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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-4.13 ms +- 80.6 us per loop (mean +- std. dev. of 7 runs, 1 loop each)\n-6.18 ms +- 25.9 us per loop (mean +- std. dev. of 7 runs, 1 loop each)\n+10.9 ms +- 2.19 ms per loop (mean +- std. dev. of 7 runs, 1 loop each)\n+19.9 ms +- 2.7 ms 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-3.45 ms +- 30.1 us per loop (mean +- std. dev. of 7 runs, 1 loop each)\n-5.83 ms +- 19 us per loop (mean +- std. dev. of 7 runs, 1 loop each)\n+9.95 ms +- 2.14 ms per loop (mean +- std. dev. of 7 runs, 1 loop each)\n+26.7 ms +- 5.02 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-12.1 ms +- 28.8 us per loop (mean +- std. dev. of 7 runs, 1 loop each)\n-14.1 ms +- 11.3 us per loop (mean +- std. dev. of 7 runs, 1 loop each)\n+39.8 ms +- 3.06 ms per loop (mean +- std. dev. of 7 runs, 1 loop each)\n+43.1 ms +- 5.54 ms 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([0.10251134, 0.85299618, 0.56613854])\n+Out[7]: array([0.3128215 , 0.78171961, 0.38668424])\n \n In [8]: rng.random(3, dtype=np.float32)\n-Out[8]: array([0.00238842, 0.97557807, 0.6222447 ], dtype=float32)\n+Out[8]: array([0.9865558, 0.8202648, 0.9328461], dtype=float32)\n \n In [9]: rng.integers(0, 256, size=3, dtype=np.uint8)\n-Out[9]: array([ 94, 222,  12], dtype=uint8)\n+Out[9]: array([ 23, 124, 136], dtype=uint8)\n 
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