.2’s release, two different
interfaces for processing XML have become common: SAX2 (version 2 of the Simple
API for XML) provides an event-driven interface with some similarities to
xmllib
, and the DOM (Document Object Model) provides a tree-based
interface, transforming an XML document into a tree of nodes that can be
traversed and modified. Python 2.0 includes a SAX2 interface and a stripped-down
DOM interface as part of the xml
package. Here we will give a brief
overview of these new interfaces; consult the Python documentation or the source
code for complete details. The Python XML SIG is also working on improved
documentation.
SAX2 Support¶
SAX defines an event-driven interface for parsing XML. To use SAX, you must
write a SAX handler class. Handler classes inherit from various classes
provided by SAX, and override various methods that will then be called by the
XML parser. For example, the startElement()
and endElement()
methods are called for every starting and end tag encountered by the parser, the
characters()
method is called for every chunk of character data, and so
forth.
The advantage of the event-driven approach is that the whole document doesn’t have to be resident in memory at any one time, which matters if you are processing really huge documents. However, writing the SAX handler class can get very complicated if you’re trying to modify the document structure in some elaborate way.
For example, this little example program defines a handler that prints a message
for every starting and ending tag, and then parses the file hamlet.xml
using it:
from xml import sax
class SimpleHandler(sax.ContentHandler):
def startElement(self, name, attrs):
print 'Start of element:', name, attrs.keys()
def endElement(self, name):
print 'End of element:', name
# Create a parser object
parser = sax.make_parser()
# Tell it what handler to use
handler = SimpleHandler()
parser.setContentHandler( handler )
# Parse a file!
parser.parse( 'hamlet.xml' )
For more information, consult the Python documentation, or the XML HOWTO at https://pyxml.sourceforge.net/topics/howto/xml-howto.html.
DOM Support¶
The Document Object Model is a tree-based representation for an XML document. A
top-level Document
instance is the root of the tree, and has a single
child which is the top-level Element
instance. This Element
has children nodes representing character data and any sub-elements, which may
have further children of their own, and so forth. Using the DOM you can
traverse the resulting tree any way you like, access element and attribute
values, insert and delete nodes, and convert the tree back into XML.
The DOM is useful for modifying XML documents, because you can create a DOM
tree, modify it by adding new nodes or rearranging subtrees, and then produce a
new XML document as output. You can also construct a DOM tree manually and
convert it to XML, which can be a more flexible way of producing XML output than
simply writing <tag1>
…</tag1>
to a file.
The DOM implementation included with Python lives in the xml.dom.minidom
module. It’s a lightweight implementation of the Level 1 DOM with support for
XML namespaces. The parse()
and parseString()
convenience
functions are provided for generating a DOM tree:
from xml.dom import minidom
doc = minidom.parse('hamlet.xml')
doc
is a Document
instance. Document
, like all the other
DOM classes such as Element
and Text
, is a subclass of the
Node
base class. All the nodes in a DOM tree therefore support certain
common methods, such as toxml()
which returns a string containing the XML
representation of the node and its children. Each class also has special
methods of its own; for example, Element
and Document
instances have a method to find all child elements with a given tag name.
Continuing from the previous 2-line example:
perslist = doc.getElementsByTagName( 'PERSONA' )
print perslist[0].toxml()
print perslist[1].toxml()
For the Hamlet XML file, the above few lines output:
<PERSONA>CLAUDIUS, king of Denmark. </PERSONA>
<PERSONA>HAMLET, son to the late, and nephew to the present king.</PERSONA>
The root element of the document is available as doc.documentElement
, and
its children can be easily modified by deleting, adding, or removing nodes:
root = doc.documentElement
# Remove the first child
root.removeChild( root.childNodes[0] )
# Move the new first child to the end
root.appendChild( root.childNodes[0] )
# Insert the new first child (originally,
# the third child) before the 20th child.
root.insertBefore( root.childNodes[0], root.childNodes[20] )
Again, I will refer you to the Python documentation for a complete listing of
the different Node
classes and their various methods.
Relationship to PyXML¶
The XML Special Interest Group has been working on XML-related Python code for a
while. Its code distribution, called PyXML, is available from the SIG’s web
pages at https://www.python.org/community/sigs/current/xml-sig. The PyXML distribution also used
the package name xml
. If you’ve written programs that used PyXML, you’re
probably wondering about its compatibility with the 2.0 xml
package.
The answer is that Python 2.0’s xml
package isn’t compatible with PyXML,
but can be made compatible by installing a recent version PyXML. Many
applications can get by with the XML support that is included with Python 2.0,
but more complicated applications will require that the full PyXML package will
be installed. When installed, PyXML versions 0.6.0 or greater will replace the
xml
package shipped with Python, and will be a strict superset of the
standard package, adding a bunch of additional features. Some of the additional
features in PyXML include:
4DOM, a full DOM implementation from FourThought, Inc.
The xmlproc validating parser, written by Lars Marius Garshol.
The
sgmlop
parser accelerator module, written by Fredrik Lundh.
Module changes¶
Lots of improvements and bugfixes were made to Python’s extensive standard
library; some of the affected modules include readline
,
ConfigParser
, cgi
, calendar
, posix
, readline
,
xmllib
, aifc
, chunk
, wave
, random
, shelve
,
and nntplib
. Consult the CVS logs for the exact patch-by-patch details.
Brian Gallew contributed OpenSSL support for the socket
module. OpenSSL
is an implementation of the Secure Socket Layer, which encrypts the data being
sent over a socket. When compiling Python, you can edit Modules/Setup
to include SSL support, which adds an additional function to the socket
module: socket.ssl(socket, keyfile, certfile)
, which takes a socket
object and returns an SSL socket. The httplib
and urllib
modules
were also changed to support https://
URLs, though no one has implemented
FTP or SMTP over SSL.
The httplib
module has been rewritten by Greg Stein to support HTTP/1.1.
Backward compatibility with the 1.5 version of httplib
is provided,
though using HTTP/1.1 features such as pipelining will require rewriting code to
use a different set of interfaces.
The Tkinter
module now supports Tcl/Tk version 8.1, 8.2, or 8.3, and
support for the older 7.x versions has been dropped. The Tkinter module now
supports displaying Unicode strings in Tk widgets. Also, Fredrik Lundh
contributed an optimization which makes operations like create_line
and
create_polygon
much faster, especially when using lots of coordinates.
The curses
module has been greatly extended, starting from Oliver
Andrich’s enhanced version, to provide many additional functions from ncurses
and SYSV curses, such as colour, alternative character set support, pads, and
mouse support. This means the module is no longer compatible with operating
systems that only have BSD curses, but there don’t seem to be any currently
maintained OSes that fall into this category.
As mentioned in the earlier discussion of 2.0’s Unicode support, the underlying
implementation of the regular expressions provided by the re
module has
been changed. SRE, a new regular expression engine written by Fredrik Lundh and
partially funded by Hewlett Packard, supports matching against both 8-bit
strings and Unicode strings.
New modules¶
A number of new modules were added. We’ll simply list them with brief descriptions; consult the 2.0 documentation for the details of a particular module.
atexit
: For registering functions to be called before the Python interpreter exits. Code that currently setssys.exitfunc
directly should be changed to use theatexit
module instead, importingatexit
and callingatexit.register()
with the function to be called on exit. (Contributed by Skip Montanaro.)codecs
,encodings
,unicodedata
: Added as part of the new Unicode support.filecmp
: Supersedes the oldcmp
,cmpcache
anddircmp
modules, which have now become deprecated. (Contributed by Gordon MacMillan and Moshe Zadka.)gettext
: This module provides internationalization (I18N) and localization (L10N) support for Python programs by providing an interface to the GNU gettext message catalog library. (Integrated by Barry Warsaw, from separate contributions by Martin von Löwis, Peter Funk, and James Henstridge.)linuxaudiodev
: Support for the/dev/audio
device on Linux, a twin to the existingsunaudiodev
module. (Contributed by Peter Bosch, with fixes by Jeremy Hylton.)mmap
: An interface to memory-mapped files on both Windows and Unix. A file’s contents can be mapped directly into memory, at which point it behaves like a mutable string, so its contents can be read and modified. They can even be passed to functions that expect ordinary strings, such as there
module. (Contributed by Sam Rushing, with some extensions by A.M. Kuchling.)pyexpat
: An interface to the Expat XML parser. (Contributed by Paul Prescod.)robotparser
: Parse arobots.txt
file, which is used for writing web spiders that politely avoid certain areas of a web site. The parser accepts the contents of arobots.txt
file, builds a set of rules from it, and can then answer questions about the fetchability of a given URL. (Contributed by Skip Montanaro.)tabnanny
: A module/script to check Python source code for ambiguous indentation. (Contributed by Tim Peters.)UserString
: A base class useful for deriving objects that behave like strings.webbrowser
: A module that provides a platform independent way to launch a web browser on a specific URL. For each platform, various browsers are tried in a specific order. The user can alter which browser is launched by setting the BROWSER environment variable. (Originally inspired by Eric S. Raymond’s patch tourllib
which added similar functionality, but the final module comes from code originally implemented by Fred Drake asTools/idle/BrowserControl.py
, and adapted for the standard library by Fred.)_winreg
: An interface to the Windows registry._winreg
is an adaptation of functions that have been part of PythonWin since 1995, but has now been added to the core distribution, and enhanced to support Unicode._winreg
was written by Bill Tutt and Mark Hammond.zipfile
: A module for reading and writing ZIP-format archives. These are archives produced by PKZIP on DOS/Windows or zip on Unix, not to be confused with gzip-format files (which are supported by thegzip
module) (Contributed by James C. Ahlstrom.)imputil
: A module that provides a simpler way for writing customized import hooks, in comparison to the existingihooks
module. (Implemented by Greg Stein, with much discussion on python-dev along the way.)
IDLE Improvements¶
IDLE is the official Python cross-platform IDE, written using Tkinter. Python 2.0 includes IDLE 0.6, which adds a number of new features and improvements. A partial list:
UI improvements and optimizations, especially in the area of syntax highlighting and auto-indentation.
The class browser now shows more information, such as the top level functions in a module.
Tab width is now a user settable option. When opening an existing Python file, IDLE automatically detects the indentation conventions, and adapts.
There is now support for calling browsers on various platforms, used to open the Python documentation in a browser.
IDLE now has a command line, which is largely similar to the vanilla Python interpreter.
Call tips were added in many places.
IDLE can now be installed as a package.
In the editor window, there is now a line/column bar at the bottom.
Three new keystroke commands: Check module (Alt-F5), Import module (F5) and Run script (Ctrl-F5).
Deleted and Deprecated Modules¶
A few modules have been dropped because they’re obsolete, or because there are
now better ways to do the same thing. The stdwin
module is gone; it was
for a platform-independent windowing toolkit that’s no longer developed.
A number of modules have been moved to the lib-old
subdirectory:
cmp
, cmpcache
, dircmp
, dump
, find
,
grep
, packmail
, poly
, util
, whatsound
,
zmod
. If you have code which relies on a module that’s been moved to
lib-old
, you can simply add that directory to sys.path
to get them
back, but you’re encouraged to update any code that uses these modules.
Acknowledgements¶
The authors would like to thank the following people for offering suggestions on various drafts of this article: David Bolen, Mark Hammond, Gregg Hauser, Jeremy Hylton, Fredrik Lundh, Detlef Lannert, Aahz Maruch, Skip Montanaro, Vladimir Marangozov, Tobias Polzin, Guido van Rossum, Neil Schemenauer, and Russ Schmidt.