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Using Iteration and a Python Function

For more information on concepts covered in this lesson, you can check out Python Inner Functions: What Are They Good For?.

00:00 Using Iteration and a Python Function. The example seen previously implements a recursive solution that uses memoization as an optimization strategy. In this section, you’ll code a function that uses iteration. The code seen next on-screen implements an iterative version of your Fibonacci sequence algorithm.

00:25 Line 3 defines fibonacci_of(), which takes a positive integer, n, as an argument. Lines 5 to 8 perform the usual validation of n.

00:45 Lines 11 and 12 handle the base cases where n is either 0 or 1. Line 14 defines two local variables, previous and fib_number, and initializes them with the first two numbers in the Fibonacci sequence.

01:03 Line 15 starts a for loop that iterates from 2 to n + 1. The loop uses an underscore for the loop variable because it’s a throwaway variable and you won’t be using this value in the code.

01:16 Line 18 computes the next Fibonacci number in the sequence and remembers the previous one. And finally, line 20 returns the requested Fibonacci number.

01:30 To give this code a try, make sure you’ve saved it as fibonacci_func.py, and then open an interactive session in the same directory you saved the file in.

02:06 This implementation of fibonacci_of() is quite minimal. It uses iterable unpacking to compute the Fibonacci numbers during the loops, which is quite efficient memory-wise. However, every time you call the function with a different value of n, it needs to recompute the sequence entirely.

02:25 One possible solution for this would be to make use of closures and make the function remember the already-computed values between calls. You can learn more about this in this Real Python course.

02:38 In the next section, we’ll take a look back at what you’ve covered in this course.

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