Returning Functions from Functions

What thus is happening in this case? Because it generates another function, inner_function, our outer_function is upsetting the Higher-Order Function status. Now, this inner function maintains its hold on the surrounding state (in this example, message), thereby acting as a sort of smart little closure. Using "Hello World," when we ring out outer_function returns that inner_function, which we then plug into the variable greet. And what would you suppose? Jabbing greetings later screams out "Hello World." Nice, right? This capacity allows us to create what are known as function factories—that is, essentially bespoke behaviors producing other functions.

def power_factory(n):
   def nth_power(x):
       return x ** n
   return nth_power
   
square = power_factory(2)
cube = power_factory(3)

print(square(2))  # prints 4
print(cube(2))    # prints 8

Peep at this next one: power_factory. Our function factory computes powers using our generating functions. using power_factory with two results in a function that squares integers; using power_factory with three results in a function that cubes them. Neat, huh?

This approach of returning functions from other functions increases abstraction and reusability of programming.
It allows us to design function factories that spew functions linked to complete tasks.
We can also create closures using it that hold hold of and apply their surrounding state.

We will walk over some practical cases illustrating the magic of returning functions from functions in the following leg of our trip.

Practical Examples of Returning Functions from Functions

Using some practical examples, let's dissect the notion of returning functions from other functions. These will demonstrate in practical code situations how helpful this idea is. Recall our earlier discussion on function factories? Let's now explore still another delicious illustration of their application.

def make_multiplier_of(n):
   def multiplier(x):
       return x * n
   return multiplier
   
times3 = make_multiplier_of(3)
times5 = make_multiplier_of(5)

print(times3(9))  # prints 27
print(times5(3))  # prints 15
print(times5(times3(2)))  # prints 30

Here, make_multiplier_of is our function factory producing operations to multiply objects by a certain quantity. Thanks to a little magic known as closures, the returned functions never forget the number they are meant to multiply by. And there's more! Super crucial in object-oriented programming, this idea also allows us to perform some data hiding and encapsulation.

def outer():
   hidden = {}

   def add(key, value):
       hidden[key] = value

   def view():
       print(hidden)

   return add, view

add_secret, view_secret = outer()
add_secret('some_key', 'some_value')
view_secret()  # prints {'some_key': 'some_value'}

In this one, work on a secret dictionary, the outer function returns two more functions, add and view. Using the add and view tools lets you change this dictionary like a tiny secret you cannot access straight from outside. Pulling off data masking and encapsulation is easy and fantastic.

For the huge production of particular kinds of functions, you can build function factories by using returning functions from other functions.
Must-know ideas in object-oriented programming, data hiding and encapsulation is also conveniently done here.
Closures help these returning functions to recall the surrounding condition of affairs.

Stay around since we will then discuss the reasons behind the great advantage of applying higher-order functions and returning functions from other functions.

Benefits of Using Higher-Order Functions

Let's chat about why using Higher-Order Functions, especially those that return functions, is pretty awesome for our Python coding adventures. They bring tons of benefits that make our code more efficient, easier to read, and super reusable. Ready to dive in? Here we go! One big perk is that these functions help us follow the good ol' DRY (Don't Repeat Yourself) principle, which is all about cutting down on redundant code. By returning functions, we can whip up function factories to spawn functions with specific tasks, saving us from writing the same stuff over and over.

def make_adder(n):
   def add(x):
       return x + n
   return add

add_5 = make_adder(5)
add_10 = make_adder(10)

print(add_5(3))  # prints 8
print(add_10(3))  # prints 13

See how, in this example, we didn't have to craft separate functions just to add 5 and 10? Instead, we've got a slick function factory that churns out exactly what we need. Plus, Higher-Order Functions are great for making your code look clean and understandable. By tucking away complex operations inside functions that return other functions, your code becomes a breeze to read. And let's not forget about data hiding and encapsulation! Returning functions that work on data tucked away in their scope gives us a way to control how data gets used, keeping it safe from unwanted meddling.

Higher-Order Functions rock at promoting code reusability and sticking to the DRY principle.
They boost code readability, making complex actions less of a head-scratcher.
They provide neat ways to hide data and encapsulate it, which is super handy in object-oriented programming.

Next up, we'll be diving into some common blunders you might encounter with Higher-Order Functions and how to steer clear of them.

Advanced Techniques with Returning Functions from Functions

All set to expand on your Python competency? Let's explore some sophisticated methods for retrieving functions from ones that will truly improve your coding performance. First on our agenda is whipping up decorations with returned functions. Decorators are like magic wraps allowing you to add additional taste to the behavior of a function without permanently altering its character.

def my_decorator(func):
   def wrapper():
       print("Before function call")
       func()
       print("After function call")
   return wrapper

def say_hello():
   print("Hello!")

say_hello = my_decorator(say_hello)

say_hello()

Consider this: Here our Higher-Order Function is my_decorator. It performs a function and spits out a new one around the original function with some added moves. Let us then discuss simple state machine operations using returned functions. Imagine a state machine as a gizmo in one of multiple states at any one moment that changes between them depending on particular inputs.

def start():
   def stop():
       print("Stopping")
       return start
   print("Starting")
   return stop

machine = start
for i in range(5):
   machine = machine()

Start and stop are our state-representing roles in this simple state machine here. Every one speaks out a message and returns what is to happen.

Your ticket to designing decorators and allowing a function to be more active without permanently changing it is returned functions.
Simple state machines also benefit from them since every state is highlighted by its own purpose.

Real-World Applications of Returning Functions from Functions

Returning functions from functions is quite useful in the real-world programming scene, not only some elegant idea for textbooks. Let's explore some amazing current methods this is happening in. Among the most common applications is... Writing decorators! Web frameworks such as Flask and Django abound in these ingenious tools. Consider Flask: decorators link HTTP requests with particular purposes. Quite tidy, right?

from flask import Flask
app = Flask(__name__)

@app.route('/')
def hello_world():
   return 'Hello, World!'

@app.route('/') is a decorator here asking Flask to run the hello_world function anytime someone visits the app's homepage. Callback functions are another large field in which returning functions excel. Ever notice how functions sent around in event-driven programming—that is, in GUIs or even JavaScript—are used later? For ya, that is callbacks. On demand, you can also roll out lightweight, anonymous functions—or lambdas—for simple chores like sorting a list of dictionaries by a certain key.

data = [{'name': 'John', 'age': 30}, {'name': 'Jane', 'age': 25}, {'name': 'Joe', 'age': 35}]
data.sort(key=lambda x: x['age'])
print(data)

Here, we determine the sorting sequence depending on the age inside the dictionaries by means of a lambda. How great is that?

Making decorators, used all over frameworks like Flask and Django, usually starts with returning functions from other methods.
For callback functions, a pillar of event-driven programming, it is absolutely necessary.
For a variety of chores, you can create lightweight, anonymous functions (lambdas) just on demand.

Comparing Python's Higher-Order Functions with Other Languages

Higher-Order Functions go beyond Python in nature. Loads of contemporary programming languages, particularly those groving in the functional programming realm, have them too. The twist is that every language accomplishes them very differently. Consider JavaScript, which respects functions as first-class citizens much as Python does. You can thus toss functions into variables, apply them as function arguments, or even have them as function return values. JavaScript has its own syntax and style, nevertheless, for arranging and calling for these features.

function createGreeting(name) {
   return function() {
       console.log('Hello, ' + name);
   }
}

let greetJohn = createGreeting('John');
greetJohn();  // prints 'Hello, John'

The createGreeting method in this JavaScript fragment spins forth another crucial ability—that of greeting someone particular. We then pack this ability into a variable for eventual use. Java, meantime, performs things rather differently. Though with Java 8 functional interfaces and lambda expressions came into use, giving us some of that Higher-Order Function magic, functions are not first-class citizens here.

import java.util.function.Function;

public class Main {
   public static void main(String[] args) {
       Function> makeAdder = x -> y -> x + y;
       Function add5 = makeAdder.apply(5);
       System.out.println(add5.apply(3)); // prints 8
   }
}

In this Java example, a lambda expression shapes a Function that hands back another Function. Then that Function works with an argument. Beautiful!

Like Python, JavaScript allows functions to roam free; you name it, assign them to variables, send them as arguments, return them from other functions.
Java keeps functions under closer control, but Java 8's functional interfaces and lambda expressions enable you replicate the first-class citizen experience.

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