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In [1]: import numpy.random\n \n In [2]: rng = np.random.default_rng()\n \n In [3]: %timeit -n 1 rng.standard_normal(100000)\n    ...: %timeit -n 1 numpy.random.standard_normal(100000)\n    ...: \n-7.25 ms +- 74.8 us per loop (mean +- std. dev. of 7 runs, 1 loop each)\n-14.7 ms +- 17.5 us per loop (mean +- std. dev. of 7 runs, 1 loop each)\n+3.44 ms +- 38.5 us per loop (mean +- std. dev. of 7 runs, 1 loop each)\n+7.52 ms +- 18.9 us per loop (mean +- std. dev. of 7 runs, 1 loop each)\n 
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In [4]: %timeit -n 1 rng.standard_exponential(100000)\n    ...: %timeit -n 1 numpy.random.standard_exponential(100000)\n    ...: \n-6.09 ms +- 42.2 us per loop (mean +- std. dev. of 7 runs, 1 loop each)\n-17.3 ms +- 64.3 us per loop (mean +- std. dev. of 7 runs, 1 loop each)\n+3.15 ms +- 20 us per loop (mean +- std. dev. of 7 runs, 1 loop each)\n+7.51 ms +- 16.8 us per loop (mean +- std. dev. of 7 runs, 1 loop each)\n 
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In [5]: %timeit -n 1 rng.standard_gamma(3.0, 100000)\n    ...: %timeit -n 1 numpy.random.standard_gamma(3.0, 100000)\n    ...: \n-29.6 ms +- 3.38 ms per loop (mean +- std. dev. of 7 runs, 1 loop each)\n-34 ms +- 1.45 ms per loop (mean +- std. dev. of 7 runs, 1 loop each)\n+12.7 ms +- 63.8 us per loop (mean +- std. dev. of 7 runs, 1 loop each)\n+18.4 ms +- 29.1 us per loop (mean +- std. dev. of 7 runs, 1 loop each)\n 
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In [6]: rng = np.random.default_rng()\n \n In [7]: rng.random(3, dtype=np.float64)\n-Out[7]: array([0.19452855, 0.8623312 , 0.87792506])\n+Out[7]: array([0.96395281, 0.04954194, 0.25917649])\n \n In [8]: rng.random(3, dtype=np.float32)\n-Out[8]: array([0.06192333, 0.66640437, 0.17871135], dtype=float32)\n+Out[8]: array([0.7857391 , 0.9779958 , 0.98701364], dtype=float32)\n \n In [9]: rng.integers(0, 256, size=3, dtype=np.uint8)\n-Out[9]: array([156, 162, 124], dtype=uint8)\n+Out[9]: array([62, 82, 77], dtype=uint8)\n 
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