Understanding closures in Python: getting started with closures

Walker AI 2021-01-20 20:30:15
understanding closures python getting started


This article was first published in : Walker AI

python What is a closure in ? What's the use of closures ? Why use closures ? Today we'll take this 3 One problem is to know closures step by step .

Closures and functions are closely related , It's necessary to introduce some background knowledge before introducing closures , Such as nested functions 、 The scope of variables and so on .

1. Scope

Scope is the range of variables that can be accessed at runtime , Variables defined in a function are local variables , The scope of action of local variables can only be within the internal scope of the function , It cannot be referenced outside a function .

The variables defined in the outermost layer of the module are global variables , It's globally visible , Of course, you can also read global variables in functions . You can't access local variables outside the function . for example :

a = 1
def foo():
print(a) # 1 
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def foo():
print(a) # NameError: name 'num' is not defined 
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2. Nested function

Functions can be defined not only in the outermost layer of a module , It can also be defined inside another function , Functions defined in functions like this are called nested functions (nested function). For nested functions , It has access to nonlocals declared in its outer scope (non-local) Variable , For example, variables in the code example a Can be nested functions printer Normal visit .

def foo():
#foo It's a peripheral function 
a = 1
# printer It's a nested function 
def printer():
print(a)
printer()
foo() # 1
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So is there a possibility that even if it is out of the scope of the function itself , Local variables can also be accessed ?

The answer is closures !

We change the above functions into higher order functions ( Accept a function as an argument , Or the function returned as a result is a higher-order function ) Writing .

def foo():
#foo It's a peripheral function 
a = 1
# printer It's a nested function 
def printer():
print(a)
return printer
x = foo()
x() # 1
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This code has exactly the same effect as the previous example , The same output 1. The difference is in the internal function printer Directly returned as a return value .

In general , Local variables in a function are only available during the execution of the function , once foo() After execution , We would think of variables as a Will no longer be available . However , Here we find foo After execution , Calling x When a The value of the variable is output normally , That's what closures do , Closures make it possible for local variables to be accessed outside the function .

3. Closure

People sometimes confuse closures with anonymous functions . There are historical reasons for that : Define the function inside the function Less common , Not until you start using anonymous functions . and , Closure problems occur only when nested functions are involved . therefore , Many people know these two concepts at the same time .

Actually , A closure is a function that extends its scope , It contains references in the function definition body 、 But not in the definition body Non global variables . It doesn't matter if the function is anonymous , The key is that it can access non global variables defined outside the definition body .

Generally speaking, closures , seeing the name of a thing one thinks of its function , It's a sealed package , It's wrapped in free variables , Just like property values defined in a class , The visible range of the free variable is accompanied by the package , Where can I access this package , Where can I access this free variable . Where is the package bound ? Add a print sentence to the above code :

 def foo():
# foo It's a peripheral function 
a = 1
# printer It's a nested function 
def printer():
print(a)
return printer
x = foo()
print(x.__closure__[0].cell_contents) # 1 
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You can see that it's in the function object __closure__ Properties of the ,__closure__ Is a meta ancestor object function responsible for closure binding , Binding of free variables . The value of this property is usually None, If this function is a closure , So what it returns is a result of cell Tuple objects made up of objects .cell Object's cell_contents Attributes are free variables in closures . This explains why local variables are separated from functions , It can also be accessed outside the function , Because it's stored in the cell_contents It's in .

4. The benefits of closures

Closures avoid using global variables , Besides , Closures allow functions to be manipulated with certain data ( Environmental Science ) Connected . This is very similar to object-oriented programming , In face object programming , Object allows us to put some data ( Object properties ) Associated with one or more methods .

Generally speaking , When there is only one method in an object , Using closures is a better choice . Let's take an example of calculating the mean value , If there's one called avg Function of , Its function is to calculate the mean value of the increasing series of values ; for example , Throughout history The average closing price of a commodity . New prices are added every day , So the average takes into account all the prices so far , As shown below :

>>> avg(10) #10.0 
>>> avg(11) #10.5 
>>> avg(12) #11.0 
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In the past , We can design a class :

class Averager():
def __init__(self):
self.series = []
def __call__(self, new_value):
self.series.append(new_value)
total = sum(self.series)
return total/len(self.series)
avg = Averager()
avg(10) #10.0
avg(11) #10.5
avg(12) #11.0
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In this case, we use closures to implement .

def make_averager():
series = []
def averager(new_value):
series.append(new_value)
total = sum(series)
return total/len(series)
return averager
avg = make_averager()
avg(10) #10.0
avg(11) #10.5
avg(12) #11.0
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call make_averager when , Return to one averager Function object . Every time you call averager when , It adds parameters to the list , Then calculate the current average . This is more elegant than using classes to implement , In addition, decorators are also an application scenario based on closures .

5. The pit of closure

After reading the above explanation of closures, you think closures are nothing more than that ? In actual use, we often fall into the trap inadvertently , Take a look at the following example :

def create_multipliers():
return [lambda x: x * i for i in range(5)]
for multiplier in create_multipliers():
print(multiplier(2))
# Expected output 0, 2, 4, 6, 8
# The result is 8, 8, 8, 8, 8
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We expect output 0, 2, 4, 6, 8. The result is 8, 8, 8, 8, 8. Why is this problem ? Let's change the code :

def create_multipliers():
multipliers = [lambda x: x * i for i in range(5)]
print([m.__closure__[0].cell_contents for m in multipliers])
create_multipliers() # [4, 4, 4, 4, 4] 
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You can see the function binding i It's all worth it 4 That is, after the cycle, finally i The value of , This is because Python The closure of is delayed binding , This means that the value of the variable used in the closure , It is obtained by querying when an internal function is called .

The right way to use it is to put i The value of is passed as a parameter :

def create_multipliers():
return [lambda x,i=i: x * i for i in range(5)]
s = create_multipliers()
for multiplier in s:
print(multiplier(2)) # 0, 2, 4, 6, 8
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We use default parameters to pass i, As with closures, the default parameter is bound to __defaults__ Attribute .

print([f.__defaults__ for f in s]) # [(0,), (1,), (2,), (3,), (4,)] 
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PS: More technical dry goods , Quick attention 【 official account | xingzhe_ai】, Discuss it with the traveler !

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https://pythonmana.com/2021/01/20210120193217547Q.html

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