What are they, what are they good for and why people still use C/C++ with much more complicated syntax? 15.2. In this notebook we are going to discuss tools designed to provide C-like performance to Python code: Cython and Numba. But we have already encountered situations when it was not possible to avoid running Python loops that easily end up being painfully slow. scikit-learn or TensorFlow).Īs we discussed earlier, NumPy and SciPy integrate optimized and precompiled C code into Python and, therefore, might provide a significant speed up. Python also dominates the data-science due to availability of packages such as NumPy, SciPy and versatile machine learning tools (e.g. On the contrary, Python is a great tool for various tasks that do not require running expensive simulations (web-development, scripting). It doesn’t make Python a bad programming language. There are two major reasons for that: Python is a dynamically typed language and Python is an interpreted language.
But Fortran, C, C++ dominate high-performance programming. Python has plenty of appeal to the programming community: it’s simple, interactive and free.
0 kommentar(er)
