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We began analyzing https://numpy.org/doc/stable/reference/random/index.html, but it redirected us to https://numpy.org/doc/stable/reference/random/index.html. The analysis below is for the second page.
Title[redir]:
Random sampling — NumPy v2.3 Manual
Description:
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Title[redir]:
Random sampling — NumPy v2.3 Manual
Description:
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Keywords {🔍}
random, generator, numpy, rng, array, functions, randomstate, routines, legacy, bit, generators, bitgenerator, defaultrng, vary, seed, state, module, parallel, generation, nprandomdefaultrng, rngrandom, integers, seeds, seedsequence, algorithm, api, pcg, compatibility, whats, performance, implements, number, users, instance, import, generate, integer, independent, bitgenerators, version, structure, sampling, upgrading, pcgdxsm, applications, examples, numba, cython, cffi, numpyrandom,
Topics {✒️}
bit generator-provided stream routines nondeterministic data legacy random generation convenience functions standard library advanced options alternative bit generators bit generators seeding arbitrary 128-bit integer backward compatibility functions pseudo-random sequences parallel rng streams generator instance owns legacy randomstate class array produce random doubles security migration advice numpy api consult upgrading pcg64 entropy upgrading pcg64 unit gaussian distribution distributed applications bit generators random generator bitgenerator classes implementing 64-bit values parallel generation internal implementation details arbitrary-sized integers generator instance secrets module legacy randomstate core rng algorithm legacy infrastructure numpy version 1 numpy implements generator instances generator takes comparing performance random examples large positive integers seedsequence implements pseudo-randomness random unsigned 32 previous numpy numpy developers included generators
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