7 .. if you add new entries, keep the alphabetical sorting!
12 The typical Python prompt of the interactive shell. Often seen for code
13 examples that can be tried right away in the interpreter.
16 The typical Python prompt of the interactive shell when entering code for
17 an indented code block.
20 A tool that tries to convert Python 2.x code to Python 3.x code by
21 handling most of the incompatibilites that can be detected by parsing the
22 source and traversing the parse tree.
24 2to3 is available in the standard library as :mod:`lib2to3`; a standalone
25 entry point is provided as :file:`Tools/scripts/2to3`.
28 Abstract Base Classes (abbreviated ABCs) complement :term:`duck-typing` by
29 providing a way to define interfaces when other techniques like :func:`hasattr`
30 would be clumsy. Python comes with many builtin ABCs for data structures
31 (in the :mod:`collections` module), numbers (in the :mod:`numbers`
32 module), and streams (in the :mod:`io` module). You can create your own
33 ABC with the :mod:`abc` module.
36 A value passed to a function or method, assigned to a name local to
37 the body. A function or method may have both positional arguments and
38 keyword arguments in its definition. Positional and keyword arguments
39 may be variable-length: ``*`` accepts or passes (if in the function
40 definition or call) several positional arguments in a list, while ``**``
41 does the same for keyword arguments in a dictionary.
43 Any expression may be used within the argument list, and the evaluated
44 value is passed to the local variable.
47 Benevolent Dictator For Life, a.k.a. `Guido van Rossum
48 <http://www.python.org/~guido/>`_, Python's creator.
51 Python source code is compiled into bytecode, the internal representation
52 of a Python program in the interpreter. The bytecode is also cached in
53 ``.pyc`` and ``.pyo`` files so that executing the same file is faster the
54 second time (recompilation from source to bytecode can be avoided). This
55 "intermediate language" is said to run on a "virtual machine" that calls
56 the subroutines corresponding to each bytecode.
59 Any class which does not inherit from :class:`object`. See
60 :term:`new-style class`.
63 The implicit conversion of an instance of one type to another during an
64 operation which involves two arguments of the same type. For example,
65 ``int(3.15)`` converts the floating point number to the integer ``3``, but
66 in ``3+4.5``, each argument is of a different type (one int, one float),
67 and both must be converted to the same type before they can be added or it
68 will raise a ``TypeError``. Coercion between two operands can be
69 performed with the ``coerce`` builtin function; thus, ``3+4.5`` is
70 equivalent to calling ``operator.add(*coerce(3, 4.5))`` and results in
71 ``operator.add(3.0, 4.5)``. Without coercion, all arguments of even
72 compatible types would have to be normalized to the same value by the
73 programmer, e.g., ``float(3)+4.5`` rather than just ``3+4.5``.
76 An extension of the familiar real number system in which all numbers are
77 expressed as a sum of a real part and an imaginary part. Imaginary
78 numbers are real multiples of the imaginary unit (the square root of
79 ``-1``), often written ``i`` in mathematics or ``j`` in
80 engineering. Python has builtin support for complex numbers, which are
81 written with this latter notation; the imaginary part is written with a
82 ``j`` suffix, e.g., ``3+1j``. To get access to complex equivalents of the
83 :mod:`math` module, use :mod:`cmath`. Use of complex numbers is a fairly
84 advanced mathematical feature. If you're not aware of a need for them,
85 it's almost certain you can safely ignore them.
88 An objects that controls the environment seen in a :keyword:`with`
89 statement by defining :meth:`__enter__` and :meth:`__exit__` methods.
93 A function returning another function, usually applied as a function
94 transformation using the ``@wrapper`` syntax. Common examples for
95 decorators are :func:`classmethod` and :func:`staticmethod`.
97 The decorator syntax is merely syntactic sugar, the following two
98 function definitions are semantically equivalent::
109 Any *new-style* object that defines the methods :meth:`__get__`,
110 :meth:`__set__`, or :meth:`__delete__`. When a class attribute is a
111 descriptor, its special binding behavior is triggered upon attribute
112 lookup. Normally, using *a.b* to get, set or delete an attribute looks up
113 the object named *b* in the class dictionary for *a*, but if *b* is a
114 descriptor, the respective descriptor method gets called. Understanding
115 descriptors is a key to a deep understanding of Python because they are
116 the basis for many features including functions, methods, properties,
117 class methods, static methods, and reference to super classes.
119 For more information about descriptors' methods, see :ref:`descriptors`.
122 An associative array, where arbitrary keys are mapped to values. The use
123 of :class:`dict` much resembles that for :class:`list`, but the keys can
124 be any object with a :meth:`__hash__` function, not just integers starting
125 from zero. Called a hash in Perl.
128 Pythonic programming style that determines an object's type by inspection
129 of its method or attribute signature rather than by explicit relationship
130 to some type object ("If it looks like a duck and quacks like a duck, it
131 must be a duck.") By emphasizing interfaces rather than specific types,
132 well-designed code improves its flexibility by allowing polymorphic
133 substitution. Duck-typing avoids tests using :func:`type` or
134 :func:`isinstance`. (Note, however, that duck-typing can be complemented
135 with abstract base classes.) Instead, it typically employs :func:`hasattr`
136 tests or :term:`EAFP` programming.
139 Easier to ask for forgiveness than permission. This common Python coding
140 style assumes the existence of valid keys or attributes and catches
141 exceptions if the assumption proves false. This clean and fast style is
142 characterized by the presence of many :keyword:`try` and :keyword:`except`
143 statements. The technique contrasts with the :term:`LBYL` style that is
144 common in many other languages such as C.
147 A piece of syntax which can be evaluated to some value. In other words,
148 an expression is an accumulation of expression elements like literals, names,
149 attribute access, operators or function calls that all return a value.
150 In contrast to other languages, not all language constructs are expressions,
151 but there are also :term:`statement`\s that cannot be used as expressions,
152 such as :keyword:`print` or :keyword:`if`. Assignments are also not
156 A module written in C, using Python's C API to interact with the core and
160 A series of statements which returns some value to a caller. It can also
161 be passed zero or more arguments which may be used in the execution of
162 the body. See also :term:`argument` and :term:`method`.
165 A pseudo module which programmers can use to enable new language features
166 which are not compatible with the current interpreter. For example, the
167 expression ``11/4`` currently evaluates to ``2``. If the module in which
168 it is executed had enabled *true division* by executing::
170 from __future__ import division
172 the expression ``11/4`` would evaluate to ``2.75``. By importing the
173 :mod:`__future__` module and evaluating its variables, you can see when a
174 new feature was first added to the language and when it will become the
177 >>> import __future__
178 >>> __future__.division
179 _Feature((2, 2, 0, 'alpha', 2), (3, 0, 0, 'alpha', 0), 8192)
182 The process of freeing memory when it is not used anymore. Python
183 performs garbage collection via reference counting and a cyclic garbage
184 collector that is able to detect and break reference cycles.
187 A function that returns an iterator. It looks like a normal function
188 except that values are returned to the caller using a :keyword:`yield`
189 statement instead of a :keyword:`return` statement. Generator functions
190 often contain one or more :keyword:`for` or :keyword:`while` loops that
191 :keyword:`yield` elements back to the caller. The function execution is
192 stopped at the :keyword:`yield` keyword (returning the result) and is
193 resumed there when the next element is requested by calling the
194 :meth:`next` method of the returned iterator.
196 .. index:: single: generator expression
199 An expression that returns a generator. It looks like a normal expression
200 followed by a :keyword:`for` expression defining a loop variable, range,
201 and an optional :keyword:`if` expression. The combined expression
202 generates values for an enclosing function::
204 >>> sum(i*i for i in range(10)) # sum of squares 0, 1, 4, ... 81
208 See :term:`global interpreter lock`.
210 global interpreter lock
211 The lock used by Python threads to assure that only one thread can be run
212 at a time. This simplifies Python by assuring that no two processes can
213 access the same memory at the same time. Locking the entire interpreter
214 makes it easier for the interpreter to be multi-threaded, at the expense
215 of some parallelism on multi-processor machines. Efforts have been made
216 in the past to create a "free-threaded" interpreter (one which locks
217 shared data at a much finer granularity), but performance suffered in the
218 common single-processor case.
221 An object is *hashable* if it has a hash value that never changes during
222 its lifetime (it needs a :meth:`__hash__` method), and can be compared to
223 other objects (it needs an :meth:`__eq__` or :meth:`__cmp__` method).
224 Hashable objects that compare equal must have the same hash value.
226 Hashability makes an object usable as a dictionary key and a set member,
227 because these data structures use the hash value internally.
229 All of Python's immutable built-in objects are hashable, while all mutable
230 containers (such as lists or dictionaries) are not. Objects that are
231 instances of user-defined classes are hashable by default; they all
232 compare unequal, and their hash value is their :func:`id`.
235 An Integrated Development Environment for Python. IDLE is a basic editor
236 and interpreter environment that ships with the standard distribution of
237 Python. Good for beginners, it also serves as clear example code for
238 those wanting to implement a moderately sophisticated, multi-platform GUI
242 An object with fixed value. Immutable objects are numbers, strings or
243 tuples (and more). Such an object cannot be altered. A new object has to
244 be created if a different value has to be stored. They play an important
245 role in places where a constant hash value is needed, for example as a key
249 Mathematical division discarding any remainder. For example, the
250 expression ``11/4`` currently evaluates to ``2`` in contrast to the
251 ``2.75`` returned by float division. Also called *floor division*.
252 When dividing two integers the outcome will always be another integer
253 (having the floor function applied to it). However, if one of the operands
254 is another numeric type (such as a :class:`float`), the result will be
255 coerced (see :term:`coercion`) to a common type. For example, an integer
256 divided by a float will result in a float value, possibly with a decimal
257 fraction. Integer division can be forced by using the ``//`` operator
258 instead of the ``/`` operator. See also :term:`__future__`.
261 Python has an interactive interpreter which means that you can try out
262 things and immediately see their results. Just launch ``python`` with no
263 arguments (possibly by selecting it from your computer's main menu). It is
264 a very powerful way to test out new ideas or inspect modules and packages
265 (remember ``help(x)``).
268 Python is an interpreted language, as opposed to a compiled one. This
269 means that the source files can be run directly without first creating an
270 executable which is then run. Interpreted languages typically have a
271 shorter development/debug cycle than compiled ones, though their programs
272 generally also run more slowly. See also :term:`interactive`.
275 A container object capable of returning its members one at a
276 time. Examples of iterables include all sequence types (such as
277 :class:`list`, :class:`str`, and :class:`tuple`) and some non-sequence
278 types like :class:`dict` and :class:`file` and objects of any classes you
279 define with an :meth:`__iter__` or :meth:`__getitem__` method. Iterables
280 can be used in a :keyword:`for` loop and in many other places where a
281 sequence is needed (:func:`zip`, :func:`map`, ...). When an iterable
282 object is passed as an argument to the builtin function :func:`iter`, it
283 returns an iterator for the object. This iterator is good for one pass
284 over the set of values. When using iterables, it is usually not necessary
285 to call :func:`iter` or deal with iterator objects yourself. The ``for``
286 statement does that automatically for you, creating a temporary unnamed
287 variable to hold the iterator for the duration of the loop. See also
288 :term:`iterator`, :term:`sequence`, and :term:`generator`.
291 An object representing a stream of data. Repeated calls to the iterator's
292 :meth:`next` method return successive items in the stream. When no more
293 data is available a :exc:`StopIteration` exception is raised instead. At
294 this point, the iterator object is exhausted and any further calls to its
295 :meth:`next` method just raise :exc:`StopIteration` again. Iterators are
296 required to have an :meth:`__iter__` method that returns the iterator
297 object itself so every iterator is also iterable and may be used in most
298 places where other iterables are accepted. One notable exception is code
299 that attempts multiple iteration passes. A container object (such as a
300 :class:`list`) produces a fresh new iterator each time you pass it to the
301 :func:`iter` function or use it in a :keyword:`for` loop. Attempting this
302 with an iterator will just return the same exhausted iterator object used
303 in the previous iteration pass, making it appear like an empty container.
305 More information can be found in :ref:`typeiter`.
308 Arguments which are preceded with a ``variable_name=`` in the call.
309 The variable name designates the local name in the function to which the
310 value is assigned. ``**`` is used to accept or pass a dictionary of
311 keyword arguments. See :term:`argument`.
314 An anonymous inline function consisting of a single :term:`expression`
315 which is evaluated when the function is called. The syntax to create
316 a lambda function is ``lambda [arguments]: expression``
319 Look before you leap. This coding style explicitly tests for
320 pre-conditions before making calls or lookups. This style contrasts with
321 the :term:`EAFP` approach and is characterized by the presence of many
322 :keyword:`if` statements.
325 A compact way to process all or a subset of elements in a sequence and
326 return a list with the results. ``result = ["0x%02x" % x for x in
327 range(256) if x % 2 == 0]`` generates a list of strings containing hex
328 numbers (0x..) that are even and in the range from 0 to 255. The
329 :keyword:`if` clause is optional. If omitted, all elements in
330 ``range(256)`` are processed.
333 A container object (such as :class:`dict`) that supports arbitrary key
334 lookups using the special method :meth:`__getitem__`.
337 The class of a class. Class definitions create a class name, a class
338 dictionary, and a list of base classes. The metaclass is responsible for
339 taking those three arguments and creating the class. Most object oriented
340 programming languages provide a default implementation. What makes Python
341 special is that it is possible to create custom metaclasses. Most users
342 never need this tool, but when the need arises, metaclasses can provide
343 powerful, elegant solutions. They have been used for logging attribute
344 access, adding thread-safety, tracking object creation, implementing
345 singletons, and many other tasks.
347 More information can be found in :ref:`metaclasses`.
350 A function that is defined inside a class body. If called as an attribute
351 of an instance of that class, the method will get the instance object as
352 its first :term:`argument` (which is usually called ``self``).
353 See :term:`function` and :term:`nested scope`.
356 Mutable objects can change their value but keep their :func:`id`. See
357 also :term:`immutable`.
360 Any tuple subclass whose indexable fields are also accessible with
361 named attributes (for example, :func:`time.localtime` returns a
362 tuple-like object where the *year* is accessible either with an
363 index such as ``t[0]`` or with a named attribute like ``t.tm_year``).
365 A named tuple can be a built-in type such as :class:`time.struct_time`,
366 or it can be created with a regular class definition. A full featured
367 named tuple can also be created with the factory function
368 :func:`collections.namedtuple`. The latter approach automatically
369 provides extra features such as a self-documenting representation like
370 ``Employee(name='jones', title='programmer')``.
373 The place where a variable is stored. Namespaces are implemented as
374 dictionaries. There are the local, global and builtin namespaces as well
375 as nested namespaces in objects (in methods). Namespaces support
376 modularity by preventing naming conflicts. For instance, the functions
377 :func:`__builtin__.open` and :func:`os.open` are distinguished by their
378 namespaces. Namespaces also aid readability and maintainability by making
379 it clear which module implements a function. For instance, writing
380 :func:`random.seed` or :func:`itertools.izip` makes it clear that those
381 functions are implemented by the :mod:`random` and :mod:`itertools`
382 modules respectively.
385 The ability to refer to a variable in an enclosing definition. For
386 instance, a function defined inside another function can refer to
387 variables in the outer function. Note that nested scopes work only for
388 reference and not for assignment which will always write to the innermost
389 scope. In contrast, local variables both read and write in the innermost
390 scope. Likewise, global variables read and write to the global namespace.
393 Any class that inherits from :class:`object`. This includes all built-in
394 types like :class:`list` and :class:`dict`. Only new-style classes can
395 use Python's newer, versatile features like :attr:`__slots__`,
396 descriptors, properties, :meth:`__getattribute__`, class methods, and
399 More information can be found in :ref:`newstyle`.
402 The arguments assigned to local names inside a function or method,
403 determined by the order in which they were given in the call. ``*`` is
404 used to either accept multiple positional arguments (when in the
405 definition), or pass several arguments as a list to a function. See
409 Nickname for the next major Python version, 3.0 (coined long ago
410 when the release of version 3 was something in the distant future.) This
411 is also abbreviated "Py3k".
414 An idea or piece of code which closely follows the most common idioms of
415 the Python language, rather than implementing code using concepts common
416 in other languages. For example, a common idiom in Python is the :keyword:`for`
417 loop structure; other languages don't have this easy keyword, so people
418 use a numerical counter instead::
420 for i in range(len(food)):
423 As opposed to the cleaner, Pythonic method::
429 The number of places where a certain object is referenced to. When the
430 reference count drops to zero, an object is deallocated. While reference
431 counting is invisible on the Python code level, it is used on the
432 implementation level to keep track of allocated memory.
435 A declaration inside a :term:`new-style class` that saves memory by
436 pre-declaring space for instance attributes and eliminating instance
437 dictionaries. Though popular, the technique is somewhat tricky to get
438 right and is best reserved for rare cases where there are large numbers of
439 instances in a memory-critical application.
442 An :term:`iterable` which supports efficient element access using integer
443 indices via the :meth:`__getitem__` and :meth:`__len__` special methods.
444 Some built-in sequence types are :class:`list`, :class:`str`,
445 :class:`tuple`, and :class:`unicode`. Note that :class:`dict` also
446 supports :meth:`__getitem__` and :meth:`__len__`, but is considered a
447 mapping rather than a sequence because the lookups use arbitrary
448 :term:`immutable` keys rather than integers.
451 An object usually containing a portion of a :term:`sequence`. A slice is
452 created using the subscript notation, ``[]`` with colons between numbers
453 when several are given, such as in ``variable_name[1:3:5]``. The bracket
454 (subscript) notation uses :class:`slice` objects internally (or in older
455 versions, :meth:`__getslice__` and :meth:`__setslice__`).
458 A statement is part of a suite (a "block" of code). A statement is either
459 an :term:`expression` or a one of several constructs with a keyword, such
460 as :keyword:`if`, :keyword:`while` or :keyword:`print`.
463 The type of a Python object determines what kind of object it is; every
464 object has a type. An object's type is accessible as its
465 :attr:`__class__` attribute or can be retrieved with ``type(obj)``.
468 Listing of Python design principles and philosophies that are helpful in
469 understanding and using the language. The listing can be found by typing
470 "``import this``" at the interactive prompt.