- The default Python prompt of the interactive shell. Often seen for code
examples which can be executed interactively in the interpreter.
- The default Python prompt of the interactive shell when entering code for
an indented code block or within a pair of matching left and right
delimiters (parentheses, square brackets or curly braces).
A tool that tries to convert Python 2.x code to Python 3.x code by
handling most of the incompatibilites which can be detected by parsing the
source and traversing the parse tree.
2to3 is available in the standard library as lib2to3; a standalone
entry point is provided as Tools/scripts/2to3. See
2to3 - Automated Python 2 to 3 code translation.
- abstract base class
- Abstract Base Classes (abbreviated ABCs) complement duck-typing by
providing a way to define interfaces when other techniques like hasattr()
would be clumsy. Python comes with many builtin ABCs for data structures
(in the collections module), numbers (in the numbers
module), and streams (in the io module). You can create your own
ABC with the abc module.
A value passed to a function or method, assigned to a named local
variable in the function body. A function or method may have both
positional arguments and keyword arguments in its definition.
Positional and keyword arguments may be variable-length: * accepts
or passes (if in the function definition or call) several positional
arguments in a list, while ** does the same for keyword arguments
in a dictionary.
Any expression may be used within the argument list, and the evaluated
value is passed to the local variable.
- A value associated with an object which is referenced by name using
dotted expressions. For example, if an object o has an attribute
a it would be referenced as o.a.
- Benevolent Dictator For Life, a.k.a. Guido van Rossum, Python’s creator.
- Python source code is compiled into bytecode, the internal representation
of a Python program in the interpreter. The bytecode is also cached in
.pyc and .pyo files so that executing the same file is faster the
second time (recompilation from source to bytecode can be avoided). This
“intermediate language” is said to run on a virtual machine
that executes the machine code corresponding to each bytecode.
- A template for creating user-defined objects. Class definitions
normally contain method definitions which operate on instances of the
- The implicit conversion of an instance of one type to another during an
operation which involves two arguments of the same type. For example,
int(3.15) converts the floating point number to the integer 3, but
in 3+4.5, each argument is of a different type (one int, one float),
and both must be converted to the same type before they can be added or it
will raise a TypeError. Without coercion, all arguments of even
compatible types would have to be normalized to the same value by the
programmer, e.g., float(3)+4.5 rather than just 3+4.5.
- complex number
- An extension of the familiar real number system in which all numbers are
expressed as a sum of a real part and an imaginary part. Imaginary
numbers are real multiples of the imaginary unit (the square root of
-1), often written i in mathematics or j in
engineering. Python has builtin support for complex numbers, which are
written with this latter notation; the imaginary part is written with a
j suffix, e.g., 3+1j. To get access to complex equivalents of the
math module, use cmath. Use of complex numbers is a fairly
advanced mathematical feature. If you’re not aware of a need for them,
it’s almost certain you can safely ignore them.
- context manager
- An object which controls the environment seen in a with
statement by defining __enter__() and __exit__() methods.
See PEP 343.
- The canonical implementation of the Python programming language. The
term “CPython” is used in contexts when necessary to distinguish this
implementation from others such as Jython or IronPython.
A function returning another function, usually applied as a function
transformation using the @wrapper syntax. Common examples for
decorators are classmethod() and staticmethod().
The decorator syntax is merely syntactic sugar, the following two
function definitions are semantically equivalent:
f = staticmethod(f)
The same concept exists for classes, but is less commonly used there. See
the documentation for function definitions and
class definitions for more about decorators.
Any object which defines the methods __get__(), __set__(), or
__delete__(). When a class attribute is a descriptor, its special
binding behavior is triggered upon attribute lookup. Normally, using
a.b to get, set or delete an attribute looks up the object named b in
the class dictionary for a, but if b is a descriptor, the respective
descriptor method gets called. Understanding descriptors is a key to a
deep understanding of Python because they are the basis for many features
including functions, methods, properties, class methods, static methods,
and reference to super classes.
For more information about descriptors’ methods, see Implementing Descriptors.
- An associative array, where arbitrary keys are mapped to values. The use
of dict closely resembles that for list, but the keys can
be any object with a __hash__() function, not just integers.
Called a hash in Perl.
- A string literal which appears as the first expression in a class,
function or module. While ignored when the suite is executed, it is
recognized by the compiler and put into the __doc__ attribute
of the enclosing class, function or module. Since it is available via
introspection, it is the canonical place for documentation of the
- A pythonic programming style which determines an object’s type by inspection
of its method or attribute signature rather than by explicit relationship
to some type object (“If it looks like a duck and quacks like a duck, it
must be a duck.”) By emphasizing interfaces rather than specific types,
well-designed code improves its flexibility by allowing polymorphic
substitution. Duck-typing avoids tests using type() or
isinstance(). (Note, however, that duck-typing can be complemented
with abstract base classes.) Instead, it typically employs hasattr()
tests or EAFP programming.
- Easier to ask for forgiveness than permission. This common Python coding
style assumes the existence of valid keys or attributes and catches
exceptions if the assumption proves false. This clean and fast style is
characterized by the presence of many try and except
statements. The technique contrasts with the LBYL style
common to many other languages such as C.
- A piece of syntax which can be evaluated to some value. In other words,
an expression is an accumulation of expression elements like literals,
names, attribute access, operators or function calls which all return a
value. In contrast to many other languages, not all language constructs
are expressions. There are also statements which cannot be used
as expressions, such as if. Assignments are also statements,
- extension module
- A module written in C or C++, using Python’s C API to interact with the core and
with user code.
- floor division
- Mathematical division discarding any remainder. The floor division
operator is //. For example, the expression 11//4 evaluates to
2 in contrast to the 2.75 returned by float true division.
- A series of statements which returns some value to a caller. It can also
be passed zero or more arguments which may be used in the execution of
the body. See also argument and method.
A pseudo module which programmers can use to enable new language features
which are not compatible with the current interpreter.
By importing the __future__ module and evaluating its variables,
you can see when a new feature was first added to the language and when it
becomes the default:
>>> import __future__
_Feature((2, 2, 0, 'alpha', 2), (3, 0, 0, 'alpha', 0), 8192)
- garbage collection
- The process of freeing memory when it is not used anymore. Python
performs garbage collection via reference counting and a cyclic garbage
collector that is able to detect and break reference cycles.
- A function which returns an iterator. It looks like a normal function
except that values are returned to the caller using a yield
statement instead of a return statement. Generator functions
often contain one or more for or while loops which
yield elements back to the caller. The function execution is
stopped at the yield keyword (returning the result) and is
resumed there when the next element is requested by calling the
__next__() method of the returned iterator.
- generator expression
An expression that returns a generator. It looks like a normal expression
followed by a for expression defining a loop variable, range,
and an optional if expression. The combined expression
generates values for an enclosing function:
>>> sum(i*i for i in range(10)) # sum of squares 0, 1, 4, ... 81
- See global interpreter lock.
- global interpreter lock
- The lock used by Python threads to assure that only one thread
executes in the CPython virtual machine at a time.
This simplifies the CPython implementation by assuring that no two
processes can access the same memory at the same time. Locking the
entire interpreter makes it easier for the interpreter to be
multi-threaded, at the expense of much of the parallelism afforded by
multi-processor machines. Efforts have been made in the past to
create a “free-threaded” interpreter (one which locks shared data at a
much finer granularity), but so far none have been successful because
performance suffered in the common single-processor case.
An object is hashable if it has a hash value which never changes during
its lifetime (it needs a __hash__() method), and can be compared to
other objects (it needs an __eq__() method). Hashable objects which
compare equal must have the same hash value.
Hashability makes an object usable as a dictionary key and a set member,
because these data structures use the hash value internally.
All of Python’s immutable built-in objects are hashable, while no mutable
containers (such as lists or dictionaries) are. Objects which are
instances of user-defined classes are hashable by default; they all
compare unequal, and their hash value is their id().
- An Integrated Development Environment for Python. IDLE is a basic editor
and interpreter environment which ships with the standard distribution of
Python. Good for beginners, it also serves as clear example code for
those wanting to implement a moderately sophisticated, multi-platform GUI
- An object with a fixed value. Immutable objects include numbers, strings and
tuples. Such an object cannot be altered. A new object has to
be created if a different value has to be stored. They play an important
role in places where a constant hash value is needed, for example as a key
in a dictionary.
- Python has an interactive interpreter which means you can enter
statements and expressions at the interpreter prompt, immediately
execute them and see their results. Just launch python with no
arguments (possibly by selecting it from your computer’s main
menu). It is a very powerful way to test out new ideas or inspect
modules and packages (remember help(x)).
- Python is an interpreted language, as opposed to a compiled one,
though the distinction can be blurry because of the presence of the
bytecode compiler. This means that source files can be run directly
without explicitly creating an executable which is then run.
Interpreted languages typically have a shorter development/debug cycle
than compiled ones, though their programs generally also run more
slowly. See also interactive.
- A container object capable of returning its members one at a
time. Examples of iterables include all sequence types (such as
list, str, and tuple) and some non-sequence
types like dict and file and objects of any classes you
define with an __iter__() or __getitem__() method. Iterables
can be used in a for loop and in many other places where a
sequence is needed (zip(), map(), ...). When an iterable
object is passed as an argument to the builtin function iter(), it
returns an iterator for the object. This iterator is good for one pass
over the set of values. When using iterables, it is usually not necessary
to call iter() or deal with iterator objects yourself. The for
statement does that automatically for you, creating a temporary unnamed
variable to hold the iterator for the duration of the loop. See also
iterator, sequence, and generator.
An object representing a stream of data. Repeated calls to the iterator’s
__next__() (or passing it to the builtin function) next()
method return successive items in the stream. When no more data are
available a StopIteration exception is raised instead. At this
point, the iterator object is exhausted and any further calls to its
next() method just raise StopIteration again. Iterators are
required to have an __iter__() method that returns the iterator
object itself so every iterator is also iterable and may be used in most
places where other iterables are accepted. One notable exception is code
which attempts multiple iteration passes. A container object (such as a
list) produces a fresh new iterator each time you pass it to the
iter() function or use it in a for loop. Attempting this
with an iterator will just return the same exhausted iterator object used
in the previous iteration pass, making it appear like an empty container.
More information can be found in Iterator Types.
- keyword argument
- Arguments which are preceded with a variable_name= in the call.
The variable name designates the local name in the function to which the
value is assigned. ** is used to accept or pass a dictionary of
keyword arguments. See argument.
- An anonymous inline function consisting of a single expression
which is evaluated when the function is called. The syntax to create
a lambda function is lambda [arguments]: expression
- Look before you leap. This coding style explicitly tests for
pre-conditions before making calls or lookups. This style contrasts with
the EAFP approach and is characterized by the presence of many
- A built-in Python sequence. Despite its name it is more akin
to an array in other languages than to a linked list since access to
elements are O(1).
- list comprehension
- A compact way to process all or part of the elements in a sequence and
return a list with the results. result = ["0x%02x" % x for x in
range(256) if x % 2 == 0] generates a list of strings containing
even hex numbers (0x..) in the range from 0 to 255. The if
clause is optional. If omitted, all elements in range(256) are
- A container object (such as dict) which supports arbitrary key
lookups using the special method __getitem__().
The class of a class. Class definitions create a class name, a class
dictionary, and a list of base classes. The metaclass is responsible for
taking those three arguments and creating the class. Most object oriented
programming languages provide a default implementation. What makes Python
special is that it is possible to create custom metaclasses. Most users
never need this tool, but when the need arises, metaclasses can provide
powerful, elegant solutions. They have been used for logging attribute
access, adding thread-safety, tracking object creation, implementing
singletons, and many other tasks.
More information can be found in Customizing class creation.
- A function which is defined inside a class body. If called as an attribute
of an instance of that class, the method will get the instance object as
its first argument (which is usually called self).
See function and nested scope.
- Mutable objects can change their value but keep their id(). See
- named tuple
Any tuple-like class whose indexable elements are also accessible using
named attributes (for example, time.localtime() returns a
tuple-like object where the year is accessible either with an
index such as t or with a named attribute like t.tm_year).
A named tuple can be a built-in type such as time.struct_time,
or it can be created with a regular class definition. A full featured
named tuple can also be created with the factory function
collections.namedtuple(). The latter approach automatically
provides extra features such as a self-documenting representation like
- The place where a variable is stored. Namespaces are implemented as
dictionaries. There are the local, global and builtin namespaces as well
as nested namespaces in objects (in methods). Namespaces support
modularity by preventing naming conflicts. For instance, the functions
builtins.open() and os.open() are distinguished by their
namespaces. Namespaces also aid readability and maintainability by making
it clear which module implements a function. For instance, writing
random.seed() or itertools.izip() makes it clear that those
functions are implemented by the random and itertools
- nested scope
- The ability to refer to a variable in an enclosing definition. For
instance, a function defined inside another function can refer to
variables in the outer function. Note that nested scopes work only for
reference and not for assignment which will always write to the innermost
scope. In contrast, local variables both read and write in the innermost
scope. Likewise, global variables read and write to the global namespace.
- new-style class
- Old name for the flavor of classes now used for all class objects. In
earlier Python versions, only new-style classes could use Python’s newer,
versatile features like __slots__, descriptors, properties,
__getattribute__(), class methods, and static methods.
- Any data with state (attributes or value) and defined behavior
(methods). Also the ultimate base class of any new-style
- positional argument
- The arguments assigned to local names inside a function or method,
determined by the order in which they were given in the call. * is
used to either accept multiple positional arguments (when in the
definition), or pass several arguments as a list to a function. See
- Python 3000
- Nickname for the Python 3.x release line (coined long ago when the release
of version 3 was something in the distant future.) This is also
An idea or piece of code which closely follows the most common idioms
of the Python language, rather than implementing code using concepts
common to other languages. For example, a common idiom in Python is
to loop over all elements of an iterable using a for
statement. Many other languages don’t have this type of construct, so
people unfamiliar with Python sometimes use a numerical counter instead:
for i in range(len(food)):
As opposed to the cleaner, Pythonic method:
for piece in food:
- reference count
- The number of references to an object. When the reference count of an
object drops to zero, it is deallocated. Reference counting is
generally not visible to Python code, but it is a key element of the
CPython implementation. The sys module defines a
getrefcount() function that programmers can call to return the
reference count for a particular object.
- A declaration inside a class that saves memory by pre-declaring space for
instance attributes and eliminating instance dictionaries. Though
popular, the technique is somewhat tricky to get right and is best
reserved for rare cases where there are large numbers of instances in a
- An iterable which supports efficient element access using integer
indices via the __getitem__() special method and defines a
len() method that returns the length of the sequence.
Some built-in sequence types are list, str,
tuple, and unicode. Note that dict also
supports __getitem__() and __len__(), but is considered a
mapping rather than a sequence because the lookups use arbitrary
immutable keys rather than integers.
- An object usually containing a portion of a sequence. A slice is
created using the subscript notation,  with colons between numbers
when several are given, such as in variable_name[1:3:5]. The bracket
(subscript) notation uses slice objects internally.
- special method
- A method that is called implicitly by Python to execute a certain
operation on a type, such as addition. Such methods have names starting
and ending with double underscores. Special methods are documented in
Special method names.
- A statement is part of a suite (a “block” of code). A statement is either
an expression or a one of several constructs with a keyword, such
as if, while or for.
- triple-quoted string
- A string which is bound by three instances of either a quotation mark
(“) or an apostrophe (‘). While they don’t provide any functionality
not available with single-quoted strings, they are useful for a number
of reasons. They allow you to include unescaped single and double
quotes within a string and they can span multiple lines without the
use of the continuation character, making them especially useful when
- The type of a Python object determines what kind of object it is; every
object has a type. An object’s type is accessible as its
__class__ attribute or can be retrieved with type(obj).
- The objects returned from dict.keys(), dict.items(), and
dict.items() are called dictionary views. They are lazy sequences
that will see changes in the underlying dictionary. To force the
dictionary view to become a full list use list(dictview). See
Dictionary view objects.
- virtual machine
- A computer defined entirely in software. Python’s virtual machine
executes the bytecode emitted by the bytecode compiler.
- Zen of Python
- Listing of Python design principles and philosophies that are helpful in
understanding and using the language. The listing can be found by typing
“import this” at the interactive prompt.