The constraint: your problem must fit vectorized operations. Element-wise math, matrix algebra, reductions, conditionals (np.where computes both branches and masks the result -- redundant work, but still faster than a Python loop on large arrays) -- NumPy handles all of these. What it can't help with: sequential dependencies where each step feeds the next, recursive structures, and small arrays where NumPy's per-call overhead costs more than the computation itself.
В России изменились программы в автошколах22:30。关于这个话题，搜狗输入法提供了深入分析
。关于这个话题，手游提供了深入分析
Число пострадавших при ударе ракетами Storm Shadow по российскому городу резко выросло20:46。超级权重对此有专业解读
C library + Cython declarations