Making things unnecessarily complex (aka over-engineering). Implementing certain design patterns for the heck of it is s

Python is one of the world’s most popular and in-demand programming languages. Indeed, if you are in the hot field of Data Science, Python is, most probably, your daily driver. But why?

As we said, to make something out of these primitive functions, we would have to compose them and create higher abstractions. So let’s do that and create our itertools extensions.

My name is Dimitris Poulopoulos and I’m a machine learning engineer working for Arrikto. I have worked on designing and implementing AI and software solutions for major clients such as the European Commission, Eurostat, IMF, the European Central Bank, OECD, and IKEA.

However, this story is not for beginners. Some experience with the language is required to understand the examples and their value. Thus, without further ado let’s dive into the itertools module of Python, to discover a few hidden-in-plain-sight treasures of the language.

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Every function that we examine here is part of Python’s itertools library. That means that to import them you just write from itertools import x, where x is the function at hand. You will see that most of them, do not do anything useful by themselves, but the magic starts when you compose them.

If you haven’t use partial before, you can find it in the functools namespace. In this example, it takes the drop function, passes 1 as the argument n, and returns a new function that expects the it argument. Thus, if you pass a list in the tail function, it chops off the first element and it returns the remaining items.

Let’s walk it through step by step. The compose function creates a sequence of tuples. The first tuple is (0, 1). Thus, the fibonacci function yields the y value of the tuple, which is 1. It then pass the tuple (0, 1) to the next_num function, which returns 1 and 0 1 = 1. So, the fibonacci function yields also 1. Finally, the compose function passes the tuple (1, 1) to the next_num function, which returns 1 and 1 1 = 2. The fibonacci function in turn yields 2.

Generally when I have — I talked about two different ways this can be done and I really glossed over the technicalities of each one. I would generally give an overarching 'idea' of what needed to be done and let the reader figure out the rest.

This story is a study on the great talk delivered by Joel Grus, on PyData Seattle 2015. It’s a great demonstration on how to use functional programming techniques to make your Python code clean and more efficient. I hope this is the start of your journey into Python’s excellent itertools library.

preface: are you ready to take your Python skills to the next level? Can you walk through the examples in this story without squinting? Functional programming in Python might be daunting for some, but still, very fulfilling.

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I admit it looks a bit complex, but if you squint hard enough, you’ll understand its inner workings. repeat generates an infinite sequence of xs and then, accumulate takes each of them and calculates the running total according to some function f that we define. So, the lambda function, takes as inputs the previously accumulated value, and a new x that we ignore, and passes the accumulated value through the function, again and again. To test it, we could do something like that:

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