Python is a versatile language that has seen growing popularity in recent years. Python offers many advantages over other popular languages, such as Java, C#, Ruby, and Javascript. In this article, we explore some of Python's key strengths and why it is a great choice for your tech stack.
Known for its ease of use, readability and straightforward syntax that is easy to learn, Python also has a large and active community that contributes a wealth of modules, libraries, and frameworks. This makes Python an ideal choice for rapidly prototyping and developing complex applications.
Python is a powerful language with a wide range of application domains. Python is particularly well suited for scientific computing, data analysis, data science, AI/ML, and web development. Python's rich ecosystem provides many tools and libraries that make these tasks easier.
Leading Python Application Domains
Python was originally applied widely to system integration, scripting of code written in low level languages like C/C++ and applications where a Python interpreter could be embedded into the running program. Python was never intended as a replacement for these languages but as an addition to the toolbox, bringing with it increased programmer productivity and code readability.
Scientific Computing
In the late nineties and early two-thousands, Python became more widely adopted in scientific computing circles where its ease of use and readability increased productivity compared to lower level languages like C/C++. The Python language ecosystem had also begun to take shape with the creation of important libraries like NumPy for numerical computing and SciPy for scientific computing.
Python's strength in scientific computing comes from its ability to wrap C/C++ libraries behind a simplified and interactive interface. As an interpreted language, python's performance is comparatively slow. However, scientific applications delegate the computationally intensive functionality to performant libraries. NumPy and SciPy wrap numerical computing libraries that have been developed, verified and optimised since the 1960s. These highly performant libraries were typically written in languages like Fortran, C and C++. UI toolkits such as PyQT and PyGTK, also wrap frameworks in C++ and allow scientists to rapidly prototype applications and analysis tools that work across multiple operating systems.
Web Applications, Backend Systems, REST APIs
In the early 2000s, web development was dominated by server-side scripting languages like PHP, Perl, and ASP. However, Python's popularity for scripting, automation, network services, tools and scientific computing led to the development of important web frameworks like Django and Flask. These frameworks made it possible to rapidly develop complex web applications and adapt to changing requirements. These frameworks have matured over time and, along with new entrants such as FastAPI, are now leading choices for back-end systems and APIs. Python is now one of the most popular web development languages.
Many 3rd party services also provide python bindings to their REST APIs which means that it is easy to integrate with them from a python backend.
Big Data, Data Science, AI and Machine Learning
In building the ecosystem for modern data applications, Python was a natural choice. Its rich integration with numerical computing libraries and data-wrangling tools from the scientific computing domain has been adapted and enhanced to great effect. The powerful tools and libraries available in the Python ecosystem make exploratory data integration, preparation and analysis tasks easier and more efficient. As a result, Python has completely taken over this application domain.
Several important libraries have been developed for data science, machine learning and artificial intelligence. TensorFlow, PyTorch and Keras are the most popular libraries for deep learning. Scikit-learn is a comprehensive toolkit for machine learning tasks. Natural Language ToolKit (NLTK) provides industry-leading text processing capabilities. Python's popularity is due to its expressiveness, ease of use and the vast amount of high-quality code and libraries available. With Jupyter notebooks, python has also become the de-facto standard for exploratory data analysis.
Addressing Python's Shortcomings
The Python community is taking steps to address two of its most notable shortcomings, performance and concurrency. An early design decision, the Global Interpreter Lock or GIL, became a significant technical hurdle when languages needed to adapt to take advantage of multicore processors. The Python community is actively working to remove the GIL entirely and unlock dramatic performance improvements. PyPy, a self-hosted alternative python runtime with a JIT compiler, is significantly faster than CPython, the default implementation. The latest versions of CPython have also seen marked improvements in performance due to compiler optimisations.
Regardless of the speed of the language, for a large class of applications, I/O operations and the network are the most significant bottleneck. With the addition of asyncio in Python 3, it became possible to write asynchronous code using coroutines that satisfy most concurrency requirements and allow execution to continue without blocking on I/O or waiting on a network operation. Where the main application has to perform CPU-intensive computations, the recommended approach for most interpreted languages is to delegate these functions to performant compiled languages such as C, C++ and Rust.
So Why Choose Python for your Tech Stack?
We have established that Python is a leader in the web backend domain and a dominant language in the Data domain. The key to this choice is not in any one of Python's strengths. It is in how well they tie together. Any modern startup or software project needs to be able to marshal resources effectively. With Python, building modern data-driven products and platforms is a seamless experience. Not only can data extraction and analysis tools and libraries be integrated directly into the application itself, but ML and AI models can be served up directly from the same backend technology. This is a very compelling argument because it allows an effective organisation where the engineering and data teams speak the same language and are able to collaborate and share code effectively.
The Python language and ecosystem are continuing to evolve and grow. Aside from performance, other exciting new developments promise to extend Python's reach even further. With Webassembly, it will be possible to run Python code in the browser. This will enable new ways of developing rich frontend applications and integrating with legacy systems. Micropython is a specialised implementation of Python for constrained environments such as microcontrollers. It will be possible to develop sophisticated IoT applications in pure Python.
If you are choosing a tech stack for your next project, Python should be high on your list of options. Python's versatility, ease of use, rich set of libraries and tools make it an ideal choice for building all kinds of applications. Whether you are looking to develop a web application, data science platform, or a simple script to automate a task, Python is an excellent choice.