Save Big on Coursera Plus. 7,000+ courses at $160 off. Limited Time Only! Grab it Discover the intricacies of Python for scientific computing in this 43-minute talk by Tamás Gál at the NHR@FAU HPC Cafe. Explore Python's internals, understand its popularity among scientists, and learn why libraries like NumPy, Pandas, and Numba are crucial for efficient scientific coding. Gain insights into writing scalable code, optimizing performance, and addressing common issues when dealing with large datasets. Delve into topics such as CPython, bytecode, memory management, the Global Interpreter Lock, and performance comparisons. Learn about structuring code with classes, leveraging NumPy's ndarray, and utilizing tools like Numexpr and Numba JIT for improved efficiency. Acquire the knowledge to choose the right tools and languages for scientific computing tasks, and consider the importance of green coding practices.