My Love/Hate Relationship with Python: A 45-Year Programmer's Perspective

Python, the versatile and powerful programming language, is a darling of the analytics and AI world. Its rich ecosystem of libraries and tools has made it a go-to choice for data scientists and developers alike. Yet, for someone like me, a seasoned programmer with 45 years of experience, my relationship with Python is a complex mix of love and hate. In this blog post, I'll take you on a journey through my thoughts and experiences with Python, highlighting both its strengths and the aspects that continue to irk me.

1. The Good: Tooling Beyond Imagination
   Python's ecosystem is nothing short of astounding. As a company deeply entrenched in analytics and AI, the wealth of libraries, frameworks, and resources available to us is a treasure trove. From NumPy, SciPy, and Pandas for data manipulation, to TensorFlow and PyTorch for deep learning, Python has it all. The convenience of having this vast toolbox at our disposal is undoubtedly one of Python's biggest strengths.
2. The Bad: Lack of Clear Scope Markers
   One of my biggest gripes with Python is its lack of clear scope markers. Coming from a background of C/C++ and JavaScript, I miss the comforting embrace of curly braces ({}) or parentheses (()). These symbols provide a visual structure to the code, making it easier to discern scope. Python's reliance on indentation for flow control can be frustrating at times, especially when you're dealing with nested loops and conditionals. I yearn for the days when code structure was dictated by the programmer, not by the language.
3. The Ugly: Performance Quandary
   Python's performance is a double-edged sword. While the extensive libraries and high-level abstractions make development a breeze, they also highlight performance issues. The interpreted nature of Python can be a bottleneck, especially when dealing with computationally intensive tasks. Many times, I've found myself optimizing code or resorting to external libraries written in C to improve performance. It's a trade-off between ease of development and execution speed that keeps me on my toes.
4. The Frustrating: Threading and the Global Interpreter Lock
   Python's Global Interpreter Lock (GIL) is a necessary evil, but it's also a constant source of frustration. The GIL ensures that only one thread executes Python bytecode at a time, which can lead to suboptimal performance in multi-threaded applications. Even the smartest of developers can run into performance bottlenecks when dealing with CPU-bound tasks. While there are workarounds like multiprocessing or using external libraries, they add complexity to what should be a straightforward task.

My love/hate relationship with Python as a seasoned programmer in the analytics and AI field is a testament to the language's strengths and weaknesses. Its extensive tooling and ease of use have revolutionized the way we work, but the absence of clear scope markers, performance concerns, and the GIL's presence continue to be thorns in my side.

In the end, Python remains a powerful ally in the world of analytics and AI, but it's essential to recognize and work around its quirks. For me, it's a dance between embracing its strengths and taming its idiosyncrasies, a dance that keeps my passion for programming alive even after 45 years in the field.

Darren Pegg is CTO at DataGPT - A Place to ask questions

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