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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-1.49 ms +- 68.8 us per loop (mean +- std. dev. of 7 runs, 1 loop each)\n-2.63 ms +- 81.4 us per loop (mean +- std. dev. of 7 runs, 1 loop each)\n+2.33 ms +- 307 us per loop (mean +- std. dev. of 7 runs, 1 loop each)\n+4.61 ms +- 157 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-813 us +- 73.3 us per loop (mean +- std. dev. of 7 runs, 1 loop each)\n-2.06 ms +- 24 us per loop (mean +- std. dev. of 7 runs, 1 loop each)\n+The slowest run took 7.21 times longer than the fastest. This could mean that an intermediate result is being cached.\n+3.18 ms +- 2.97 ms per loop (mean +- std. dev. of 7 runs, 1 loop each)\n+7.88 ms +- 2.56 ms 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-2.94 ms +- 105 us per loop (mean +- std. dev. of 7 runs, 1 loop each)\n-4.98 ms +- 178 us per loop (mean +- std. dev. of 7 runs, 1 loop each)\n+8.42 ms +- 4.19 ms per loop (mean +- std. dev. of 7 runs, 1 loop each)\n+8.56 ms +- 41.5 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([0.24782288, 0.96689871, 0.72806661])\n+Out[7]: array([0.1887054 , 0.02088624, 0.26052049])\n \n In [8]: rng.random(3, dtype=np.float32)\n-Out[8]: array([0.4407423 , 0.26955348, 0.02589738], dtype=float32)\n+Out[8]: array([0.2037934 , 0.6804217 , 0.16867453], dtype=float32)\n \n In [9]: rng.integers(0, 256, size=3, dtype=np.uint8)\n-Out[9]: array([100, 192, 153], dtype=uint8)\n+Out[9]: array([111, 155, 175], dtype=uint8)\n 
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