55-летняя телеведущая описала себя в постели и довела коллегу до истерики20:49
Still, development continued apace. IBM retired the Lotus branding around 2002,
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Мария Большакова (редактор отдела «Интернет и СМИ»)
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.