Python Recursion Example | Recursion In Python Programming Tutorial is today’s topic. Recursion is the method of programming or coding the problem, in which the function calls itself one or more times in its body. Usually, it is returning a return value of this function call. If the function definition satisfies the condition of recursion, we call this function a recursive function.

Recursion is a process of defining something in terms of itself.

A Real-world example would be to place two parallel mirrors facing each other like the movie in inception. Any object in between them would be reflected recursively and you will see infinite reflections of that object.

A recursive function is the function defined in terms of itself via self-referential expressions.

The recursive function has to fulfill an essential condition to be used in a program: it has to terminate.

The recursive function terminates if with every recursive call the solution of the problem is downsized and moves towards the base case.

The base case is a case where a problem can be solved without further recursion.

The recursion can end up in an infinite loop if the base case is not met in the calls.

Factorial of any number is the product of all the integers from 1 to that number. For instance, the factorial of 6 (denoted as 6!) is 1*2*3*4*5* = 120.

See the following program of Recursion in Python.

```
# app.py
def factorial(x):
if x == 1:
return 1
else:
return (x * factorial(x-1))
number = 5
print("The factorial of", number, "is", factorial(number))
```

See the output.

```
➜ pyt python3 app.py
The factorial of 5 is 120
➜ pyt
```

In the above example, factorial() is the recursive function as it calls itself.

When we call this function with the positive integer, it will recursively call itself by decreasing a number.

Each function calls multiples the number with the factorial of number 1 until the number is equal to one.

When working with a recursion, we should define the base case for which we already know an answer. In an above example, we are finding the factorial of the integer number, and we already know that a factorial of 1 is 1 so this is our base case.

Each successive recursive call to a function should bring it closer to the base case, which is precisely what we are doing in the above example. We use a base case in recursive function so that the function stops calling itself when the base case is reached. Without the base case, the function would keep calling itself indefinitely.

To demonstrate this structure, let’s write the recursive function for calculating n!:

- Decompose the original problem into more straightforward instances of a same problem. This is the recursive case:

```
n! = n x (n−1) x (n−2) x (n−3) ⋅⋅⋅⋅ x 3 x 2 x 1
n! = n x (n−1)!
```

- As a large problem, we need to broke down into successively less complex ones, those subproblems must eventually become so simple that they could solved without any further subdivision. This is the base case:

```
n! = n x (n−1)!
n! = n x (n−1) x (n−2)!
n! = n x (n−1) x (n−2) x (n−3)!
⋅
⋅
n! = n x (n−1) x (n−2) x (n−3) ⋅⋅⋅⋅ x 3!
n! = n x (n−1) x (n−2) x (n−3) ⋅⋅⋅⋅ x 3 x 2!
n! = n x (n−1) x (n−2) x (n−3) ⋅⋅⋅⋅ x 3 x 2 x 1!
```

Here, 1! is our base case, and it equals 1.

Recursive functions make a code look clean and elegant.

The complex task can be broken down into simpler sub-problems using recursion.

Sequence generation is more comfortable with a recursion than using the nested iteration.

A logic behind recursion is hard to follow through.

The recursive calls are expensive (inefficient) as they take up the lot of memory and time.

Recursive functions are very hard to debug.

Finally, Python Recursion Example | Recursion In Python Programming Tutorial is over.

Happy Coding !

In the programming world, Data types play an important role. Each Variable is stored in different data types and responsible for various functions. Python had two different objects, and They are mutable and immutable objects.

Magic Methods are the special methods which gives us the ability to access built in syntactical features such as ‘<’, ‘>’, ‘==’, ‘+’ etc.. You must have worked with such methods without knowing them to be as magic methods. Magic methods can be identified with their names which start with __ and ends with __ like __init__, __call__, __str__ etc. These methods are also called Dunder Methods, because of their name starting and ending with Double Underscore (Dunder).

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Python any() function returns True if any element of an iterable is True otherwise any() function returns False. The syntax is any().