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 value passed to a function or method, assigned to a name local to
21 the body. A function or method may have both positional arguments and
22 keyword arguments in its definition. Positional and keyword arguments
23 may be variable-length: ``*`` accepts or passes (if in the function
24 definition or call) several positional arguments in a list, while ``**``
25 does the same for keyword arguments in a dictionary.
27 Any expression may be used within the argument list, and the evaluated
28 value is passed to the local variable.
31 Benevolent Dictator For Life, a.k.a. `Guido van Rossum
32 <http://www.python.org/~guido/>`_, Python's creator.
35 Python source code is compiled into bytecode, the internal representation
36 of a Python program in the interpreter. The bytecode is also cached in
37 ``.pyc`` and ``.pyo`` files so that executing the same file is faster the
38 second time (recompilation from source to bytecode can be avoided). This
39 "intermediate language" is said to run on a "virtual machine" that calls
40 the subroutines corresponding to each bytecode.
43 Any class which does not inherit from :class:`object`. See
44 :term:`new-style class`.
47 The implicit conversion of an instance of one type to another during an
48 operation which involves two arguments of the same type. For example,
49 ``int(3.15)`` converts the floating point number to the integer ``3``, but
50 in ``3+4.5``, each argument is of a different type (one int, one float),
51 and both must be converted to the same type before they can be added or it
52 will raise a ``TypeError``. Coercion between two operands can be
53 performed with the ``coerce`` builtin function; thus, ``3+4.5`` is
54 equivalent to calling ``operator.add(*coerce(3, 4.5))`` and results in
55 ``operator.add(3.0, 4.5)``. Without coercion, all arguments of even
56 compatible types would have to be normalized to the same value by the
57 programmer, e.g., ``float(3)+4.5`` rather than just ``3+4.5``.
60 An extension of the familiar real number system in which all numbers are
61 expressed as a sum of a real part and an imaginary part. Imaginary
62 numbers are real multiples of the imaginary unit (the square root of
63 ``-1``), often written ``i`` in mathematics or ``j`` in
64 engineering. Python has builtin support for complex numbers, which are
65 written with this latter notation; the imaginary part is written with a
66 ``j`` suffix, e.g., ``3+1j``. To get access to complex equivalents of the
67 :mod:`math` module, use :mod:`cmath`. Use of complex numbers is a fairly
68 advanced mathematical feature. If you're not aware of a need for them,
69 it's almost certain you can safely ignore them.
72 An objects that controls the environment seen in a :keyword:`with`
73 statement by defining :meth:`__enter__` and :meth:`__exit__` methods.
77 A function returning another function, usually applied as a function
78 transformation using the ``@wrapper`` syntax. Common examples for
79 decorators are :func:`classmethod` and :func:`staticmethod`.
81 The decorator syntax is merely syntactic sugar, the following two
82 function definitions are semantically equivalent::
93 Any *new-style* object that defines the methods :meth:`__get__`,
94 :meth:`__set__`, or :meth:`__delete__`. When a class attribute is a
95 descriptor, its special binding behavior is triggered upon attribute
96 lookup. Normally, using *a.b* to get, set or delete an attribute looks up
97 the object named *b* in the class dictionary for *a*, but if *b* is a
98 descriptor, the respective descriptor method gets called. Understanding
99 descriptors is a key to a deep understanding of Python because they are
100 the basis for many features including functions, methods, properties,
101 class methods, static methods, and reference to super classes.
103 For more information about descriptors' methods, see :ref:`descriptors`.
106 An associative array, where arbitrary keys are mapped to values. The use
107 of :class:`dict` much resembles that for :class:`list`, but the keys can
108 be any object with a :meth:`__hash__` function, not just integers starting
109 from zero. Called a hash in Perl.
112 Pythonic programming style that determines an object's type by inspection
113 of its method or attribute signature rather than by explicit relationship
114 to some type object ("If it looks like a duck and quacks like a duck, it
115 must be a duck.") By emphasizing interfaces rather than specific types,
116 well-designed code improves its flexibility by allowing polymorphic
117 substitution. Duck-typing avoids tests using :func:`type` or
118 :func:`isinstance`. Instead, it typically employs :func:`hasattr` tests or
119 :term:`EAFP` programming.
122 Easier to ask for forgiveness than permission. This common Python coding
123 style assumes the existence of valid keys or attributes and catches
124 exceptions if the assumption proves false. This clean and fast style is
125 characterized by the presence of many :keyword:`try` and :keyword:`except`
126 statements. The technique contrasts with the :term:`LBYL` style that is
127 common in many other languages such as C.
130 A piece of syntax which can be evaluated to some value. In other words,
131 an expression is an accumulation of expression elements like literals, names,
132 attribute access, operators or function calls that all return a value.
133 In contrast to other languages, not all language constructs are expressions,
134 but there are also :term:`statement`\s that cannot be used as expressions,
135 such as :keyword:`print` or :keyword:`if`. Assignments are also not
139 A module written in C, using Python's C API to interact with the core and
143 A series of statements which returns some value to a caller. It can also
144 be passed zero or more arguments which may be used in the execution of
145 the body. See also :term:`argument` and :term:`method`.
148 A pseudo module which programmers can use to enable new language features
149 which are not compatible with the current interpreter. For example, the
150 expression ``11/4`` currently evaluates to ``2``. If the module in which
151 it is executed had enabled *true division* by executing::
153 from __future__ import division
155 the expression ``11/4`` would evaluate to ``2.75``. By importing the
156 :mod:`__future__` module and evaluating its variables, you can see when a
157 new feature was first added to the language and when it will become the
160 >>> import __future__
161 >>> __future__.division
162 _Feature((2, 2, 0, 'alpha', 2), (3, 0, 0, 'alpha', 0), 8192)
165 The process of freeing memory when it is not used anymore. Python
166 performs garbage collection via reference counting and a cyclic garbage
167 collector that is able to detect and break reference cycles.
170 A function that returns an iterator. It looks like a normal function
171 except that values are returned to the caller using a :keyword:`yield`
172 statement instead of a :keyword:`return` statement. Generator functions
173 often contain one or more :keyword:`for` or :keyword:`while` loops that
174 :keyword:`yield` elements back to the caller. The function execution is
175 stopped at the :keyword:`yield` keyword (returning the result) and is
176 resumed there when the next element is requested by calling the
177 :meth:`next` method of the returned iterator.
179 .. index:: single: generator expression
182 An expression that returns a generator. It looks like a normal expression
183 followed by a :keyword:`for` expression defining a loop variable, range,
184 and an optional :keyword:`if` expression. The combined expression
185 generates values for an enclosing function::
187 >>> sum(i*i for i in range(10)) # sum of squares 0, 1, 4, ... 81
191 See :term:`global interpreter lock`.
193 global interpreter lock
194 The lock used by Python threads to assure that only one thread can be run
195 at a time. This simplifies Python by assuring that no two processes can
196 access the same memory at the same time. Locking the entire interpreter
197 makes it easier for the interpreter to be multi-threaded, at the expense
198 of some parallelism on multi-processor machines. Efforts have been made
199 in the past to create a "free-threaded" interpreter (one which locks
200 shared data at a much finer granularity), but performance suffered in the
201 common single-processor case.
204 An object is *hashable* if it has a hash value that never changes during
205 its lifetime (it needs a :meth:`__hash__` method), and can be compared to
206 other objects (it needs an :meth:`__eq__` or :meth:`__cmp__` method).
207 Hashable objects that compare equal must have the same hash value.
209 Hashability makes an object usable as a dictionary key and a set member,
210 because these data structures use the hash value internally.
212 All of Python's immutable built-in objects are hashable, while all mutable
213 containers (such as lists or dictionaries) are not. Objects that are
214 instances of user-defined classes are hashable by default; they all
215 compare unequal, and their hash value is their :func:`id`.
218 An Integrated Development Environment for Python. IDLE is a basic editor
219 and interpreter environment that ships with the standard distribution of
220 Python. Good for beginners, it also serves as clear example code for
221 those wanting to implement a moderately sophisticated, multi-platform GUI
225 An object with fixed value. Immutable objects are numbers, strings or
226 tuples (and more). Such an object cannot be altered. A new object has to
227 be created if a different value has to be stored. They play an important
228 role in places where a constant hash value is needed, for example as a key
232 Mathematical division discarding any remainder. For example, the
233 expression ``11/4`` currently evaluates to ``2`` in contrast to the
234 ``2.75`` returned by float division. Also called *floor division*.
235 When dividing two integers the outcome will always be another integer
236 (having the floor function applied to it). However, if one of the operands
237 is another numeric type (such as a :class:`float`), the result will be
238 coerced (see :term:`coercion`) to a common type. For example, an integer
239 divided by a float will result in a float value, possibly with a decimal
240 fraction. Integer division can be forced by using the ``//`` operator
241 instead of the ``/`` operator. See also :term:`__future__`.
244 Python has an interactive interpreter which means that you can try out
245 things and immediately see their results. Just launch ``python`` with no
246 arguments (possibly by selecting it from your computer's main menu). It is
247 a very powerful way to test out new ideas or inspect modules and packages
248 (remember ``help(x)``).
251 Python is an interpreted language, as opposed to a compiled one. This
252 means that the source files can be run directly without first creating an
253 executable which is then run. Interpreted languages typically have a
254 shorter development/debug cycle than compiled ones, though their programs
255 generally also run more slowly. See also :term:`interactive`.
258 A container object capable of returning its members one at a
259 time. Examples of iterables include all sequence types (such as
260 :class:`list`, :class:`str`, and :class:`tuple`) and some non-sequence
261 types like :class:`dict` and :class:`file` and objects of any classes you
262 define with an :meth:`__iter__` or :meth:`__getitem__` method. Iterables
263 can be used in a :keyword:`for` loop and in many other places where a
264 sequence is needed (:func:`zip`, :func:`map`, ...). When an iterable
265 object is passed as an argument to the builtin function :func:`iter`, it
266 returns an iterator for the object. This iterator is good for one pass
267 over the set of values. When using iterables, it is usually not necessary
268 to call :func:`iter` or deal with iterator objects yourself. The ``for``
269 statement does that automatically for you, creating a temporary unnamed
270 variable to hold the iterator for the duration of the loop. See also
271 :term:`iterator`, :term:`sequence`, and :term:`generator`.
274 An object representing a stream of data. Repeated calls to the iterator's
275 :meth:`next` method return successive items in the stream. When no more
276 data is available a :exc:`StopIteration` exception is raised instead. At
277 this point, the iterator object is exhausted and any further calls to its
278 :meth:`next` method just raise :exc:`StopIteration` again. Iterators are
279 required to have an :meth:`__iter__` method that returns the iterator
280 object itself so every iterator is also iterable and may be used in most
281 places where other iterables are accepted. One notable exception is code
282 that attempts multiple iteration passes. A container object (such as a
283 :class:`list`) produces a fresh new iterator each time you pass it to the
284 :func:`iter` function or use it in a :keyword:`for` loop. Attempting this
285 with an iterator will just return the same exhausted iterator object used
286 in the previous iteration pass, making it appear like an empty container.
288 More information can be found in :ref:`typeiter`.
291 Arguments which are preceded with a ``variable_name=`` in the call.
292 The variable name designates the local name in the function to which the
293 value is assigned. ``**`` is used to accept or pass a dictionary of
294 keyword arguments. See :term:`argument`.
297 An anonymous inline function consisting of a single :term:`expression`
298 which is evaluated when the function is called. The syntax to create
299 a lambda function is ``lambda [arguments]: expression``
302 Look before you leap. This coding style explicitly tests for
303 pre-conditions before making calls or lookups. This style contrasts with
304 the :term:`EAFP` approach and is characterized by the presence of many
305 :keyword:`if` statements.
308 A compact way to process all or a subset of elements in a sequence and
309 return a list with the results. ``result = ["0x%02x" % x for x in
310 range(256) if x % 2 == 0]`` generates a list of strings containing hex
311 numbers (0x..) that are even and in the range from 0 to 255. The
312 :keyword:`if` clause is optional. If omitted, all elements in
313 ``range(256)`` are processed.
316 A container object (such as :class:`dict`) that supports arbitrary key
317 lookups using the special method :meth:`__getitem__`.
320 The class of a class. Class definitions create a class name, a class
321 dictionary, and a list of base classes. The metaclass is responsible for
322 taking those three arguments and creating the class. Most object oriented
323 programming languages provide a default implementation. What makes Python
324 special is that it is possible to create custom metaclasses. Most users
325 never need this tool, but when the need arises, metaclasses can provide
326 powerful, elegant solutions. They have been used for logging attribute
327 access, adding thread-safety, tracking object creation, implementing
328 singletons, and many other tasks.
330 More information can be found in :ref:`metaclasses`.
333 A function that is defined inside a class body. If called as an attribute
334 of an instance of that class, the method will get the instance object as
335 its first :term:`argument` (which is usually called ``self``).
336 See :term:`function` and :term:`nested scope`.
339 Mutable objects can change their value but keep their :func:`id`. See
340 also :term:`immutable`.
343 Any tuple subclass whose indexable fields are also accessible with
344 named attributes (for example, :func:`time.localtime` returns a
345 tuple-like object where the *year* is accessible either with an
346 index such as ``t[0]`` or with a named attribute like ``t.tm_year``).
348 A named tuple can be a built-in type such as :class:`time.struct_time`,
349 or it can be created with a regular class definition. A full featured
350 named tuple can also be created with the factory function
351 :func:`collections.namedtuple`. The latter approach automatically
352 provides extra features such as a self-documenting representation like
353 ``Employee(name='jones', title='programmer')``.
356 The place where a variable is stored. Namespaces are implemented as
357 dictionaries. There are the local, global and builtin namespaces as well
358 as nested namespaces in objects (in methods). Namespaces support
359 modularity by preventing naming conflicts. For instance, the functions
360 :func:`__builtin__.open` and :func:`os.open` are distinguished by their
361 namespaces. Namespaces also aid readability and maintainability by making
362 it clear which module implements a function. For instance, writing
363 :func:`random.seed` or :func:`itertools.izip` makes it clear that those
364 functions are implemented by the :mod:`random` and :mod:`itertools`
365 modules respectively.
368 The ability to refer to a variable in an enclosing definition. For
369 instance, a function defined inside another function can refer to
370 variables in the outer function. Note that nested scopes work only for
371 reference and not for assignment which will always write to the innermost
372 scope. In contrast, local variables both read and write in the innermost
373 scope. Likewise, global variables read and write to the global namespace.
376 Any class that inherits from :class:`object`. This includes all built-in
377 types like :class:`list` and :class:`dict`. Only new-style classes can
378 use Python's newer, versatile features like :attr:`__slots__`,
379 descriptors, properties, :meth:`__getattribute__`, class methods, and
382 More information can be found in :ref:`newstyle`.
385 The arguments assigned to local names inside a function or method,
386 determined by the order in which they were given in the call. ``*`` is
387 used to either accept multiple positional arguments (when in the
388 definition), or pass several arguments as a list to a function. See
392 Nickname for the next major Python version, 3.0 (coined long ago when the
393 release of version 3 was something in the distant future.)
396 An idea or piece of code which closely follows the most common idioms of
397 the Python language, rather than implementing code using concepts common
398 in other languages. For example, a common idiom in Python is the :keyword:`for`
399 loop structure; other languages don't have this easy keyword, so people
400 use a numerical counter instead::
402 for i in range(len(food)):
405 As opposed to the cleaner, Pythonic method::
411 The number of places where a certain object is referenced to. When the
412 reference count drops to zero, an object is deallocated. While reference
413 counting is invisible on the Python code level, it is used on the
414 implementation level to keep track of allocated memory.
417 A declaration inside a :term:`new-style class` that saves memory by
418 pre-declaring space for instance attributes and eliminating instance
419 dictionaries. Though popular, the technique is somewhat tricky to get
420 right and is best reserved for rare cases where there are large numbers of
421 instances in a memory-critical application.
424 An :term:`iterable` which supports efficient element access using integer
425 indices via the :meth:`__getitem__` and :meth:`__len__` special methods.
426 Some built-in sequence types are :class:`list`, :class:`str`,
427 :class:`tuple`, and :class:`unicode`. Note that :class:`dict` also
428 supports :meth:`__getitem__` and :meth:`__len__`, but is considered a
429 mapping rather than a sequence because the lookups use arbitrary
430 :term:`immutable` keys rather than integers.
433 An object usually containing a portion of a :term:`sequence`. A slice is
434 created using the subscript notation, ``[]`` with colons between numbers
435 when several are given, such as in ``variable_name[1:3:5]``. The bracket
436 (subscript) notation uses :class:`slice` objects internally (or in older
437 versions, :meth:`__getslice__` and :meth:`__setslice__`).
440 A statement is part of a suite (a "block" of code). A statement is either
441 an :term:`expression` or a one of several constructs with a keyword, such
442 as :keyword:`if`, :keyword:`while` or :keyword:`print`.
445 The type of a Python object determines what kind of object it is; every
446 object has a type. An object's type is accessible as its
447 :attr:`__class__` attribute or can be retrieved with ``type(obj)``.
450 Listing of Python design principles and philosophies that are helpful in
451 understanding and using the language. The listing can be found by typing
452 "``import this``" at the interactive prompt.