{"id":7293,"date":"2021-01-06T08:05:54","date_gmt":"2021-01-06T02:35:54","guid":{"rendered":"http:\/\/www.pythonpool.com\/?p=7293"},"modified":"2026-07-13T12:30:45","modified_gmt":"2026-07-13T07:00:45","slug":"numpy-random-uniform-function","status":"publish","type":"post","link":"https:\/\/www.pythonpool.com\/numpy-random-uniform-function\/","title":{"rendered":"NumPy random.uniform: Generate Reproducible Uniform Samples"},"content":{"rendered":"

Quick answer:<\/strong> Generate uniform samples with a NumPy random Generator by defining low, high, and size. Use a local seeded generator for reproducible tests and experiments, and validate bounds, output shape, dtype, and the statistical assumptions of the downstream task.<\/p>\n

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Uniform sampling draws values across a chosen interval; define the bounds, output shape, generator, and reproducibility policy before using the samples.<\/figcaption><\/figure>\n

numpy.random.uniform<\/code> generates floating-point samples from a uniform distribution. For new NumPy code, the recommended pattern is to create a random number generator with np.random.default_rng()<\/code> and call rng.uniform()<\/code> from that generator.<\/p>\n

A uniform distribution means every value in the interval has the same probability density. In NumPy, the usual interval is [low, high)<\/code>: low<\/code> is included and high<\/code> is treated as the upper boundary. The official docs also note a floating-point rounding edge case where the high value can appear, so avoid writing code that depends on the exact endpoint.<\/p>\n

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