Coding in Python is fun because there are many different ways to accomplish the same functionalities

Programmers and developers can tap into a variety of languages to build applications, websites, and other programs. Ultimately, their preferred language will end up being the one with which they are the most comfortable and the one that gets the job done most effectively. But one particular language emerging at the top, among all the new programming languages, is Python. The list of programming languages is so massive that it is natural for people to have a wide variety of opinions about which one might be the best. Yet, Python remains at the top. It is undoubtedly considered one of the top programming languages at the same level as JavaScript or C++, and it is also one of the most-used languages by businesses and enterprises. Here are the top 10 tips for you to write the best Python code as a Data Scientist.

Code Everyday

Consistency is very important when you are learning a new language. It is recommended to make a commitment to code every day. It may be hard to believe, but muscle memory plays a large part in programming. Committing to coding every day will really help develop that muscle memory.

Write It Out

As you progress on your journey as a new programmer, you may wonder if you should be taking notes. Yes, you should! In fact, research suggests that taking notes by hand is most beneficial for long-term retention. This will be especially beneficial for those working towards the goal of becoming a full-time developer, as many interviews will involve writing code on a whiteboard.

Go Interactive!

Whether you are learning about basic Python data structures (strings, lists, dictionaries, etc.) for the first time, or you are debugging an application, the interactive Python shell will be one of your best learning tools.

Take Breaks

Breaks are especially important when you are debugging. If you hit a bug and can't quite figure out what is going wrong, take a break. Step away from your computer, go for a walk, or chat with a friend. In programming, your code must follow the rules of a language and logic exactly, so even missing a quotation mark will break everything. Fresh eyes make a big difference.

Become a Bug Bounty Hunter

When debugging, it is important to have a methodological approach to help you find where things are breaking down. Going through your code in the order in which it is executed and making sure each part works is a great way to do this.

Work Together

Though coding may seem like a solitary activity, it actually works best when you work together. It is extremely important when you are learning to code in Python that you surround yourself with other people who are learning as well. This will allow you to share the tips and tricks you learn along the way.

Teach

There are many ways to do this: whiteboarding with other Python lovers, writing blog posts explaining newly learned concepts, recording videos in which you explain something you learned, or simply talking to yourself at your computer. Each of these strategies will solidify your understanding as well as expose any gaps in your understanding.

Pair Program

Pair programming has many benefits: it gives you a chance to not only have someone review your code, but also see how someone else might be thinking about a problem. Being exposed to multiple ideas and ways of thinking will help you in problem-solving when you got back to coding on your own.

Build Something, Anything

What you build is not as important as how you build it. The journey of the building is truly what will teach you the most. You can only learn so much from reading Real Python articles and courses. Most of your learning will come from using Python to build something. The problems you will solve will teach you a lot.

Contribute to Open Source In the open-source model, software source code is available publicly, and anyone can collaborate. There are many Python libraries that are open-source projects and make contributions. Additionally, many companies publish open-source projects. This means you can work with code written and produced by the engineers working in these companies.