About functools module function analysis in Python

This article mainlyintroducesabout the functools modulefunctionanalysis in Python, and explains functools.cmp_to_key, functools respectively .total_ordering, functools.reduce, functools.partial, functools.update_wrapper and functools.wraps usage, friends in need can refer to

Python The built-in functools module provides some commonly used higher-order functions, which are special functions used to process other functions. In other words, you can use this module to process callable objects.

functools module functionOverview

• ##functools.cmp_to_key(func)

• functools.total_ordering(cls)

##functools.reduce(function, iterable[, initializer])
functools.partial (func[, args][, *keywords])
functools.update_wrapper(wrapper, wrapped[, assigned][, updated])
functools.wraps(wrapped[, assigned][, updated])

• 
  

##functools.cmp_to_key()

Syntax:

functools.cmp_to_key(func)

This function is used to convert the old comparison function into a keyword function.

Old-style comparison function: receives two parameters and returns the comparison result. If the return value is less than zero, the former is less than the latter, if the return value is greater than zero, the opposite is true, and if the return value is equal to zero, the two are equal.

Keyword function: receives a parameter and returns its corresponding comparable object. For example,

sort

ed(), min(), max(), heapq.nlargest(), heapq.nsm

allest(), itertools.groupby() can all be used as keywords function. In Python 3, there are many places that no longer support the old comparison functions. At this time, you can use cmp_to_key() for conversion.

Example:

sorted(iterable, key=cmp_to_key(cmp_func))

functools.total_ordering()

Syntax:

functools.total_ordering(cls)

This is a class decorator used to automatically implement comparison operations of classes.

We only need to implement the eq() method and any one of the following methods lt(), le(), gt(), ge() in the class, then total_ordering() can automatically help us implement it The remaining comparison operations.

Example:

@total_ordering
class Student: 
  def eq(self, other):
    return ((self.lastname.lower(), self.firstname.lower()) ==
        (other.lastname.lower(), other.firstname.lower()))
  def lt(self, other):
    return ((self.lastname.lower(), self.firstname.lower()) <
        (other.lastname.lower(), other.firstname.lower()))

functools.reduce()

Syntax:

functools.reduce(function, iterable[, initializer])

This function is the same as Python’s built-in reduce() function and is mainly used to write code that is compatible with Python 3 .

functools.partial()

Syntax:

functools.partial(func[, *args][, * *keywords])

This function returns a partial object. The effect of calling this object is equivalent to calling the func function and passing in the positional parameters args and keyword parameters keywords. If the object is called with positional parameters, these parameters will be added to args. If keyword arguments are passed in, they will be added to keywords.

The equivalent implementation of the partial() function is roughly as follows:

def partial(func, *args, **keywords): 
  def newfunc(*fargs, **fkeywords):
    newkeywords = keywords.copy()
    newkeywords.update(fkeywords)
    return func(*(args + fargs), **newkeywords)
  newfunc.func = func
  newfunc.args = args
  newfunc.keywords = keywords
  return newfunc

partial() function is mainly used to "freeze" some parameters of a function. Returns a function object with fewer parameters and simpler usage.

Example:

>>> from functools import partial
>>> basetwo = partial(int, base=2)
>>> basetwo.doc = 'Convert base 2 string to an int.'
>>> basetwo('10010')
18

functools.update_wrapper()

Syntax:

functools.update_wrapper(wrapper, wrapped[, assigned][, updated])

This function is used to
update

the wrapper function (wrapper) so that it looks like the original The function is the same. The optional parameter is a tuple. The assigned tuple specifies the

attribute to be replaced directly with the value of the original function, and the updated tuple specifies the attribute to be updated against the original function. The default values ​​of these two parameters are module-level constants: WRAPPER_ASSIGNMENTS and WRAPPER_UPDATES respectively. The former specifies direct assignment of the name, module, and doc attributes of the wrapper function, while the latter specifies the update of the dict attribute of the wrapper function.

该函数主要用于装饰器函数的定义中，置于包装函数之前。如果没有对包装函数进行更新，那么被装饰后的函数所具有的元信息就会变为包装函数的元信息，而不是原函数的元信息。

functools.wraps()

语法：

functools.wraps(wrapped[, assigned][, updated])

wraps() 简化了 update_wrapper() 函数的调用。它等价于 partial(update_wrapper, wrapped=wrapped, assigned, updated=updated)。

示例：

>>> from functools import wraps
>>> def my_decorator(f):
...   @wraps(f)
...   def wrapper(*args, **kwds):
...     print 'Calling decorated function'
...     return f(*args, **kwds)
...   return wrapper

>>> @my_decorator
... def example():
...   """Docstring"""
...   print 'Called example function'

>>> example()
Calling decorated function 
Called example function 
>>> example.name
'example' 
>>> example.doc
'Docstring'

如果不使用这个函数，示例中的函数名就会变成 wrapper ，并且原函数 example() 的说明文档（docstring）就会丢失。

The above is the detailed content of About functools module function analysis in Python. For more information, please follow other related articles on the PHP Chinese website!