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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-7.3 ms +- 72.9 us per loop (mean +- std. dev. of 7 runs, 1 loop each)\n-14.6 ms +- 36.9 us per loop (mean +- std. dev. of 7 runs, 1 loop each)\n+4.15 ms +- 79.9 us per loop (mean +- std. dev. of 7 runs, 1 loop each)\n+6.2 ms +- 22.2 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-6.17 ms +- 61.5 us per loop (mean +- std. dev. of 7 runs, 1 loop each)\n-17.4 ms +- 223 us per loop (mean +- std. dev. of 7 runs, 1 loop each)\n+3.46 ms +- 15.6 us per loop (mean +- std. dev. of 7 runs, 1 loop each)\n+5.83 ms +- 11.3 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-27.1 ms +- 302 us per loop (mean +- std. dev. of 7 runs, 1 loop each)\n-32.6 ms +- 522 us per loop (mean +- std. dev. of 7 runs, 1 loop each)\n+12.1 ms +- 18.6 us per loop (mean +- std. dev. of 7 runs, 1 loop each)\n+14.2 ms +- 12 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([0.23463919, 0.72393835, 0.14778879])\n+Out[7]: array([0.62899335, 0.03774   , 0.21407664])\n \n In [8]: rng.random(3, dtype=np.float32)\n-Out[8]: array([0.87651783, 0.8591692 , 0.7715133 ], dtype=float32)\n+Out[8]: array([0.32505184, 0.85750914, 0.6715027 ], dtype=float32)\n \n In [9]: rng.integers(0, 256, size=3, dtype=np.uint8)\n-Out[9]: array([153,  30, 207], dtype=uint8)\n+Out[9]: array([232, 105, 192], dtype=uint8)\n 
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