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                          Python syntax and semantics

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   This article's lead section may be too short to adequately summarize the   
   key points. Please consider expanding the lead to provide an accessible    
   overview of all important aspects of the article. (January 2021)           

   This article is written like a manual or guidebook. Please help rewrite    
   this article from a descriptive, neutral point of view, and remove advice  
   or instruction. (January 2021) (Learn how and when to remove this template 
   message)                                                                   

   The syntax of the Python programming language is the set of rules that
   defines how a Python program will be written and interpreted (by both the
   runtime system and by human readers). The Python language has many
   similarities to Perl, C, and Java. However, there are some definite
   differences between the languages.

Contents

     * 1 Design philosophy
     * 2 Keywords
     * 3 Indentation
     * 4 Data structures
          * 4.1 Base types
          * 4.2 Collection types
          * 4.3 Object system
     * 5 Literals
          * 5.1 Strings
               * 5.1.1 Normal string literals
               * 5.1.2 Multi-line string literals
               * 5.1.3 Raw strings
               * 5.1.4 Concatenation of adjacent string literals
               * 5.1.5 Unicode
          * 5.2 Numbers
          * 5.3 Lists, tuples, sets, dictionaries
     * 6 Operators
          * 6.1 Arithmetic
          * 6.2 Comparison operators
          * 6.3 Logical operators
     * 7 Functional programming
          * 7.1 Comprehensions
          * 7.2 First-class functions
          * 7.3 Closures
          * 7.4 Generators
          * 7.5 Generator expressions
          * 7.6 Dictionary and set comprehensions
     * 8 Objects
          * 8.1 With statement
          * 8.2 Properties
          * 8.3 Descriptors
          * 8.4 Class and static methods
     * 9 Exceptions
     * 10 Comments and docstrings
     * 11 Function annotations
     * 12 Decorators
     * 13 Easter eggs
     * 14 References
     * 15 External links

Design philosophyEdit

   Python was designed to be a highly readable language.^[1] It has a
   relatively uncluttered visual layout and uses English keywords frequently
   where other languages use punctuation. Python aims to be simple and
   consistent in the design of its syntax, encapsulated in the mantra "There
   should be one— and preferably only one —obvious way to do it", from the
   Zen of Python.^[2]

   This mantra is deliberately opposed to the Perl and Ruby mantra, "there's
   more than one way to do it".

KeywordsEdit

   Python has 35 keywords or reserved words; they cannot be used as
   identifiers.^[3]^[4]

     * and
     * as
     * assert
     * async^[note 1]
     * await^[note 1]
     * break
     * class
     * continue
     * def
     * del
     * elif
     * else
     * except
     * False^[note 2]
     * finally
     * for
     * from
     * global
     * if
     * import
     * in
     * is
     * lambda
     * None
     * nonlocal^[note 3]
     * not
     * or
     * pass
     * raise
     * return
     * True^[note 2]
     * try
     * while
     * with
     * yield

   In addition, Python also has 3 soft keywords. Unlike regular hard
   keywords, soft keywords are reserved words only in the limited contexts
   where interpreting them as keywords would make syntactic sense. These
   words can be used as identifiers elsewhere; You can define a function or
   variable named match or case. ^[6]^[7]

     * _^[note 4]
     * case^[note 4]
     * match^[note 4]

   Notes

    1. ^ ^a ^b async and await were introduced in Python 3.5.^[5]
    2. ^ ^a ^b True and False became keywords in Python 3.0. Previously they
       were global variables.
    3. ^ nonlocal was introduced in Python 3.0.
    4. ^ ^a ^b ^c match, case and _ were introduced as keywords in Python
       3.10.

IndentationEdit

   Python uses whitespace to delimit control flow blocks (following the
   off-side rule). Python borrows this feature from its predecessor ABC:
   instead of punctuation or keywords, it uses indentation to indicate the
   run of a block.

   In so-called "free-format" languages—that use the block structure derived
   from ALGOL—blocks of code are set off with braces ({ }) or keywords. In
   most coding conventions for these languages, programmers conventionally
   indent the code within a block, to visually set it apart from the
   surrounding code.

   A recursive function named foo, which is passed a single parameter, x, and
   if the parameter is 0 will call a different function named bar and
   otherwise will call baz, passing x, and also call itself recursively,
   passing x-1 as the parameter, could be implemented like this in Python:

 def foo(x):
     if x == 0:
         bar()
     else:
         baz(x)
         foo(x - 1)

   and could be written like this in C with K&R indent style:

 void foo(int x)
 {
     if (x == 0) {
         bar();
     } else {
         baz(x);
         foo(x - 1);
     }
 }

   Incorrectly indented code could be misread by a human reader differently
   than it would be interpreted by a compiler or interpreter. For example, if
   the function call foo(x - 1) on the last line in the example above was
   erroneously indented to be outside the if/else block:

 def foo(x):
     if x == 0:
         bar()
     else:
         baz(x)
     foo(x - 1)

   it would cause the last line to always be executed, even when x is 0,
   resulting in an endless recursion.

   While both space and tab characters are accepted as forms of indentation
   and any multiple of spaces can be used, spaces are recommended^[8] and 4
   spaces (as in the above examples) are recommended and are by far the most
   commonly used.^[9]^[10]^[unreliable source?] Mixing spaces and tabs on
   consecutive lines is not allowed starting with Python 3^[11] because that
   can create bugs which are difficult to see, since many tools do not
   visually distinguish spaces and tabs.

Data structuresEdit

   See also: Python (programming language) § Typing

   Since Python is a dynamically typed language, Python values, not
   variables, carry type information. All variables in Python hold references
   to objects, and these references are passed to functions. Some people
   (including Guido van Rossum himself) have called this parameter-passing
   scheme "call by object reference". An object reference means a name, and
   the passed reference is an "alias", i.e. a copy of the reference to the
   same object, just as in C/C++. The object's value may be changed in the
   called function with the "alias", for example:

 >>> alist = ['a', 'b', 'c']
 >>> def my_func(al):
 ...     al.append('x')
 ...     print(al)
 ...
 >>> my_func(alist)
 ['a', 'b', 'c', 'x']
 >>> alist
 ['a', 'b', 'c', 'x']

   Function my_func changes the value of alist with the formal argument al,
   which is an alias of alist. However, any attempt to operate (assign a new
   object reference to) on the alias itself will have no effect on the
   original object.^[clarification needed]

 >>> alist = ['a', 'b', 'c']
 >>> def my_func(al):
 ...     # al.append('x')
 ...     al = al + ['x'] # a new list created and assigned to al means al is no more alias for alist
 ...     print(al)
 ...
 >>> my_func(alist)
 ['a', 'b', 'c', 'x']
 >>> alist
 ['a', 'b', 'c']

   In Python, non-innermost-local and not-declared-global accessible names
   are all aliases.

   Among dynamically typed languages, Python is moderately type-checked.
   Implicit conversion is defined for numeric types (as well as booleans), so
   one may validly multiply a complex number by an integer (for instance)
   without explicit casting. However, there is no implicit conversion
   between, for example, numbers and strings; a string is an invalid argument
   to a mathematical function expecting a number.

  Base typesEdit

   Python has a broad range of basic data types. Alongside conventional
   integer and floating-point arithmetic, it transparently supports
   arbitrary-precision arithmetic, complex numbers, and decimal numbers.

   Python supports a wide variety of string operations. Strings in Python are
   immutable, so a string operation such as a substitution of characters,
   that in other programming languages might alter the string in place,
   returns a new string in Python. Performance considerations sometimes push
   for using special techniques in programs that modify strings intensively,
   such as joining character arrays into strings only as needed.

  Collection typesEdit

   One of the very useful aspects of Python is the concept of collection (or
   container) types. In general a collection is an object that contains other
   objects in a way that is easily referenced or indexed. Collections come in
   two basic forms: sequences and mappings.

   The ordered sequential types are lists (dynamic arrays), tuples, and
   strings. All sequences are indexed positionally (0 through length − 1) and
   all but strings can contain any type of object, including multiple types
   in the same sequence. Both strings and tuples are immutable, making them
   perfect candidates for dictionary keys (see below). Lists, on the other
   hand, are mutable; elements can be inserted, deleted, modified, appended,
   or sorted in-place.

   Mappings, on the other hand, are (often unordered) types implemented in
   the form of dictionaries which "map" a set of immutable keys to
   corresponding elements (much like a mathematical function). For example,
   one could define a dictionary having a string "toast" mapped to the
   integer 42 or vice versa. The keys in a dictionary must be of an immutable
   Python type, such as an integer or a string, because under the hood they
   are implemented via a hash function. This makes for much faster lookup
   times, but requires keys not change.

   Dictionaries are central to the internals of Python as they reside at the
   core of all objects and classes: the mappings between variable names
   (strings) and the values which the names reference are stored as
   dictionaries (see Object system). Since these dictionaries are directly
   accessible (via an object's __dict__ attribute), metaprogramming is a
   straightforward and natural process in Python.

   A set collection type is an unindexed, unordered collection that contains
   no duplicates, and implements set theoretic operations such as union,
   intersection, difference, symmetric difference, and subset testing. There
   are two types of sets: set and frozenset, the only difference being that
   set is mutable and frozenset is immutable. Elements in a set must be
   hashable. Thus, for example, a frozenset can be an element of a regular
   set whereas the opposite is not true.

   Python also provides extensive collection manipulating abilities such as
   built in containment checking and a generic iteration protocol.

  Object systemEdit

   In Python, everything is an object, even classes. Classes, as objects,
   have a class, which is known as their metaclass. Python also supports
   multiple inheritance and mixins.

   The language supports extensive introspection of types and classes. Types
   can be read and compared—types are instances of type. The attributes of an
   object can be extracted as a dictionary.

   Operators can be overloaded in Python by defining special member functions
   - for instance, defining a method named __add__ on a class permits one to
   use the + operator on objects of that class.

LiteralsEdit

  StringsEdit

   Python has various kinds of string literals.

    Normal string literalsEdit

   Either single or double quotes can be used to quote strings. Unlike in
   Unix shell languages, Perl or Perl-influenced languages such as Ruby or
   Groovy, single quotes and double quotes function identically, i.e. there
   is no string interpolation of $foo expressions. However, interpolation can
   be done in various ways: with "f-strings" (since Python 3.6^[12]), using
   the format method or the old % string-format operator.

   For instance, all of these Python statements:

 print(f"I just printed {num} pages to the printer {printer}")

 print("I just printed {} pages to the printer {}".format(num, printer))
 print("I just printed {0} pages to the printer {1}".format(num, printer))
 print("I just printed {num} pages to the printer {printer}".format(num=num, printer=printer))

 print("I just printed %s pages to the printer %s" % (num, printer))
 print("I just printed %(num)s pages to the printer %(printer)s" % {"num": num, "printer": printer})

   are equivalent to the Perl statement:

 print "I just printed $num pages to the printer $printer\n"

   They build a string using the variables num and printer.

    Multi-line string literalsEdit

   There are also multi-line strings, which begin and end with a series of
   three single or double quotes and function like here documents in Perl and
   Ruby.

   A simple example with variable interpolation (using the format method) is:

 print("""Dear {recipient},

 I wish you to leave Sunnydale and never return.

 Not Quite Love,
 {sender}
 """.format(sender="Buffy the Vampire Slayer", recipient="Spike"))

    Raw stringsEdit

   Finally, all of the previously mentioned string types come in "raw"
   varieties (denoted by placing a literal r before the opening quote), which
   do no backslash-interpolation and hence are very useful for regular
   expressions; compare "@-quoting" in C#. Raw strings were originally
   included specifically for regular expressions. Due to limitations of the
   tokenizer, raw strings may not have a trailing backslash.^[13] Creating a
   raw string holding a Windows path ending with a backslash requires some
   variety of workaround (commonly, using forward slashes instead of
   backslashes, since Windows accepts both).

   Examples include:

 >>> # A Windows path, even raw strings cannot end in a backslash
 >>> r"C:\Foo\Bar\Baz\"
   File "<stdin>", line 1
     r"C:\Foo\Bar\Baz\"
                      ^
 SyntaxError: EOL while scanning string literal

 >>> dos_path = r"C:\Foo\Bar\Baz\ " # avoids the error by adding
 >>> dos_path.rstrip()              # and removing trailing space
 'C:\\Foo\\Bar\\Baz\\'

 >>> quoted_dos_path = r'"{}"'.format(dos_path)
 >>> quoted_dos_path
 '"C:\\Foo\\Bar\\Baz\\ "'

 >>> # A regular expression matching a quoted string with possible backslash quoting
 >>> re.match(r'"(([^"\\]|\\.)*)"', quoted_dos_path).group(1).rstrip()
 'C:\\Foo\\Bar\\Baz\\'

 >>> code = 'foo(2, bar)'
 >>> # Reverse the arguments in a two-arg function call
 >>> re.sub(r'\(([^,]*?),([^ ,]*?)\)', r'(\2, \1)', code)
 'foo(2, bar)'
 >>> # Note that this won't work if either argument has parens or commas in it.

    Concatenation of adjacent string literalsEdit

   String literals (using possibly different quote conventions) appearing
   contiguously and only separated by whitespace (including new lines), are
   allowed and are aggregated into a single longer string.^[14] Thus

 title = "One Good Turn: " \
         'A Natural History of the Screwdriver and the Screw'

   is equivalent to

 title = "One Good Turn: A Natural History of the Screwdriver and the Screw"

    UnicodeEdit

   Since Python 3.0, the default character set is UTF-8 both for source code
   and the interpreter. In UTF-8, unicode strings are handled like
   traditional byte strings. This example will work:

 s = "Γειά" # Hello in Greek
 print(s)

  NumbersEdit

   Numeric literals in Python are of the normal sort, e.g. 0, -1, 3.4,
   3.5e-8.

   Python has arbitrary-length integers and automatically increases their
   storage size as necessary. Prior to Python 3, there were two kinds of
   integral numbers: traditional fixed size integers and "long" integers of
   arbitrary size. The conversion to "long" integers was performed
   automatically when required, and thus the programmer usually didn't have
   to be aware of the two integral types. In newer language versions the
   distinction is completely gone and all integers behave like
   arbitrary-length integers.

   Python supports normal floating point numbers, which are created when a
   dot is used in a literal (e.g. 1.1), when an integer and a floating point
   number are used in an expression, or as a result of some mathematical
   operations ("true division" via the / operator, or exponentiation with a
   negative exponent).

   Python also supports complex numbers natively. Complex numbers are
   indicated with the J or j suffix, e.g. 3 + 4j.

  Lists, tuples, sets, dictionariesEdit

   Python has syntactic support for the creation of container types.

   Lists (class list) are mutable sequences of items of arbitrary types, and
   can be created either with the special syntax

 a_list = [1, 2, 3, "a dog"]

   or using normal object creation

 a_second_list = list()
 a_second_list.append(4)
 a_second_list.append(5)

   Tuples (class tuple) are immutable sequences of items of arbitrary types.
   There is also a special syntax to create tuples

 a_tuple = 1, 2, 3, "four"
 a_tuple = (1, 2, 3, "four")

   Although tuples are created by separating items with commas, the whole
   construct is usually wrapped in parentheses to increase readability. An
   empty tuple is denoted by (), while a tuple with a single value can be
   created with (1,).

   Sets (class set) are mutable containers of hashable items^[15] of
   arbitrary types, with no duplicates. The items are not ordered, but sets
   support iteration over the items. The syntax for set creation uses curly
   brackets

 some_set = {0, (), False}

   Python sets are very much like mathematical sets, and support operations
   like set intersection and union. Python also features a frozenset class
   for immutable sets, see Collection types.

   Dictionaries (class dict) are mutable mappings tying keys and
   corresponding values. Python has special syntax to create dictionaries
   ({key: value})

 a_dictionary = {"key 1": "value 1", 2: 3, 4: []}

   The dictionary syntax is similar to the set syntax, the difference is the
   presence of colons. The empty literal {} results in an empty dictionary
   rather than an empty set, which is instead created using the non-literal
   constructor: set().

OperatorsEdit

  ArithmeticEdit

   Python includes the +, -, *, / ("true division"), // (floor division), %
   (modulus), and ** (exponentiation) operators, with their usual
   mathematical precedence.

   In Python 3, x / y performs "true division", meaning that it always
   returns a float, even if both x and y are integers that divide evenly.

 >>> 4 / 2
 2.0

   and // performs integer division or floor division, returning the floor of
   the quotient as an integer.

   In Python 2 (and most other programming languages), unless explicitly
   requested, x / y performed integer division, returning a float only if
   either input was a float. However, because Python is a dynamically typed
   language, it was not always possible to tell which operation was being
   performed, which often led to subtle bugs, thus prompting the introduction
   of the // operator and the change in semantics of the / operator in Python
   3.

  Comparison operatorsEdit

   The comparison operators, i.e. ==, !=, <, >, <=, >=, is, is not, in and
   not in^[16] are used on all manner of values. Numbers, strings, sequences,
   and mappings can all be compared. In Python 3, disparate types (such as a
   str and an int) do not have a consistent relative ordering. While it was
   possible to compare whether some string was greater-than or less-than some
   integer in Python 2, this was considered a historical design quirk and was
   ultimately removed in Python 3.

   Chained comparison expressions such as a < b < c have roughly the meaning
   that they have in mathematics, rather than the unusual meaning found in C
   and similar languages. The terms are evaluated and compared in order. The
   operation has short-circuit semantics, meaning that evaluation is
   guaranteed to stop as soon as a verdict is clear: if a < b is false, c is
   never evaluated as the expression cannot possibly be true anymore.

   For expressions without side effects, a < b < c is equivalent to a < b and
   b < c. However, there is a substantial difference when the expressions
   have side effects. a < f(x) < b will evaluate f(x) exactly once, whereas a
   < f(x) and f(x) < b will evaluate it twice if the value of a is less than
   f(x) and once otherwise.

  Logical operatorsEdit

   In all versions of Python, boolean operators treat zero values or empty
   values such as "", 0, None, 0.0, [], and {} as false, while in general
   treating non-empty, non-zero values as true. The boolean values True and
   False were added to the language in Python 2.2.1 as constants (subclassed
   from 1 and 0) and were changed to be full blown keywords in Python 3. The
   binary comparison operators such as == and > return either True or False.

   The boolean operators and and or use minimal evaluation. For example, y ==
   0 or x/y > 100 will never raise a divide-by-zero exception. These
   operators return the value of the last operand evaluated, rather than True
   or False. Thus the expression (4 and 5) evaluates to 5, and (4 or 5)
   evaluates to 4.

Functional programmingEdit

   As mentioned above, another strength of Python is the availability of a
   functional programming style. As may be expected, this makes working with
   lists and other collections much more straightforward.

  ComprehensionsEdit

   Link: mw-deduplicated-inline-style
   Main article: List comprehension

   One such construction is the list comprehension, which can be expressed
   with the following format:

 L = [mapping_expression for element in source_list if filter_expression]

   Using list comprehension to calculate the first five powers of two:

 powers_of_two = [2**n for n in range(1, 6)]

   The Quicksort algorithm can be expressed elegantly (albeit inefficiently)
   using list comprehensions:

 def qsort(L):
     if L == []:
         return []
     pivot = L[0]
     return (qsort([x for x in L[1:] if x < pivot]) +
             [pivot] +
             qsort([x for x in L[1:] if x >= pivot]))

   Python 2.7+^[17] also supports set comprehensions^[18] and dictionary
   comprehensions.^[19]

  First-class functionsEdit

   In Python, functions are first-class objects that can be created and
   passed around dynamically.

   Python's limited support for anonymous functions is the lambda construct.
   An example is the anonymous function which squares its input, called with
   the argument of 5:

 f = lambda x: x**2
 f(5)

   Lambdas are limited to containing an expression rather than statements,
   although control flow can still be implemented less elegantly within
   lambda by using short-circuiting,^[20] and more idiomatically with
   conditional expressions.^[21]

  ClosuresEdit

   Python has had support for lexical closures since version 2.2. Here's an
   example function that returns a function that approximates the derivative
   of the given function:

 def derivative(f, dx):
     """Return a function that approximates the derivative of f
     using an interval of dx, which should be appropriately small.
     """
     def function(x):
         return (f(x + dx) - f(x)) / dx
     return function

   Python's syntax, though, sometimes leads programmers of other languages to
   think that closures are not supported. Variable scope in Python is
   implicitly determined by the scope in which one assigns a value to the
   variable, unless scope is explicitly declared with global or
   nonlocal.^[22]

   Note that the closure's binding of a name to some value is not mutable
   from within the function. Given:

 >>> def foo(a, b):
 ...     print(f'a: {a}')
 ...     print(f'b: {b}')
 ...     def bar(c):
 ...         b = c
 ...         print(f'b*: {b}')
 ...     bar(a)
 ...     print(f'b: {b}')
 ...
 >>> foo(1, 2)
 a: 1
 b: 2
 b*: 1
 b: 2

   and you can see that b, as visible from the closure's scope, retains the
   value it had; the changed binding of b inside the inner function did not
   propagate out. The way around this is to use a nonlocal b statement in
   bar. In Python 2 (which lacks nonlocal), the usual workaround is to use a
   mutable value and change that value, not the binding. E.g., a list with
   one element.

  GeneratorsEdit

   Introduced in Python 2.2 as an optional feature and finalized in version
   2.3, generators are Python's mechanism for lazy evaluation of a function
   that would otherwise return a space-prohibitive or computationally
   intensive list.

   This is an example to lazily generate the prime numbers:

 from itertools import count

 def generate_primes(stop_at=None):
     primes = []
     for n in count(start=2):
         if stop_at is not None and n > stop_at:
             return # raises the StopIteration exception
         composite = False
         for p in primes:
             if not n % p:
                 composite = True
                 break
             elif p ** 2 > n:
                 break
         if not composite:
             primes.append(n)
             yield n

   When calling this function, the returned value can be iterated over much
   like a list:

 for i in generate_primes(100):  # iterate over the primes between 0 and 100
     print(i)

 for i in generate_primes():  # iterate over ALL primes indefinitely
     print(i)

   The definition of a generator appears identical to that of a function,
   except the keyword yield is used in place of return. However, a generator
   is an object with persistent state, which can repeatedly enter and leave
   the same scope. A generator call can then be used in place of a list, or
   other structure whose elements will be iterated over. Whenever the for
   loop in the example requires the next item, the generator is called, and
   yields the next item.

   Generators don't have to be infinite like the prime-number example above.
   When a generator terminates, an internal exception is raised which
   indicates to any calling context that there are no more values. A for loop
   or other iteration will then terminate.

  Generator expressionsEdit

   Link: mw-deduplicated-inline-style
   Further information: List comprehension

   Introduced in Python 2.4, generator expressions are the lazy evaluation
   equivalent of list comprehensions. Using the prime number generator
   provided in the above section, we might define a lazy, but not quite
   infinite collection.

 from itertools import islice

 primes_under_million = (i for i in generate_primes() if i < 1000000)
 two_thousandth_prime = islice(primes_under_million, 1999, 2000).next()

   Most of the memory and time needed to generate this many primes will not
   be used until the needed element is actually accessed. Unfortunately, you
   cannot perform simple indexing and slicing of generators, but must use the
   itertools module or "roll your own" loops. In contrast, a list
   comprehension is functionally equivalent, but is greedy in performing all
   the work:

 primes_under_million = [i for i in generate_primes(2000000) if i < 1000000]
 two_thousandth_prime = primes_under_million[1999]

   The list comprehension will immediately create a large list (with 78498
   items, in the example, but transiently creating a list of primes under two
   million), even if most elements are never accessed. The generator
   comprehension is more parsimonious.

  Dictionary and set comprehensionsEdit

   While lists and generators had comprehensions/expressions, in Python
   versions older than 2.7 the other Python built-in collection types (dicts
   and sets) had to be kludged in using lists or generators:

 >>> dict((n, n*n) for n in range(5))
 {0: 0, 1: 1, 2: 4, 3: 9, 4: 16}

   Python 2.7 and 3.0 unified all collection types by introducing dictionary
   and set comprehensions, similar to list comprehensions:

 >>> [n*n for n in range(5)]  # regular list comprehension
 [0, 1, 4, 9, 16]
 >>>
 >>> {n*n for n in range(5)}  # set comprehension
 {0, 1, 4, 9, 16}
 >>>
 >>> {n: n*n for n in range(5)}  # dict comprehension
 {0: 0, 1: 1, 2: 4, 3: 9, 4: 16}

ObjectsEdit

   Python supports most object oriented programming (OOP) techniques. It
   allows polymorphism, not only within a class hierarchy but also by duck
   typing. Any object can be used for any type, and it will work so long as
   it has the proper methods and attributes. And everything in Python is an
   object, including classes, functions, numbers and modules. Python also has
   support for metaclasses, an advanced tool for enhancing classes'
   functionality. Naturally, inheritance, including multiple inheritance, is
   supported. Python has very limited support for private variables using
   name mangling which is rarely used in practice as information hiding is
   seen by some as unpythonic, in that it suggests that the class in question
   contains unaesthetic or ill-planned internals. The slogan "we're all
   responsible users here" is used to describe this attitude.^[23]

     As is true for modules, classes in Python do not put an absolute barrier
     between definition and user, but rather rely on the politeness of the
     user not to "break into the definition."

     — 9. Classes, The Python 2.6 Tutorial (2013)

   OOP doctrines such as the use of accessor methods to read data members are
   not enforced in Python. Just as Python offers functional-programming
   constructs but does not attempt to demand referential transparency, it
   offers an object system but does not demand OOP behavior. Moreover, it is
   always possible to redefine the class using properties (see Properties) so
   that when a certain variable is set or retrieved in calling code, it
   really invokes a function call, so that spam.eggs = toast might really
   invoke spam.set_eggs(toast). This nullifies the practical advantage of
   accessor functions, and it remains OOP because the property eggs becomes a
   legitimate part of the object's interface: it need not reflect an
   implementation detail.

   In version 2.2 of Python, "new-style" classes were introduced. With
   new-style classes, objects and types were unified, allowing the
   subclassing of types. Even entirely new types can be defined, complete
   with custom behavior for infix operators. This allows for many radical
   things to be done syntactically within Python. A new method resolution
   order for multiple inheritance was also adopted with Python 2.3. It is
   also possible to run custom code while accessing or setting attributes,
   though the details of those techniques have evolved between Python
   versions.

  With statementEdit

   The with statement handles resources, and allows users to work with the
   Context Manager protocol.^[24] One function (__enter__()) is called when
   entering scope and another (__exit__()) when leaving. This prevents
   forgetting to free the resource and also handles more complicated
   situations such as freeing the resource when an exception occurs while it
   is in use. Context Managers are often used with files, database
   connections, test cases, etc.

  PropertiesEdit

   Properties allow specially defined methods to be invoked on an object
   instance by using the same syntax as used for attribute access. An example
   of a class defining some properties is:

 class MyClass:
     def __init__(self):
         self._a = None

     @property
     def a(self):
         return self._a

     @a.setter  # makes the property writable
     def a(self, value):
         self._a = value

  DescriptorsEdit

   A class that defines one or more of the three special methods
   __get__(self, instance, owner), __set__(self, instance, value),
   __delete__(self, instance) can be used as a descriptor. Creating an
   instance of a descriptor as a class member of a second class makes the
   instance a property of the second class.^[25]

  Class and static methodsEdit

   Python allows the creation of class methods and static methods via the use
   of the @classmethod and @staticmethod decorators. The first argument to a
   class method is the class object instead of the self-reference to the
   instance. A static method has no special first argument. Neither the
   instance, nor the class object is passed to a static method.

ExceptionsEdit

   Python supports (and extensively uses) exception handling as a means of
   testing for error conditions and other "exceptional" events in a program.
   Indeed, it is even possible to trap the exception caused by a syntax
   error.

   Python style calls for the use of exceptions whenever an error condition
   might arise. Rather than testing for access to a file or resource before
   actually using it, it is conventional in Python to just go ahead and try
   to use it, catching the exception if access is rejected.

   Exceptions can also be used as a more general means of non-local transfer
   of control, even when an error is not at issue. For instance, the Mailman
   mailing list software, written in Python, uses exceptions to jump out of
   deeply nested message-handling logic when a decision has been made to
   reject a message or hold it for moderator approval.

   Exceptions are often used as an alternative to the if-block, especially in
   threaded situations. A commonly invoked motto is EAFP, or "It is Easier to
   Ask for Forgiveness than Permission,"^[26] which is attributed to Grace
   Hopper.^[27]^[28] The alternative, known as LBYL, or "Look Before You
   Leap", explicitly tests for pre-conditions.^[29]

   In this first code sample, following the LBYL approach, there is an
   explicit check for the attribute before access:

 if hasattr(spam, 'eggs'):
     ham = spam.eggs
 else:
     handle_missing_attr()

   This second sample follows the EAFP paradigm:

 try:
     ham = spam.eggs
 except AttributeError:
     handle_missing_attr()

   These two code samples have the same effect, although there will be
   performance differences. When spam has the attribute eggs, the EAFP sample
   will run faster. When spam does not have the attribute eggs (the
   "exceptional" case), the EAFP sample will run slower. The Python profiler
   can be used in specific cases to determine performance characteristics. If
   exceptional cases are rare, then the EAFP version will have superior
   average performance than the alternative. In addition, it avoids the whole
   class of time-of-check-to-time-of-use (TOCTTOU) vulnerabilities, other
   race conditions,^[28]^[30] and is compatible with duck typing. A drawback
   of EAFP is that it can be used only with statements; an exception cannot
   be caught in a generator expression, list comprehension, or lambda
   function.

Comments and docstringsEdit

   Python has two ways to annotate Python code. One is by using comments to
   indicate what some part of the code does. Single-line comments begin with
   the hash character (#) and continue until the end of the line. Comments
   spanning more than one line are achieved by inserting a multi-line string
   (with """ or ''' as the delimiter on each end) that is not used in
   assignment or otherwise evaluated, but sits in between other statements.

   Commenting a piece of code:

 import sys

 def getline():
     return sys.stdin.readline()  # Get one line and return it

   Commenting a piece of code with multiple lines:

 def getline():
     return sys.stdin.readline()    """this function
                                       gets one line
                                       and returns it"""

   Docstrings (documentation strings), that is, strings that are located
   alone without assignment as the first indented line within a module,
   class, method or function, automatically set their contents as an
   attribute named __doc__, which is intended to store a human-readable
   description of the object's purpose, behavior, and usage. The built-in
   help function generates its output based on __doc__ attributes. Such
   strings can be delimited with " or ' for single line strings, or may span
   multiple lines if delimited with either """ or ''' which is Python's
   notation for specifying multi-line strings. However, the style guide for
   the language specifies that triple double quotes (""") are preferred for
   both single and multi-line docstrings.^[31]

   Single line docstring:

 def getline():
     """Get one line from stdin and return it."""
     return sys.stdin.readline()

   Multi-line docstring:

 def getline():
     """Get one line
        from stdin
        and return it.
     """
     return sys.stdin.readline()

   Docstrings can be as large as the programmer wants and contain line
   breaks. In contrast with comments, docstrings are themselves Python
   objects and are part of the interpreted code that Python runs. That means
   that a running program can retrieve its own docstrings and manipulate that
   information, but the normal usage is to give other programmers information
   about how to invoke the object being documented in the docstring.

   There are tools available that can extract the docstrings from Python code
   and generate documentation. Docstring documentation can also be accessed
   from the interpreter with the help() function, or from the shell with the
   pydoc command pydoc.

   The doctest standard module uses interactions copied from Python shell
   sessions into docstrings to create tests, whereas the docopt module uses
   them to define command-line options.

Function annotationsEdit

   Function annotations (type hints) are defined in PEP 3107.^[32] They allow
   attaching data to the arguments and return of a function. The behaviour of
   annotations is not defined by the language, and is left to third party
   frameworks. For example, a library could be written to handle static
   typing:^[32]

 def haul(item: Haulable, *vargs: PackAnimal) -> Distance

DecoratorsEdit

   Link: mw-deduplicated-inline-style
   See also: Advice (computer science)

   A decorator is any callable Python object that is used to modify a
   function, method or class definition. A decorator is passed the original
   object being defined and returns a modified object, which is then bound to
   the name in the definition. Python decorators were inspired in part by
   Java annotations, and have a similar syntax; the decorator syntax is pure
   syntactic sugar, using @ as the keyword:

 @viking_chorus
 def menu_item():
     print("spam")

   is equivalent to

 def menu_item():
     print("spam")
 menu_item = viking_chorus(menu_item)

   Decorators are a form of metaprogramming; they enhance the action of the
   function or method they decorate. For example, in the sample below,
   viking_chorus might cause menu_item to be run 8 times (see Spam sketch)
   for each time it is called:

 def viking_chorus(myfunc):
     def inner_func(*args, **kwargs):
         for i in range(8):
             myfunc(*args, **kwargs)
     return inner_func

   Canonical uses of function decorators are for creating class methods or
   static methods, adding function attributes, tracing, setting pre- and
   postconditions, and synchronization,^[33] but can be used for far more,
   including tail recursion elimination,^[34] memoization and even improving
   the writing of other decorators.^[35]

   Decorators can be chained by placing several on adjacent lines:

 @invincible
 @favourite_colour("Blue")
 def black_knight():
     pass

   is equivalent to

 def black_knight():
     pass
 black_knight = invincible(favourite_colour("Blue")(black_knight))

   or, using intermediate variables

 def black_knight():
     pass
 blue_decorator = favourite_colour("Blue")
 decorated_by_blue = blue_decorator(black_knight)
 black_knight = invincible(decorated_by_blue)

   In the example above, the favourite_colour decorator factory takes an
   argument. Decorator factories must return a decorator, which is then
   called with the object to be decorated as its argument:

 def favourite_colour(colour):
     def decorator(func):
         def wrapper():
             print(colour)
             func()
         return wrapper
     return decorator

   This would then decorate the black_knight function such that the colour,
   "Blue", would be printed prior to the black_knight function running.
   Closure ensures that the colour argument is accessible to the innermost
   wrapper function even when it is returned and goes out of scope, which is
   what allows decorators to work.

   Despite the name, Python decorators are not an implementation of the
   decorator pattern. The decorator pattern is a design pattern used in
   statically typed object-oriented programming languages to allow
   functionality to be added to objects at run time; Python decorators add
   functionality to functions and methods at definition time, and thus are a
   higher-level construct than decorator-pattern classes. The decorator
   pattern itself is trivially implementable in Python, because the language
   is duck typed, and so is not usually considered as such.^[clarification
   needed]

Easter eggsEdit

   Users of curly bracket languages, such as C or Java, sometimes expect or
   wish Python to follow a block-delimiter convention. Brace-delimited block
   syntax has been repeatedly requested, and consistently rejected by core
   developers. The Python interpreter contains an easter egg that summarizes
   its developers' feelings on this issue. The code from __future__ import
   braces raises the exception SyntaxError: not a chance. The __future__
   module is normally used to provide features from future versions of
   Python.

   Another hidden message, the Zen of Python (a summary of Python design
   philosophy), is displayed when trying to import this.

   The message Hello world! is printed when the import statement import
   __hello__ is used. In Python 2.7, instead of Hello world! it prints Hello
   world....

   Importing the antigravity module opens a web browser to xkcd comic 353
   that portrays a humorous fictional use for such a module, intended to
   demonstrate the ease with which Python modules enable additional
   functionality.^[36] In Python 3, this module also contains an
   implementation of the "geohash" algorithm, a reference to xkcd comic
   426.^[37]

ReferencesEdit

   Link: mw-deduplicated-inline-style
    1. ^ "Readability counts." - PEP 20 - The Zen of Python Archived
       2014-12-05 at the Wayback Machine
    2. ^ "PEP 20 - The Zen of Python". Python Software Foundation.
       2004-08-23. Archived from the original on 2008-12-03. Retrieved
       2008-11-24.
    3. ^
       Link: mw-deduplicated-inline-style
       "2. Lexical analysis". Python 3 documentation. Python Software
       Foundation. Retrieved 2021-03-11.{{cite web}}: CS1 maint: url-status
       (link)
    4. ^
       Link: mw-deduplicated-inline-style
       "2. Lexical analysis". Python v2.7.18 documentation. Python Software
       Foundation. Retrieved 2021-03-11.{{cite web}}: CS1 maint: url-status
       (link)
    5. ^
       Link: mw-deduplicated-inline-style
       "New Keywords". Python v3.5 documentation. Docs.python.org. Archived
       from the original on 2016-06-18. Retrieved 2016-06-01.
    6. ^
       Link: mw-deduplicated-inline-style
       "2. Lexical analysis". Python 3 documentation. Python Software
       Foundation. Retrieved 2022-01-22.{{cite web}}: CS1 maint: url-status
       (link)
    7. ^
       Link: mw-deduplicated-inline-style
       "PEP 622 -- Structural Pattern Matching". 2020-06-23. Retrieved
       2022-01-22.
    8. ^
       Link: mw-deduplicated-inline-style
       "PEP 8 -- Style Guide for Python Code". Python.org. Retrieved
       2021-03-17.
    9. ^
       Link: mw-deduplicated-inline-style
       Hoffa, Felipe (2017-07-26). "400,000 GitHub repositories, 1 billion
       files, 14 terabytes of code: Spaces or Tabs?". Medium. Retrieved
       2021-03-11.
   10. ^
       Link: mw-deduplicated-inline-style
       "Tabs or Spaces". ukupat.github.io. Retrieved 2021-03-11.
   11. ^
       Link: mw-deduplicated-inline-style
       "PEP 8 -- Style Guide for Python Code". Python.org. Retrieved
       2021-03-11.
   12. ^
       Link: mw-deduplicated-inline-style
       "PEP 498 - Literal String Interpolation". What’s New In Python 3.6.
       2016-12-23. Archived from the original on 2017-03-30. Retrieved
       2017-03-29.
   13. ^
       Link: mw-deduplicated-inline-style
       "2. Lexical analysis". Python v2.7.5 documentation. Docs.python.org.
       Archived from the original on 2012-10-23. Retrieved 2013-08-16.
   14. ^
       Link: mw-deduplicated-inline-style
       "2. Lexical analysis". Python v2.7.5 documentation. Docs.python.org.
       Archived from the original on 2012-10-23. Retrieved 2013-08-16.
   15. ^ Hashable items are usually immutable, but not necessarily so by
       definition. See python.org/3/glossary.htm
   16. ^
       Link: mw-deduplicated-inline-style
       "6. Expressions — Python 3.9.2 documentation". docs.python.org.
       Retrieved 2021-03-17.
   17. ^
       Link: mw-deduplicated-inline-style
       "Archived copy". Archived from the original on 2016-01-27. Retrieved
       2016-01-19.{{cite web}}: CS1 maint: archived copy as title (link)
   18. ^
       Link: mw-deduplicated-inline-style
       "Archived copy". Archived from the original on 2016-01-26. Retrieved
       2016-01-19.{{cite web}}: CS1 maint: archived copy as title (link)
   19. ^
       Link: mw-deduplicated-inline-style
       "Archived copy". Archived from the original on 2016-01-26. Retrieved
       2016-01-19.{{cite web}}: CS1 maint: archived copy as title (link)
   20. ^
       Link: mw-deduplicated-inline-style
       David Mertz. "Functional Programming in Python". IBM developerWorks.
       Archived from the original on 2007-02-20. Retrieved 2007-08-27.
   21. ^
       Link: mw-deduplicated-inline-style
       "PEP 308 -- Conditional Expressions". Archived from the original on
       2016-03-13. Retrieved 2016-04-14.
   22. ^ The nonlocal keyword was adopted by PEP 3104 Archived 2014-12-02 at
       the Wayback Machine
   23. ^
       Link: mw-deduplicated-inline-style
       "Python Style Guide". docs.python-guide.org. Archived from the
       original on 2015-03-09. Retrieved 2015-03-08.
   24. ^
       Link: mw-deduplicated-inline-style
       "PEP 343 -- The "with" Statement". Archived from the original on
       2014-12-14. Retrieved 2014-08-15.
   25. ^
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       "Glossary — Python 3.9.2 documentation". docs.python.org. Retrieved
       2021-03-23.
   26. ^ EAFP Archived 2012-10-26 at the Wayback Machine, Python Glossary
   27. ^
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       Hamblen, Diane. "Only the Limits of Our Imagination: An exclusive
       interview with RADM Grace M. Hopper". Department of the Navy
       Information Technology Magazine. Archived from the original on January
       14, 2009. Retrieved 2007-01-31.
   28. ^ ^a ^b Python in a nutshell, Alex Martelli, p. 134
   29. ^ LBYL Archived 2018-01-21 at the Wayback Machine, Python Glossary
   30. ^
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       Alex Martelli (19 May 2003). "EAFP v. LBYL". python-list mailing list.
       Archived from the original on 14 July 2012. Retrieved 18 July 2011.
   31. ^
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       "PEP 8 -- Style Guide for Python Code". Python.org. Retrieved
       2021-03-23.
   32. ^ ^a ^b
       Link: mw-deduplicated-inline-style
       "PEP 3107 -- Function Annotations". Archived from the original on
       2015-01-06. Retrieved 2014-08-15.
   33. ^
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       "Python 2.4 Decorators: Reducing code duplication and consolidating
       knowledge". Dr. Dobb's. 2005-05-01. Archived from the original on
       2007-02-06. Retrieved 2007-02-08.
   34. ^
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       "New Tail Recursion Decorator". ASPN: Python Cookbook. 2006-11-14.
       Archived from the original on 2007-02-09. Retrieved 2007-02-08.
   35. ^
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       "The decorator module". Archived from the original on 2007-02-10.
       Retrieved 2007-02-08.
   36. ^
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       cpython: The Python programming language, Python, 2017-10-15, archived
       from the original on 2017-09-15, retrieved 2017-10-15
   37. ^
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       "Another hidden treasure. · python/cpython@b1614a7". GitHub. Retrieved
       2017-10-15.

External linksEdit

     * Python tutorial written by the author of Python, Guido van Rossum.
   Retrieved from
   "https://en.wikipedia.org/w/index.php?title=Python_syntax_and_semantics&oldid=1082284544"
   Last edited on 12 April 2022, at 09:35
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