Oh boy, where do we even start.

My colleague, an ardent mathematician, kept telling me about functional programming for what basically is my whole programming life. I come from an economist background so for me the terms Object-Oriented and functional did not mean much. Also, I think early on in our journeys on the path to learn how to write code we don’t concern ourselves with paradigms (which is what those terms meant).

However, as you get better and better at the programming and stop worrying about indentation errors and learning syntax, your brain craves for what Raymond Hettinger calls “there must be a better way” moments. Those moments where you step back, look at whats infront of you and realise that you might need to abstract stuff or maybe figure out a better way to organise your code.

So after coding in python for 3 years, I listened to my mathsy friend and learned a bit of Haskell which is a great functional programming language. Basically the poster boy of functional programming. I will try to convey the lessons I learned from that to you in this blog thing and showcase some things in python that would allow you to do functional programming.

Also I won’t go into Object Oriented stuff. I think I’m definitely not qualified to talk about all that. But my aim will be to talk why I at the very least started thinking more functionally about my code and hopefully make a case on why you should at least try it.

Functional Programming

Disclaimer: This is how I understand the subject matter. I’m not a math scientist person. If you want more details heres the wikipedia link for convenience.

Maths is something I love, but don’t do well enough. So you could call me an enthusiastic cheerleader for maths. So I’ll keep this light on the maths but sprinkle in notation as and when.

So what is a function? A function is something that associates every element of a set of inputs to a unique element of an output set. Or at least something along those lines anyway. When it comes to the world outside of maths, a function in say python looks something like func(input1, input2). Well in that case the inputs are mapped to an output (if nothing is returned it got mapped to the None value).

Now python doesn’t check what comes into the function versus what is supposed to come into the function. Same goes for the output side which means as soon as it hits a snag, because you haven’t passed the right things into it, will kick up a fuss (document your code). But I digress.

Functional programming sort of forces you to think about your code as a set of functions. A program is then defined almost like this:


Let us break it down a bit. You have some input x and then you pass it to function h which then does something to it (maps it to some output). After this is done it then passes whatever comes out of it to the next function (z in this case). Let me rewrite this with more relatable names


One great thing when your code conceptually looks like this (if you’re say a data scientist or someone building pipelines) is that you get a nice flow of data or any other input through your system in pipeline. Say I forgot a step (and I did) of creating statistics before I generate my report. Well the simple way of fixing this issue in our little functional world is to simply insert another function. You know what has to come in and what has to come out in each stage after all so if you respect that flow you’re all set to go!

After adding that extra step we get:


Before we rush off and start looking at python I think its important to bring up the concept of composition. As you might have seen writing all of those brackets constantly is annoying and for larger programs it would get more and more annoying over time. This is where composition comes in handy.

Composition essentially allows you to join functions together into a much larger function (basically what we’ve been doing to write our ‘pipeline’). In maths you would show composition like this:

$$f \circ g$$

which is identical to


so our old pipeline can be expressed as

$$pipe = publish \circ report \circ stats \circ clean \circ load$$

and then we can do


Remember, respect the order of what has to come in and what has to come out!


Since not only our pipeline, but the whole program in itself has to be function composed out of smaller functions, functional programmers have tools at their disposal that help them solve very specific issues.

The one I wanted bring up is map. Not as in Mercator and projections and such (looking at you Dan), but more going from one thing to another.

Mapping means (pedantic maths people see disclaimer above) that you have some sort of structure (often times a list) and a function and then proceed to apply the given function onto the elements of the structure.

So think of an array (list in python for example), then we would take a function like say f and applied it to each element of the list. So it might look something like this if we tried to square all the values from 0 to 3.

$$ \begin{align*} &input: [0, 1, 2, 3] \\
&function: square \\
&produces:[square(0), square(1), square(2), square(3)] \end{align*} $$

Partial Functions

Another common (and useful!) concept to be aware off is partial functions. As you may or may not know you can pass in default arguments when you define functions in python. Meaning that if you don’t pass values for a given parameter, it will fill it with defaults. Similarly if you are using a function over and over and need to pass in the same arguments over and over, might as well create a prefilled version of it!

We’ll cover how this is done in python later and it will be much clearer!

Functional stuff in python

By now you might be quite annoyed by all the abstract stuff. So for the sake of actually having some python in here lets translate our pipeline to python.


The more eagle-eyed among you might have realised I am not using composition. That is because that doesn’t exist by default in python unfortunately. But you could write your own composition function like this:

def compose(outter, inner):
#     0      1      2       3        4       5
    return lambda *args, **kwargs: outter(inner(x))

There are nicer ways to do this but this is the simplest and fastest way I could think of at the moment. Lets break it down!

  1. return whatever follows
  2. we will be returning an anonymous function (more info)
  3. the *args bit captures any arguments we pass in
  4. the **kwargs will contain any key=value style arguments (keyword arguments)
  5. thats the outter function
  6. is the inner function that we’re wrapping up

Neat. There are nicer and more user friendly composition functions we can create but for the sake of our little exploration that one is just a nice and simple example. Better ones could be functions that take in more than 2 functions for example compose(publish, report, stats, clean, load) could literally produce our whole pipeline.


One thing I want to mention is generators. Some of you might be familiar with the list comprehension syntax in python which lets you generate lists like the squared values from 1 to 3 (see Map section above) easily like so

[square(val) for val in range(4)]

Now the issue is that when you get to ranges or data that is really large, you are in essence creating a maaaaasive array in memory. What if I wanted to create an array and the filter out the values that I don’t need. Well generators come in handy!

A generator is basically what it says in the name, it is a procedure or object that will generate certain values. Say we had a generator that would generate square values and as we looped through it would give us the square of the next number. Now it doesn’t have all these values in memory. It just knows how to get the next one and the one after. If we had such as thing, then we could filter our values as we go along rather than create a massive array and then filter.

Funny enough, map as a function already exist in python and is a generator!

squareGen = map(square, range(4))

This would give us a generator, not a list and if we wanted it to be a list we would have to pass the generator to the list function. Now if we wanted to filter some values we could use filter which takes in a function (which has to be able to go from input to a True/False value) and an iterable. It will only keep values in the iterable if after it has passed the each value to the filter function, the function returns True. So for a filter that wants values that are even, it will pass the number 0 in, then check with if it is something it wants to keep using the function supplied and in this case it would!

To put it in code

filter(lambda x: x % 2 == 0, range(42))

We can then use the map and the filter together!

filter(lambda x: x % 2 == 0, map(square, range(42)))

This will run instantly, but unless you pass the resulting generator into a list function, or loop through it in some other way, you’ll be stuck with a filter object.

Functools, Itertools and other nice things

So where do all these functional things sit in python? Well things like map, filter and range sit already very nicely in python without the need for imports. However python has 2 libraries functools and itertools that will cover you for quite a lot of the functional programming needs.

Why itertools? Well, as you learn more functional programming and use more of it you’ll eventually see that you need to use for loops and such, however all the clever tools in itertools will help you create useful iterators that you can apply functions to instead of looping in the traditional sense of for and while loops.

The other little package I would like to highlight is toolz. This package is like itertools and functools in one and on steroids. It has a nice little drop in replacement called cytoolz, but I’ll leave the googling to you. toolz is great as it has things that make doing functional programming much easier, like a nice compose function that we already covered.

functools and toolz have in them tons of stuff but one thing I promised to talk to you about is partial so I will briefly cover it here. Say we usually find ourselves using rounding and constantly want to round to the same accuracy (lets say 3 decimal places). So instead of typing round(my_value, 3) we can do something like this

from functools import partial
round3 = partial(round, ndigits=3)

This allows us to use round3 and it will always be rounded to 3 decimal places.

Side Effects

Ok last thing I want to cover is side effects. So you know how in math if I gave you the function

$$ y = x^2 $$

You know what the graph would look like regardless of when you plotted it. Day, night, morning, afternoon, if it rains, if it snows etc. Basically, the function describes the process of going from one thing to another right? So this is true always. In a world where this is true in programming, there would be much less coding errors.

The basic idea is that a maths function would not depend if some file exists on disk. In programming, you need to read files sometimes and they could be missing, hence you’d get an error! Oh no! You want to limit those or at least know where they can happen! Reading files and writing files, connecting to databases basically relying on and changing state outside of your pipeline can cause issues and make your functions not behave like maths functions!

This becomes an issue if you run things out of order for example. So keeping your side effect heavy functions segregated and clearly labelled as such etc will save you a headache or two in future.

I think I haven’t explicitly stated this, but you might infer this (and definitely realise this when you work in FP), another property of the maths functions is that one thing comes in one comes out. Conceptually. If you stick with this and avoid coding in side effects where you don’t need to you’ll be better off.


This whole post is a massive simplication. Massive simpliciation! Of a subject matter that is quite expansive. I think if you are keen to learn some of the things I’ve described here, go learn haskell. And if you are tempted to give it a bash read the Haskell Book

Anyway, fascinating subject, useful for data scientists (easy pipelining) and super usefull to think about in order to maintain the potential points of failure clearly labelled.