# 100 Days of Fibonacci - Day 10, Python

It is yet another time for a post in my 100 days of Fibonacci challenge. Today I am containing myself a bit and provide a link between a concept in programming and its root in mathematics.

The concept is list comprehension in Python. Python is a widely used programming language especially used as an easy language appropriate for beginners. The languages is in particular close to English. Furthermore it enforces good style by scoping by indentation.

As mentioned I zoom in on the list comprehension feature in Python. I relate this feature with the way sets are defined in math. Lastly I talk a bit about the interpretation of infinite sets in programming.

# Fibonacci in Python

The implementation is the direct recursive implementation accompanied by dynamic programming by momoization. This as my Java implementation. It is not new to implement it like this, and simply provides a predicate to use in the list comprehension.

The cache is here used as a global variable. Global variables are something one should by all means avoid, but here the amount of code is small and the main goal of this post is not software architecture.

From here we can know create the list of the 20 first Fibonacci numbers using list comprehension in Python. We print it directly to provide an output.

This is a one-liner for mapping the list of numbers from 0 to 19 to the list their corresponding Fibonacci numbers.

The implementation is available from my supporting Git repository.

# List Comprehension

The notation used above looks mostly like a very compact `for`

-loop. For this
relation to work we need to slightly open up our understanding of lists and
iteration in programming languages.

First we need to view a list as a set. It should be quite clear that a list
indeed can be a set. For that to work we need operations like *intersection*,
*union*, *difference*, etc. Any programmer should be able to quickly implement
those. The contrary, that lists are sets, are not given. Lists are inherently
ordered, where sets are unordered.

The other thing is the `for`

-loop. `for`

-loops are usually viewed as iterating
all elements in a list one-by-one, and doing something to them. Instead we
can see it as quantification. For a second we should forget everything about
the order of the list and details about iterations, and just see it as we
perform an operation on every single element in the *set*.

We now have the mindset set for the following mathematical interpretation of the above list comprehension to work. The following is a direct translation.

which would usually be written as follows.

The reason why the last formulation is kind of futile is that Python uses strict evaluation: All terms are evaluated. The set of naturals is infinite and hence the computation will not halt.

The last formulation is, however, possible to formulate in programming.
We just need a lazy evaluated language. As such we can formulate it, and use
it, in the Haskell programming language. Following works given an
implementation of the `fib`

function:

And what is all this worth? Well, a lot of material programmers have to implement was first formulated in a mathematical language. This relations seeks the demystify the relation between programming and math. In fact it can (and is / will be in other posts) be argued that math and programming is in fact the same thing. But for some reason the two areas are somewhat disjoint. This, in my opinion, largely because of two cultures in communicating.

# Conclusion

In this post i implemented Fibonacci in python. There is nothing new to the implementation. It was a directly recursive implementation supported by a cache for speed.

The concept elaborated in this post is list comprehension and its relationship the the mathematical formulation of sets. Ultimately my message is that we as programmers should be able to both formulate and interpret in other frameworks. Also those seemingly very distant frameworks, like mathematics is for many programmers.