Why is Python an excellent language for data science?

If you have ever been among a group of “data scientist programmers” chatting animatedly over coffee or at happy hour, you have probably witnessed a very common discussion about the answer to the question: which is the best programming language for data science? It's not a one-answer question and you might hear the name of languages like C, C++, Java, R, Julia and, of course, Python.

Although each of these languages has characteristics that make them very useful in data science, I wouldn't be surprised if you told me that Python is an option that has increasingly appeared in answers to questions of this type. The next question, you can already guess, is the one that titles this short article and that I will try to answer here: why is Python an excellent language for Data Science?

In truth, we need to remember first of all that the option to use a programming language in this area, instead of just graphical and interactive tools such as Power BI or Excel, is a private decision for each scientist or team, which depends on the complexity and nature of the data they are working with. Graphical tools are excellent for quick analysis and interactive visualizations, while programming languages offer greater flexibility, control, and precision for data manipulation. It is not my objective here to deal with this specific issue, but if the reader gives me permission to give my opinion here as well, I believe that the solution is a combination of both strategies. But let's get back to the main topic of the article. If the option is for a programming language, why Python?

I can name some good reasons for this!

1. Python is easy to learn

Of course, this is not a feature that favors just the field of data science, but all areas in which Python is applied. However, in the case of data science, this is particularly useful because it is not uncommon for data scientists from areas other than program development (such as mathematics or statistics) to need to learn how to program, and quickly.

Python stands out here because its learning curve is smooth and brief, compared to languages like C or C++, for example. The language offers a clear and intuitive syntax, making it ideal for beginners and data scientists just starting their journey. In addition, it has a wide community of developers, teachers, courses and teaching materials that accelerate language learning.

2. Python has a large and collaborative community

Being widely used and having a solid and very welcoming user base, Python is a language whose features are shared among its members - including the data scientists who use it. With several professionals and enthusiasts contributing to the community with their knowledge and experience, the language has remarkable support for problem solving and dissemination of good practices. The Python community develops and maintains a wide range of specialized data science libraries, which are continually improved and updated thanks to the collective efforts of its members. This keeps the language relevant and powerful for cutting-edge data science applications.

Additionally, an active community means that many common problems have already been addressed and resolved by others. This allows data scientists to find solutions quickly, without having to reinvent the wheel.

3. Plenty of tools for Data Science

Do you need functions that handle numeric and mathematical data? Or that process information from different sources, such as databases, files or the internet? Or even if they manipulate large masses of data? You might even need objects that perform data analysis, or plot graphs, or provide machine learning capabilities, or any other task that data scientists do every day.

Python has all this and more. The wide variety of resources and libraries available for the language is one of the main factors contributing to its large market share. This diversity allows developers and data scientists to leverage ready-made solutions for a wide range of tasks, from data manipulation to machine learning and visualization. Additionally, the active community of developers continues to build and improve these tools, ensuring that Python is always up-to-date and relevant to the ever-evolving needs in the field of data science. And the best part: these libraries are usually free!

4. Python - and most of its libraries - is open source

Python is an open source programming language. This means that its source code is open and free, so anyone can view and modify it. Furthermore, most libraries and resources developed for the language embody this community spirit and are also open source. This includes the top data science tools available today.

These characteristics make Python a very economical choice for the area, allowing data scientists, regardless of their location or financial resources, to use powerful tools without economic barriers.

5. Flexibility

Python is an incredibly versatile language. Multiplatform, multiparadigm and multifunctional, it can be used to create a variety of different programs in different types of scenarios. This versatility contributes to it being one of the most used programming languages today.

In data science, this flexibility is crucial as data scientists often need to handle a variety of tasks such as data collection, cleaning, visualization, modeling, and deployment. Python allows them to easily transition between these steps, using the same skills and libraries. Furthermore, Python can be easily integrated with other tools and languages. You can use Python for data preprocessing and then pass the data to graphical data analysis or machine learning tools.

6. Speed in development and testing

It is very fast to develop in Python. Given the clarity of the language, high-level code can be written quickly and reviewed without difficulty (one of the characteristics of the language is that it is strongly oriented towards the use of tabs, making the code more readable and elegant). As it is an interpreted language, Python allows operations in its scripts to almost always be immediately tested after implementation. Some features of the language also allow rapid prototyping, for testing and demonstrations. And the language's garbage collection system allows the developer to worry about what interests him, without wasting time freeing the memory allocated by objects created during the program.

This is perhaps one of the most important language features for data science. We live in an era in which data production increases exponentially in quantity and speed, and the way in which this information must be treated changes at the same speed. Python is agile; you develop, test and deliver very quickly, giving the scientists and teams that adopt it a competitive advantage and the ability to quickly adapt to market demands.

In summary, there are several reasons why Python is always one of the most cited good tools for use in data science. We saw some of them here, but, believe me, there are more. In any case, this list is enough to provide arguments for pro-Python debaters at future meetings of “data scientist programmers” during coffee hours or happy hours.