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The keys can be any object** with __hash__() and __eq__() methods.
Called a hash in Perl. **dictionary view The objects returned from dict.keys(), dict.values(), and dict.items() are called** dictionary views.
They provide a dynamic view on the dictionary’s entries, which means that when the dictionary changes, the view reflects these changes.
To force the dictionary view to become a full list use list(dictview). See dict-views. **docstring 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 object. **duck-typing A programming style which does not look at an object’s type to determine if it has the right** interface; instead, the method or attribute is simply called or used (“If it looks like a duck and quacks like...
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._ **EAFP Easier to ask for forgiveness than permission.** This common Python coding style assumes the existence of valid keys or attributes and catches exceptions if...
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. **expression 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 val...
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, not expressions. **extension module A module written in C or C++, using Python’s C API to interact with the core and** with user code. **f-string String literals prefixed with 'f' or 'F' are commonly called “f-strings” which is short for formatted** [string literals.
See also PEP 498.](https://www.python.org/dev/peps/pep-0498) **file object An object exposing a file-oriented API (with methods such as read() or write()) to an underly-** ing resource.
Depending on the way it was created, a file object can mediate access to a real on-disk file or to another type of storage or communication device (for example standard input/output, in-memory buffers, sockets, pipes, etc.).
File objects are also called file-like objects or streams. There are actually three categories of file objects: raw binary files, buffered binary files and text files. Their interfaces are defined in the io module.
The canonical way to create a file object is by using the ----- ``` open() function. ``` **file-like object A synonym for file object.** **finder An object that tries to find the loader for a module that is being imported.** Since Python 3.3, there are two types of finder: meta path finders for use with sys.me...
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.
Note that (-11) // 4 is -3 because that is -2.75 rounded downward. See PEP 238.](https://www.python.org/dev/peps/pep-0238) **function 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 parameter, method, and the_ function section. **function annotation An annotation of a function parameter or return value.** Function annotations are usually used for type hints: for example this function is expected to take two ``` int arguments and is also expected to have an int return value: def su...
Python performs garbage** collection via reference counting and a cyclic garbage collector that is able to detect and break reference cycles.
The garbage collector can be controlled using the gc module. **generator A function which returns a generator iterator.
It looks like a normal function except that it** contains yield expressions for producing a series of values usable in a for-loop or that can be retrieved one at a time with the next() function. Usually refers to a generator function, but may refer to a generator iterator in some contexts.
In cases where the intended meaning isn’t clear, using the full terms avoids ambiguity. **generator iterator An object created by a generator function.** Each yield temporarily suspends processing, remembering the location execution state (including local variables and pending try-statements).
When the generator iterator resumes, it picks up where it left off (in contrast to functions which start fresh on every invocation). **generator expression An expression that returns an iterator.
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: ----- **generic function A function composed of multiple functions implementing the same operation for different** types.
Which implementation should be used during a call is determined by the dispatch algorithm. [See also the single dispatch glossary entry, the functools.singledispatch() decorator, and PEP](https://www.python.org/dev/peps/pep-0443) **[443.](https://www.python.org/dev/peps/pep-0443)** **GIL See global interpreter lock.*...
This simplifies the CPython implementation by making the object model (including critical built-in types such as dict) implicitly safe against concurrent access.
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. However, some extension modules, either standard or third-party, are designed so as to release the GIL when doing computationally-intensive tasks such...
Also, the GIL is always released when doing I/O. Past efforts to create a “free-threaded” interpreter (one which locks shared data at a much finer granularity) have not been successful because performance suffered in the common single-processor case.
It is believed that overcoming this performance issue would make the implementation much more complicated and therefore costlier to maintain. **hash-based pyc A bytecode cache file that uses the hash rather than the last-modified time of the corre-** sponding source file to determine its validity.
See pyc-invalidation. **hashable 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; mutable containers (such as lists or dictionaries) are...
Objects which are instances of user-defined classes are hashable by default.
They all compare unequal (except with themselves), and their hash value is derived from their id(). **IDLE An Integrated Development Environment for Python.
IDLE is a basic editor and interpreter envi-** ronment which ships with the standard distribution of Python. **immutable 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. **import path A list of locations (or path entries) that are searched by the path based finder for modules to** import.
During import, this list of locations usually comes from sys.path, but for subpackages it may also come from the parent package’s __path__ attribute. **importing The process by which Python code in one module is made available to Python code in another** module. **importer An object that both finds and loads a module...
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)). **interpreted 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. **interpreter shutdown When asked to shut down, the Python interpreter enters a special phase where it** gradually releases all allocated resources, such as modules and various critical internal structures.
It also makes several calls to the garbage collector. This can trigger the execution of code in user-defined destructors or weakref callbacks.
Code executed during the shutdown phase can encounter various exceptions as the resources it relies on may not function anymore (common examples are library modules or the warnings machinery). The main reason for interpreter shutdown is that the __main__ module or the script being run has finished executing. **iterab...
When an iterable object is passed as an argument to the built-in 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. **iterator An object representing a stream of data.
Repeated calls to the iterator’s __next__() method** (or passing it to the built-in function next()) 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 typeiter. **key function A key function or collation function is a callable that returns a value used for sorting or** or...
For example, locale.strxfrm() is used to produce a sort key that is aware of locale specific sort conventions. A number of tools in Python accept key functions to control how elements are ordered or grouped. They include min(), max(), sorted(), list.sort(), heapq.merge(), heapq.nsmallest(), heapq. ``` nlargest(), a...
For example. the str.lower() method can serve as a key function for case insensitive sorts. Alternatively, a key function can be built from a lambda expression such as lambda r: (r[0], r[2]).
Also, the operator module provides three key function constructors: attrgetter(), itemgetter(), and methodcaller().
See the Sorting HOW TO for examples of how to create and use key functions. **keyword argument See argument.** **lambda 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 [parameters]: expression **LBYL 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 ``` if statements. ``` ----- In a multi-threaded environment, the LBYL approach can risk introducing a race condition between “the looking” and “the leaping”.
For example, the code, if key in mapping: return mapping[key] can fail if another thread removes key from mapping after the test, but before the lookup.
This issue can be solved with locks or by using the EAFP approach. **list 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 is 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 = ['{:#04x}'.format(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 processed. **loader An object that loads a module. It must define a method named load_module(). A loader is typically** [returned by a finder.
See PEP 302 for details and importlib.abc.Loader for an abstract base class.](https://www.python.org/dev/peps/pep-0302) **mapping A container object that supports arbitrary key lookups and implements the methods specified** in the Mapping or MutableMapping abstract base classes.
Examples include dict, collections. ``` defaultdict, collections.OrderedDict and collections.Counter. ``` **meta path finder A finder returned by a search of sys.meta_path.
Meta path finders are related to, but** different from path entry finders. See importlib.abc.MetaPathFinder for the methods that meta path finders implement. **metaclass 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 metaclasses. **method 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._ **method resolution order Method Resolution Order is the order in which base classes are searched for** [a member during lookup.
See The Python 2.3 Method Resolution Order for details of the algorithm](https://www.python.org/download/releases/2.3/mro/) used by the Python interpreter since the 2.3 release. **module An object that serves as an organizational unit of Python code.** Modules have a namespace containing arbitrary Python objects.
Modules are loaded into Python by the process of importing. See also package. **module spec A namespace containing the import-related information used to load a module.
An instance** of importlib.machinery.ModuleSpec. **MRO See method resolution order.** **mutable Mutable objects can change their value but keep their id().
See also immutable.** **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[0] or with a named attribute like t.tm_year). A named tuple can be a bui...
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 Employee(name='jones', title='programmer'). ----- **namespace The place where a variable is stored.** Namespaces are implemented as dictionaries.
There are the local, global and built-in 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.islice() makes it clear that those functions are implemented by the random ``` and itertools modules, respectively. **[namespace package A PEP 420 package which serves only as a container for subpackages.
Namespace](https://www.python.org/dev/peps/pep-0420)** packages may have no physical representation, and specifically are not like a regular package because they have no __init__.py file. See also module. **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 by default work only for reference and not for assignment.
Local variables both read and write in the innermost scope. Likewise, global variables read and write to the global namespace.
The nonlocal allows writing to outer scopes. **new-style class Old name for the flavor of classes now used for all class objects.
In earlier Python ver-** sions, only new-style classes could use Python’s newer, versatile features like __slots__, descriptors, properties, __getattribute__(), class methods, and static methods. **object Any data with state (attributes or value) and defined behavior (methods).
Also the ultimate base** class of any new-style class. **package A Python module which can contain submodules or recursively, subpackages.
Technically, a pack-** age is a Python module with an __path__ attribute. See also regular package and namespace package. **parameter A named entity in a function (or method) definition that specifies an argument (or in some** cases, arguments) that the function can accept.
There are five kinds of parameter: - positional-or-keyword: specifies an argument that can be passed either positionally or as a keyword _argument.
This is the default kind of parameter, for example foo and bar in the following:_ ``` def func(foo, bar=None): ... ``` - positional-only: specifies an argument that can be supplied only by position.
Python has no syntax for defining positional-only parameters. However, some built-in functions have positionalonly parameters (e.g.
abs()). - keyword-only: specifies an argument that can be supplied only by keyword.
Keyword-only parameters can be defined by including a single var-positional parameter or bare * in the parameter list of the function definition before them, for example kw_only1 and kw_only2 in the following: ``` def func(arg, *, kw_only1, kw_only2): ... ``` - var-positional: specifies that an arbitrary sequ...
Such a parameter can be defined by prepending the parameter name with *, for example args in the following: ``` def func(*args, **kwargs): ... ``` - var-keyword: specifies that arbitrarily many keyword arguments can be provided (in addition to any keyword arguments already accepted by other parameters).