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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-6.33 ms +- 70.2 us per loop (mean +- std. dev. of 7 runs, 1 loop each)\n-13.3 ms +- 43.2 us per loop (mean +- std. dev. of 7 runs, 1 loop each)\n+4.76 ms +- 272 us per loop (mean +- std. dev. of 7 runs, 1 loop each)\n+6.66 ms +- 167 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-5.18 ms +- 61 us per loop (mean +- std. dev. of 7 runs, 1 loop each)\n-15.5 ms +- 19.9 us per loop (mean +- std. dev. of 7 runs, 1 loop each)\n+3.91 ms +- 119 us per loop (mean +- std. dev. of 7 runs, 1 loop each)\n+6.08 ms +- 141 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-27.2 ms +- 26.5 us per loop (mean +- std. dev. of 7 runs, 1 loop each)\n-32.2 ms +- 39.1 us per loop (mean +- std. dev. of 7 runs, 1 loop each)\n+13.3 ms +- 725 us per loop (mean +- std. dev. of 7 runs, 1 loop each)\n+15.4 ms +- 355 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([0.78096509, 0.76423919, 0.57495978])\n+Out[7]: array([0.08975275, 0.94397116, 0.14762362])\n \n In [8]: rng.random(3, dtype=np.float32)\n-Out[8]: array([0.8527657 , 0.97919476, 0.71092075], dtype=float32)\n+Out[8]: array([0.5901301 , 0.52016   , 0.35208422], dtype=float32)\n \n In [9]: rng.integers(0, 256, size=3, dtype=np.uint8)\n-Out[9]: array([ 17, 167, 130], dtype=uint8)\n+Out[9]: array([ 99, 103, 220], dtype=uint8)\n 
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