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I recommend that you spend 10–15 minutes browsing the PEP8 style guide to pick up some improvements. You simply don’t have to put energy into thinking about stylistic choices.
How to implement the logic is still 100% up to you. Should you use type hints to indicate the data types of the parameters? You should probably write a docstring to explain what the function does. There are still plenty of decisions left up to the author of the code. The correct formatting is: def my_funciton(a, b): return a + b
You now don’t need to think about the correct stylistic version of the code snippet above. On the contrary, once you have accepted PEP8 as the authority for most stylistic decisions, you can focus your energy on other aspects of your code. For a slick-looking summary of PEP8, you can check out Kenneth Reitz’s stylish version.Įvery Python developer should (unless your company has their own style guide) adhere to PEP8. This is the officially suggested convention for code style. The Solutionįear not! In Python there is PEP8. Yuck! So what, then? Should your company hold internal meetings where you go through how each case should be handled? Sounds awfully boring. If you act randomly with regard to which choices you do, your code becomes a mess like this: def MyFUNCTION( a,b) : return a+b
How should you write the function name? Perhaps in a single word def myfunction(a, b)? Perhaps in snake-case def my_function(a, b)? Or maybe in camel-case def myFunction(a, b)? Or maybe Pascal-case def MyFunction(a, b)?įor every line of code, there are considerations like this. Should you write a function as def my_function(a, b) or def my_funciton(a,b) or perhaps def my_function( a, b ). Choose whether to have spaces between parameters in function definitions. However, there is also a seemingly infinite amount of ways to do the following: This encourages creativity and novel solutions to problems. In most programming languages, there are many ways to solve the same problem. In the end, I will give you a few cool resources that can help you further on your road to code awesomeness 🔥Ģ - You are Nothing Without Style - PEP8 The Problem Think of the blog post as a beginning rather than a complete picture. In this blog post, I will show get you started with some pointers for writing high-quality code in Python. As such, the code should be of high quality. Similarly, as a data scientist, you should take pride in your code. Almost every exceptional writer takes pride in their writing. I suggest that you adopt the mindset that high-quality code is something you take pride in. If you try to improve then the fruits of your labour will be visible for everyone to see in the long run. Your code quality will not change significantly between today and tomorrow. However, learning to write high-quality code is an incremental process. Hopefully, you have now been convinced that high-quality code is useful. High-quality code is extendable to new situations. When code is of high quality, it should be possible to scale out to new situations more smoothly. If your code is awful, then this might not be as simple as it seems. The data volume and data velocity going through the data pipeline will be higher. You are asked to scale out the project to meet increasing demands. Say you have a data science project that is widely successful. High-quality code is understandable in the future. And even for the person that wrote the code, it might be almost nonsensical a few months down the road. Certain parts of the code will be mystical and not really understandable for anyone else than the person that wrote it. When the code is haphazardly written the onboarding process slows down. This is true in data science as well as in software engineering.
When newcomers arrive at a project, they have to learn the ropes of how the codebase works. High-quality code allows you to fail early and fail fast. This leads later down the road to time-consuming bug fixes and, at worst, production failures. With bad code quality, it is easy for errors and questionable edge cases to go unnoticed. Quite a bit actually! Here are three situations where high code quality can save you countless hours: Well, as you probably suspected by the title of this blog post, I am here to tell you that code quality for data scientists do in fact matter. Why not simply disregard code quality and have one less thing to worry about 😌 Learning to write high-quality code takes time and effort. When presented with the option to care about code quality or not, it is tempting to choose the path of least resistance. Others say that data scientists are not software engineers and adopt the following mantra: Some people say that code quality is really important. 
When starting out as a data scientist one is told conflicting tales regarding code quality.
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