GeezerVison

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 programming, 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.

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".

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

In addition, Python 3 also has 4 soft keywords, including type added in Python 3.12. 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, in other words, match and case are valid names for functions and variables.^[6][7]

• _^{[note 4]}
• case^{[note 4]}
• match^{[note 4]}
• type

Function annotations (type hints) are defined in PEP 3107.^[8] They allow attaching data to the arguments and return of a function. The act 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:^[8]

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

While annotations are optional in Python, the rest of this article will use annotations to provide clarity.

In Python, code is organized into files called modules, and namespaces are defined by the individual modules. Since modules can be contained in hierarchical packages, then namespaces are hierarchical too.^[9][10] In general when a module is imported then the names defined in the module are defined via that module's namespace, and are accessed in from the calling modules by using the fully qualified name.

# assume ModuleA defines two functions : func1() and func2() and one class : Class1
import ModuleA

ModuleA.func1()
ModuleA.func2()
a: ModuleA.Class1 = ModuleA.Class1()

The from ... import ... statement can be used to insert the relevant names directly into the calling module's namespace, and those names can be accessed from the calling module without the qualified name:

# assume ModuleA defines two functions : func1() and func2() and one class : Class1
from ModuleA import func1

func1()
func2() # this will fail as an undefined name, as will the full name ModuleA.func2()
a: Class1 = Class1() # this will fail as an undefined name, as will the full name ModuleA.Class1()

Since this directly imports names (without qualification) it can overwrite existing names with no warnings.

A special form of the statement is from ... import * which imports all names defined in the named package directly in the calling module's namespace. Use of this form of import, although supported within the language, is generally discouraged as it pollutes the namespace of the calling module and will cause already defined names to be overwritten in the case of name clashes.^[11] However, this page will present code as if the line "from typing import *" were included, for referring to collection types.

The different import statements are demonstrated here:

# imports the argument parsing module
import argparse
# imports the Pattern class from the regular expressions module
from re import Pattern
# imports all symbols inside the typing module
from typing import *

Using from import statements in Python can simplify verbose namespaces, such as nested namespaces.

from selenium.webdriver import Firefox
from selenium.webdriver.common.action_chains import ActionChains
from selenium.webdriver.common.by import By
from selenium.webdriver.common.keys import Keys
from selenium.webdriver.remote.webelement import WebElement

if __name__ == "__main__":
    driver: Firefox = Firefox()
    element: WebElement = driver.find_element(By.ID, "myInputField")
    element.send_keys(f"Hello World{Keys.ENTER}")
    action: ActionChains = ActionChains(driver)
    action.key_down(Keys.CONTROL).send_keys("a").key_up(Keys.CONTROL).perform()

Python also supports import x as y as a way of providing an alias or alternative name for use by the calling module. Widely used third-party packages have conventional short aliases, such as np for NumPy and pa for PyArrow:^[12]

import numpy as np
import pyarrow as pa
from numpy.typing import NDArray

a: NDArray[np.float32] = np.arange(1000, dtype=np.float32)
t: pa.Table = pa.table({"a": a})

When a module is imported, the Python interpreter first checks if it exists in the sys.modules cache, and reuses it if it had been imported previously, otherwise it loads it. When loading, it searches it in sys.path, and compiles it to bytecode or interprets its contents. All code in the global scope of the module is executed. However, this can be mitigated using an explicit main function, which behaves similarly to an entry point in most compiled languages, using the entry point idiom described as follows.

import sys

def main(argv: list[str]) -> int:
    argc: int = len(argv)  # get length of argv
    n: int = int(argv[1])
    print(n + 1)
    return 0

if __name__ == "__main__":
    sys.exit(main(sys.argv))

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: int) -> None:
    if x == 0:
        bar()
    else:
        baz(x)
        foo(x - 1)

and could be written like this in C:

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: int) -> None:
    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^[16] and four spaces (as in the above examples) are recommended and are by far the most commonly used.^[17][18] Mixing spaces and tabs on consecutive lines is not allowed starting with Python 3^[19] because that can create bugs which are difficult to see, since many text editors do not visually distinguish spaces and tabs.

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 Python creator 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:

my_list: list[str] = ["a", "b", "c"]
def my_func(l: list[str]) -> None:
    l.append("x")
    print(l)

print(my_func(my_list))
# prints ['a', 'b', 'c', 'x']
print(my_list)
# prints ['a', 'b', 'c', 'x']

Function my_func changes the value of my_list with the formal argument l, which is an alias of my_list. However, any attempt to operate (assign a new object reference to) on the alias itself will have no effect on the original object.

my_list: list[str] = ["a", "b", "c"]

def my_func(l: list[str]) -> None:
    # l.append("x")
    l = l + ["x"]  # a new list created and assigned to l means l is no more alias for my_list
    print(l)

print(my_func(my_list))
# prints ['a', 'b', 'c', 'x']
print(my_list)
# prints ['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.

A strength of Python is the availability of a functional programming style, which makes working with lists and other collections much more straightforward.

l: list[Any] = [mapping_expression for element in source_list if filter_expression]

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

T: TypeVar = TypeVar("T")

def qsort(l: list[T]) -> list[T]:
    if l == []:
        return []
    pivot: T = 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: Callable[[int], int] = lambda x: x**2
f(5)

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

called_yet = False

def incorrect_tracker() -> None:
    called_yet = True # Creates a new variable instead of updating the other called_yet

incorrect_tracker()
print(called_yet) # False

called_yet = False

def tracker() -> None:
    global called_yet
    called_yet = True # Reassigns called_yet from the global scope

tracker()
print(called_yet) # True

import itertools

def generate_primes(stop_at: Optional[int] = None) -> Iterator[int]:
    primes: list[int] = []
    for n in itertools.count(start = 2):
        if stop_at is not None and n > stop_at:
            return # raises the StopIteration exception
        composite: bool = 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)

import itertools

primes_under_million: Iterator[int] = (i for i in generate_primes() if i < 1000000)
two_thousandth_prime: Iterator[int] = itertools.islice(primes_under_million, 1999, 2000).next()

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

squares = dict((n, n * n) for n in range(5))
# in Python 3.5 and later the type of squares is dict[int, int]
print(squares)
# prints {0: 0, 1: 1, 2: 4, 3: 9, 4: 16}

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

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.^[35]

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."

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) -> None:
        self._a: int = 0

    @property
    def a(self) -> int:
        return self._a

    @a.setter  # makes the property writable
    def a(self, value: int) -> None:
        self._a = value

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,"^[38] which is attributed to Grace Hopper.^[39][40] The alternative, known as LBYL, or "Look Before You Leap", explicitly tests for pre-conditions.^[41]

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,^[40][42] 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.

There are 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() -> str:
    return sys.stdin.readline()  # Get one line and return it

Commenting a piece of code with multiple lines:

def getline() -> str:
    """This function gets one line and returns it.

    As a demonstration, this is a multiline docstring.

    This full string can be accessed as getline.__doc__.
    """
    return sys.stdin.readline()

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.^[43]

Single-line docstring:

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

Multi-line docstring:

def getline() -> str:
    """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.

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 symbol. However, unlike general attributes in most programming languages, which are used to append metadata either for the compiler or for reading at reflection, Python decorators are not used for attaching metadata, but rather as executable wrappers.

@viking_chorus
def menu_item() -> None:
    print("spam")

is equivalent to

def menu_item() -> None:
    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:

R: TypeVar = TypeVar("R")

def viking_chorus(myfunc: Callable[..., R]) -> Callable[..., None]:
    def inner_func(*args: *tuple[Any, ...], **kwargs: Unpack[dict[str, Any]]) -> None:
        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,^[44] but can be used for far more, including tail recursion elimination,^[45] memoization and even improving the writing of other decorators.^[46]

Decorators can be chained by placing several on adjacent lines:

@invincible
@favourite_colour("Blue")
def black_knight() -> None:
    pass

is equivalent to

def black_knight() -> None:
    pass
black_knight = invincible(favourite_colour("Blue")(black_knight))

or, using intermediate variables

def black_knight() -> None:
    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: str) -> Callable[[Callable[[], R]], Callable[[], R]]:
    def decorator(func: Callable[[], R]) -> Callable[[], R]:
        def wrapper() -> R:
            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.

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.^[47] In Python 3, this module also contains an implementation of the "geohash" algorithm, a reference to xkcd comic 426.^[48]

• "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.