# recursion in python

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Recursion is a widely-discussed concept not just in programming, but also in day-to-day language. An example from the English language that beautifully captures recursion is “To understand recursion, you must first understand recursion”. Similarly, the saying “A human is someone whose mother is human” offers another simple explanation.

Now, pivoting to programming, you may ask: How is this relevant?

In the realm of problem-solving, often there’s a need to break down a large, intricate problem into smaller, manageable parts. While you might be accustomed to using loops or iterations for such purposes, sometimes, recursion provides a more elegant and intuitive solution.

So, what exactly is recursion in Python? A function is termed as recursive when it makes a call to itself, but it’s imperative that this function has a stopping point or a termination condition. This ensures that the function doesn’t end up calling itself endlessly.

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## Recursion in Practice

List-Based Recursion Example

Consider a simple task of summing all numbers in a given list. A non-recursive approach to achieve this would be:

The above approach is straightforward: we iterate through each element and accumulate the sum. But how can we approach this using recursion?

Here, if the list contains just one element, that element is returned (acting as the termination condition). Otherwise, the function adds the first element to the sum of the rest of the list (achieved through a recursive call).

Factorial Using Recursion

Factorials are often calculated using recursion in programming. The mathematical definition states: n! = n * (n-1)!, given n > 1 and f(1) = 1. For instance, 3! = 3 x 2 x 1 = 6. Here’s how you can compute factorials recursively in Python:

In the above code, as long as the input is greater than 1, the function keeps calling itself, thus calculating the factorial in a recursive manner.

## Constraints of Using Recursion

It’s essential to understand the limitations of recursion. Each time a function calls itself, it uses some memory to store return values. Because of this, a recursive function can sometimes use a lot more memory compared to its iterative counterpart. In Python, recursion is limited to a depth of 1000 calls. If you try to surpass this, as demonstrated below:

You’ll be met with the error:

Some languages might crash your program under such conditions. While you can tweak the maximum recursion depth in Python, as shown:

Remember that this isn’t a foolproof solution. There will always be a threshold, and for problems like calculating large factorials, a recursive function might not be the most efficient choice. However, for tasks like directory traversal, recursion can be quite handy.

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Harm Mon, 15 Jun 2015

Thanks for the Tutorial!

Frank Tue, 16 Jun 2015

Thanks Harm! More tutorials coming soon

Aj Tue, 23 Jun 2015

That's a big number :o,
Thanks for the Tutorials you helped me a lot!

Frank Tue, 23 Jun 2015

Thanks! More tutorials coming soon

John Youn Wed, 24 Jun 2015

Thanks a lot. I'm looking forward to more tutorials.

Christian Ransom Sun, 26 Jul 2015

In the second example on line 7 it says:

return list[0] + sum(list[1:])

What does "[1:]" do? I looked and didn't see anything about that elsewhere in the tutorials.

Frank Sun, 26 Jul 2015

Hi Christian, [1:] returns everything from the second character.

Fin Mon, 07 Sep 2015

I want to say thank you for your awesome tutorial. thank you. :)

Frank Mon, 07 Sep 2015

Thanks Fin!

Fred Pouyet Thu, 17 Sep 2015

I agree with Fin. Thanks a lot for putting together this tutorial which is simple to grasp and not boring unlike the vast majority of the tutorials