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.
Excellent Automation Capabilities
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.
Compact and Swift Syntax
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.
A Huge Global Community
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. The wide availability of open-source libraries makes it simple for developers to solve problems quickly and create new programs that do exactly what they need in the least amount of time.
Community is a key part of any language’s success and has been a key factor for Python. Should a data scientist run into a problem with their code, they can easily get feedback from around the world through Python’s thriving online community of data scientists. The community is strengthened further by the big names in tech who have announced that they use Python, as well. Among the well-known entities that use Python are Intel, Pixar, NASA, Facebook, and Google.
Easy to Learn
One of Python’s most commonly discussed advantages over other languages is the ease with which beginners can learn it. Python’s syntax is clear and readable, making it intuitive to understand. This allows aspiring data scientists to learn Python quicker than other languages and put it into practice effectively with a minimum amount of training.
Python’s rapid learning curve is important for the data science industry for a couple of reasons. Primarily, there is a surge in demand for data scientists on the rise, with an astonishing 19% growth in new data scientist positions predicted for the next 10 to 20 years. If people can learn Python quickly and easily, it will be easier for new data scientists to enter the industry and fill all those new jobs. Compared to the more complicated languages used in other computer science fields, Python’s approachability makes data science an appealing career choice.
Ideal for AI, ML, and Deep Learning
AI and machine learning (ML) are becoming industry-standard tools for data analysis. These tools take Python’s automation capabilities to a new level, enabling intelligent, autonomous, accurate data analysis in a robust and adaptable framework with a swift turnaround time. Python’s functionality for AI, ML, and deep learning has grown over the years alongside its popularity, with users developing tools to help other developers create their own AI programs through Python.
Pandas, for example, which is a popular library for data analysis, is also a top choice when it comes to developing AI and ML projects. Many of the things that draw data scientists to Python originally contribute to what makes it the ideal language for AI and ML, as well. Its simplicity, compatibility, and reliability are all extremely valuable in the development of AI tools with functional machine learning, deep learning, and computer vision capabilities.
Data Science’s First Choice
The data science industry is always evolving, especially as its outlook grows more and more promising for the decades ahead. Python has been the language of choice for the industry for years, but it hasn’t shown much sign of slowing down, with only a couple of languages coming close to its global popularity. The accessibility, functionality, and advanced applications of Python give it a sure spot in the data science industry’s all-time most important programming languages.