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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-2.08 ms +- 28.1 us per loop (mean +- std. dev. of 7 runs, 1 loop each)\n-3.56 ms +- 35.6 us per loop (mean +- std. dev. of 7 runs, 1 loop each)\n+1.17 ms +- 17 us per loop (mean +- std. dev. of 7 runs, 1 loop each)\n+2.06 ms +- 46.3 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-1.05 ms +- 16.1 us per loop (mean +- std. dev. of 7 runs, 1 loop each)\n-2.52 ms +- 26.2 us per loop (mean +- std. dev. of 7 runs, 1 loop each)\n+520 us +- 9.69 us per loop (mean +- std. dev. of 7 runs, 1 loop each)\n+1.36 ms +- 10.7 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-3.55 ms +- 32.8 us per loop (mean +- std. dev. of 7 runs, 1 loop each)\n-7.17 ms +- 38 us per loop (mean +- std. dev. of 7 runs, 1 loop each)\n+2.13 ms +- 19.2 us per loop (mean +- std. dev. of 7 runs, 1 loop each)\n+4.13 ms +- 18.5 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([0.31372568, 0.72167143, 0.11277527])\n+Out[7]: array([0.20177295, 0.80481925, 0.25923581])\n \n In [8]: rng.random(3, dtype=np.float32)\n-Out[8]: array([0.1022529 , 0.65221405, 0.698686  ], dtype=float32)\n+Out[8]: array([0.6782615 , 0.70966625, 0.8387832 ], dtype=float32)\n \n In [9]: rng.integers(0, 256, size=3, dtype=np.uint8)\n-Out[9]: array([102,  67, 157], dtype=uint8)\n+Out[9]: array([ 82, 164,  39], dtype=uint8)\n 
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