A snippet of Python code with keywords highlighted in bold yellow font

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. It supports multiple programming paradigms, including structured, object-oriented programming, and functional programming, and boasts a dynamic type system and automatic memory management.

Python's syntax is simple and consistent, adhering to the principle that "There should be one— and preferably only one —obvious way to do it." The language incorporates built-in data types and structures, control flow mechanisms, first-class functions, and modules for better code reusability and organization. Python also uses English keywords where other languages use punctuation, contributing to its uncluttered visual layout.

The language provides robust error handling through exceptions, and includes a debugger in the standard library for efficient problem-solving. Python's syntax, designed for readability and ease of use, makes it a popular choice among beginners and professionals alike.

Design philosophy

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".

Keywords

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

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

Indentation

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] 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 text editors do not visually distinguish spaces and tabs.

Data structures

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.

>>> 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']
>>> print(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.

Literals

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})

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

print('''Dear {recipient},

I wish you to leave Sunnydale and never return.

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

>>> # 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.

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

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

s = "Γειά" # Hello in Greek
print(s)

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

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

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

some_set = {0, (), False}

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

Operators

>>> 4 / 2
2.0

Functional programming

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.

L = [mapping_expression for element in source_list if filter_expression]

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

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]))

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

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

>>> 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

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

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)

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()

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

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

>>> [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}

Objects

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.

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

Exceptions

Python supports (and extensively uses) exception handling as a means of testing for error conditions and other "exceptional" events in a program.

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 docstrings

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 annotations

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

Decorators

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.

Easter eggs

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]

References

External links

• "The Python Language Reference".

• Van Rossum, Guido. "The Python Tutorial". (written by the author of Python)

• Ramalho, Luciano (April 2022). Fluent Python, 2nd Edition. O'Reilly Media, Inc. ISBN 9781492056355.