To cover this topic, we will go through three parts, With that, let’s cover some of the good practices, which will not only help us to create a working but also a beautiful piece of code □. And that's the idea behind this article, to provide python practitioners with a set of curated guidelines, from which they can pick and choose. That said while working in a team or even in open source collaboration, it helps to agree to a certain set of rules. Well, don't get me wrong, subjectively is a good thing and I think it is what leads to innovations. Observing several ML engineers and Data scientists working with me, I have noticed nearly all of them have their own unique style of coding. Writing code that will work tomorrow and is intuitive enough for anyone to understand and follow - well now we have hit the super hard stuff □. Writing code that will work tomorrow is hard. Don't forget to say hi on LinkedIn and/or Twitter. To read similar articles, refer to my blog or my medium page. This article is a chapter taken from my ongoing (and still only partially complete) book on Data science titled, “A Lazy Guide to Data Science”.
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