Python里的一些语法糖

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关键词

  • Syntactic Sugar
  • Operators
  • Assignment Expressions
  • for loops
  • comprehensions
  • constructor

Operators

Operator Method
+ __add__
- __sub__
* __mul__
/ __truediv__
// __floordiv__
** __pow__
in __contains__

every time we use an operator, Python calls the corresponding special method for us. For example: 1 + 2 equals to (1).__add__(2) but way more complex and unreadable.

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class NumberOperator:
    def __init__(self, number: int):
        self.number = number

    def __add__(self, other):
        v = self.number + other.number
        return NumberOperator(v)

def add():
    one = NumberOperator(1)
    two = NumberOperator(2)
    three = one + two
    print(f"one = {one.number}")
    print(f"two = {two.number}")
    print(f"three = {three.number}")

membership tests

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def membership_test():
    print(1 in [1, 2])
    print(1 not in [1, 2])
    print(3 in [1, 2])
    print(3 not in [1, 2])

Chained Conditions

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def chained_conditions(number: int) -> bool:
    if 1 <= number <= 10:
        return True
    else:
        return False

Conditional expressions

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or_test  ::= and_test | or_test "or" and_test
and_test ::= not_test | and_test "and" not_test
not_test ::= comparison | "not" not_test

conditional_expression ::= or_test ["if" or_test "else" expression]
expression             ::= conditional_expression | lambda_expr

Assignment Expressions

Traditional assignments built with the = operator don’t return a value, so they’re statements but not expressions. In contrast, assignment expressions are assignments that return a value. You can build them with the walrus operator (:=).

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# this is an assignment expression, so it's syntactically correct.
def assignment_expression():
    while (line := input("Type some text: ")) != "stop":
        print(line)
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# this is a normal assignment, it doesn't return a value, so it's syntactically incorrect
def assignment():
    # Unresolved reference 'line'
    # while (line = input("Type some text: ")) != "stop":
    #     print(line)
    pass

Unpacking

Unpacking an iterable means assigning its values to a series of variables one by one.

Iterable Unpacking

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# Once Python runs this assignment, the values are unpacked into the corresponding variable by position.
one, two three, four = [1, 2, 3, 4]

a = 200
b = 100
# using the unpacking syntactic sugar, we can swap values between variables without assigning temporary variable
a, b = b, a

*args and **kwargs

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# 1
# (2, 3, 4)
# {'hello': 'hello', 'world': 'world'}
# args will be bound to positional arguments, and kwargs will be bound to keyword arguments
def args_func(number:int, *args, **kwargs):
    print(number)
    print(args)
    print(kwargs)


if __name__ == '__main__':
    args_func(1, 2, 3, 4, hello="hello", world="world")
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# I suppose that there are two different types of arguments in Python's arguments,
# The first type is named argument and the other type is anonymous argument.
# In this situation, "name" is a named argument, and "hello" and "world" is anonymous arguments,
# so "**kwargs" will be bound to anonymous arguments rather than being bound to all arguments
def show_args(number: int, *args, name: str, **kwargs):
    args_func(number, *args, **kwargs)
    print(name)


if __name__ == '__main__':
    # TypeError: show_args() missing 1 required keyword-only argument: 'name'
    # show_args(1, 2, 3, "name", 4, hello="hello", world="world")

    # TypeError: show_args() missing 1 required keyword-only argument: 'name'
    # show_args(1, 2, 3, 4, "name", hello="hello", world="world")
    
    show_args(1, 2, 3, 4, name="name", hello="hello", world="world")
    # 1
		# (2, 3, 4)
		# {'hello': 'hello', 'world': 'world'}
		# name

What will be happening if we pass a list as a *args and pass a dict as a **kwargs

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def show_args2(*args, **kwargs):
    print(args)
    print(kwargs)


def call_show_args2():
    list1 = [1, 2, 3, 4]
    list2 = [3, 4, 5, 6]

    dict1 = {"k1": "v1", "k2": "v2"}
    show_args2(list1, list2, dict1=dict1)
    # both of list1 and list2 are bound to *args, it's worth noticing that each list is an entirety which being passed to *args
    # ([1, 2, 3, 4], [3, 4, 5, 6])
    # {'dict1': {'k1': 'v1', 'k2': 'v2'}}

    show_args2(list1, dict1=dict1)
    # ([1, 2, 3, 4],)
    # {'dict1': {'k1': 'v1', 'k2': 'v2'}}

    show_args2(*list1, dict1=dict1)
    # using *list1 will decompose list into a series of variables bound to *args
    # (1, 2, 3, 4)
    # {'dict1': {'k1': 'v1', 'k2': 'v2'}}

Loops and Comprehensions

Exploring for Loops

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def simple_loops():
    numbers = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
    for number in numbers:
        print(number, end=', ')

def iter_loops():
    numbers = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
    it = iter(numbers)
    while True:
        try:
            number = next(it)
            print(number, end=' ')
        except StopIteration:
            break

if __name__ == '__main__':
    iter_loops()

Using Comprehensions

say that we have a list of numbers as strings, and we want to convert them into numeric values and build a new list of squared values.

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def square_string_list():
    strings = ["1", "2", "3", "4", "5", "6", "7", "8", "9"]
    numbers = [int(num)**2 for num in strings]
    print(numbers)
    # [1, 4, 9, 16, 25, 36, 49, 64, 81]

if __name__ == '__main__':
    square_string_list()

Decorators

Decorators are functions that take another function as an argument and extend their behavior dynamically without explicitly modifying it. In Python, you have a dedicated syntax that allows you to apply a decorator to a given function: In this piece of syntax, the @decorator part tells Python to call decorator() with the func object as an argument.

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@decorator
def func():
    <body>

The following code produces a TypeError called ** 'NoneType' object is not callable** since decorator function prev returns nothing to be executed:

  1. before f() executing, the decorator prev() was executed;
  2. the execution of prev() returns nothing which means the () in f() has nothing to execute;
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def prev(*args, **kwargs):
    print(f'args = {args}')
    print(f'kwargs = {kwargs}')

@prev
def f():
    print('running')

if __name__ == '__main__':
    f()

The following code is correct.

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def prev(*args, **kwargs):
    print(f'args = {args}')
    print(f'kwargs = {kwargs}')
    return args[0]

@prev
def f():
    print('running')

if __name__ == '__main__':
    f()

# args = (<function f at 0x10b944ca0>,)
# kwargs = {}
# running

Here is a bizarre statement that we never call f() but its decorator prev() was invoked automatically by compiler. It's because decorator is loaded during loading modules

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def prev(*args, **kwargs):
    print(f'args = {args}')
    print(f'kwargs = {kwargs}')
    return args[0]

@prev
def f():
    print('running')


if __name__ == '__main__':
    pass

decorator with lambda

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def decorator_with_arguments(*args, **kwargs):
    def running(name: str, age: int):
        print(f'args = {args}')
        print(f'kwargs = {kwargs}')
        print(f'name = {name}')
        print(f'age = {age}')
        outer = args[0]
        return outer(name=name, age=age)

    return running


@decorator_with_arguments
def func_with_arguments(name: str, age: int):
    print(f'name = {name}, age = {age}')


if __name__ == '__main__':
    func_with_arguments(name='tony', age=20)

# args = (<function func_with_arguments at 0x10b553ca0>,)
# kwargs = {}
# name = tony
# age = 20
# name = tony, age = 20

decorator with lambda two:

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def decorator_with_arguments2(func):
    def wrapper(*args, **kwargs):
        print(f'args = {args}')
        print(f'kwargs = {kwargs}')
        return func(*args, **kwargs)
    return wrapper

@decorator_with_arguments2
def func_with_arguments2(name: str, age: int):
    print(f'func_with_arguments2 : name = {name}, age = {age}')

if __name__ == '__main__':
    func_with_arguments2(name='tony', age=20)

# args = ()
# kwargs = {'name': 'tony', 'age': 20}
# func_with_arguments2 : name = tony, age = 20

F-String Literals

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debit = 300
credit = 450

print(f"Debit: ${debit:.2f}, Credit: ${credit:.2f}, Balance: ${credit - debit:.2f}")

The yield from Construct

Introduced with PEP 255, generator functions are a special kind of function that return a lazy iterator. These are objects that you can loop over like a list. However, unlike lists, lazy iterators do not store their contents in memory.

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def gen_infinite_sequence():
    num = 0
    while True:
        yield num
        num += 1

if __name__ == '__main__':
    gen = gen_infinite_sequence()
    print(gen)
    for i in range(5):
        print(next(gen), end=' ')

# <generator object gen_infinite_sequence at 0x10be6cf90>
# 0 1 2 3 4

Understanding Generators

Python yield statement and the code that follows it. yield indicates where a value is sent back to the caller, but unlike return, you don’t exit the function afterward.

Instead, the state of the function is remembered. That way, when next() is called on a generator object (either explicitly or implicitly within a for loop), the previously yielded variable num is incremented, and then yielded again.

Profiling Generator Performance

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def profiling_yield():
    import cProfile
    cProfile.run('sum([i * 2 for i in range(10000)])')

if __name__ == '__main__':
    profiling_yield()
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Ordered by: standard name

ncalls  tottime  percall  cumtime  percall filename:lineno(function)
     1    0.001    0.001    0.001    0.001 <string>:1(<listcomp>)
     1    0.000    0.000    0.001    0.001 <string>:1(<module>)
     1    0.000    0.000    0.001    0.001 {built-in method builtins.exec}
     1    0.000    0.000    0.000    0.000 {built-in method builtins.sum}
     1    0.000    0.000    0.000    0.000 {method 'disable' of '_lsprof.Profiler' objects}

Understanding the python yield statement

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def multi_yield():
    yield_string = "first string"
    yield yield_string
    yield_string = "second string"
    yield yield_string

if __name__ == '__main__':
    gen = multi_yield()
    print(next(gen))
    print(next(gen))
# first string
# second string

Using Advanced Generator Methods

method description
generator.send() Resumes the execution and “sends” a value into the generator function. The value argument becomes the result of the current yield expression. The send() method returns the next value yielded by the generator, or raises StopIteration if the generator exits without yielding another value.
generator.throw() .throw() allows you to throw exceptions with the generator. I
generator.close() As its name implies, .close() allows you to stop a generator.

The with Statement

Python’s with statement is a handy tool that allows you to manipulate context manager objects. These objects automatically handle the setup and teardown phases whenever you’re dealing with external resources such as files.

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with open("hello.txt", mode="w", encoding="utf-8") as file:
    file.write("Hello, World!")

# equals to
    
file = open("hello.txt", mode="w", encoding="utf-8")

try:
    file.write("Hello, World!")
finally:
    file.close()

List Comprehension

List comprehensions provide a concise way to create lists. Common applications are to make new lists where each element is the result of some operations applied to each member of another sequence or iterable, or to create a subsequence of those elements that satisfy a certain condition.

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squares = []

for x in range(10):
  	squares.append(x**2)

we can obtain the same result with

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squares = [x ** 2 for x in range(10)]

This is also simliar to the following code, except that the following code return <map object at 0x100d01760> which is a stream of data that is lazily initiated when the it's called.

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squares = map(lambda x: x**2, range(10))
print(squares)
# <map object at 0x100d01760>
for s in squares:
  print(s, end=' ')
  # 0 1 4 9 16 25 36 49 64 81

A list comprehension consists of brackets containing an expression followed by a for clause, then zero or more for or if clauses. The result will be a new list resulting from evaluating the expression in the context of the for and if clauses which follow it. For example, this listcomp combines the elements of two lists if they are not equal:

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def lc2():
    arr = [(x, y)
           for x in [1, 2, 3]
           for y in [3, 1, 4]
           if x != y]
    for item in arr:
        print(item, end=' ')
        # (1, 3) (1, 4) (2, 3) (2, 1) (2, 4) (3, 1) (3, 4)

The forementioned code is equivalent to the following code but way more readable:

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for_arr = []
for x in [1,2,3]:
    for y in [3,1,4]:
        if x != y:
            for_arr.append((x, y))
for item in for_arr:
    print(item, end=' ')
# (1, 3) (1, 4) (2, 3) (2, 1) (2, 4) (3, 1) (3, 4)

If the expression is a tuple (e.g. the (x, y) in the previous example), it must be parenthesized.

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[x, x**2 for x in range(6)]
#   File "<stdin>", line 1, in <module>
#    [x, x**2 for x in range(6)]

Nested List Comprehensions

The initial expression in a list comprehension can be any arbitrary expression, including another list comprehension.

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def nested_list_comprehensions():
    matrix = [
        [1, 2, 3, 4],
        [5, 6, 7, 8],
        [9, 10, 11, 12],
    ]
    print([[row[i] for row in matrix] for i in range(len(matrix))])
    # [[1, 5, 9], [2, 6, 10], [3, 7, 11]]
    print([[row[i] for row in matrix] for i in range(4)])
    # [[1, 5, 9], [2, 6, 10], [3, 7, 11], [4, 8, 12]]

Dictionary And dict comprehensions

In addition, dict comprehensions can be used to create dictionaries from arbitrary key and value expressions:

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def dictionaries():
    v = {x: x ** 2 for x in (2, 4, 6)}
    print(v)
    # {2: 4, 4: 16, 6: 36}