In the past two lectures, we wrote most of our code in procedural programming (our code was a sequence of instructions), we also test the speed and memory usage as our ways to measure code performance, but we didn’t say anything about scalability. Today, we will write more scalable, reusable, testable, and predictable code.
Unlike “CS introduction with Python” courses, the goal here, from the beginning, is to use Python effectively so we need to learn as much Python features as we could. However, if you came from another programming language, you will probably search on applying that language’s features in Python. In that case, you will miss what Python offers. For example, if you’ve never seen it tuple unpacking before, you will probably not search for them, and you may end up not using this feature just because it is specific to Python (Ramalho, 2022). So, you need to keep your eyes open.
As they say “What is more important than doing the work is organising it”. So, what we aim to do today is to reach some level of simple, efficient, readable, and robust code. It is not another list of rules to memorise, it is how you share your thinking process with other coders. Also, a big part of your job as a coder is to share your ideas, contribute, or at least do your tasks within your team/company.
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