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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-1.36 ms +- 27.9 us per loop (mean +- std. dev. of 7 runs, 1 loop each)\n-2.63 ms +- 68.7 us per loop (mean +- std. dev. of 7 runs, 1 loop each)\n+1.37 ms +- 28.5 us per loop (mean +- std. dev. of 7 runs, 1 loop each)\n+2.62 ms +- 22.8 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-730 us +- 15.6 us per loop (mean +- std. dev. of 7 runs, 1 loop each)\n-1.92 ms +- 8.22 us per loop (mean +- std. dev. of 7 runs, 1 loop each)\n+742 us +- 35.1 us per loop (mean +- std. dev. of 7 runs, 1 loop each)\n+1.92 ms +- 11 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-2.69 ms +- 26.5 us per loop (mean +- std. dev. of 7 runs, 1 loop each)\n-5.04 ms +- 22.4 us per loop (mean +- std. dev. of 7 runs, 1 loop each)\n+2.69 ms +- 44.2 us per loop (mean +- std. dev. of 7 runs, 1 loop each)\n+5.06 ms +- 14.3 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([0.46632479, 0.91027615, 0.7069302 ])\n+Out[7]: array([0.59799319, 0.21212049, 0.99022154])\n \n In [8]: rng.random(3, dtype=np.float32)\n-Out[8]: array([0.80933726, 0.15478891, 0.15383297], dtype=float32)\n+Out[8]: array([0.5102923 , 0.09887606, 0.8792059 ], dtype=float32)\n \n In [9]: rng.integers(0, 256, size=3, dtype=np.uint8)\n-Out[9]: array([139,  91, 165], dtype=uint8)\n+Out[9]: array([45, 71, 91], dtype=uint8)\n 
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