Why Is Python the Language of Choice for Data Scientists?
Python has grown to become one of the most popular and well-liked programming languages in the world, used by millions of developers since its creation in 1991. For data scientists in particular, Python has a strong, long-time base of developers. Why is Python the language of choice for so many practitioners and why does the data science industry gravitate so much towards it? The answer comes down to a combination of factors that check more boxes than virtually any other language.
Automation is key to success in data science, allowing rapid analysis of large, complex datasets. Python’s easily scalable tools and modules, along with its user-friendly syntax, make it a great choice for automation projects.
The Python ecosystem is so feature-rich and adaptable that data scientists can easily choose exactly the tools they need without any side effects or clunky structure requirements. Additionally, Python’s active online community has generated countless functional and reliable tools, packages, and libraries purpose-built for automating data analysis. Pytest is a particularly popular framework, especially for testing purposes.
A top priority for any developer is the degree to which a language enables them to be more productive. This is a key reason why data scientists choose Python over other languages. In fact, StackOverflow found in its 2020 developer survey that Python is the 3rd most-loved programming language in the world, preferred and used by over 66% of developers.
Compared to close competitor Java, for example, Python’s syntax is drastically more compressed and readable, requiring far fewer characters to do similar tasks. This makes writing the code faster but also simplifies debugging since having fewer characters means there are fewer opportunities for errors. The friendliness of the Python language makes it excellent for integrating easily with other frameworks and architectures for data analysis, as well.
The sheer speed and efficiency of Python result in rapid prototyping capabilities that allow developers to test their analysis programs faster than nearly any other language. Additionally, Python’s cross-platform accessibility makes collaborative development, prototyping, and testing easier than many other languages.
Programmers rely a lot on each other for new tools and resources, from forums to get feedback on bugs to open-source libraries for expanding a language’s features. Since Python is so popular, it has both of these in abundance. Custom libraries are available for virtually any niche a developer could want, including some highly effective libraries for data science applications. Popular libraries among data scientists include Pandas, SciPy, and NumPy.
