Cyberattacks don't stop at the time-honored Apache HTTP server, but a smart configuration, timely updates, and carefully considered security strategies can keep it from going under.
Although Python is a popular language, in the high-performance world, it is not known for being fast. A number of tactics have been employed to make Python faster. We look at three: Numba, Cython, and ctypes.
The Joblib Python Library handles frequent problems – like parallelization, memorization, and saving and loading objects – in almost no time, giving programmers more freedom to push on with their core tasks.
The Python Data Analysis Library, or Pandas, is built on top of the fast math library NumPy and makes analysis of large volumes of data an easy and efficient experience.
